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Текст
Understanding Innovation Christoph Meinel Larry Leifer Editors Design Thinking Research Achieving Real Innovation
Understanding Innovation Series Editors Christoph Meinel, Potsdam, Germany Larry Leifer, Stanford, USA
“Everyone loves an innovation, an idea that sells.” Few definitions of innovation are more succinct. It cuts to the core. Yet in doing so, it lays bare the reality that selling depends on factors outside the innovation envelope. The “let’s get creative” imperative does not control its own destiny. Expressed another way, in how many ways can we define innovation? A corollary lies in asking, in how many ways can the innovative enterprise be organized? For a third iteration, in how many ways can the innovation process be structured? Now we have a question worth addressing. “Understanding Innovation” is a book series designed to expose the reader to the breadth and depth of design thinking modalities in pursuit of innovations that sell. It is not our intent to give the reader a definitive protocol or paradigm. In fact, the very expectation of “one right answer” would be misguided. Instead we offer a journey of discovery, one that is radical, relevant, and rigorous.
Christoph Meinel • Larry Leifer Editors Design Thinking Research Achieving Real Innovation
Editors Christoph Meinel Hasso Plattner Institute and Digital Engineering Faculty University of Potsdam Potsdam, Germany Larry Leifer Stanford University Stanford, CA, USA ISSN 2197-5752 ISSN 2197-5760 (electronic) Understanding Innovation ISBN 978-3-031-09296-1 ISBN 978-3-031-09297-8 (eBook) https://doi.org/10.1007/978-3-031-09297-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Foreword As year 13 of the Hasso Plattner Design Thinking Research Program comes to a close, we can take this opportunity to reflect on over a decade of close cooperation between the d.school at Stanford University in California and the School of Design Thinking at the Hasso Plattner Institute for Digital Engineering in Potsdam. Humancentered design is an integral part of the digital engineering curriculum. At the same time, students from other fields also have the possibility to learn Design Thinking methods and create impact through human-centered design. There is an increasing interest in Design Thinking. In universities around the world, Design Thinking programs have been established. The aim has been to complement the education of students through a practical, user-centered approach focused on real-life projects. However, the methodology has not just appeared in academic settings. Practitioners have also adopted Design Thinking in business and industry. Design Thinking offers a powerful framework to foster innovation in products, services, and operations. There is no blueprint to develop an innovation culture. It is a step-by-step process that evolves with the individual contributions of team members. Researchers and practitioners alike identify, iterate, and test ideas as part of a Design Thinking process. In parallel, the Hasso Plattner Design Thinking Research Program, a research initiative conducted jointly by Stanford University in California and the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany, has been working diligently to learn more about the approach. Affiliated researchers in North America and Europe have meanwhile conducted more than 160 research projects investigating, illuminating, and making sense of Design Thinking. Our understanding of the methodology has increased exponentially and we have a solid body of new knowledge on the characteristics and mechanisms of effective Design Thinking tools, team dynamics, and its application in various contexts. Cutting-edge research, of course, includes data on interactions in virtual and hybrid environments—topics that have gained increasing importance in the last year. Collaboration between different university faculties has increased. Levers for value generation based on human-centric principles can be found in many academic v
vi Foreword disciplines. Concrete outputs are captured in this volume. One excellent example is a cooperation with neuroscientists. Design Thinking researchers have leveraged newly developed research methodologies, approaches, and knowledge to foster new ideas and research in neurodesign, emerging in both Stanford and Potsdam. In 2019, the Hasso Plattner Institute in Potsdam was the first to offer a neurodesign curriculum for its IT students. In the last three years, the Hasso Plattner Institute has held several neuroscience symposiums for researchers and established formal and informal agreements for collaborative work with internationally recognized researchers from several countries and continents. This work illuminates the biological basis of creativity, collaboration, innovation, and human-centered design. In each of the projects highlighted in this volume, co-creation helps teams harness Design Thinking for value capture and provides value to the (research) community. Tools and mindsets are calibrated and recalibrated as researchers make strides to integrate new datasets into established models and to develop new models as necessary. These developments demonstrate once again the fruitful research conducted by multi-disciplinary teams in the area of design thinking research. One way of bringing the findings to innovators everywhere is through the book series “Design Thinking Research” published by Springer. This series presents a comprehensive collection of research studies carried out by scholars at both the Hasso Plattner Institute in Potsdam and Stanford University. In addition to providing the findings of the most recent projects, the 13th volume of the series pushes the boundaries and searches for insights on achieving “real innovation.” The Design Thinking Research Program includes a growing community of researchers in a transatlantic context and has influenced many others around the world. The program enables a rich exchange between current doctoral candidates, alumni, researchers, and practitioners from diverse disciplines. Through collaboration and partnerships of many kinds, the Design Thinking Research Program brings new perspectives, insights, and lasting value not only to the program and its related researchers, but also to Design Thinking itself and the growing community of Design Thinkers. We urge you to make a contribution to the already rich exchange between innovators and researchers. New actors and established stakeholders are invited to reach out and encourage cooperation and experimentation in their personal ecosystems. In so doing, the practice and understanding of Design Thinking continues to broaden and deepen, as it carries on its course of defining solutions for the challenges facing our world. Palo Alto, CA, USA Hasso Plattner
Introduction: “The Impact of REAL Prototyping and Thinking” Is there any chance of removing “THINKING” and replacing it with “QUESTIONING”? John Arnold, the founder of the Design Program at Stanford, famously asserted that “DESIGN is all about QUESTIONS” in a class shortly after he came to Stanford. Notably, he had been more or less forced out of the mechanical engineering faculty at another university because of his insistence on teaching engineering students to be creative, whereby the focus of the engineering mainstream was all about being a good physicist or mathematician. In order to make strides forward in this field, we needed to be creative and to value creativity. This is still the best strategy. Creativity must be then applied to BUILDING real prototypes. Only in this way are we able to move beyond “thinking” to “testing.” Real IMPACT comes when we test with prospective HUMAN users of the product, the prototype, or the service. The Design Thinking process enables researchers to think outside the box about future solutions. REAL innovation demands REAL divergent thinking and building. Where might we look to measure divergence? How about looking at the brains of creative thinkers versus trend thinkers. This volume of Design Thinking Research presents us with dramatic examples of just this view. One research team, for instance, measures differences in brain activity and thereby compares the behavior of divergent with trend thinkers. While we have to be aware of the context in which we are working, at the same time we must be ready to rethink our preconceived ideas. Design Thinking is a learning experience. As such, each learning experience should consider new methods, new capabilities, and new ways of thinking, and yield convergence in the Design Thinking mindset. Since 2008, research teams from the Hasso Plattner Institute for Digital Engineering in Potsdam, Germany. and from Stanford University, USA, have engaged in the joint Design Thinking Research Program, which is financed and supported by the Hasso Plattner Foundation. In this volume, multidisciplinary research teams from the HPI-Stanford Design Thinking Research Program scientifically investigate Design Thinking, digital vii
viii Introduction: “The Impact of REAL Prototyping and Thinking” transformation, and innovation in their various dimensions. Researchers are continually engaged in ideating, advancing, and appraising. Creative collaboration is a hallmark of the teams. In regularly scheduled community building workshops, researchers share insights from current investigations and obtain constructive feedback. This process is all part of the multidisciplinary approach to which researchers aspire. Scientific evidence supports all design activities undertaken by our research teams. For many years, members of our Design Thinking research teams have asked and continue to ask why subjects do what they do. For impact, we test prototypes with real users (including each other). Design Thinking behavior can be quantified, and there are tools and frameworks in place that help us advance our knowledge of Design Thinking. Let us consider the novel use of haptic information (gestures) versus the spoken and/or written word. From our various political situations, we have discovered that gestures often reveal more about the speaker’s emotions and deep beliefs than their words. The role of emotions in technical work and innovation has long been regarded with suspicion. The PhD research of some of our doctoral candidates has now revealed that emotions play a significantly greater role in technical decision making than was previously thought. Similar findings have been produced for business decision makers, for example, by psychology Professor Brian Knutson at Stanford University. Throughout the history of our book series, we have looked critically at the issues and values that yield innovation—or fail to do so. Our design research has made many different measures of team innovation a reality: its causes, limits, and implementation. Very importantly, we have gathered evidence to measure what does and does not make a difference in the ability of a culture or organization to innovate, as well as the impact of these innovations (real or imagined) on the company culture. We have measured the emotions and the logic of innovation teams. No one variable is predictive of a corporate design team’s performance. All the variables and their context need to be understood and measured. The chapters in this volume address the complex relationships between Design Thinking, business models, and entrepreneurship. Creative solutions and continuous development are the goals needed to redesign work practices. Following these goals allows teams to evolve in response to internal and external factors. To achieve these solutions and this development we need creative thought and communication. Our teams measure the impact of Design Thinking education on young professionals’ predisposition to entrepreneurial outcomes in the short term and long term. The entrepreneurial outcomes of former Design Thinking students have increased significantly over the last decade. Relevant coursework contributes to the creation of an ecosystem, which propels these entrepreneurs forward, as it transcends its original context. Other teams theorize about how to build an appropriate framework to assess the impact of Design Thinking in organizations. However, before we can build a framework we have to understand what challenges can be addressed by practitioners in the organizational context.
Introduction: “The Impact of REAL Prototyping and Thinking” ix Engage. Collaborate. Interact. Join us in exploring the hidden world of team innovation. Read our books. Get in the driver’s seat and steer innovation. Design your own research program. Then, share your ideas with us. It is our intention that this series volume will be a starting point for dialogue in many realms. Please join us in our quest for noteworthy hypotheses, and in understanding why these hypotheses are critical to the implementation of Design Thinking and Design Thinking Research in academia and in industry. Road Map Through This Book Over the past year, researchers from HPI and Stanford University have immersed themselves in a wide range of research projects on Design Thinking. Volume 13 in the series Design Thinking Research highlights the findings of affiliated researchers for program year 13. The breadth of topics is impressive. We have been able to arrange the contributions into three parts, each addressing a different aspect of Design Thinking research. Part 1: Value Creation in Virtual Innovation Spaces The demand for virtual solutions has increased dramatically over the last two years. In the virtual realm, interaction takes many new directions. Researchers in the HPDTRP tackle the provocative questions of value creation in virtual innovation spaces. They do this by way of nuanced information, which they have obtained, on how individuals interact, and then develop cooperative scenarios, and negotiate ambiguity. Teams develop appropriate measurement tools for leveraging success in outcomes, examine existing barriers for technology users, and stake out programmatic changes for facilitating virtual Design Thinking. In the first text, Stephanie Balters, Theresa J. Weinstein, Grace Hawthorne, and Allan Reiss proceed with the goal of designing better (i.e., new and more efficient) team performance. The team sets out to show to what extent participation in an interpersonal trust activity prior to teamwork in an in-person or virtual setting increases the interpersonal trust and creativity level of the collaborative outcome. In order to test this hypothesis, functional near-infrared spectroscopy (fNIRS) hyperscanning is used. The desired outcome is for DT educators and practitioners to design novel and effective DT activities that correlate positively to team trust and collaboration. The research team consisting of So Yeon Park, Mark E. Whiting, and Michael Shanks raise the question of why ambiguity is so common in online interactions. By design, ambiguity is possible, with online actions curated and designed by platforms. Platforms in turn are the mediators. Park, Whiting, and Shanks address the desired goal of removing ambiguity in online actions through targeted implementations
x Introduction: “The Impact of REAL Prototyping and Thinking” while also improving clarity. The team provides complete design implications for this purpose that target technical functioning and context. This chapter explores how social media is designed, and how the scientific community and public react when using it. The following chapter prepared by Carolin Marx examines links between Design Thinking and digital transformation. Carolin Marx presents a study that defines context-dependent and cause-effect relationships to investigate the role dynamic capabilities can play within this network of effects. By studying these relationships, practitioners can test hypotheses for specific contributions of Design Thinking on digital transformation. The chapter offers results from two empirical studies. Selina Mayer, Martin Schwemmle, Claudia Nicolai, and Ulrich Weinberg introduce theoretical reflections for the process of providing optimized virtual education experiences. This chapter provides theoretical foundations for evolving conceptions. In reviewing various theories, the reader is inspired to use ideas to improve Design Thinking education in the virtual realm. Danyang Fan, Kate Glatzko, and Sean Follmer underscore the importance of reflecting the diagram structure with the navigation scheme of online whiteboards to provide a spatial overview for users to reference. These tools are often exclusionary to people who are blind and visually impaired. This work provides a number of recommendations and endeavors to improve the accessibility of online whiteboards. The authors elaborate upon an accessibility gap when interacting with linked-node diagrams within online white boards and relate the results of user studies with university students to better understand the accessibility of current tools. Researchers and students then collaboratively evaluated several existing audio and haptic approaches. They describe design guidelines and future directions that might improve user access and understanding of linked-node diagrams. Part 2: Fostering Innovation Behavior and Co-evolution Contributions in the second part of this book tackle the question of how innovation behavior can be fostered and augmented. The researchers identify perceived problems or target the creation of novel approaches to augment existing practice. The DT project teams are engaged in formulating the right questions and sharing key insights for DT practitioners and society. The practice of programming changes rapidly. Marcel Taeumel, Jens Lincke, Patrick Rein, and Robert Hirschfeld begin with the assumption that newcomers to the programming field face significant challenges at the beginning in understanding mindsets and tools. The team applies the idea of patterns to stake out traditional and modern practices of exploratory programming. The workspace tool is evaluated. The team extracted data into a novel pattern language that focuses on the typical question/response cycle that programmers follow. Their findings will benefit project team efficiency.
Introduction: “The Impact of REAL Prototyping and Thinking” xi In the next chapter, the lead author’s dissertation project with an industry partner is front and center. Lena Mayer, Katharina Hölzle, Karen von Schmieden, Reem Refaie, Hanadi Traifeh, and Christoph Meinel contribute to the publication. Narratives from innovation practitioners are translated into a quantitative study. The empirical study examines the links between employee job insecurity and innovation behavior. The team further discusses the organizational factors that can foster employee innovation behavior. Parastoo Abtahi, Sidney Q. Hough, Jackie Yang, Sean Follmer, and James A. Landay grapple with how next-generation computing systems challenge traditional ideas of good design and user-centered processes. The authors explore state exposure timing strategies that enable users to develop up-to-date mental models of systems that are in a constant process of learning and evolving. When user expectations are not met, according to the research teams, systems should allow for the co-evolution of mental models and dynamic systems. Jonathan Antonio Edelman, Babajide Alamu Owoyele, Joaquin Santuber, and Stefan Konigorski argue that there is a need for more education and training on value creation in developing better healthcare systems. The authors discuss two approaches (MEDGI and PretoVids) that they have developed within the past three years. They have achieved significant inroads in increased customer engagement, reduced risk, and improved profit by encouraging medical practitioners and industry actors to leverage Design for Value Creation (D4VC) in healthcare. After reviewing design literature in the field of healthcare, the authors show how design can mediate and facilitate the creation of value for many stakeholders. Part 3: Problematizing Design Thinking as a Concept This volume’s third part is concerned with building comparative frameworks in the process of problematizing DT as a concept. The chapters in this section help the reader to visualize existing models, stake out relationships between DT education and societal change, and gain a better understanding of DT and its evolution over the past 50 years. Julia von Thienen, Constantin Hartmann, and Christoph Meinel define the variable “human needs” as part of DT—but how does this relate to the variable (radical) innovation? The authors examine need theories of three authors: John Arnold, Abraham Maslow, and Robert McKim. Many questions are raised, such as: What is the future of DT? Is DT only implemented to provide incremental innovation—in other words, improvement within existing solutions? The researchers conclude with a discussion on the diversity of human needs concepts in DT. The outlook shows that new development provides a comprehensive framework of human needs to analyze product risks and create systemic benefits. Jan Auernhammer, Matteo Zallio, Lawrence Domingo, and Larry Leifer stake out the evolution of multiple human-centered design approaches over the preceding 50 years. It is noted that these approaches were developed and adopted in response to
xii Introduction: “The Impact of REAL Prototyping and Thinking” socio-material and socio-economic challenges. In this work, emphasis is put on the need to continue exploring creative design approaches to new challenges. The author team, composed of Sheri D. Sheppard, Helen L. Chen, George Toye, Timo Bunk, Nada Elfiki, Felix Kempf, Johannes Lamprecht, and Micah Lande, focuses on alumni of specific mechanical engineering courses (ME310 and ME218) at Stanford University. The authors examine the long-term impact of specific engineering design courses on graduates’ career paths, as well as the subjects’ practical utilization of design after graduation. This research helps the team assess to what extent enrollment in specific coursework leads to innovation and entrepreneurship among alumni. In addition, the team is able to pinpoint specific improvements for current engineering design education. Jan Auernhammer and Bernard Roth outline the theory of productive thinking in design. In the process, they identify five different types of productive thinking. These strands of thinking contribute to an evolving type of productive culture that gives individuals a diverse toolbox with which to design in many realms. The authors emphasize the goal of cultivating a harmonious Productive Culture. Xiao Ge, Chunchen Xu, Nanami Furue, Daigo Misaki, Cinoo Lee, and Hazel Rose Markus address a gap in research and practice: that is, the role of culture in understanding and promoting creativity in design education. Creative behavior can connect, preserve, or offer new ideas and change. The authors examine how creativity and creative problem solving are shaped in the cultural contexts of the USA and East Asia using the culture cycle framework. The author’s pilot studies challenge the popular view that only agentic change-makers are to be seen as creative problem solvers. The research results should be encouraging to designers from non-Western societies who wish to incorporate culturally varied ideas about creative problem solving into design processes. Nicole M. Ardoin, Alison W. Bowers, Veronica Lin, and Indira Phukan, researchers at the Social Ecology Lab at Stanford University, argue that Design Thinking is a useful, appropriate, and necessary approach to support collective action in addressing sustainability problems. The authors argue for greater alignment between Design Thinking and sustainability action. The Social Ecology Lab has consistently found Design Thinking useful in generating, iterating, reflecting, and adapting, especially when working with a diverse group of individuals. The researchers seek to understand the relationship between Design Thinking and sustainability. In doing so, they discuss propositions as to whether sustainability issues can be reframed as broader societal considerations. Outlook Creativity and innovation are the building blocks of Design Thinking. Understanding how researchers can bridge the gap between theory and practice is essential to the work currently being done in the field of Design Thinking. With this objective in mind, research teams assess how creativity is manifested in many different
Introduction: “The Impact of REAL Prototyping and Thinking” xiii situations. We define three areas of investigation in this year’s volume. In the first area, attempts are made to bring about value creation in virtual innovation spaces. In a second line of research, project teams show how to foster innovation behavior and implement co-evolution. A third group of researchers builds comparative frameworks and problematizes Design Thinking as a concept. The ongoing research, collaboration, and discovery in the field of Design Thinking generated by our affiliated researchers is laid out in this volume to inspire readers and to capture a snapshot of work in progress that has a broad impact on academia and industry. Included in this year’s volume, immediately following the introduction, the editors and several authors have come together to discuss and stake out a philosophy for the field. This chapter outlines a humanistic and creative philosophy of design that has evolved over several decades at Stanford University. This philosophy of design is often referred to as human-centered design and Design Thinking. It is design that incorporates humanistic and creative qualities, including creative thinking modes, attitudes and human values, creative attributes, visual and collaborative abilities, activities and practices, useful techniques, and a supportive environment while also addressing blocks to creativity. Developing and cultivating these qualities aims to encourage creative design in individuals and teams that not only satisfies people’s profound needs, but also resolves and harmonizes societal and ecological tensions. The aim is to develop innovators. A critical number of creative individuals who collaboratively support each other in the challenges inherent in designing innovation and entrepreneurial activities have the ability to spark an era of innovation. For over a decade, the HPI-Stanford Design Thinking Research Program has transmitted valuable insights on how and why Design Thinking works. In this volume, concrete examples serve as guides to others active in the field. On the way to new frameworks, tools, and systems, our scholars from their various academic backgrounds work in interdisciplinary teams and share their successes and failures with us in this volume. We invite you to visit www.hpi.de/dtrp. This website provides an overview of the research projects included in the HPI-Stanford Design Thinking Research Program, from 2008 to the present. Here you can also find the names and affiliations of participating team members. Another good resource is the website: https://thisisdesignthinking.net. This site offers a comprehensive look at current research projects in the field of Design Thinking. Contributors to the site offer unparalleled access to the strengths and weaknesses of particular strategies. The challenges facing our world today are also problematized and examined through the lens of Design Thinking. Educators and design practitioners can obtain timely information on challenges in the field of Design Thinking and join in the conversation. Please send experiences, stories, and inquiries to thisisdesignthinking@hpi.de. Achieving real innovation is possible. You have the building blocks in your hands. Live Design Thinking—ask the question “why?”, but don’t stop there. Keep on asking questions. We wish you success in your endeavors and encourage you to contact us directly. It would be our pleasure to hear from you.
Acknowledgments We thank all the authors for sharing their research results in this publication. Our special thanks go to Dr. Sharon Nemeth for her constant support in reviewing the contributions and to Jill Grinager for the publication’s project management. xv
Contents A Humanistic and Creative Philosophy of Design . . . . . . . . . . . . . . . . . . Jan Auernhammer, Larry Leifer, Christoph Meinel, and Bernard Roth Part I 1 Value Creation in Virtual Innovation Spaces Interpersonal Trust Activity to Increase Team Creativity Outcome: An fNIRS Hyperscanning Approach . . . . . . . . . . . . . . . . . . . Stephanie Balters, Theresa J. Weinstein, Grace Hawthorne, and Allan L. Reiss 19 Dancing with Ambiguity Online: When Our Online Actions Cause Confusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . So Yeon Park, Mark E. Whiting, and Michael Shanks 37 Design Thinking for Digital Transformation: Reconciling Theory and Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carolin Marx 57 Experiences of Facilitating Virtual Design Thinking: Theoretical Reflections and Practical Implications . . . . . . . . . . . . . . . . . . . . . . . . . . Selina Mayer, Martin Schwemmle, Claudia Nicolai, and Ulrich Weinberg 79 Accessibility of Linked-Node Diagrams on Collaborative Whiteboards for Screen Reader Users: Challenges and Opportunities . . . . . . . . . . . . . Danyang Fan, Kate Glazko, and Sean Follmer 97 Part II Fostering Innovation Behavior and Co-evolution A Pattern Language of an Exploratory Programming Workspace . . . . . 111 Marcel Taeumel, Jens Lincke, Patrick Rein, and Robert Hirschfeld xvii
xviii Contents Practice-to-Research: Translating Company Phenomena into Empirical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Lena Mayer, Katharina Hölzle, Karen von Schmieden, Reem Refaie, Hanadi Traifeh, and Christoph Meinel Timely State Exposure for the Coevolution of Mental Models and Dynamic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Parastoo Abtahi, Sidney Q. Hough, Jackie Junrui Yang, Sean Follmer, and James A. Landay Designing for Value Creation: Principles, Methods, and Case Insights from Embedding Designing-as-Performance in Digital Health Education and Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Jonathan Antonio Edelman, Babajide Alamu Owoyele, Joaquin Santuber, and Stefan Konigorski Part III Problematizing Design Thinking as a Concept Different Concepts of Human Needs and Their Relation to Innovation Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Julia von Thienen, Constantin Hartmann, and Christoph Meinel Facets of Human-Centered Design: The Evolution of Designing by, with, and for People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Jan Auernhammer, Matteo Zallio, Lawrence Domingo, and Larry Leifer Decades of Alumni: Designing a Study on the Long-Term Impact of Design Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Sheri D. Sheppard, Helen L. Chen, George Toye, Timo Bunk, Nada Elfiki, Felix Kempf, J. L. Lamprecht, and Micah Lande Different Types of Productive Thinking in Design: From Rational to Social Design Thinking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Jan Auernhammer and Bernard Roth The Cultural Construction of Creative Problem-Solving: A Critical Reflection on Creative Design Thinking, Teaching, and Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Xiao Ge, Chunchen Xu, Nanami Furue, Daigo Misaki, Cinoo Lee, and Hazel Rose Markus Design Thinking as a Catalyst and Support for Sustainability Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Nicole M. Ardoin, Alison W. Bowers, Veronica Lin, and Indira Phukan
Contributors Parastoo Abtahi Department of Mechanical Engineering, Stanford School of Engineering, Stanford University, Stanford, CA, USA Nicole M. Ardoin Graduate School of Education, Stanford University, Stanford, CA, USA Stanford Woods Institute for the Environment, Stanford University, Stanford, CA, USA Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA, USA Jan Auernhammer ME Design Group, Center for Design Research, Stanford University, Stanford, CA, USA Stephanie Balters Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University School of Medicine, Stanford, CA, USA Alison W. Bowers Graduate School of Education, Stanford University, Stanford, CA, USA Timo Bunk Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA Helen L. Chen Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA Lawrence Domingo ME Design Group, Center for Design Research, Stanford University, Stanford, CA, USA Jonathan Antonio Edelman Stanford, California, USA Nada Elfiki Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA xix
xx Contributors Danyang Fan Department Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA Sean Follmer Department Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA Nanami Furue School of Management, Tokyo University of Science, Tokyo, Japan Xiao Ge Center for Design Research, Stanford University, Stanford, CA, USA Kate Glatzko Department Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA Constantin Hartmann Hasso Plattner Institute, Potsdam University, Potsdam, Germany Grace Hawthorne Hasso Plattner Institute of Design (d.school), CA, USA Robert Hirschfeld Hasso Platter Institute, Potsdam, Germany Katharina Hölzle HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany Sidney Q. Hough Department of Mechanical Engineering, Stanford School of Engineering, Stanford University, Stanford, CA, USA Felix Kempf Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA Stefan Konigorski Hasso Plattner Institute for Digital Engineering, Potsdam, Germany J. L. Lamprecht Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA James A. Landay Department of Mechanical Engineering, Stanford School of Engineering, Stanford University, Stanford, CA, USA Micah Lande Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA Cinoo Lee Stanford SPARQ, Stanford University, Stanford, CA, USA Larry Leifer ME Design Group, Center for Design Research, Stanford University, Stanford, CA, USA Jens Lincke Hasso Platter Institute, Potsdam, Germany Veronica Lin Graduate School of Education, Stanford University, Stanford, CA, USA Hazel Rose Markus Stanford SPARQ, Stanford University, Stanford, CA, USA Carolin Marx Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
Contributors xxi Lena Mayer HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany Selina Mayer HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany Christoph Meinel Hasso Plattner Institute and Digital Engineering Faculty, University of Potsdam, Potsdam, Germany Daigo Misaki Faculty of Engineering, Kogakuin University, Tokyo, Japan Claudia Nicolai HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany Babajide Alamu Owoyele Hasso Plattner Institute for Digital Engineering, Potsdam, Germany So Yeon Park Stanford University, Stanford, CA, USA Indira Phukan Graduate School of Education, Stanford University, Stanford, CA, USA Reem Refaie HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany Patrick Rein Hasso Platter Institute, Potsdam, Germany Allan L. Reiss Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University School of Medicine, Stanford, CA, USA Bernard Roth ME Design Group and Hasso Plattner Institute of Design, Stanford University, Stanford, CA, USA Joaquin Santuber Hasso Plattner Institute for Digital Engineering, Potsdam, Germany Martin Schwemmle HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany Michael Shanks Stanford University, Stanford, CA, USA Sheri D. Sheppard Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA Marcel Taeumel Hasso Platter Institute, Potsdam, Germany George Toye Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA Hanadi Traifeh HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany Karen von Schmieden HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
xxii Contributors Julia von Thienen Hasso Plattner Institute, Potsdam University, Potsdam, Germany Ulrich Weinberg HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany Theresa J. Weinstein Hasso-Plattner-Institute, Potsdam University, Potsdam, Germany Mark E. Whiting University of Pennsylvania, Philadelphia, PA, USA Chunchen Xu Stanford SPARQ, Stanford University, Stanford, CA, USA Jackie Yang Department of Mechanical Engineering, Stanford School of Engineering, Stanford University, Stanford, CA, USA Matteo Zallio Inclusive Design Group, Engineering Design Center, University of Cambridge, Cambridge, UK
A Humanistic and Creative Philosophy of Design Jan Auernhammer, Larry Leifer, Christoph Meinel, and Bernard Roth Abstract This chapter outlines a humanistic and creative Philosophy of Design that evolved over several decades at Stanford University. This Philosophy of Design is often referred to as Human-centered Design and Design Thinking. It incorporates humanistic and creative qualities, including creative thinking modes, attitudes and human values, creative attributes, visual and collaborative abilities, blocks to creativity, activities and practices, useful techniques, and a supportive environment. Developing and cultivating these qualities aims to encourage creative design in individuals and teams that satisfies people’s profound needs and resolves and harmonizes societal and ecological tensions. The intention is to develop innovators. A critical number of creative individuals who collaboratively support and help each other in the challenges inherent in designing innovation and entrepreneurial activities can spark an era of innovation. 1 Introduction Over the last seven decades, Design has evolved at Stanford University from creative product design to design thinking, tackling many diverse challenges. In the beginning, the integration of arts and engineering education established the Joined Product Design Program. Over time, many different disciplines, such as biology, computer science, electronics, management, marketing, and medicine, combined into a J. Auernhammer (*) · L. Leifer ME Design Group and Center for Design Research, Stanford University, Stanford, CA, USA e-mail: jan.auernhammer@stanford.edu C. Meinel Hasso Plattner Institute and Digital Engineering Faculty, University of Potsdam, Potsdam, Germany e-mail: Christoph.Meinel@hpi.de B. Roth ME Design Group and Hasso Plattner Institute of Design, Stanford University, Stanford, CA, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_1 1
2 J. Auernhammer et al. comprehensive design practice. The focus shifted from designing artifacts for people to interactions and experiences design, biodesign, software design, life design, design for extreme affordability, entrepreneurship, and organizational design, to name a few (Auernhammer & Leifer, 2019; Buchenau & Fulton Suri, 2000; Burnett & Evans, 2016; Miller et al., 2001; Moggridge, 2007; Srinivasan et al., 1997; Winograd, 1996). Underneath the different pan-disciplinary design practices is a shared Philosophy of Design often referred to as Human-centered Design and Design Thinking. This philosophy is deeply embedded in the humanistic psychology of creativity (Adams, 1974; Arnold, 1959; Guilford, 1959; Hartman, 1959; Maslow, 1959; McKim, 1959, 1972; Roth, 1973). It emphasizes designing for the whole person and people in need (Arnold, 1959; McKim, 1959). It also centers on enabling creativity in designers. John Arnold, Bob McKim, and many members within the wider design community developed educational programs and practices to encourage the creative potential in people through perceptual, imaginative, expressive, and collaborative exercises and project-based learning (Adams, 1974; Arnold, 1959, 1962a; Kelley & Kelley, 2013; Leifer, 1998; McKim, 1972, 1980a, 1980b; Roth, 1973, 2015; Stein-Greenberg, 2021). This Philosophy of Design incorporates several humanistic and creative qualities. These qualities are (1) perceptual, imaginative, and expressive thinking modes, (2) attitudes and human values, (3) attributes, (4) abilities, (5) blocks, (6) activities and practices, (7) techniques, and (8) environment (Auernhammer & Roth, 2021). These qualities are a holistic view of human creativity in design practices. This chapter outlines the humanistic and creative qualities in design that have been cultivated over decades. This Philosophy of Design expanded globally into academic programs, such as the Hasso Plattner Design Thinking Research program and companies, such as IDEO, Apple, Concept2, Edge Innovation, and Wet Design, contributing to society with various innovations. Many companies, such as P&G, SAP, IBM, Intuit, and Samsung, and numerous Universities, through global collaboration, such as ME310, developed a design approach inspired by this humanistic and creative Philosophy of Design. 2 Humanistic and Creative Qualities in Design The qualities inherent in the Philosophy of Design are based on humanistic psychology on creativity (Auernhammer & Roth, 2021). Creativity incorporates a dynamic interplay of (1) thinking modes, including perceptual, imaginative, and expressive modes (Arnold, 1959; McKim, 1980a). Designing for people requires (2) attitudes and human values to recenter to understand people’s needs and experiences, going beyond stereotypes (Arnold, 1959; McKim, 1959, 1980a; Wertheimer, 1945). Developing creative behavior is associated with nurturing creative (3) attributes, such as problem and need-sensitivity, openness to experience, and tolerance for ambiguity (Arnold, 1959; Guilford, 1950; Rogers, 1954). Making a tangible design that others can react to requires (4) abilities, such as visualizing, making, and communicating (Arnold, 1959; McKim, 1980a). Developing tangible design
A Humanistic and Creative Philosophy of Design 3 solutions requires diverse abilities, such as collaborating with many diverse people. In such collaborations, (5) blocks to creativity prevent people from creating novel and valuable design solutions (Adams, 2019; Arnold, 1959; Maslow, 1962; Papanek, 1973). Actively overcoming these creativity blocks in everyday interactions in teams and organizations encourages creative behavior and a culture conducive to creativity. Various (6) activities and practices, such as need-finding, visualization, mock-up building, and evaluation, enable collaboration among diverse stakeholders, resolve structural tensions, and satisfy people’s needs (McKim, 1980a, 1982). Various (7) techniques are useful in these activities to stimulate, e.g., divergent thinking and group creativity (Arnold, 1959, 1962b). A conducive (8) environment for creativity is imperative for the various activities and practices to be productive. Such an environment provides psychological safety and freedom to play unreservedly with materials and learn from unsuccessful attempts without fear of making a mistake (Arnold, 1959; Arnold et al., 1960; McKim, 1980a). 2.1 Thinking Modes The creative accomplishment of innovative design is primarily an embodied mental activity (Arnold, 1959; McKim, 1980a). It consists of mental activities, such as perceptual, imaginative, and expressive activities (Faste, 1994; McKim, 1980a). The perceptual processes of organizing, identifying, and interpreting sensory input are imperative when designing (Arnheim, 1967; McKim, 1980a). Perceiving a situation from different centers makes it possible to overcome stereotypes, grasp people’s needs, understand the human-made environment, and perceive the whole situation in new ways (McKim, 1980a; Nelson, 2003; Wertheimer, 1945). Recentering and redesigning situations is an imaginative act, which incorporates mental activities, such as envisioning mental imagery, visual recall, and directed fantasy (McKim, 1980a). Communicating these envisioned situations requires expressive embodied mental activities, including visualization, idea sketching, mock-up building, and enacting (McKim, 1980a). These mental activities are imperative in finding needs, envisioning situations, and experiencing and evaluating the design within the situation. Perceptual, imaginative, and expressive mental activities occur dynamically as, for example, a sketch is perceived in a new way triggering a new mental image (Bamberger & Schön, 1983; Koffka, 1927; McKim, 1980a; Schön, 1992). Moving dynamically between perceiving, imagining, and expressing makes it possible to explore diverse design solutions. In such creative acts, designing for people and resolving structural tensions to provide value requires certain attitudes and human values.
4 J. Auernhammer et al. 2.2 Attitudes and Human Values Positive attitudes towards other living organisms are essential in recentering, overcoming one-sided, superficial views (McKim, 1980a; Wertheimer, 1945). Such attitudes are essential in perceiving how others experience the situation and designers’ own role in the situation. Human values and attitudes, such as questioning and keen observation, are imperative in perceiving the situations as a whole to grasp people’s needs and tensions (Arnold, 1959). McKim (1959) emphasizes the importance of human values in design as follows: [. . .] knowledge of and concern for human values and needs is of prime importance to the designer. Design is, after all, a response to human needs; needs which are all too often lost sight of in this age of intense technology. Human values and attitudes are essential in recentering from self to other people, enabling perceiving the situation as a new whole, overcoming ego-perspective and stereotypes, grasping people’s needs, and enabling designing new human experiences (Arnheim, 2009; McKim, 1980a; Wertheimer, 1945). In contrast, negative assumptions and self-centric attitudes often prevent designers from seeing the whole situation from diverse points of view. Designers’ values and needs influence the design at each step (Rittel, 1987). This Philosophy of Design focuses on creatively designing for people to create value by resolving structural tensions and satisfying human needs. Technology has no value by itself. Designs create value and meaning in everyday life situations when they empower people, satisfy profound needs, harmonize tensions, and reinterpret existing designs (Krippendorff, 2006; McKim, 1959; Wertheimer, 1945). Without human values and attitudes (i.e., mindsets), designers do not recognize people’s needs and tensions, and their designs have little value in the real world, satisfying merely designers’ interests (McKim, 1959; Papanek, 1973). 2.3 Attributes Discovering or finding a profound need or problem requires attributes of openness to experiences and problem or need-sensitivity. This means designers’ attributes are imperative in innovation (Arnold, 1959). Sensitivity to human needs and problems and openness to experiences are qualities attributed to creative individuals (Guilford, 1950; Rogers, 1954). Finding needs and problems identify opportunities for innovation and often sets creative individuals apart from experts (Getzels, 1980). In everyday life, people ignore or do not notice other people’s needs and wider societal and ecological tensions. Developing the attributes of need and problem-sensitivity and curiosity through openness to experience allows uncovering opportunities for new tangible designs that satisfy needs and resolve tensions. Exercising practices, such as engaging with people in their life situations, develops an understanding of the profound need and tension.
A Humanistic and Creative Philosophy of Design 5 For resolving tensions and satisfying needs through design, fluency in thinking modes and flexibility in creation and use of techniques are essential attributes. The emerging need and problem from the situation demands different “thinking languages,” such as symbolic, visual, kinesthetic, or emotional languages (Adams, 2019; Faste, 1994). It requires attributes of flexibility to dynamically respond appropriately to situations and inherent needs. Similarly, fluency in perception, recentering, imagination, expression, and communication is imperative in grasping situations and inherent needs to redesign them in more meaningful and valuable ways. Attitudes to consider one group over another and functional fixedness inhibit recentering and grasping the whole situation, preventing an understanding of the actual needs, tensions, and opportunities (Duncker, 1945; McKim, 1980a; Wertheimer, 1945). Engaging in a situation that is unclear and ambiguous, and thereby preventing a clear path to action, is often unbearable for people (Wertheimer, 1945). Therefore, the attribute of tolerance for ambiguity enables designers to explore situations without hasty conclusions and premature closure (Rogers, 1954). Such tolerance for ambiguity increases in importance when not merely finding a suitable solution, but instead what is a truly valuable solution for many people. Such value creation incorporates the attribute of originality. Originality is the attribute inherent in creative individuals to produce novelty (Guilford, 1950). An existing solution may resolve the tension. However, developing the attribute of originality, which is the curiosity and desire to contribute with a novel design, may provide a more profound experience for people by designing for the whole person and pluralistic society (McKim, 1959; Rittel, 1987). Original designs can be created through collaborative dialogues of reinterpreting existing solutions through new product semantics (Krippendorff, 2006). Originality is highly dependent on Zeitgeist, as one design solution resolves tension and, over time, creates another, making design a neverending temporal-dependent effort (Rittel & Webber, 1973). Any undisputable creative contribution to society requires attributes of drive, motivation, and confidence (Amabile, 1996; Arnold, 1959). Motivation often emerges in the thinker from the felt need and structural tension, determining the direction towards harmonizing it (Wertheimer, 1945). Designing and developing tangible solutions demands attributes of drive to increase motivation over longer periods, which is required to overcome the many obstacles and pitfalls involved in innovation (Arnold, 1959). Such creative contribution demands building, testing, breaking, and rebuilding the design repeatedly until a working design solution is determined for many potential structural tensions, such as environmentally friendly manufacturability and satisfying diverse people’s needs. Overcoming these pitfalls requires confidence in creative ability. Part of this ability is to find the necessary resources and people with essential skills. Collaboratively developing countless projects, in which people help each other, develops creative confidence, i.e. selfefficacy in design (Arnold, 1962a; Kelley & Kelley, 2013). Problem and needsensitivity, openness to experience, tolerance for ambiguity, fluency, flexibility, originality, drive, motivation, and confidence are imperative creative attributes in
6 J. Auernhammer et al. design. These attributes in design team members are ultimately the essential attributes for entrepreneurship and innovation. 2.4 Abilities Any truly creative design task requires many diverse abilities. Creative abilities are distinct from expertise. Expertise is knowing what and how from past experience, while creative ability is figuring out who has the tension, why this tension emerges, when it occurs, how it occurs, and what (i.e., solution-methods) the structural tension in society is and how it can be resolved. Diverse abilities are required to develop, build, and enact a tangible design that resolves the tension. In such creative collaboration, team members require the ability to express and communicate often incomplete situations, needs, problems, ideas, and concepts. Visual thinking abilities support perceiving, imagining, expressing, and communicating these various aspects (McKim, 1980a). Such visual abilities incorporate sketching, mock-up building, and enacting situations (e.g., role-playing) to directly experience the designed situation from various centers (Arnheim, 2009; McKim, 1980a; Simsarian, 2003). Developing a shared language between diverse groups is essential for creative collaboration, including communicating, debating, experiencing, and evaluating designs. In collaboration with non-designers, a frequent challenge is that designers notice people’s naiveté about design and lack of articulable knowledge, resulting in designers creating what they want, rather than for people’s needs and experiences (Gilmore et al., 1999). Abilities and attitudes are required to develop a shared visual language to enable creative collaboration and to help non-designers take part in the collaborative activity of conveying and translating need-situations into concepts and tangible designs. Developing abilities of collaboration, visual thinking, communication is imperative in creative accomplishments and innovation (Arnheim, 2004; McKim, 1980a). 2.5 Blocks Inherent in any collaborative and creative practice are blocks (Adams, 2019; Arnold, 1959; Duncker, 1945; Maslow, 1962; Papanek, 1973). Perceptual blocks, such as functional fixedness, prevent designers from perceiving critical aspects of and new means for the situational tension (Adams, 2019; Arnold, 1962a; Duncker, 1945; Wertheimer, 1945). The perceptual block of functional fixedness is the reason why one cannot simply ask people what they need (Duncker, 1945). It is impossible to know what to look for in the case of innovation (Arnold, 1962a). In such cases, strategies, such as recentering, reframing, experimenting, and learning from mistakes make it possible to see the problem in a new way and lead to new directions that resolve structural tension creatively (Duncker, 1945; McKim, 1980a; Roth,
A Humanistic and Creative Philosophy of Design 7 2015; Schön, 1983; Wertheimer, 1945). Similarly, stereotype vision is a selective mechanism in perceiving objects and people based on familiarity and socially conditioned images (McKim, 1980a). Visual clues that are surprising and “unacceptable” images allow designers to recognize their own cultural stereotypes. Designing for stereotypes rather than for actual people prevents designers from satisfying needs and produces tensions for people. Such a stereotype vision of the “default user” is a discriminatory bias in design (Perez, 2019). Overcoming perceptual blocks to recognize the situation as a new whole (i.e., recentering) is imperative in creative design (Arnold, 1959; Wertheimer, 1945). Similarly, cultural and environmental blocks prevent individuals from collaborating creatively and designing for people’s actual needs (Adams, 2019; Arnold, 1959). Such blocks are taboos, no humor is permitted, and there is an overemphasis on technical-rationality, unsupportive environments, and unproductive criticism (Adams, 2019; Arnold, 1959). Such cultural blocks can kill creativity in organizations (Amabile, 1998). Developing a space of psychological safety and freedom in collaborations encourages debating taboos, productive criticisms, and incorporates diverse points of view (Auernhammer, 2012; Edmondson, 1999; Lewin, 1936; Rogers, 1954; Sutton & Hargadon, 1996). Such psychological safety and freedom are particularly relevant for keeping motivation high over longer periods (Arnold et al., 1960). For example, the culture and coaching practices in the Product Realization Lab enable students to receive help and support to figure out how to build, break, and rebuild a tangible design. This culture is essential for creativity and encourages creative behavior in educational settings. Without the necessary support, creative accomplishments are unlikely to happen as people are occupied with navigating cultural pitfalls rather than engaging in and focusing on creative tasks. In creative collaborations, emotional blocks prevent people from exploring new directions and learning. Inherent fears of making mistakes, taking risks, personal ambiguities and inabilities to move consciously between idea production and evaluation and reality and fantasy are strong blocks to creativity (Adams, 2019; Arnold, 1959; Rogers, 1954). Practicing the ability to move fluently between different thinking modes and flexibly between different thinking languages, engaging in ambiguous situations for longer periods, and building many design projects can develop confidence in creative ability (Adams, 2019; Arnold, 1959; Kelley & Kelley, 2013). An environment in which students can explore and develop projects freely and safely, while earning a degree, allows engaging in creative projects and helps overcome blocks to creativity. This Philosophy of Design focuses on developing innovators (i.e., people) by overcoming their blocks to creativity, enabling their creativity potential. 2.6 Activities and Practices Various activities, exercises, and practices are essential in collaboratively uncovering needs and tensions within the living world and visually imagining new
8 J. Auernhammer et al. situations and experiences. Collaborative practices, such as need-finding, visualizing, mock-up building, enacting, and evaluating through direct experience, are vital for understanding the situation as new wholes and resolving tensions through new designs. People’s needs and behavior emerge in relation to the designed, cultural, and natural environment (Gibson, 2014; Maslow, 1954; McKim, 1959). Finding and grasping moments of people’s needs and problems is supported by diverse practices, as problems are envisaged, posed, formulated, and created in various ways (Getzels & Csikszentmihalyi, 1976). For example, need-finding practices enable designers to engage with people and situations to directly experience tensions from different centers. Environmental problems are felt when, for example, the pollution in drinking water and trash floating in oceans are experienced directly. Such pollution exists as people do not grasp and experience the structural tension their everyday behavior produces. Therefore, practices that enable designers to experience structural tensions and recenter to grasp the whole, result in new understanding and motivation towards changing the situation (Wertheimer, 1945). Visualizing practices support grasping the situation from different centers, uncovering hidden interrelations, and envisioning new valuable whole situations (McKim, 1980a). Building mock-ups and various prototyping practices allow exploring and evaluating various aspects of the interventions that change the whole towards resolving the structural tensions (Barkan & Iansiti, 1993; Buchenau & Fulton Suri, 2000; Fulton Suri, 2000, 2003; Gilmore et al., 1999; Houde & Hill, 1997; McKim, 1980a; Simsarian, 2003; Verplank et al., 1993). These are dynamic practices and not a cookbook pattern of step-by-step processes (McKim, 1980a, 1980b). Recentering changes the entire meaning of the situation, resulting in completely new questions and perspectives. Prototyping and evaluating validates and informs the grasped need. These are not separate process ends. Practices are intertwined and are required depending on the emergent understanding of the situation. Creative design requires responding dynamically with the necessary activities and practices rather than following a cookbook pattern. Such cookbook patterns or machine models disregard human experience and emerging changes inherent in the whole situation (Koffka, 1927; McKim, 1980b). The Philosophy of Design emphasizes designers as people with qualities, such as attitudes, attributes, and abilities, and not as mechanistic information processors. 2.7 Techniques Techniques and tools can be useful in diverse design activities and practices (Arnold, 1962b). Techniques such as Brainstorming and Synectics aim to enable teams to develop psychological safety and freedom and to produce a collaborative environment conducive to creativity (Arnold, 1959; Gordon, 1961; Osborn, 1957; Prince, 1970). Morphological analysis and attribute listing help create many combinations and increase divergent thinking (Crawford, 1954; Zwicky, 1948). Over the decades,
A Humanistic and Creative Philosophy of Design 9 many useful techniques have been developed for various purposes and uses (e.g., Buchenau & Fulton Suri, 2000; Fulton Suri & Marsh, 2000; Ideo., 2015; Jones, 1992; Kumar, 2012; Roozenburg & Eekels, 1995; Wasson, 2000). However, developments in the movements of design methods and design thinking/cognition resulted in method-centricity rather than problem and peoplecentricity. This mechanistic doctrine of human thinking emerged in debates in the early developments of experimental psychology (Koffka, 1927; Selz, 1922; Wallas, 1926). It developed further into the information processing theory advocated by Simon and colleagues (Newell et al., 1958; Newell & Simon, 1956, 1972). In design, the design process incorporates mechanistic models and related methods (Archer, 1965; Hubka & Eder, 1996; Liedtka & Ogilvie, 2011; Pahl et al., 1996). It dehumanizes creative thinking and practice. In response to the design method movement, one of the leading personalities, Chris Jones (1977), expressed that he dislikes the machine language, behaviorism, and continual attempt to fix the whole of life into a logical framework. Similarly, Alexander (Alexander, 1964 revised edition; Alexander, 1971) stated that he publicly rejects the whole idea of design methods as a subject of study and suggested to “forget the whole thing.” The mechanistic and information processing theory has also been criticized and questioned for various reasons in psychology over the century (Benary, 1923; Csikszentmihalyi, 1988; Greeno & Moore, 1993; Koffka, 1927; Neisser, 1963; Varela & Thompson, 1990). Techniques are not deterministic tools to make people think and act to accomplish the next process step. They are useful tools that help to produce a conducive situation to creativity and accomplish required tasks inherent in the challenge. For example, prototyping techniques help explore and evaluate various design aspects, while brainstorming helps to encourage psychological safety and freedom through “defer judgment” (Arnold, 1959; Houde & Hill, 1997; Osborn, 1957). Creative accomplishments that contribute to society with a novel, valuable, and tangible design require developing the ability to create and respond with new solution-methods to situations and not merely utilize existing ones. Exercising and practicing various techniques can help strengthen abilities conducive to creativity. However, it is not the techniques that result in creative acts. It is the ability to creatively create and utilize various techniques that are useful depending on the situation. For example, in environments of constant and unproductive critique, consciously deferring judgment can help to encourage psychological safety and freedom. It is often the social environment that either encourages or hinders creativity. 2.8 Environment The environmental context influences people’s thought processes (Wertheimer, 1945). A mind occupied by various environmental distractions will not be able to focus on a creative challenge over longer periods. Cultural and physical environments influence designers thought processes, motivational attitudes, and abilities to express ideas freely and collaborate creatively (Adams, 2019; Amabile, 1996;
10 J. Auernhammer et al. Auernhammer & Hall, 2014). Physical environments, such as a Design Loft, allow for developing a collaborative culture in which designers help each other while working on different challenges and can play freely with materials without fear of making a mistake or being penalized (Arnold, 1959; Arnold et al., 1960; McKim, 1980a). A supportive culture in the Product Realization Lab enables designers to build and explore different design directions rapidly. The combination of designers’ willingness and freedom to use their resources, machines, and tools creates an environment conducive to creativity, allowing rapid prototyping and exploring various tangible solutions. Such a culture of help and support, encouraged by anchored physical spaces and enacted by motivated and driven designers, enables creative activities and practices. Similarly, a wider network of people with diverse abilities and skills who support design teams with advice, time, and other resources is conducive to creativity and innovation. In design education, a network of alumni who engage in courses by sponsoring projects, providing real-world problems and practices, and mentoring teams is an essential part of a supportive environment for innovation. Entrepreneurial and innovation ecosystems require a critical mass of people who support and help motivated individuals drive their projects forward. For example, making connections and the support provided by people like Nolan Bushnell, Don Valentine, and Mike Markkula was essential in helping Steve Jobs and Steve Wozniak, who were in their twenties at the time, to start Apple Computer. Such an environment of helping and supporting others is part of the humanistic and creative Philosophy of Design, enabling a culture of design, entrepreneurship, and innovation. 3 Cultivating the Philosophy of Design Since the 1950s, this humanistic and creative Philosophy of Design integrated humanistic psychology (e.g., satisfying people’s needs), engineering (e.g., solving technical problems), arts (e.g., creating esthetic product meaning), and business (e.g., establishing a scalable and sustainable operation). Design is often reduced to merely visual product languages reinterpreting existing designs or technical aspects of optimizing structures and functions with little regard to addressing the fundamental tensions and profound needs existing in the world. Such designs result from selfcentered attitudes, in which designers’ needs and interests determine thinking and activities (Arnheim, 2009; Maslow, 1954). The humanistic and creative Philosophy of Design focuses on designing for the whole person, including physical, intellectual, emotional needs and tensions in society and wider ecology (Arnold, 1959; Fuller, 1969; McKim, 1959). Developing these attitudes and human values in designers by engaging in need-finding activities is essential in order for them to be able to grasp the situation from diverse centers, beyond stereotype visions (McKim, 1959, 1980a). Such design activities are a comprehensive design philosophy of cultivating pan-disciplinary collaboration to
A Humanistic and Creative Philosophy of Design 11 respond flexibly to the emergent situation and inherent tensions and needs. The inherent tension of the situation should determine the solution and not the designers’ disciplinary practice. This pan-disciplinary collaboration requires developing the abilities to collaborate creatively, such as finding and developing shared languages for visualizing and developing incomplete mental imagery into tangible designs. Visual abilities are developed through various exercises of visual perception, imagination, and expression (Arnheim, 2004; McKim, 1980a). These abilities facilitate creative dialogues and collaboration, which is imperative for resolving profound social tensions inherent in a pluralistic society. An important task in cultivating the humanistic and creative Philosophy of Design is helping design teams to overcome emerging blocks to creativity. Perceptual blocks, such as personas based on stereotype vision, require keen observation, and deep engagement with people, as well as recentering to understand their experience and need-tension (Adams, 2019; McKim, 1980a). Exercises that generate self-awareness of stereotype vision help designers overcome such socially conditioned blocks to creativity. Cultural blocks, such as taboos, prevent teams from exploring and debating the actual tension inherent in the problem-situation (Adams, 2019). Practices from theater and improv, such as roleplaying, enable designers to enact and experience tensions within a psychologically safe and free space (Faste, 1993; Simsarian, 2003). Emotional blocks, such as the fear of making mistakes, are strong blocks to creativity (Adams, 2019; Arnold, 1959). Developing a supportive environment in which designers can freely engage in unconventional projects is conducive to overcoming emotional blocks to creativity. Exercising and practicing activities and related techniques that support finding and grasping needs, recentering and envisioning whole situations, making and enacting new situations, and experiencing and evaluating these situations can stimulate creative solutions in design (Adams, 2019; Arnold, 1962a, 1962b; McKim, 1980a). Developing an environment, including a Design Loft and Product Realization Lab, in which a culture of experimental learning, collaboration, and help flourish, supports designers in freely playing with materials to creatively explore new directions for innovation (McKim, 1980a). The humanistic and creative Philosophy of Design aims to enable and develop people’s humanistic and creative qualities. A critical mass of people that creatively, collaboratively, and dynamically approach needs in society and tensions in wider ecology by designing solutions towards harmonizing them has the potential to spark an era of innovation and sustainability. References Adams, J. L. (1974). Conceptual blockbusting: A guide to better ideas. Stanford Alumni Association. Adams, J. L. (2019). Conceptual blockbusting: A guide to better ideas (5th ed.). Basic Books. Alexander, C. (1964). Notes on the synthesis of form. Harvard University Press. Alexander, C. (1971). The state of the art in design methods. DMG Newsletter, 5(3).
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Part I Value Creation in Virtual Innovation Spaces
Interpersonal Trust Activity to Increase Team Creativity Outcome: An fNIRS Hyperscanning Approach Stephanie Balters, Theresa J. Weinstein, Grace Hawthorne, and Allan L. Reiss Abstract Organizational research demonstrates that team interpersonal trust enhances team performance and creativity. Design thinking offers many interactive team events such as warm-up games that are aimed at increasing team trust and collaboration. While effective in practice, little is understood about the underlying brain mechanisms that facilitate interpersonal trust during these activities. In this chapter, we present a novel interpersonal trust activity centered around human emotions that is designed to enhance interpersonal trust in both in-person and virtual (zoom) team interactions. We hypothesize that participating in an interpersonal trust activity prior to a collaborative design task will increase interpersonal trust and the creativity level of the collaborative outcome (i.e., creative product/innovation). We present our scientific approach for testing this hypothesis by applying the methods of functional near-infrared spectroscopy (fNIRS) hyperscanning in both in-person and virtual (video conferencing) team interactions. A better understanding of the neural signatures underlying an interpersonal trust activity will allow design thinking practitioners and educators to design novel and effective design thinking activities and interactions that can positively impact team trust and collaboration. S. Balters (*) · A. L. Reiss Department of Psychiatry and Behavioral Sciences, Center for Interdisciplinary Brain Sciences Research, Stanford University School of Medicine, Stanford, CA, USA e-mail: balters@stanford.edu; reiss@stanford.edu T. J. Weinstein Hasso-Plattner-Institute, Potsdam University, Potsdam, Germany e-mail: theresa.weinstein@hpi.de G. Hawthorne Hasso Plattner Institute of Design (d.school), Stanford, CA, USA e-mail: grace@dschool.stanford.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_2 19
20 S. Balters et al. 1 Introduction There are a myriad of empirical studies demonstrating that team emotional intelligence (i.e., “the ability of a group to develop a set of norms that manage emotional processes so as to cultivate trust, group identity, and group efficacy” (Druskat & Wolff, 2001a)) enhances team performance and creativity (Akgün et al., 2015; Barczak et al., 2010; Brattström et al., 2012; Chang et al., 2012; De Jong et al., 2016; Lee & Wong, 2019; Parke et al., 2015; Rezvani et al., 2019; Rezvani & Khosravi, 2019). Empirical results reveal that a team’s emotional intelligence promotes interpersonal trust, which, in turn fosters a collaborative culture that enhances team creativity (Akgün et al., 2015; Barczak et al., 2010; De Jong et al., 2016; Rezvani & Khosravi, 2019). Interpersonal trust can be defined as an individual’s willingness to accept vulnerability based upon positive expectations of the intentions or behavior of another (Rousseau et al., 1998). Organizational research further differentiates between affective trust (i.e., the confidence one places in a team member based on one’s feelings of caring and concern illustrated by a co-worker) and cognitive trust (i.e., one’s willingness to rely on a team member’s expertise and reliability) (McAllister, 1995). We follow the latter definition in connection with the aim of this chapter. Design thinking practice and education have adopted the concepts of emotional intelligence and interpersonal trust to improve team performance and creativity (Druskat & Wolff, 2001b; Uebernickel & Thong, 2021). Many activities used in design thinking explicitly or implicitly help improve interpersonal (team) trust. The activities include various team rituals such as team “check-ins” and “check-outs” before and after each workday, during which team members share their momentary personal sensitivities, opinions, and feelings with the team (Ney & Meinel, 2019). If a team or interpersonal conflict needs to be resolved, design thinking encourages the use of structured formats for communicating and receiving feedback (Ney & Meinel, 2019). Other commonly used activities are the so-called warm-up games which are inspired by improvisation theater (Rothouse, 2020; Talbot, 2021). While there are many different warm-up game varieties serving different purposes in the design thinking process, they all help create a positive and open atmosphere. Since the games usually involve stepping out of one’s comfort zone and often result in some form of playful body movement or verbal interaction, they contribute to establishing a work culture in which one is permitted to make mistakes and show vulnerabilities in front of colleagues without fearing rejection (West et al., 2017). The activities usually serve as primers for certain collaborative work modes and are typically applied when initiating a new design thinking work phase. Alternatively, these activities function as breaks whenever the overall team mood and collaborative atmosphere need special attention. In essence, the example activities aid in team collaboration by encouraging empathetic behavior toward the other team members, offering tools to avoid or resolve team conflict though open and respectful communication, and contribute to a psychologically safe culture.
Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . . 21 Research shows that after completing a design thinking training, participants have not only gained “creative confidence” (Jobst et al., 2012; Kelley & Kelley, 2013; Rauth et al., 2010; Royalty et al., 2012, 2014), but they have also become better and more confident collaborators through practicing their empathy and social interaction skills in a design thinking team setting (Noweski et al., 2012; Plank & Meinel, 2021; Traifeh et al., 2020; von Thienen et al., 2017). Furthermore, many trained design thinkers are equipped with practical activities and “hacks” to enhance collaboration within any team (Kerguenne, 2021; Koch, 2021; Ney & Meinel, 2019). Although design thinking methodology offers many practical activities, little is understood about the underlying social mechanisms that make these activities work. Social neuroscience now offers the tools to systematically examine how such activities affect our brains and can provide evidence for their effectiveness in increasing empathy, trust, and creativity in teams (Balters et al., 2021a, 2021b; Balters et al., 2020; Balters et al., 2021a, 2021b; Li et al., 2021; Mayseless et al., 2019). Here, we propose an emergent technology in brain-imaging—hyperscanning (i.e., measuring two brains simultaneously to derive measures of inter-brain synchrony) with functional near-infrared spectroscopy (fNIRS)—as an ideal brain-imaging technique to gain understanding in the underlying social brain mechanisms of interpersonal trust activities. In this chapter, we present an interpersonal activity that is designed to enhance interpersonal trust in in-person and virtual (zoom) design thinking teams. The activity is derived from Nonviolent Communication Practices, a communication method to increase interpersonal trust between individuals (Rosenberg & Chopra, 2015). Due to its focus on human needs, the activity we utilize in our research is strongly in line with design thinking’s human-centered approach. We hypothesize that the interpersonal trust activity prior to a creativity exercise will increase interpersonal trust among participants in the same group and enhance the originality and effectiveness of their collaborative outcome (i.e., creative product/innovation). In this chapter, we describe our neuroscientific approach for applying the methods of fNIRS hyperscanning to assess whether and how an interpersonal trust activity prior to an ideation session increases collaborative outcome (i.e., creative product/innovation) in both in-person and virtual (video conferencing) team interactions. A better understanding of the neural signatures underlying an interpersonal trust activity will allow design thinking practitioners and educators to design novel and effective design thinking activities and interactions that can positively impact team trust and collaboration.
22 S. Balters et al. 2 Multidimensional Basis of Design Thinking Team Interaction Studying design thinking team interaction is a complex undertaking. It requires the consideration of many measurable constructs that can and have been quantified in the context of a design thinking team interaction. Our approach to better understanding the impact of an interpersonal trust activity on the collaborative outcome (i.e., creative product/innovation) is the quantification of multiple measurement constructs. For that matter, we cluster a variety of potentially regulating constructs broadly based within five categories (Fig. 1): (1) Behavioral constructs such as eye gaze, body movements, verbal and nonverbal interactions (Cannon & Edelman, 2019; Lasecki et al., 2014; Sonalkar et al., 2013); (2) Physiological constructs such as heart rate, blink rate, skin conductance, or hormonal changes (Balters & Steinert, 2015; Mønster et al., 2016); (3) Individual moderators such as age, culture, ethnicity, sex/gender, socioeconomic status, personality traits, creative ability, interpersonal trust (Ancona & Caldwell, 1992; Baker et al., 2016; Caldwell & O’Reilly III, 2003); (4) Outcome constructs such as levels of cooperation/collaboration based on subjective or objective measures and collaborative outcome such as creative innovation (Dong et al., 2004; Kress et al., 2012; Sjöman et al., 2015); and (5) Neuroscientific constructs such as inter-brain synchrony (IBS) between interacting partners engaged in a design thinking task (Balters et al., 2020). While we focus on examining IBS with fNIRS hyperscanning in this chapter, we suggest that a consideration of all of the constructs listed above is needed to generate a holistic model of a design thinking team’s interaction and interpersonal team trust. Understanding the multidimensional basis of design thinking team interaction: measurable constructs • Behavior: eye gaze, body movements, verbal and nonverbal interactions • Physiology: heart rate, blink rate, skin conductance or hormonal changes • Moderators: age, culture, ethnicity, sex/gender, socioeconomic status, personality traits, creative ability, interpersonal trust • Outcome: Level of cooperation (objective and subjective), collaborative outcome (i.e., creative innovation) • Inter-brain synchrony (IBS): Simultaneous scanning of two or more brains (i.e., hyperscanning) with neuroimaging modalities such as with portable functional near-infrared spectroscopy (fNIRS) Fig. 1 Potentially regulating constructs of design thinking team interactions
Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . . 2.1 23 Functional NIRS Hyperscanning Here, we propose functional near-infrared spectroscopy (fNIRS) hyperscanning (i.e., measuring two or more brains simultaneously as they interact dynamically) as a tool to quantify and understand the effects of an interpersonal trust activity on team creativity outcome. Functional NIRS is a non-invasive neuroimaging technology that assesses cortical brain activity with relatively high spatial resolution (~1 cm) compared to electroencephalography (EEG) (Scholkmann et al., 2014) and relatively high temporal resolution (~10 Hz) compared to functional magnetic resonance imaging (fMRI) (Cui et al., 2010). The technology uses near-infrared light to measure the hemodynamic response (i.e., blood oxygenation level) of the cerebral cortex as a proxy for neural activity (Cui et al., 2010; Scholkmann et al., 2014). In recent years, fNIRS systems have become increasingly portable and affordable, allowing researchers to investigate neurocognitive behavior in real-world settings (Baker et al., 2017). Researchers have extended fNIRS measurements from single brain to hyperscanning applications to investigate shared brain functions related to social interactions in a laboratory (Cui et al., 2012; Funane et al., 2011) and increasingly naturalistic settings (N. Liu et al., 2016; Mayseless et al., 2019; Miller et al., 2019). For a more comprehensive introduction to fNIRS hyperscanning, we refer the reader to Balters et al. (2020). Results from fNIRS hyperscanning studies have shown increased inter-brain synchrony (IBS) to be related to enhanced levels of interaction between team members (ref). Researchers also identified significant differences in inter-brain synchrony values between cooperation and competition tasks (Cui et al., 2012; Kruse et al., 2021; T. Liu et al., 2017). Cui et al. (2012) demonstrated that coherence between brain activation patterns in participants’ superior frontal cortices increased significantly during cooperation, but not during competition. Relatedly, T. Liu et al. (2017) found increased IBS in the right posterior superior temporal sulcus in the cooperation and competition conditions; however, only the competition condition involved significant IBS in the right inferior parietal lobule (thought to be due to additional requirements of mentalizing resources in competing contexts). Via a machine learning approach (i.e., convolutional neural networks), Kruse et al. (2021) could classify the sex composition of a dyad (i.e., female–female versus male–male dyad) in both cooperation and competition tasks, with prediction accuracy of >80%. These findings suggest that female–female and male–male dyads exhibit different brain behavior when cooperating and competing with one another. Nuanced inter-brain synchrony patterns also emerged depending on task objectives of the interacting dyad such as face-to-face deception (Zhang et al., 2017a, 2017b) and risky decision-making during gambling games (Zhang et al., 2017a, 2017b). Notably, fNIRS hyperscanning studies have emerged which focused particularly on team creativity (Lu et al., 2019, 2020; Mayseless et al., 2019). Mayseless et al. (2019) applied fNIRS hyperscanning to observe differences in IBS between partners engaging in ideation versus problem-solving activities. Lu et al. (2019) applied fNIRS hyperscanning to investigate how cooperative and competitive interaction
24 S. Balters et al. modes affect the group creative performance; and whether the sex composition of the dyad impact these interactions (and related performance) (Baker et al., 2017; Lu et al., 2020). 3 Methodology In this work, we describe the methods and procedures that we are planning to use to determine and understand the impact of an interpersonal trust activity on the collaborative outcome (i.e., creative product/innovation) and inter-brain synchrony in both virtual and in-person interactions. Actual data analysis and related results are not part of this book chapter. 3.1 Participants A total of 96 adults will participate in the study (48 females, 48 males, age range: 18–45 years). All participants will be right-handed, healthy with normal or corrected to normal hearing and vision. Participants will be randomly assigned to a previously unacquainted dyad partner. The study will follow a 2 2 between-subject design (Fig. 2). The dyads will be randomly assigned to either of the four groups (i.e., Group 1: in-person with interpersonal trust activity; Group 2: in-person without interpersonal trust activity; Group 3: virtual with interpersonal trust activity; and Group 4: virtual without interpersonal trust activity) to meet equal sex distributions and inter-dyad age differences across all groups. Sex distribution will be four female–female, four male–male, and four female–male dyads for each group. Consent will be obtained for all participants. Recruitment will be done through local advertisement via flyers, email lists, and social media. Participants will be compensated with an Amazon gift card ($25 USD per hour). The experimental procedure will last about 2 h. Fig. 2 Study design will follow a 2 2 between-subject design with 12 dyads per group
Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . . 3.2 25 Procedure Dyad partners of the in-person groups will be seated in front of each other on opposite sides of a square table. To obey COVID-related regulations, participants will wear clear medical-grade face masks (FDA approved) and keep a distance of 9 feet to one another (Fig. 3a). Participants of the virtual groups will interact with their dyad partner over zoom via two identical laptops (Lenovo Yoga 730-15IKB, 15.600 ) placed in two separate and auditorily disconnected rooms (Fig. 3b). Zoom settings will be set to full screen without self-view windows. Despite interacting in two different rooms, participants will wear clear medical-grade face masks to avoid bias between the conditions. Functional NIRS caps will be attached while participants are in the same room or while zoom connection is already established. Participants will be instructed to not talk to one another during the setup. Before starting the experiment, participants will be given 3 min to introduce themselves to one another. After the experiment, each participant will complete additional assessments in separate rooms to assess personality traits, creative ability, and prior proficiency with virtual (zoom) interactions. 3.3 Experimental Tasks The study is designed to assess whether and how an interpersonal trust activity prior to an ideation session increases collaborative outcome (i.e., creative product/innovation) in design thinking teams. Given the recent digitalization of team interactions, the aim is to incorporate both in-person and virtual (zoom) team interactions into our methodology (i.e., between-subject design). Our goal is to create experimental paradigms that require participants to collaborate in settings that are of significance to design thinking practices. In addition to our interpersonal trust activity, we chose to integrate a naturalistic creative innovation task as well a consensus decisionmaking task as a control condition (Fig. 4). During each of the three tasks, dyads will collaborate on the task challenge for a continuous time of 8 min with little instruction and no intervention. Participants will fill out a questionnaire after each task (see Fig. 3 Experimental setup for the in-person group (a) and the virtual group (b)
26 S. Balters et al. Fig. 4 Experimental procedure for the between-subject study in both in-person and virtual condition Needs we all have acceptance affection appreciation belonging cooperation communication closeness community companionship compassion consideration consistency empathy inclusion intimacy love mutuality nurturing respect/self-respect safety security stability support to know and to be known to see and be seen to understand and be understood trust warmth Introduction: For the next eight minutes collaborate with your partner. Together, identify four needs from the list above that are most meaningful to both of you. To emphasize the importance of each need, describe a situation from your own life when that need was not met and how it made you feel. As a partner, listen actively, acknowledge the feelings of the one who shared, and describe why the need is also meaningful to you. Fig. 5 Interpersonal trust activity below). Dyads will be randomly assigned to task order 1 (i.e., interpersonal trust activity prior to creative innovation task) or task order 2 (i.e., no interpersonal trust activity prior to the creative innovation task) in both in-person and virtual conditions, resulting in four different groups. During the interpersonal trust task, participants will be asked to engage in a modified version of a Nonviolent Communication (NVC) exercise that is used to increase interpersonal trust between individuals (Rosenberg & Chopra, 2015). Participants will be provided with a NVC list of “Needs We All Have” (we selected the needs within the “connectedness” section, please see Fig. 5). Participants will be asked to collaborate and identify four needs from the list that are most meaningful to them. To emphasize the importance of each need, they will be asked to describe a situation from their life when that need was not met and how it made them feel. The partner will be instructed to actively listen, to acknowledge the feelings of the one who shared and to describe why the need is also meaningful to
Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . . 27 them. Dyads will be told that they will describe their list of needs to an experimenter after completion of the task. During the creative innovation task, participants will be asked to collaborate and design a solution that can increase water conservation in the highest possible number of households. The solution can take any form (i.e., product, process, campaign, etc.). Dyads will be told that they will have to describe their solution to an experimenter after completion of the task. To avoid time effects, we decided to include a control task prior to the creative innovation task in the control condition. The task will have the same length as the interpersonal trust activity. During the consensus decision-making control task, participants will be asked to collaborate and identify the four most important traffic rules that enhance safety on US highways. To emphasize the importance of each rule, participants will be asked to describe how the rule enhances safety on US highways and why the rule is more important than other rules. Dyads will be told that they will have to describe their rules to an experimenter after completion of the task. 3.4 Assessments during the Experiment Questionnaires during the experiment will capture subjective levels of cooperation and level of interpersonal trust (Fig. 4). Video and audio recordings will be captured to derive objective levels of cooperation. Subjective Cooperation Index Participants will be asked to rate the overall cooperation of the team, the cooperation rating of themselves and their partner each on a scale of 1 (not at all) to 9 (extremely). Level of Interpersonal Trust Our study explores two kinds of interpersonal trust important in team dynamics: affective and cognitive trust. “Affective trust” is the confidence one places in a team member based on one’s feelings of caring and concern illustrated by that co-worker (McAllister, 1995). “Cognitive trust” is based on one’s willingness to rely on a team member’s expertise and reliability (Johnson & Grayson, 2005; McAllister, 1995). Participants will rate affective and cognitive trust based on the McAllister’s interpersonal trust questionnaire with Likert-type 7-point scales with 1 indicating total disagreement and 7 indicating complete agreement with the statements (McAllister, 1995). Affective and cognitive trust items will be averaged to a combined interpersonal trust index (Barczak et al., 2010). Objective Cooperation Index Audio data will be used to code the level of cooperation and level of leadership for each task and dyad. The level of cooperation will be coded as the number of turn-taking while speaking and the level of leadership will be coded as the ratio of speaking time for each participant.
28 3.5 S. Balters et al. Level of Team Creativity Outcome After completion of the creative innovation tasks, participants will individually write down the solution idea generated by their team. Ideas will be checked for congruency between dyad partners. Two trained raters will independently assess the quality of product ideas on a 5-point Likert scale from 1 (low) to 7 (high). Raters will view all videos before scoring. Product ideas will be scored for originality (how original and infrequent) and effectiveness (efficiency of the solution, whether it improved/ incentivized water conservation) following previous work (Mayseless et al., 2019). Inter-rater reliability index (as measured by Intra Class Correlation Coefficient (ICC)) will be assessed for both measures, and average score of originality and efficacy will be used as team creativity outcome. 3.6 Post Experimental Assessments To control for potential bias between the four interaction groups, participants’ personality traits, creative ability, and prior zoom experience will be assessed after the experiment (in two separate rooms). Personality Traits To capture personality traits, participants will be asked to complete the NEO-FFI-3 survey (McCrae & Costa Jr., 2007) and Adult Attachment Scale survey (Collins & Read, 1990). Creative Ability The Alternate Uses Task (AUT) will be applied to assess individual levels of divergent thinking and creativity (Guilford, 1967). Participants will be presented with pictures of four common objects. The name of each object as well as each object’s most common everyday use will be indicated in parenthesis next to the pictures. Participants will be asked to list as many alternative uses as possible for each object within a total duration of 8 min. Participants are instructed to be as creative as possible. The items will be a pillow, key, toothbrush, and ceramic plate. Before performing the task, participants will be presented with an example of alternate uses for a paper cup to familiarize them with the task. Only responses that do not replicate the common uses given will be counted and included. Scoring will include fluency (number of responses) and originality (rarity of the response). Final scores will be calculated based on the average score of all items. Scoring of originality will follow the procedures described in Torrance (1974). Original responses will be defined as statistically infrequent responses within the population of the study. Zoom Experience Participants will be asked about their prior experience and proficiency with zoom video conferencing.
Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . . 3.7 29 Functional NIRS Data Acquisition The cortical hemodynamic activity of each participant will be recorded using a continuous wave fNIRS system (NIRSport2 System, NIRX, Germany) with two wavelengths (760 and 850 mm) and a sampling frequency of 10.2 Hz. A total of 128 optodes (64 sources 64 detectors) will be divided between the two participants resulting in 100 measurement channels per participant. Optodes will be placed over the entire cortex according to the international 10–20 EEG placement system (Fig. 6a). Additionally, 16 short channels per participant will be placed across the cortex to capture and correct for background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations; pink dots in Fig. 6a). Plastic connectors will be utilized between each source/detector channel pair to maintain an estimated 3 cm distance. 3.8 Functional NIRS Analysis The raw fNIRS data will be analyzed using the NIRS Brain AnalyzIR Toolbox (Santosa et al., 2018). Raw data will be converted to optical density data and further corrected for motion artifacts by applying a wavelet motion correction procedure (Molavi & Dumont, 2012). Short separation prefiltering will be applied (Santosa et al., 2020, ‘ShortDistanceFilter’ function) to reduce systemic physiological noises (i.e., respiration and cardiac oscillations). Data will then be transformed into concentration changes of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) according to the Modified Beer–Lambert Law (Wyatt et al., 1986). Noisy channels will be manually identified and removed from further analysis using a correlation-based method (Cui et al., 2010). Since NIRS data are relative values, HbO and HbR data will be converted to z-scores and regions of interest (ROI) will be A B BA-4 left BA-7 left BA-9 left BA-10 left BA-17 left BA-19 left BA-21 left BA-22 left BA-47 left BA-4 right BA-7 right BA-9 right BA-10 right BA-17 right BA-19 right BA-21 right BA-22 right BA-47 right Fig. 6 (a) We use 100 measurement channels (blue lines) and 16 short channels (pink lines). (b) Source localization analysis identified 18 ROI across the cortex
30 S. Balters et al. created based on source localizations. All channels that share a common fNIRS source will be averaged together resulting in 18 ROIs centered on source locations (Fig. 6b for ROI localizations). 3.9 Functional NIRS Dynamic Inter-Brain Synchrony (IBS) States Analyses Wavelet Transform Coherence (WTC) will be used to assess similarity between NIRS signals of partners. WTC can identify locally phase locked behavior between two time series by measuring cross-correlation between the time series as a function of frequency and time (Baker et al., 2016; Cui et al., 2012). For a more in-depth explanation of WTC, please see (Grinsted et al., 2004). The wavelet transform coherence (WTC) package in Matlab (Grinsted et al., 2004) will be used. WTC will be calculated between each ROI and the rest of ROIs on the converted HbO time series (a total of 324 combinations: 18 ROIs 18 ROIs). The inter-brain synchrony (IBS) between same ROI pairings will then be averaged. This will result in 171 IBS pairings. The average coherence value between 0.15 and 0.015 Hz will be calculated. This frequency band allows us to exclude noise associated with cardiac pulsation (about 1 Hz) and respiration (0.2–0.3 Hz). Finally, averaged coherence values will be converted to Fisher z-statistics (Cui et al., 2012). “For the dynamic IBS network analysis, the interpersonal trust activity, control task and creative innovation tasks will be analyzed separately following the procedure described in Li et al. (2021). The IBS calculated by WTC across the entire task duration for each dyad will be segmented by using a sliding window approach. The window size will be set to 15 s and shifted in an increment of 1 s along the entire task. Within each time window, WTC values between the same pair of ROIs will be averaged, resulting in an IBS matrix for each time window. The 8-min measurement duration will then be segmented into a series of windowed IBS matrices (18 ROIs 18 ROIs 451 windows) for each dyad. To characterize the dynamic IBS (dIBS) states during cooperation tasks, k-means clustering will be applied to the concatenated windowed IBS matrices averaged across the group. The k-means clustering approach can estimate the similarity between the windowed IBS matrices and will identify various clusters representing distinct IBS states derived from these windowed IBS matrices. The number of clusters will be determined using the elbow criterion of the cluster cost, computed as the ratio between within-cluster distance to between-cluster distance. We will compute the cost for different k values (e.g., from 1 to 15), and then plot these cost values as a function of the cluster number. With this approach, we will seek to minimize the within-cluster distance and maximize the between-cluster distance, while controlling the number of the clusters. The appropriate number of k is generally selected at the elbow of the curve, optimally balancing the cluster cost and cluster number. In each iteration of cluster estimation, we will estimate the similarity between the windowed IBS matrices using the L1
Interpersonal Trust Activity to Increase Team Creativity Outcome: An. . . 31 distance function (Manhattan distance) and will repeat 1000 times to decrease chances of escaping local minima, with randomly initialized centroid positions. Cluster centroids obtained from the group-averaged IBS matrices will then be used as the initial centroids for the individual dyad-level clustering analysis, resulting in final dIBS states for each dyad and each cooperation task. To identify the true taskrelated regional connections in each clustered dIBS state, we will construct a series of permuted IBS matrices as a ‘baseline’ for each dIBS state (i.e., an unrelatedpaired-participants’ IBS matrix as compared to the real-paired-participants’ dIBS matrices). This will be calculated by using the time series of two unrelated players from the whole participant group (i.e., two players from different dyads and cooperation tasks). Out of a large number of possible pairings, permuted IBS matrices will be formed by randomly selecting a set of 400 unrelated pairs (permuted pairs). We will then perform a series of two-sample t-tests on single regional connection level between individual dIBS matrices and the permuted IBS matrices to identify significant task-related regional connection in the dIBS matrices.” (Li et al., 2021). 3.10 Experimental Hypothesis Through our experimental methodology, we intend to address the following experimental hypothesis: H1: Participating in an interpersonal trust activity prior to an innovation event increases a team’s creativity outcome. By means of fNIRS hyperscanning, we will further explore whether and how an interpersonal trust activity prior to an ideation session increases collaborative outcome (i.e., creative product/innovation) in both in-person and virtual team interactions. The aim is to distill neural inter-brain signatures of effective interpersonal trust activities to understand the underlying brain function of effective activities. 4 Planned Analyses and Conclusion To test our hypothesis, we will first execute a stimulus check and test whether interpersonal trust levels are higher after the interpersonal trust activity in contrast to the control task in both the in-person and virtual interaction condition (i.e., repeated measures ANOVA across all four groups). We expect that interpersonal trust is higher after the interpersonal trust activity in both interaction conditions in contrast to the control task. Next, we will test our experimental hypothesis H1. We will use the team creativity outcome scores and run repeated measures ANOVA across all four groups. We expect higher creative outcomes for the two groups who have immersed themselves in the interpersonal trust activity in contrast to those groups who have engaged in the control task prior to the creative innovation task. We also do not expect differences in creative outcome between the two interaction
32 S. Balters et al. conditions (i.e., in-person versus virtual) for those who engaged in either interpersonal trust activity or control task. As a third analysis, we will assess the differences of properties of dIBS states, which will be characterized by three metrics (Li et al., 2021): (1) Fractional windows of each state (i.e., percentage of total windows a dyad spent in each dIBS state); (2) Number of transitions between states (i.e., total number of switches between any two states during the task); and (3) Global efficiency of each state (i.e., elementary graph-based metric that represents the integration of the entire network, reflecting how active and efficient a network can share information between regions). Repeated analysis of variance (ANOVA) will be used to assess whether there are significant differences between these dIBS states in terms of their properties when comparing (i) interpersonal trust activity and control task as well as (ii) creative innovation task with and without prior interpersonal trust activity. A better understanding of the neural signatures underlying the interpersonal trust activity will allow design thinking practitioners and educators to design novel and effective design thinking activities and interactions in the future. Following the success of the present investigation, we plan to extend the methods and measures presented in this chapter. In future work, we are specifically interested in examining the impact of design experience (e.g., design thinking experts versus novices) on the effectiveness of this interpersonal trust activity. Furthermore, we aim to extend two-person hyperscanning to hyperscanning of three persons simultaneously. It is our hope that this work will provide new and valuable information on human social interaction within working teams in the design thinking and related areas. References Akgün, A. E., Keskin, H., Cebecioglu, A. Y., & Dogan, D. (2015). Antecedents and consequences of collective empathy in software development project teams. Information & Management, 52(2), 247–259. Ancona, D. G., & Caldwell, D. F. (1992). Demography and design: Predictors of new product team performance. Organization Science, 3(3), 321–341. Baker, J. M., Liu, N., Cui, X., Vrticka, P., Saggar, M., Hosseini, S. H., & Reiss, A. L. (2016). Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning. Scientific Reports, 6(1), 1–11. Baker, J. M., Rojas-Valverde, D., Gutiérrez, R., Winkler, M., Fuhrimann, S., Eskenazi, B., Reiss, A. L., & Mora, A. M. (2017). Portable functional neuroimaging as an environmental epidemiology tool: A how-to guide for the use of fNIRS in field studies. Environmental Health Perspectives, 125(9), 094502. Balters, S., Baker, J. M., Hawthorne, G., & Reiss, A. L. (2020). Capturing human interaction in the virtual age: A perspective on the future of fNIRS Hyperscanning. Frontiers in Human Neuroscience, 14, 458. Balters, S., Baker, J. M., Hawthorne, G., & Reiss, A. L. (2021a). Inter-brain synchrony and innovation in a zoom world using analog and digital manipulatives. In Design thinking research (pp. 9–32). Springer.
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Dancing with Ambiguity Online: When Our Online Actions Cause Confusion So Yeon Park, Mark E. Whiting, and Michael Shanks Abstract Online social actions are often ambiguous, leading us to wonder: Why did this person unfollow me? Why did my friend like this negative content? Such ambiguity is common and perceived as a natural part of our ubiquitous online interactions. However, as online actions are curated and designed by platforms, this ambiguity is, at least in part, something platforms can control—for example, some platforms provide explicit dislike functionality, while others do not provide features to clearly signal such sentiment. Our understanding of this ambiguity around online actions is limited. We are unaware of the wide spectrum of situations in which people are confused by others’ online actions and how widespread such confusion might be. We conducted a survey study to identify when such ambiguity occurs—when people wonder why online actions are taken. We found that ambiguity of online actions occurs in non-nuanced situations. Specifically, some people wondered why online actions were taken when simply certain actions, content, or stakeholders were involved. For example, malicious content caused ambiguity, regardless of whether others posted or interacted with such content. Our findings suggest that more platform features may help to improve the clarity of people’s actions as well as the extent of the impact of these actions, which may help to avoid such uncertainty. 1 Introduction Our use of social platforms hinges on the assumption that other people understand the actions we take. We may like something because of our legitimate excitement about the content, or for virtue signaling and moral grandstanding [Tosi & Warmke, 2016]. We may share a link to a community sincerely or ironically, or for any one of S. Y. Park (*) · M. Shanks Stanford University, Stanford, CA, USA M. E. Whiting University of Pennsylvania, Philadelphia, PA, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_3 37
38 S. Y. Park et al. numerous other reasons. Despite the ubiquity of the actions (e.g., posting, liking, sharing) that we take and receive on social media platforms, being heavily mediated by the platforms (i.e., designing how they look, feel, and function) often hinder clear interpretation of these online actions. As a result, most people, most of the time, have uncertainty about the posts and digital traces they are seeing on platforms [Hayes et al., 2016; Lee et al., 2016; Park et al., 2021; Sumner et al., 2018]. This causes ambiguity in online actions that often leaves people feeling surprised or bewildered and can lead to misinterpretations and misunderstandings. Yet to address this phenomenon, we need to first understand the sources of ambiguity, not only technically (i.e., what design choices regarding the interaction cause this), but also contextually; what situations lead to uncertainty? The advent of the Facebook papers [Accountable Tech, 2021] and related revelations—namely, that social platform companies likely understand a lot more about the nuanced impacts of their products than they let on—has struck a chord with the public. These revelations have generated an uproar surrounding the topic of how platforms should act for our mental health and our society. However, as deeper analysis of these efforts revealed, the truth is much more complicated and the data much more nuanced than first impressions may indicate. In a world designed for one-click simplicity, it may appear that social platforms have simplified their products to provide a fundamental reduction of certain social interactions. What is simpler than 280 characters—a rule enforced for tweets?1 But in practice, this quest for simplicity may remove cues that, if present, make clear to others what we are actually thinking when we use the Internet. An underlying challenge with this problem context is that misunderstanding is largely a latent negative outcome that can have devastating consequences much later, including imprisonment.2 Users performing online actions may rarely realize they have been misunderstood, platforms may not have clear ways to estimate when such misunderstandings occur, and viewers often have no immediate way to ask why something has happened (other than to comment on the post). This last challenge is exemplified when a famous person takes an online action that becomes a point of debate in the media, such as former President Trump’s tweet regarding termination of sanctions against North Korea on a day when no sanctions had been issued,3 or the oddities fans found in Britney Spears’ social media posts (now found as having been controlled under her conservatorship).4 1 https://developer.twitter.com/en/docs/counting-characters,%20Accessed%20November%201,% 202021. 2 https://evangelicalfocus.com/world/2559/christian-in-pakistan-sentenced-to-life-imprisonmentfor-blasphemy, https://www.smh.com.au/national/sydneyman-unlawfully-imprisoned-in-egyptianprison-since-january-20200920-p55xff.html, Accessed November 16, 2020. 3 https://www.vox.com/world/2019/3/26/18282408/trump-north-korea-tweet-bloomberg-sanctions, Accessed November 1, 2021. 4 https://www.newyorker.com/news/american-chronicles/britney-spears-conservatorshipnightmaregt, https://www.refinery29.com/en-us/2021/07/ 10555863/who-runs-britney-spearsinstagram-real-fake, Accessed November 20, 2021.
Dancing with Ambiguity Online: When Our Online Actions Cause Confusion 39 In an effort to understand situations that lead to uncertainty, we surveyed thousands of Twitter users and asked them when they had wondered why someone took an online action. We conducted inductive thematic analysis on 1042 free-text responses, and we found that ambiguity was perceived in instances where only certain actions, content, and stakeholders were involved. We found that people’s bewilderment or confusion by others’ online actions do not only occur in situations that are nuanced. This is also the case for some generalizable contexts that are dependent upon one of three factors related to taking online actions: How an action was carried out (action), what the action was taken on (content), and who was involved in the action (stakeholder). With these findings, we contribute to an improved understanding of when online actions are ambiguous and cause users to wonder why they were taken, as well as design implications for social platforms to help reduce the ambiguity inherent in their design of interactions. This work gives voice to the unspoken uncertainty that plagues our experiences online. We hope that by investigating how individual experiences of the social Internet manifest and addressing these issues, a broader discussion of sociodynamics in online platforms, and platform design decisions in general, can be initiated. While platforms conduct extensive internal research that has undoubtedly elucidated many of these aspects [Accountable Tech, 2021], the possibility for the broader scientific community and general public to understand and act on insights about how the design of social media affects us remains elusive. 2 Related Works 2.1 Online Actions on Social Media Social media is an integral part of our lives. Statistics have shown that Facebook users spend on average 58 minutes daily on the platform, and in October 2020, for example, made “an average of 5 comments, 12 post likes, 1 share, and 1 Page like.”5 Our engagement on social media comes in the form of diverse online actions that vary in the amount of cognitive effort they require—the lowest effort required, after consuming, is contributing. This is often operationalized as mere button clicks, such as liking and retweeting [Kim & Yang, 2017; Muntinga et al., 2011]. Among these contributions, however, there are varying levels of cognitive effort. While clicking the Like button has been found to be the “default reaction” that accounts for 78.9% of such low-effort contributions [Tian et al., 2017], other Facebook Reactions6 have 5 https://www.socialpilot.co/blog/social-media-statistics#fb-usage-stats, Accessed August 25, 2021. Reactions are “an extension of the Like button, to give you more ways to share your reaction to a post in a quick and easy way,” and these are made up of six emojis including original “like” (thumbs up) button as well as “love” (heart) and four facial expressions of “happy,” “wow,” “angry,” and “sad.” https://about.fb.com/news/2016/02/reactions-now-available-globally, Accessed September 4, 2021. 6
40 S. Y. Park et al. been found to be more “thoughtful behaviors” and “significantly more deliberate than Likes in their use” [Sumner et al., 2020]. An extreme example of a relative lack of deliberation behind a Like in social media was labeled in previous research as “mindless” liking [Park et al., 2021]. Online actions on social media are perceived to express not only different levels of deliberation, but also to have different intentions associated with them. The user’s intent in clicking the Like button—in addition to analogous buttons of Favorite and Upvote—has been classified into six categories: (1) to indicate that they actually “like” the content as the label of the emoticon connotes, (2) to acknowledge one’s viewing of the content to others, (3) to indicate social support to others, (4) to virtue signal7 for social grooming purposes, (5) for utilitarian reasons (e.g., clicking Like with the intention of returning to the liked content later or of affecting the platform’s algorithm to receive more content similar to the one that was liked), and (6) for a mindless reason (e.g., by accident) [Park et al., 2021; Sumner et al., 2018]. Analyzing Likes, researchers found that “52% were meant to convey content-related thoughts, 23% were meant to communicate relational-based sentiments, and 24% were said to contain both types of meaning” [Sumner et al., 2018], similar to findings in earlier work [Lee et al., 2016]. People receiving the action of liking were well aware of the variety of meanings behind the action [Sumner et al., 2018], and thus, despite the positive connotation of the word “like” itself, the action was not always received positively (e.g., it was used self-servingly to create certain impressions [LoweCalverley & Grieve, 2018]). Other low-effort “lightweight acts of communication” [Hayes et al., 2016] include retweeting on Twitter. Similar to liking, retweeting is yet another “speech act that we can only perform online, and whose linguistic meaning is not entirely obvious” [Marsili, 2020]. Retweeting can be seen as copying, rebroadcasting, and endorsing others among many other interpretations [Boyd et al., 2010; Weller, 2016]; in fact, Boyd et al. have provided ten reasons for retweeting with a greater degree of nuance [Boyd et al., 2010] than the aforementioned six reasons for three actions [Park et al., 2021; Sumner et al., 2018]. Notably, some were found to value retweeting while others “lament[ed] users’ selfish motivations” of attention seeking [Boyd et al., 2010], similar to that of the social grooming purpose for liking. 2.2 Ambiguity of Online Actions Despite being aware of these various intents behind taking online actions on social media, users are still confused by them. Ambiguity is prevalent and is felt by users on both sides of online actions alike—actors and recipients/observers [LoweCalverley & Grieve, 2018; Park et al., 2021; Sumner et al., 2020]. Due to these online actions—also termed paralinguistic digital affordances [Hayes et al., 2016]— 7 https://www.spectator.co.uk/article/easy-virtue, Accessed January 6, 2022.
Dancing with Ambiguity Online: When Our Online Actions Cause Confusion 41 being “somewhat ambiguous” in nature, reasons attributed to others taking such actions are not reliably inferred [Sumner et al., 2020]. This can lead to failure in achieving the desired interpersonal functions behind the online action [Hayes et al., 2016], for example, when an online action is perceived as being antisocial when it is in fact intended for social connection. Research has found that how people felt about these online actions, operationalized as having a positive and negative affect, is significantly predicted by the reason they “most readily associated with the action” [Park et al., 2021]. Further, what recipients of these actions feel may be completely different from what was expected or intended. Perhaps due to this, many users also feel “pressure regarding how to interpret and provide these cues in an appropriate way” [Sumner et al., 2020]. This may have also been the reason for the huge success of a system enabling Twitter users to share links with greater visibility [Gilbert, 2012]. While research finds that there are cases in which this ambiguity is advantageous—when carrying out “unsocial behaviors” (e.g., unresponsiveness) [Lopez & Ovaska, 2013] or other scenarios where users wish to mitigate social difficulties and costs (e.g., saving face) [Aoki & Woodruff, 2005]—the overall desire among users is to find clarity in ambiguity with more social information. Social information—of “who’s interested in what we say” and “whether our audience is bored and looking out the window or hanging on our every word”—helps us to “act appropriately” [Gilbert, 2012]. By seeking such information about others, uncertainty, which causes cognitive stress and uncomfortable feelings, can be reduced and lead to relationship development [Berger & Calabrese, 1974; Ramirez Jr et al., 2016]. Social anxiety stemming from this uncertainty was also found to be mediated by actions for reducing anxiety (e.g., actively inquiring others, passively observing) [Courtois et al., 2012]. However, this social information may not be readily acquired, leading to social media users’ asymmetric experiences online [Fetchenhauer & Dunning, 2010]. This information asymmetry was found to cause failure in “learn[ing] about others’ true level of trustworthiness” and in people seeing “others as driven more by self-interest than they really are” [Fetchenhauer & Dunning, 2010]. This consequently presents an example of people’s tendency to assume negative intent in others’ actions [Nasby et al., 1980]. 2.3 Ambiguity of Content on Social Media There is also ambiguity in the content shared on social media, especially when it comes to the veracity of shared news. It is unclear to some users whether a piece of news is true or false, and making sense of this ambiguity depends heavily on contextual factors such as who shared the content, how it was shared, and who is receiving it [Nekmat, 2020; Sterrett et al., 2019; Vraga & Tully, 2021]. Research has found that people are more likely to trust social media content if it is shared by people who they trust as a public figure, who is within their network, or who is perceived as being like-minded [Metzger et al., 2010; Sterrett et al., 2019]. Social
42 S. Y. Park et al. diversity “among people who share a story about a scientific discovery” was also found to “enhance the perceived credibility” of shared content [Hayat et al., 2019]. How the information is endorsed by others was also found to be important in evaluating credibility. People are disposed to attribute credibility to information and sources if others do so “without much scrutiny of the site content or source itself” [Metzger et al., 2010] as per the “consensus heuristic” [Chaiken, 1987]. Such findings can be alarming in light of research such as that which found people more inclined to accept information than to reject it [Lewandowsky et al., 2012] and that only those having as well as valuing greater news literacy “are more skeptical of information quality on social media” [Vraga & Tully, 2021]. All these studies show that while people resort to various cues to ascertain the stance they should take, more often than not they lean toward the interpretation of what is available, yet these strategies may not lead to the correct clarification of ambiguity. 3 Methods To evaluate when people desire more understanding around others’ online actions, we conducted a survey of Twitter users in which they answered questions about their past experiences on Twitter and described situations that motivated them to wonder what others meant online. In this section we outline the design of the survey instrument, our data coding approach, and the participant recruitment process. 3.1 Survey Instrument The survey evaluated when users felt a sense of ambiguity around online actions. We asked participants to answer the following question as a free-text response: “In what kinds of situations do you wonder why someone has taken an online action (e.g., like, dislike, share, retweet)?” This was part of a larger survey experiment that investigated how comfortable people felt about these online actions.8 3.2 Qualitative Coding We first assigned participant numbers with the numbering protocol of “P” followed by a four-digit number. We then coded participants’ free-text responses about when they wonder about the reasons for an online action with inductive thematic analysis [Braun & Clarke, 2006]. For this, one researcher reviewed 100 responses and from 8 Detailed further in forthcoming work.
Dancing with Ambiguity Online: When Our Online Actions Cause Confusion 43 these generated initial codes. The initial codes that emerged were discussed among the researchers and finalized into themes that are independent but can be concurrently implicated (e.g., one response can implicate multiple themes). The full set of responses was coded by one researcher except for 123 text responses that the researcher deemed as requiring discussion; two researchers reached consensus for these responses that 43 of them were unclear (i.e., to be excluded from our thematic analysis as they could not be coded) and then decided which codes would apply for the remaining 80 responses.9 Of these themes, we report on the three themes that were specifically dependent upon action, content, and stakeholders in this chapter. Participant responses with spelling corrections are included with participant numbers as per the protocol used by the researchers.10 3.3 Recruitment and Compensation We recruited Amazon Mechanical Turk participants located in the USA who had completed at least 1000 tasks on the platform with a 96% acceptance rate—a common recruiting protocol [Whiting et al., 2019, 2020]. The first page of the survey checked if participants met our eligibility criteria, including their usage of Twitter and having had the experience of their content being liked within the past 30 days and having been knowingly unfollowed in the past. These filtering questions were asked amid other similar questions to avoid desirability effect confounds. Participants who did not meet the requirements were rewarded $0.40 and did not follow through with the rest of the survey. Those who met our eligibility criteria moved on to complete the rest of the survey. The final question included in the survey asked whether respondents had answered any of our questions in the survey randomly. We filtered out responses from participants who answered “yes” to this question—these participants’ compensation was not penalized. In addition, we reviewed four free-text responses (not reported here) to eliminate data from participants who provided responses that were irrelevant from our analysis. Those who provided relevant responses to all four free-text responses received $4.20 according to the median time taken to complete the survey at a rate of $15 per hour [Whiting et al. Whiting et al., 2019]; those who did not received either $2.20 or $1.00 depending on the relevance of their responses. Upon filtering of participant data, we resulted in a total of N ¼ 1042 responses that could be thematically coded for this chapter. Our participants were aged 19–78 (μ ¼ 36.9, Unclear responses included “If I see something that I need to speak up about, then I will take action” (P0042), “To make something better” (P0314), and “when Black Lives Matter tweeted many of them has taken an online action” (P0773). 10 Spelling corrections have been made for readability and they include “NSTOP” to “STOP” (P0947), “embarrasing” to “embarrassing” (P0321), “twitted” to “tweeted” (P0126), and “ot” to “it” (P0087). 9
44 S. Y. Park et al. σ ¼ 10.8), 42% female, 71% with Bachelor’s degree or above, and 83% with Twitter experience of a few years. 4 Results We unpacked free-text responses to the question of when online actions were ambiguous—in other words, what kinds of situations cause people to wonder why someone has taken an online action—using inductive thematic analysis. We found that 84.4% of responses indicated that the situations in which they wondered why people took online actions depended upon specific action, content, and/or stakeholders. Of these, most responses implicated certain actions (68.5%; e.g., liking, retweeting, following). Despite asking about online actions, we found that certain content (43.0%; e.g., offensive or controversial content) and stakeholders (27.5%; e.g., self, friends) also caused people to wonder why others were taking online actions. Below, we present the results of those who mentioned only certain actions, content, or stakeholders as being the cause for ambiguity. 4.1 Relating to Certain Actions Situations involving only specific online actions caused many to wonder why they were taken; these actions included negative and positive ones, as well as actions that could be seen as being neutral or both negative and positive. Of these, negative actions (e.g., dislike, unfollow, block) were brought up most frequently as being the valence of action that caused people to wonder the most about others’ reasons why. • • • • • “I WONDER WHEN THEY DISLIKE OR STOP FOLLOWING” (P0947) “mTurk blocks from requestor, Facebook/Twitter unfollows” (P0489) “Unfollow or unfriend” (P0315) “Usually in negative situations, like when someone dislikes a post” (P0337) “Why they revoke a friend request” (P0594) People also wondered in situations when a certain action was discontinued or undone: “Someone who has been following for a long time suddenly stops” (P0246), “I wonder why someone dislikes a post after liking it” (P0118), “When there is a sudden unfollow” (P0989). Some showed the kinds of interpretations they had for others taking such negative actions: “I wonder if someone dislikes a video just to be mean” (P0207). Discussing, commenting, and retweeting emerged as actions that could be perceived either way in terms of their valence, i.e., positive or negative. For such actions, participants wondered most often when these were carried out with negative intent or sentiment.
Dancing with Ambiguity Online: When Our Online Actions Cause Confusion • • • • • • 45 “When they comment something mean” (P0774) “When someone starts an argument in a comment section” (P0745) “making a statement I don’t agree with” (P0592) “If someone quote retweets with a negative connotation” (P0302) “When someone retweets a post to make fun of it” (P0327) “When someone comments something unnecessarily rude or mean-spirited or just nit-picking a post instead of understanding the underlying message of the post” (P0716) Neutral actions taken by others without any positive or negative connotations could also be a source of confusion and misinterpretation of their actions. • “When I know they don’t really have a strong opinion about a social issue but they decide to chime in with their opinion anyway. I think they want to make themselves out to be more of an advocate than they are” (P0029) • “I wonder why someone comments” (P0127) • “Situations where people express their opinion” (P0815) • “When someone retweets a post, [I wonder if] it is because they find it interesting information or to help someone requesting a particular service, medicine, or help” (P0145) The disproportionate effort in writing comments could also be the reason for wondering why: “When they spend time writing a really long and drawn out opinion based comment” (P0824). Deciding to engage in such “drawn out” discussions could also cause people to wonder: “When they comment when on a situation that they know will start a back and forth” (P1006). Positive actions (e.g., like, follow) could also be confusing and cause people to want to know (and even attribute) the reasons for them, as they felt these actions could be motivated by a multitude of reasons. • “If something they liked or retweet[ed] was something they actually support, endorse, or just want to share” (P0189) • “liking and re-sharing a post” (P0006) • “when he or she has retweeted on a particular post” (P0200) • “If they like something when they actually don’t like it” (P0962) • “If I feel like they are trying to garner attention for likes/follows” (P0628) The lack or absence of such a positive action also produced confusion: “when they don’t like something” (P0326), “I also wonder why people do NOT like things, sometimes” (P0381). Sometimes, the type or nature of action (i.e., an action and its counteraction) caused people to wonder why: • “When I see someone unfollowed or when I see new followers” (P0993) • “They didn’t like something, or they liked something” (P0144) • “I sometimes wonder why someone ‘likes’ or ‘dislikes’ something when they do so” (P0277)
46 S. Y. Park et al. Finally, for some, all of the above reasons caused them to wonder more broadly about people’s interactions in general, as in the case of P0883: “I wonder what cogs in the brain fire to generate that response. It is less-so when I agree with the like/ dislike/retweet and more-so when I don’t agree with it. Why/how did humans end up becoming so divided?” 4.2 Relating to Certain Content Divisive (e.g., controversial, political) and offensive (e.g., racist, hateful) content— and any interaction with such content—caused people to speculate about others and their motives for engaging in any way with the content. • “When they are engaging with a controversial post” (P0240) • “When it’s about a controversial topic which shows they are a hypocrite and a sheep” (P0032) • “If it is controversial or taboo” (P0354) • “if it’s really angry or controversial” (P0186) • “If it was extremely hateful or hurtful. I wonder what is going on in there lives to make them so malevolent” (P0243) • “Typically in scenarios where it’s political related” (P0037) • “Usually related to politics and sports” (P0134) • “if they say something cruel or racist” (P0319) • “If the content is inappropriate, controversial, or offensive” (P0551) A few participants expanded the list with more humanitarian content: “When it comes to political issues, or humanity issues” (P0020), “political views, healthcare, immigration, homelessness” (P0014). Another type of content added to divisive and offensive content related to content with low general interest: “I wonder why someone has taken an online action when: (1) the message is controversial. (2) not many people express interest on the subject. (3) the tweet contains inappropriate materials such as porn, vulgar language, etc.” (P0375). People also wondered why others interacted with such content that lacks interest from the general public (e.g., “when the content is not important” (P0574)) or “irrelevant” as expressed by P0454: “When people put personal business out there or information about others that is irrelevant and just put out there to stir up online drama. or when “Karens” take to social media to try to make a point about something petty, like McDonald’s left their fries out of their meal or something.” Informational content, whether it is truthful or questionable, were also content that made people wonder why online actions were taken. • “When it is information that is obviously wrong” (P0378) • “When what is said doesn’t make sense, seems intentionally confrontation [al] and/or aggressive, involves questionable content and/or information, anything that seems out of the ordinary for no reason” (P0063)
Dancing with Ambiguity Online: When Our Online Actions Cause Confusion 47 Few also mentioned wondering why an online action was taken when related to content that did not make sense to them: “When the tweet is just complete nonsense. Like a few words or anything that just says ‘among us.’ I don’t get memes these days” (P0173) and “when the tweet is some incomprehensible nonsense” (P0293). Additional types of content that emerged included the following: • “when it is something embarrassing” (P0321) • “When someone takes an online action for content related to race, language and other things” (P0632) • “When the content is appropriate or not appropriate” (P0164) 4.3 Relating to Certain Stakeholders Stakeholders involved in online actions—whether they were the actor, recipient, or observer of an action—could also be the reason for wondering why actions were taken. Many of the responses in which the participants’ wondering depended only on stakeholders were in relation to others who were close to them. • • • • “i only care if it is like my family or boyfriend” (P0441) “If I know them personally” (P0128) “When I have a close personal relationship with the person” (P0753) “If it’s someone I have a personal relationship with in real life (friend, acquaintance)” (P0157) People also wondered about others’ online actions when it involved themselves: “When it directly affect[s] me” (P0087) and “I said something they did not like” (P0643). To others, it depended on their state: “Times I’m not in a good mood or sad about something” (P0787). Some implicated both themselves as well as others who were perceived to be close to them: “When it happens to me on my profile and when it’s someone I know personally” (P0748). 5 Discussion We found that in addition to how the actions themselves were taken, who took the action and regarding what could also be reasons for wondering why online actions are taken. That each of these independent factors alone could simply cause people to wonder why and possibly to be confused by an online action taken shows that speculation does not always occur in nuanced situations.
48 5.1 S. Y. Park et al. Sometimes Nuance Does Not Matter, Negativity Simply Makes us Wonder Why Specific actions could alone make people wonder why they were taken, and for some, this was especially the case for the “negative action” of unfollowing. This could be due to their unawareness of differences between reasons for online vs offline relationships ending [Quercia et al., 2012; Sibona & Walczak, 2011]. Sibona and Walczak found that disliked behavior and changes in the relationship were two factors that contributed to offline unfriending decisions, while there were four types of posts that caused online unfriending on Facebook: Unimportant/frequent, polarizing, inappropriate, and everyday life posts [Sibona & Walczak, 2011]. These motivations for unfollowing online were similarly found in the context of Twitter: Kwak et al. found that “those who left many tweets within a short time, created tweets about uninteresting topics, or tweeted about the mundane details of their lives,” with whom they lacked homophily, were unfollowed [Kwak et al., 2011]. Perhaps those participants who pinpointed to merely the action of unfollowing or unfriending, and even blocking, as being questionable or inviting speculation show that such findings from a decade ago are still true today—that they may not be aware of why people end relationships online, in addition to the lack of awareness around differences of reasons between online vs offline unfriending. Another plausible explanation may be that people are not be sure which of the many reasons above triggered the action of unfollowing and would like clarity. Participant responses showed that ambiguity around unfollowing occurred when the action was perceived as being taken out of the blue (i.e., when they were least expecting it), which evidences their lack of awareness of which particular reason led to the outcome. For others, actions taken with some negativity were also cause for wondering. One of the most common occurrences of such was retweeting with negative connotation or intent, e.g., with “arguments” and “hatred” or in a “mean” way. This may be caused by the online disinhibition effect, which is the effect of people “loosen [ing] up, feel[ing] less restrained, and express[ing] themselves more openly” online [Suler, 2004]. Of such an effect, there is toxic disinhibition, which is characterized by “rude language, harsh criticisms, anger, hatred, even threats” [Suler, 2004]. People are prone to taking on or displaying disinhibitive behaviors on social media platforms that enable “dissociative anonymity, invisibility, asynchronicity, solipsistic introjection, dissociative imagination, and minimization of authority” [Suler, 2004]. As aversive communication often engenders responding to them in kind [Chen, 2015], those on the receiving or witnessing end of such online actions may be taken aback by them. While toxic disinhibition has been present online for a while, and quite commonplace according to some responses, people may still be wondering for the most part why this occurs as it does not reflect the majority of the social media users’ behavior. Moreover, less toxic disinhibition displayed offline may also cause people to be surprised by or unsure of its occurrence online. Not only were negative actions mentioned, but also negative content was brought up as a reason for why people wondered why online actions were taken. Negative
Dancing with Ambiguity Online: When Our Online Actions Cause Confusion 49 content mentioned by the participants included content that was seen as being controversial, political, taboo, angry, hateful, offensive, wrong, etc. Participant responses seemed to connote a lack of understanding of why anyone would post or engage with such content. The propagation of such negative content could be the result of a lack of thoughtfulness and gatekeeping on social media, as Rainie et al. posits: “Armies of arbiters as democratic shapers of the defining climate of social and political discourse have fallen out of favor, replaced by creators of clickbait headlines read and shared by short-attention-span social sharers” [Rainie et al., 2017]. This does not only apply to news content, but also to online content in general as users arguably post content on social media to gain visibility. Moreover, engaging with content—whether it is liking, commenting, or retweeting—helps the post gain greater visibility. While this may not have been the intent behind one’s engagement with negative content (e.g., simply a knee-jerk reaction), some users may have felt that propagation was the outcome and therefore wondered why others would help enable an inappropriate outcome for questionable content. 5.2 Ambiguity Is Amplified by the Visibility of Actions When describing ambiguity around certain actions, we found a few situations in which participants raised the possibility of someone taking an online action for the visibility of it, as mentioned in § 5.1. Examples include: “I wonder if someone dislikes a video just to be mean” (P0207) and “when I know they don’t really have a strong opinion about a social issue but they decide to chime in with their opinion anyway. I think they want to make themselves out to be more of an advocate than they are” (P0029). In these quotes, people are raising the issue of taking online actions for a performative effect, and therefore alluding to the self-interest of social grooming, which is one resulting interpretation caused by information asymmetry [Fetchenhauer & Dunning, 2010]. As found in prior literature in the collaborative editing context, visibility of edits can help to improve the knowledge of collaborators but at the same time cause conflicts [Birnholtz & Ibara, 2012]. Similarly, these actions, simply taken for utilitarian purposes, could have deleterious consequences from misinterpretation as shown by these participants. As such, critically, a lack of visibility undermines understanding and awareness. A unique property of online actions, in stark contrast with those offline, is that the visibility of each online action is implicitly designed by the platform, whereas the visibility of offline actions is often something the action performer can choose to manage. For instance, in the offline world one might merely verbally express disgust about a TV show to someone else and then move on, but doing so online (e.g., disliking a video on YouTube) starts a chain of evidence (sometimes permanent) of the action, which is visible in various ways to other stakeholders on the platform. Intriguingly, YouTube is now experimenting with reducing the visibility of the specific property of disliking—making dislike counts invisible to the public, while keeping them visible
50 S. Y. Park et al. to creators.11 While this measure has only recently been implemented, the response to it has been dramatically negative.12 Key criticisms include issues such as reducing information for stakeholders due to the removal of signals and using the feature in different ways for different videos.13 This serves as a striking example, reinforcing a core idea of our work: Designed decisions about online interactions have widereaching consequences. In addition, decisions about the visibility of an action often serve as the battle ground on which those consequences are understood. 5.3 Actions Are Especially Ambiguous when there Are Opposing Sentiments Online actions could be especially ambiguous when the recipient or observer of the action disagreed with the action taken. For example, P0592 stated wondering about the reason for actions when others make statements they “don’t agree with.” This was the case with P0883 who stated that they wondered “more-so when I don’t agree with [the like/dislike/retweet action].” This is a case in which people on social media are not able to understand the perspective or “read the minds of others,” which is considered important to “their mental states that determine their actions” [Frith & Frith, 2006]. Limited cues are communicated through the predominantly nonverbal interactions designed by social platforms—interactions that lack kinesic, oculesic, vocalic, haptic, and facial cues among many others [Antonijević, 2013; Park et al., 2021]. Due to a lack of such cues, others’ emotions, desires, and intentions are difficult to read from these pervasive online actions [Adolphs, 2002; Langton et al., 2000]. Without information to help understand others’ perspectives, the way online actions on social media are currently designed amplifies our cognitive biases such as the curse of knowledge, by which those who are informed find it extremely difficult to consider perspectives of the lesser-informed [Newton, 1990]. Ambiguity was also felt when the actors themselves seemed to be at odds with what their actions represented, as in the case of why someone would “like something when they actually don’t like it” (P0962). P0029 similarly wondered why someone decided to get involved in a conversation “when I know they don’t really have a strong opinion” and had their own interpretation for the others’ actions: “I think they want to make themselves out to be more of an advocate than they are.” As such, in giving their responses to this question, many others also provided their interpretations of questionable actions. While what P0029 surmises may be one plausible reason, another could be that due to the perceived dissociative anonymity and/or minimization of authority online, others who did not seem to have strong opinions may have 11 https://blog.youtube/news-and-events/update-to-youtube/, Accessed November 12, 2021. https://www.theverge.com/2021/11/17/22787080/youtube-dislikes-criticism-cofounder-jawedkarim-first-video-description-zoo, Accessed November 20, 2021. 13 https://youtu.be/CaaJyRvvaq8, Accessed November 12, 2021. 12
Dancing with Ambiguity Online: When Our Online Actions Cause Confusion 51 felt more comfortable to do so, leading to online disinhibition effect [Suler, 2004]. Yet another reason may be that the other person became increasingly aware of and knowledgeable about the social issue. This could have led them to form strong opinions about it, thus creating a subtle occurrence that the participant (as an observer) may not have been privy to. Given that this negative interpretation of others was assumed out of the many other interpretations could be attributed to the fact that such ambiguous online actions confirms users’ hostile attribution bias—of readily attributing ill intention in others [Nasby et al., 1980; Park & Whiting, 2020; Park et al., 2021]. 5.4 5.4.1 Design Implications People May Not Be Able to Improve, Even If They Want To Responses suggest that people are not aware of why negative actions are taken, as discussed in § 5.1. Ambiguity may be desirable for those carrying out “unsocial behaviors” [Lopez & Ovaska, 2013]; however, in so doing, this encourages people to take more of these actions which may not only cause others to wonder but also to feel upset. This perpetuates greater ambiguity for those at the receiving end of such behaviors. This perpetuation of ambiguity leads to missed opportunities for users to learn from and even respond to such actions; this is especially the case for the users who are unaware of why such “unsocial behaviors” are being directed at them and why they have happened. Hence, in the current design of online actions, this ambiguity only serves the actors, who are arguably fewer than the recipients and observers combined. Similarly, there may be some good reason for posting and engaging with what some perceive as negative content on social media, such as putting the spotlight on the negative aspects of life or misinformation that people wish to bring to others’ attention. Yet such intentions neither “come through” nor are made clear. The outcomes of taking online actions are by design more visible (§ 5.2) and salient (e.g., how many retweets or likes a tweet has received) and have therefore become a critical part of how people perceive content and actions, when the original intent may have nothing to do with the outcome. Therefore, more cues ought to be communicated with the content and actions taken, in addition to the recently implemented labels for misinformation.14 Design implementations could be short labels that cue in others as to why certain content has been shared (e.g., “concerned about this,” “this requires attention”, “this is funny (but not true)”) or why certain actions have been taken (e.g., “too frequent posts” for unfollowing, “violent” for disliking content). This may be uncomfortable for some users (e.g., difficult to accept, invasion of privacy to have such information 14 https://blog.twitter.com/en_us/topics/product/2020/updating-our-approach-to-misleading-infor mation, Accessed November 16, 2020. https://developer.twitter.com/en/docs/twitter-api/enterprise/ engagement-api/guides/interpreting-metrics, Accessed October 30, 2021.
52 S. Y. Park et al. public), and in such cases, an aggregated breakdown mediated by the platform to inform users as to why the majority of users decided to take an action, such as a piece of content retweeted mainly across multiple users to raise awareness, could be helpful. 5.4.2 People May Be Unaware of Their Individual Impact As Bernstein et al. stated, “users have scarce information about who actually sees their content, making their audience seem invisible and difficult to estimate” [Bernstein et al., 2013]. This still seems to hold true today, as people are not aware of the extent to which their actions impact others on social media—it is difficult to quantify who will be seeing one’s likes or retweets and be influenced by them at the time these actions are taken. This helps explain why some actions that do not seem to fit people’s understanding are taken (§ 5.3); specifically, that some may not understand the adverse consequences their actions or content could have. Currently, Twitter provides impression and engagement metrics, which are the number of views and the number of engagement such as retweets, favorites, replies, etc., on a given tweet.14 Yet these metrics are provided after an action has been taken, not prior. Given that propagation of information is not completely up to chance (i.e., by algorithms designed by platforms), and given the wealth of data that can enable platforms to make somewhat accurate predictions of how a given action could have repercussions, platforms can better inform users as to how their online actions may influence others and have consequences. Predictions could be instantiated with varying granularity as the designers see fit. For instance, predictions could be provided as rough estimates of how many people a piece of content or an action will be seen by and affect others, as levels of impact, or even as a binary of whether it will have a big impact or not. Such an implementation could have similar effects as the findings by Pennycook and Rand—that “a simple nudge or prompt that shifts attention to accuracy increases the quality of news that people share (typically by decreasing the sharing of false content)” [Pennycook & Rand, 2021]. This may not be in the platform’s favor, as it could lead to greater mindfulness of users and subsequently less interaction. However, in addition to enabling platforms to provide more responsible design, such an implementation could nudge users to opt more for nuanced comments in response to such mindfulness, instead of low-effort contributions of likes and retweets, which would lead to more thoughtful engagements overall. While a quantified approach that predicts the impact of a piece of content or action might be one way to help users better estimate their influence, another is to show users how their online actions could be questionable or unclear to others. By implementing clarification features, such as having a button to indicate the uncertainty people feel about having a piece of content posted or action taken, users can get a better understanding of when their own actions are not obvious to others. With a clarification feature, a user may come to understand that posting personal content causes others to wonder why they were posted in the first place, and, in response,
Dancing with Ambiguity Online: When Our Online Actions Cause Confusion 53 provide more commentary when sharing content that is personal. Rather than a content-specific or action-specific clarification feature, a more generalized approach can be taken. A feature based on an accumulation of such statistics could also be shown to users; for example, platforms could inform users that 70% of the time others questioned when the user took a negative online action and 30% of the time when they posted or interacted with questionable content. Such implementations of informing users could come at a cost, however. While these features could help increase mindfulness and awareness, users may feel more self-conscious and censor themselves more. This could curb users from taking actions as freely as they would otherwise and encourage more social grooming practices. In the latter case, this would mean taking actions that are primarily motivated by how these users feel they come across to others. Therefore, in implementing such features, platforms ought to consider the resolution in which informing occurs so that a good balance can be reached between being informative while at the same time discouraging self-censorship. 6 Limitations & Future Work The free-text responses have given us much insight into when people feel ambiguity around online actions. Yet due to recency bias, participants may have provided only the examples that are most top of mind or salient for them, leading to incomprehensive responses from any given individual or leading to biases away from subtle or nuanced effects. In an effort to address this, we used specific wording to evoke a thoughtful response that reflected specific experiences; we also provided an incentive to the participants based on the quality of their responses. In our qualitative analysis, we excluded responses from participants who did not answer the question we had posed and therefore could not be coded. This may have been a lost opportunity as we were not able to capture 4.0% of the responses we had collected. However, in capturing more than 1000 responses from participants, we have reached saturation of information and that all the nuances we are interested in are contained within the data set that we have thematically coded. We have uncovered only a subset of themes and will further conduct thematic analysis to uncover independent factors different from action, content, and stakeholders that we derived in our current analysis. As future work, we also aim to bring insights of the ambiguity felt by users into contexts in which their outcomes impact the interactants more directly, such as the bases of collaborative contexts.
54 S. Y. Park et al. 7 Conclusion Ambiguity is “baked into” the design of online actions (e.g., liking, commenting, retweeting) on social media platforms today. Whether by intention or not, users dance, or even skirt, around the ambiguity surrounding these online actions. We conducted a survey-based study and analyzed 1042 responses to why people wonder why online actions are taken on Twitter. From inductive thematic analysis, we found that it is not clear cut when people are confused by online actions: Different actions matter to different people in different ways. In particular, and more surprisingly, simply involving certain actions, content, and stakeholders can cause ambiguity for the recipients and observers. In looking closely at the responses, we find that negativity online in general—whether it is negative actions or any interaction with negative content—as well as the visibility of these actions cause people to wonder why actions are taken. We posit that current designs do not help in clarifying this ambiguity around online actions and further hinder people from being able to gauge the impact of their actions and improve upon them; we provide design suggestions for platforms to provide users strategies in navigating the current ambiguity of people’s online actions. References Accountable Tech. (2021). The Facebook Papers. https://www.facebookpapers.com Retrieved November 1, 2021. Adolphs, R. (2002). Neural systems for recognizing emotion. Current Opinion in Neurobiology, 12(2), 169–177. Antonijević, S. (2013). The immersive hand: Non-verbal communication in virtual environments. In The Immersive Internet (pp. 92–105). Springer. Aoki, P. M., & Woodruff, A. (2005). Making space for stories: Ambiguity in the design of personal communication systems. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 181–190). Berger, C. R., & Calabrese, R. J. (1974). Some explorations in initial interaction and beyond: Toward a developmental theory of interpersonal communication. Human Communication Research, 1(2), 99–112. Bernstein, M. S., Bakshy, E., Burke, M., & Karrer, B. (2013). Quantifying the invisible audience in social networks. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 21–30). Birnholtz, J., & Ibara, S. (2012). Tracking changes in collaborative writing: Edits, visibility and group maintenance. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (pp. 809–818). Boyd, D., Golder, S., & Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In 2010 43rd Hawaii international conference on system sciences (pp. 1–10). IEEE. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77–101. Chaiken, S. (1987). The heuristic model of persuasion. Social Influence: The Ontario Symposium, 5, 3–39.
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Design Thinking for Digital Transformation: Reconciling Theory and Practice Carolin Marx Abstract While scholars have made valuable steps towards claiming legitimacy for deploying Design Thinking in organizations, the underlying practices, effects, and links between Design Thinking and Digital Transformation still seem an underexplored area. This conceptual study aims to shed light on how Design Thinking can contribute to an organization’s Digital Transformation by linking explicit Design Thinking elements to dimensions of Digital Transformation and investigating the role Dynamic Capabilities might play within this effect nexus. In particular, this study proposes three modes of design-enabled Digital Transformation and integrates theoretical with practical perspectives in a conceptual framework. The holistic nature of the framework, including the variety of possible combinations, permits a nuanced investigation of specific, context-dependent cause–effect relationships. This can be used by researchers as a foundation for deriving and testing hypotheses on specific contributions Design Thinking can make to Digital Transformation and by practitioners to analyze, communicate, and manage how they utilize Design Thinking for Digital Transformation. 1 Introduction The proliferation of digital technologies and changing market dynamics increase the complexity for organizations to navigate towards successful adaptation (Bharadwaj et al., 2013; Vial, 2019). Since no industry or organization is immune to these effects (Matt et al., 2016) gaining a competitive advantage through Digital Transformation has become a strategic imperative and a top managerial priority (Verhoef et al., 2021; Vial, 2019; Warner & Wäger, 2019). Similarly, the concept of Design Thinking, described as a novel problem-solving capability is increasingly receiving attention from both researchers and practitioners—especially regarding uses that reach beyond its form-giving roots (Kolko, 2015; Micheli et al., 2019). C. Marx (*) Hasso Plattner Institute, University of Potsdam, Potsdam, Germany e-mail: carolin.marx@hpi.de © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_4 57
58 C. Marx Given that the majority of Digital Transformation activities fail under increasing pressure for change (Forth et al., 2020) while Design Thinking is heralded to become the foundation of competitive advantage (Micheli et al., 2019) doing Design Thinking for Digital Transformation holds promising potential. Within this chapter, we want to contribute to a better understanding of how organizations can make use of adopting Design Thinking practices to foster their Digital Transformation. While scholars have made valuable steps towards claiming legitimacy for deploying Design Thinking in organizations, the underlying practices, effects, and links between Design Thinking and Digital Transformation still seem to be an area that is underexplored (Magistretti, Pham, et al., 2021; Micheli et al., 2019). Only a few attempts have been made by scholars to unpack the role of Design Thinking in the context of digital technology (Verganti et al., 2020). While those scholars have made valuable contributions towards claiming legitimacy for deploying Design Thinking in organizations, the underlying practices, effects, and links between Design Thinking and Digital Transformation still seem an underexplored area (Micheli et al., 2019). Addressing this research gap, Magistretti, Ardito, et al. (2021) attempted to close the link between Design Thinking and Digital Transformation by explaining how the Dynamic Capabilities of Design Thinking can facilitate Digital Transformation in the consulting industry. We build on the Dynamic Capability perspective (Barreto, 2010; Teece et al., 1997) in this chapter and extend Magistretti, Pham et al.’s (2021) recent approach by linking Design Thinking to Hanelt et al.’s (2020) holistic Digital Transformation perspective of continuous change instead of a usually taken simplified and industryspecific view. Further, we propose that there exist multiple modes of how Design Thinking can influence Digital Transformation when applying a holistic lens. Researchers found that Design Thinking is linked to building Dynamic Capabilities (Liedtka, 2020; Magistretti, Ardito, et al., 2021) and that Dynamic Capabilities are useful for Digital Transformation (Warner & Wäger, 2019). The examination of existing research shows that, on the one hand, there is a lack of evidence for the often-emphasized strategic influence of Design Thinking beyond assumptions, anecdotes, and success stories (Liedtka, 2015; Micheli et al., 2019). On the other hand, it is still unclear how Dynamic Capabilities can be developed and how this contributes to the Digital Transformation of organizations (Helfat et al., 2009; Vial, 2019). Hence, our goal is to connect those perspectives and shed light on how Design Thinking can contribute to an organization’s Digital Transformation by linking explicit Design Thinking elements to dimensions of Digital Transformation and investigating the role Dynamic Capabilities might play within this network of effects. Therefore, we pose the following overarching question of interest:
Design Thinking for Digital Transformation: Reconciling Theory and Practice 1.1 59 How Can Design Thinking Contribute to an Organization’s Digital Transformation? We will approach this question using a reconciliation of theory and practice. In a first step, we conceptualize the phenomenon of interest—Design Thinking in organizations—based on existing research and two empirical studies we conducted on the integration of Design Thinking in practice. In a second step, theoretical perspectives on Digital Transformation and Dynamic Capabilities are analyzed and finally integrated as a conceptual framework on the role of Design Thinking in organizational Digital Transformation. We build on and extend Magistretti, Pham et al.’s (2021) valuable contribution by linking Design Thinking to Hanelt et al.’s (2020) holistic Digital Transformation perspective of continuous change and by proposing that there exist multiple modes of how Design Thinking can influence Digital Transformation. This study thereby extends existing research on Design Thinking for Digital Transformation by proposing a unified perspective and overarching framework that can inform future theoretical and empirical studies and guide practitioners. 2 Research Approach The general research approach for this conceptual work relies on the reconciliation of theory and practice. We approach our goal of creating new knowledge on the role of Design Thinking for Digital Transformation from both sides within this chapter using a combination of inductive and deductive reasoning. In a first step, to better understand the phenomenon of interest we will empirically investigate how Design Thinking is used by organizations in practice. From a largescale empirical assessment, we obtain a conceptualization of Design Thinking in organizations, develop patterns of usage, and propose antecedent conditions for the resulting network of effects. In a second step, we approach our goal of creating new knowledge on the role of Design Thinking for Digital Transformation via a theory-driven deduction including a holistic perspective of continuous change and the dynamic capability view of the firm. 3 Practical Perspectives: Design Thinking in Organizations In this sub-chapter, we will provide a conceptualization of the phenomenon of interest—Design Thinking in organizations—by describing of its elements and identifying different types of Design Thinking diffusion (application and localization within an organization) and depth (firm-level capabilities). Taken together, these
60 C. Marx perspectives facilitate a nuanced conceptualization of the usage of Design Thinking in practice that accounts for real-world application scenarios. 3.1 Design Thinking Elements The concept of Design Thinking, described as a novel problem-solving capability, has increasingly received attention both from researchers and practitioners (Kolko, 2015; Micheli et al., 2019). Design Thinking can be understood as an iterative problem-solving approach characterized by an emphasis on empathy, usercentricity, integrative thinking, collaboration, and the active use of ideation and visualization tools (Brown, 2008; Liedtka, 2015). A recent review of Design Thinking (Micheli et al., 2019) has identified ten principal attributes that shape the concept. These attributes include among others human-centeredness and a focus on empathy, the interdisciplinary approach to collaboration, and an iterative and experimental approach, which make DT particularly suitable for uncertain and ambiguous situations. Moreover, Design Thinking is problem- and solution-oriented and relies on hypothesis-driven practices that stress the role of problems before focusing on solutions (Brown, 2008; Liedtka, 2015). Finally, Design Thinking connects the abstract and the concrete world to generate innovative ideas, involving analysis and synthesis as well as rational and intuitive modes of thought (Beckman & Barry, 2007; Micheli et al., 2019). As individuals, processes, interactions, and structures are strongly intertwined when looking at Design Thinking in an organizational context we applied an integrative view for the conceptualization. Making use of existing conceptualizations on the topic of Design Thinking (Liedtka, 2020; Magistretti, Ardito, et al., 2021; Micheli et al., 2019), we developed a comprehensive list of describing elements and then aggregated the elements to overarching areas. Table 1 presents this list of elements. The overarching areas identified by aggregating the reviews consist of user-centeredness and involvement, problem-solving, iteration and experimentation, interdisciplinary collaboration, tolerance of ambiguity and failure, and blending rationality and intuition. Elements describing the area of problem-solving, for instance, include encouraging reframing the problem, building local capabilities to solve new problems, assessing and prioritizing needed capabilities or problemsolving, and allowing emergent solutions. The example shows that each area incorporates the integration of individual, interactional, structural, and process-related elements. While most of Design Thinking’s implementations are rooted in product design, “the subject matter of design is potentially universal in scope, because Design Thinking may be applied to any area of human experience” (Buchanan, 1992, p. 16). Design Thinking has emerged as a set of formal methods for addressing uncertain and ill-defined problems (Buchanan, 1992) while aiming to combine viability, feasibility, and desirability. As a response to the increasing external complexity that organizations face in dynamic and potentially disrupted markets,
Design Thinking for Digital Transformation: Reconciling Theory and Practice 61 Table 1 Elements of organizational Design Thinking (Liedtka, 2020; Magistretti, Ardito, et al., 2021; Micheli et al., 2019) Area User-centeredness and involvement Problem-solving Iteration and experimentation Interdisciplinary collaboration Tolerance of ambiguity and failure Blending rationality and intuition Design thinking element • Deep understanding of user needs • Inclusion of diverse perspectives • Providing user-driven criteria for evaluation • Building emotional engagement • Inviting ownership and engagement • Empathizing with users • Looking at sociocultural trends • Encouraging reframing of the problem • Building local capabilities to solve new problems • Assessing and prioritizing needed capabilities for problem-solving • Allowing emergent solutions • Multiple solutions coexisting alternatives • Creating an action orientation and execution • Prototyping and experimentation • Iteration between discovery ideation and experimentation • Dialogue-based conversations • Expanding the repertoire of teams • Involving key stakeholders beyond the core team • Fostering alignment across diverse perspectives • Broadening access to networks and resources and enhancing willingness to co-create • Attracting champions • Decentralizing responsibility • Brokering knowledge • Encouraging a possibility inspired mindset • Reducing visibilities of failures • Increasing psychological safety • Unleashing and embracing ambiguity • Learning by doing • Creative confidence • Logical and rational reasoning • Mitigating decision biases • Deductive thinking • Inductive thinking • Abductive thinking • Naive mind Design Thinking is applied to more complex experiences and systems such as services, business models, business strategies, and social policies (Brown & Martin, 2015; Kolko, 2015). Such Design Thinking applications may have far-reaching effects on organizations and societies, given that the users of Design Thinking are capable of changing established mindsets and behaviors. In the same vein, scholars have shown that Design Thinking can be the foundation of competitive advantage (Martin & Martin, 2009; Micheli et al., 2019) and an enabler for an organization’s paradigmatic shift in strategic vision (Liedtka, 2015).
62 C. Marx This fundamental impact on an organization is most likely an indirect one. For instance, past studies have argued that Design Thinking can contribute to an organization’s innovation capabilities (Carlgren et al., 2014) and may help to shape the organization’s culture (Elsbach & Stigliani, 2018). This transpires through an unveiling of latent customer needs over time, balancing exploratory and exploitative innovation activities (Martin & Martin, 2009), and fostering the assimilation and reconfiguration of internal know-how from innovation systems (Acklin, 2010). An academic focus of interest underlines the shift in broadening the usage and impact of Design Thinking for an organization and the individuals involved. 3.2 Depth and Diffusion of Design Thinking Elements in Organizations As outlined previously, Design Thinking, even when described via the same elements, can have different manifestations in the mode of organizational integration. In other words, organizations use Design Thinking for different purposes and with different levels of capabilities. Therefore, in order to approach a comprehensive conceptualization, we will provide a description of diffusion, described by application and localization, and depth, described by Design Thinking capabilities, as relevant factors shaping the actual Design Thinking integration. We thereby aim to identify relevant integration conditions that when applied to the organization of interest help to conceptualize the usage of Design Thinking. For both conditions, a conceptual model has been developed and applied via an online survey to organizations practicing Design Thinking. When looking at the depth of the integration of Design Thinking elements within organizations one can identify capability levels as the suitable comparative lens. Grounded in existing research (Nakata & Hwang, 2020; Wrigley et al., 2020) and six qualitative interviews with Design Thinking experts from academia and practice we developed a multidimensional model that conceptualizes the depth of organizational Design Thinking via firm-level capabilities, derived items, and applied the model to 547 organizations via an online survey. See (Marx et al., 2022) for details on the model development and the empirical application. Table 2 shows the corresponding data structure including dimensions and sub-dimensions of firm-level Design Thinking capabilities that we developed. Firm-level Design Thinking capabilities are formed by Design Thinking related actions and processes, strategy, organizational resources, and mindset. These dimensions are each formed by multiple sub-dimensions which are each reflected in a set of two to four items. Besides depth, we propose that the second crucial factor when conceptualizing Design Thinking in an organizational context is the diffusion of Design Thinking elements, hence where (localization) and for what (application) Design Thinking is used (see Marx, Haskamp, et al., 2021 for a detailed overview of the model
Design Thinking for Digital Transformation: Reconciling Theory and Practice 63 Table 2 Depth of Design Thinking integration (Marx et al., 2022) Firm-level Design Thinking capabilities Dimension Actions and processes Strategy Organizational resources Mindset Sub-dimensions • Discovery • Ideation • Experimentation • Performance measurement • Funding • Leadership and decision-making • Organizational structure • Link to strategy • Work environment • Access to resources • Learning and development • Human-centeredness • Abductive reasoning • Learning by failure developed for assessing Design Thinking diffusion in organizations). Based on Kootstra (2009 and Wrigley et al. (2020), we identified traditional design problems and products, services and experiences, business model and strategy, and culture and ecosystem as diffusion stages when looking at for what organizations use Design Thinking, hence the application. Additionally, we suggested combining the identification of the Design Thinking application stage with an assessment of the localization of Design Thinking practices within the firm. For this part, we adapted Junginger’s model (Junginger, 2009) including peripheral, somewhere, central, and everywhere as potential localizations of Design Thinking in organizations. Table 3 gives an overview of how we conceptualized the diffusion of Design Thinking integration in organizations. The results of the empirical application in both studies indicate that our models are a suitable means to assess firm-level Design Thinking depth and diffusion across industries and firm-size categories. Further, the heterogeneity observed in the data underlines the relevance of including depth and diffusion as influencing conditions on the conceptualization of Design Thinking elements in organizations. It is therefore crucial to not only look at the Design Thinking elements but first to identify which level of Design Thinking capabilities is currently reached by the organization of interest and which combination of application and localization describes it best. 3.3 An Integrative Conceptualization of Design Thinking in Organizations Figure 1 gives an overview of the synthesized empirical findings on the current use of Design Thinking in practice and the existing research on the conceptualization of
64 C. Marx Table 3 Diffusion of Design Thinking integration (Marx, Haskamp, et al., 2021) Diffusion type Application Stage Traditional design problems and products Services and experiences Business model and strategy Culture and ecosystem Localization Peripheral Somewhere Central Everywhere Explanation Design Thinking is used to solve traditional design problems of form and to develop specific products. Design Thinking is applied to the development of internal and external services and experiences. Design Thinking shapes strategy and is applied to new business model development and strategic decisionmaking. Design Thinking is part of the organizational culture and is applied beyond products, services, and strategy to an organization’s ecosystem. Design Thinking has no continuous presence in the organization but is booked as a resource on demand. It takes place separately from operational activities. Design Thinking is practiced as a part of one or two organizational functions, such as the marketing or R&D department. Design Thinking has a central position in strategic decision-making. It is linked to leadership and an overall strategy. Design Thinking shapes aspects of the organization and has the potential to transform it. Design Thinking is integral to all parts of the organization. The organization is questioned, formed, and shaped by ongoing Design Thinking. the general phenomenon from the previous sub-chapters including defining elements, diffusion, and depth. We suggest, that when investigating the role of Design Thinking for Digital Transformation it is crucial to concretize the diffusion of Design Thinking in the organization of interest reflected by application and localization and the depth of Design Thinking integration indicated by firm-level Design Thinking capabilities. In combination with the consolidated list of Design Thinking elements, the abovementioned contextual factors permit a nuanced description of the actual use of Design Thinking in practice and build the foundation for our further conceptual analysis. 4 Theoretical Perspectives: Digital Transformation and Dynamic Capabilities In this sub-chapter, we build on the above-developed practical-oriented conceptualization of our phenomenon of interest—Design Thinking in organizations—and set the focus on how it relates to Digital Transformation activities. We do this by introducing Digital Transformation from a holistic perspective, and connecting it
Design Thinking for Digital Transformation: Reconciling Theory and Practice Diffusion (Localization & Application 65 Depth (Capabilities) ORGANIZATIONAL DESIGN THINKING ELEMENTS User centeredness and involvement Problem solving • Deep understanding of user needs • Inclusion of diverse perspectives • Providing user driven criteria for evaluation • Building emotional engagement • Inviting ownership and engagement • Emphasizing with users • Looking at sociocultural trends • Encouraging reframing of the problem • Building local capabilities to solve new problems • Assessing and prioritizing needed capabilities for problem solving • Allowing emergent solutions Interdisciplinary collaboration • Dialogue-based conversations • Expanding repertoire of teams • Involving key stakeholders not part of core team • Fostering alignment across diverse perspectives • Broadening access to networks and resources and enhancing willingness to co-create • Attracting champions • Decentralize responsibility • Brokering knowledge Iteration and experimentation • • • • Tolerance of ambiguity and failure • Encouraging a possibility inspired mindset • Reducing visibilities of failures • Increasing psychological safety • Unleashing and embracing ambiguity • Learning by doing • Creative confidence Multiple solutions coexisting alternatives Creating an action orientation & execution Prototyping & experimentation Iteration between discovery ideation and experimentation Blending rationality and intuition • • • • • • Logical and rational reasoning Mitigating decision biases Deductive thinking Inductive thinking Abductive thinking Naive mind Fig. 1 Integrative conceptualization of Design Thinking in organizations via diffusion, depth, and elements to the dynamic capability view of the firm. The aim of this sub-chapter is to connect different theoretical perspectives that help to shed light on how Design Thinking might contribute to an organization’s Digital Transformation. 4.1 A Holistic Perspective on Digital Transformation The proliferation of digital technologies such as artificial intelligence, digital platforms, big data analytics, shifts in customer expectations, and changing market dynamics increase the complexity for organizations seeking successful strategic adaptation (Bharadwaj et al., 2013; Vial, 2019). Fueled by the need to adapt, organizations fundamentally alter and create capabilities, resources, operational processes, end-user experiences, and business models (Bharadwaj et al., 2013; Correani et al., 2020; Fitzgerald et al., 2014; Rogers, 2016; Verhoef et al., 2021). These demonstrate a process of Digital Transformation. Since no industry or organization is immune to the need to adapt (Matt et al., 2016) gaining competitive advantage through Digital Transformation has become a top managerial priority (Verhoef et al., 2021; Vial, 2019; Warner & Wäger, 2019).
66 C. Marx In practice, however, many companies do not recognize the potential of Digital Transformation, and many of those that do still struggle to change their habits and ways of working sufficiently to reap the maximum benefits from digital efforts (Fitzgerald et al., 2014; Parviainen et al., 2017; Vial, 2019). Recent reports underline this apprehension showing that 87% of companies at a global level believe that Digital Transformation will disrupt their industry, while only 44% state that they are prepared to respond or lead this process of disruption and only 30% of enterprises actually achieve their initial digital goals (Forth et al., 2020). While transforming mechanisms have the potential to fundamentally change the organizational setup, as well as the firm’s competitive positioning, and the core organizational design, they are extremely challenging to successfully execute. This is especially the case for incumbent firms in traditional industries facing severe barriers to transformation (Forth et al., 2020; Linderoth et al., 2018; Vial, 2019; Warner & Wäger, 2019). We see that even when organizations are increasingly alert and prioritize investments, much remains to be done in terms of getting prepared to embrace and leverage Digital Transformation’s potential for enabling innovation and value creation. Given this tension between desirability and the challenge of implementation, recent scholars argue that a more holistic conceptualization of Digital Transformation would hold academic and practical advantages. For instance, Matt et al. (2016) claim that while the majority in academia have been largely concerned with providing guidance on certain aspects of Digital Transformation, the application of a holistic approach to the phenomenon of interest is both valuable and lacking. In line with such a holistic perspective it is important to stress that organizational Digital Transformation goes beyond the use of technology. It is in fact deeply intertwined with fundamental changes affecting every aspect of the business, the ecosystem it operates in, and the people involved with it (Hanelt et al., 2020; Kane, 2019; Wessel et al., 2021). The necessity for organizations to better integrate digital technologies with their overall strategy requires a rethink of how to view and implement technologies in a way that empowers the business on a holistic level (Ellström et al., 2021; Hanelt et al., 2020). Based on this holistic perspective, we follow (Hanelt et al., 2020, p. 1187) describing Digital Transformation as “organizational change triggered and shaped by [episodic bursts, rooted in] the widespread diffusion of digital technology.” This continuous change involves and indicates a general shift “towards malleable organizational designs that are embedded in and driven by digital business ecosystems” (Hanelt et al., 2020, p. 1168). Past conceptual and empirical research (Chanias et al., 2019; Soh et al., 2019) reinforces this understanding of Digital Transformation as an ongoing and ever-changing process. Firms that undergo Digital Transformation have to constantly assess their current organizational setup against emerging opportunities and threats and adapt it to the new environment (Gannon, 2013; Westerman et al., 2014). In this regard, core to accelerating the shift towards malleable organizational design and adaptation is rapid scaling generated by data-driven operation through monitoring and user profiling followed by swift transformation and instant, feedback-based release (Hanelt et al., 2020; Huang, The University of Warwick,
Design Thinking for Digital Transformation: Reconciling Theory and Practice 67 Fig. 2 Mechanisms generating rapid adaptation and supporting changes in the organizational setup (Hanelt et al., 2020; Huang, Henfridsson, et al., 2017) et al., 2017). Digital firms have been found to adapt rapidly through those mechanisms (Huang, The University of Warwick, et al., 2017) that are related to observable changes in the organizational setup on multiple levels (Hanelt et al., 2020). Firstly, changes towards data-driven processes and empirical insight-based managerial decision-making can be linked to data-driven operation, for instance, user profiling, decision-hedging, and monitoring. Secondly, changes in organizational structures towards permeable agility support the redefinition of organizational identity and the contextualization of technology as part of swift transformation mechanisms. Third, the instant feedback-based release mechanism is fueled by changes in organizations’ value creation setup including a shift towards customer-centered business models and smart, customized products. Figure 2 gives an overview of the connection between the described mechanisms for rapid adaptation and supporting changes in the organizational setup. Building on the observation that organizations strive for rapid adaptation through data-driven operation, swift transformation and instant release in specific cases, and a malleable organizational design in general, Hanelt et al. (2020) provide a framework that embodies multiple dimensions and levels of Digital Transformation along with contextual conditions, mechanisms, and outcomes. We will use an adapted version of this framework as a holistic representation of organizational Digital Transformation for analyzing the network of effects related to Design Thinking and Dynamic Capabilities.
68 4.2 C. Marx A Dynamic Capabilities View on Digital Transformation The second theoretical lens we apply in order to understand how Design Thinking might contribute to an organization’s Digital Transformation is the dynamic capability perspective of the firm. One can distinguish between two classes of capabilities: ordinary and dynamic (Eisenhardt & Martin, 2000; Teece, 2007). Ordinary capabilities foster efficiency (doing things right) in well-delineated tasks, are usually imitable, and do not vary much in environments open to global competition. Dynamic Capabilities, on the other hand, can be described as doing the right things at the right time by creating, extending, and revising capabilities and resources (Drnevich & Kriauciunas, 2011). Teece et al. (Teece et al., 1997, p. 516) define Dynamic Capabilities in their landmark article as “the firm’s ability to integrate, build and reconfigure internal and external competencies to address rapidly changing environments.” Specifically, the value of Dynamic Capabilities lies in the “potential for helping the organization do this repeatedly, thereby helping to create a durable competitive advantage” (Teece, 2014, p. 335) especially in high-velocity, competitive markets. Barreto (2010, p. 257) suggests a new conceptualization of Dynamic Capabilities as an aggregated multidimensional construct: Here, Dynamic Capabilities are described as a “firm’s potential to systematically solve problems, formed by its propensity to sense opportunities and threats, to make timely and market-oriented decisions, and to change its resource base.” As pointed out by Vial (2019, p. 133), “there is an interesting fit between Dynamic Capabilities as a conceptual foundation and Digital Transformation as a phenomenon of interest.” Several researchers have found that Dynamic Capabilities as a theory-grounded conceptual foundation could be valuable to foster Digital Transformation and to gain a competitive advantage (Pezeshkan et al., 2016; Warner & Wäger, 2019). To explain this link in more detail we refer back to our understanding of Digital Transformation as a challenging process of continuous change whereby digital technologies create episodic bursts triggering a general shift towards malleable organizational design embedded in and driven by digital business ecosystems (Hanelt et al., 2020). To successfully drive this process and overcome barriers to continuous adaptation, it is crucial for organizations to (A) (B) (C) (D) sense disruption at the right time, assess the opportunities and threats attached to it, seize them, and reconfigure their organizational setup towards integrating digital in their strategy, business model, processes, culture, and in the use of technologies. Hence, Dynamic Capabilities can be seen as equipping firms to respond to digital disruption in a dynamic manner and in return contribute to their Digital Transformation. Helfat and Peteraf (2015) explained that attention and perception relate to sensing activities that enable firms to recognize opportunities and create them.
Design Thinking for Digital Transformation: Reconciling Theory and Practice 69 Similarly, problem-solving and reasoning relate to seizing activities that foster strategic investments and business model design. And finally, communication and social aspects influence a firm’s reconfiguring or transforming activities, which might lead to strategic asset alignment and overcoming barriers to change. The described chain of effects nicely shows the deep connection between Dynamic Capabilities and activities directly influencing the organizational setup and performance in a Digital Transformation context. Numerous empirical studies have underlined the connection between Dynamic Capabilities and elements of Digital Transformation activities like technological capabilities, digital platform capabilities, technological innovation capabilities (Karimi & Walter, 2015; Protogerou et al., 2011; Zhou et al., 2019), and the concept of digital maturity (Marx, de Paula, & Uebernickel, 2021). Other studies have broadened the view towards the role of Dynamic Capabilities within the Digital Transformation process. Warner and Wäger (2019) and Velu (2017), for instance, found that firms must build a system of Dynamic Capabilities for Digital Transformation. 4.3 Design Thinking and Dynamic Capabilities Not only have Dynamic Capabilities been linked to elements of Digital Transformation as direct or indirect outcome variables. As Dynamic Capabilities can help to “identify latent customer needs and the most promising technological opportunities [and] then orchestrate the resources needed to innovate, or co-innovate” (Teece, 2014, p. 332), also innovation studies have increasingly relied on the Dynamic Capabilities perspective. Accordingly, there is a growing body of research applying a Dynamic Capability lens to Design Thinking in organizations showing how Design Thinking activities can contribute to building Dynamic Capabilities. As firms struggle with increasingly broad and complex innovation challenges in the rapidly changing environment, the connection intuitively seems plausible. Within this academic discourse, special emphasis is given to Design Thinking elements as potential micro-foundations of Dynamic Capabilities (Felin & Foss, 2005; Winter, 2003). Those micro-foundations reveal why certain firms excel, while others fail in increasingly complex and dynamic business environments. For this reason, they are of special interest to researchers and practitioners. Like Dynamic Capabilities, Design Thinking is rooted in the role and characteristics of the organizational members, as well as in the ways and structures characterizing their interaction modes. The linkage, however, has been demonstrated on multiple levels including processes, structures, and organizational design. Dynamic Capabilities rely on environmental scanning and real-time information, prototyping, experimentation, cross-functional collaboration, and brainstorming (Eisenhardt & Martin, 2000)—similar tools and methods that constitute Design Thinking. Regarding the specific dimensions of Dynamic Capabilities, Design Thinking favors a better understanding of customers, their contexts, and latent needs, which is directly
70 C. Marx connecting empathy to sensing as part of the dynamic capability perspective (Carlgren et al., 2016). Moreover, adopting tools and methods such as visualization, storytelling, and prototyping to support rapid testing and innovation development combined with logical reasoning helps organizations to seize the identified opportunities. Reconfiguring or transforming is fostered by Design Thinking elements that steadily stimulate novel and innovative ideas, different approaches to problemsolving, and idea management to cope with changing market needs and technological dynamism as well as to embrace ambiguity (Beverland et al., 2015). Hence, a strong link emerges between Design Thinking and Teece’s (2007) Dynamic Capabilities of sensing and seizing opportunities as well as reconfiguring the innovation approach (Carlgren et al., 2014; Liedtka, 2020). Several empirical studies have supported this connection. For instance, Kurtmollaiev et al. (2018) showed that Design Thinking training in organizations had a positive effect on the participants’ sensing and seizing capabilities, which had a further positive effect on their transforming capability, team innovation output, and team operational capability. Similarly, Nagaraj et al. (2020) showed that team Design Thinking as a Dynamic Capability can overcome inertia in routines and cognition in organizations. 5 Reconciling Theory and Practice In this sub-chapter, we will build on the conceptualization of Design Thinking in organizations and the theoretical integration outlined in the previous sub-chapters. In the first step, three modes of influence are identified and tied to existing research on the impact of Design Thinking on organizational Digital Transformation. In a second step, we incorporate the practical and theoretical perspectives into one integrative framework of effects. 5.1 Modes of Influence Based on our conceptual analysis around Digital Transformation and Dynamic Capabilities we identified three potential modes of how the organizational integration of Design Thinking can contribute to a firm’s Digital Transformation. It is most plausible that within one organization all three modes happen simultaneously and each occurs on an individual, structural, and procedural level (Felin et al., 2012). It is, therefore, crucial to consider them jointly as formative and interdependent. We define Mode 1 as a direct, short-term-oriented mode. For instance, Design Thinking can be directly used in digital innovation projects as a method for the development and implementation of elements of a Digital Transformation strategy. Resulting product or service innovations have the potential to directly translate into business model change or the creation of new value direction as part of a firm’s
Design Thinking for Digital Transformation: Reconciling Theory and Practice 71 Digital Transformation. Nagaraj et al. (2020) gave an example of this direct mode by empirically linking Design Thinking to product usefulness and novelty. Similarly, Przybilla et al. (2020) showed that Design Thinking can lead to higher quality prototyping, and more innovative business models, thereby directly influencing the creation of digital solutions as crucial outcome elements of Digital Transformation. In most cases, however, stronger connections can be assumed between the integration of Design Thinking and intermediate outcomes which then affect Digital Transformation elements. Mode 2 is described as an indirect, mid-term-oriented mode where Design Thinking is linked to Digital Transformation via changes in the organizational setup and transformation mechanisms. From an individual perspective, the use of Design Thinking can foster a shift of mental models and pre-digital mindsets towards overcoming biases in decision-making (Liedtka, 2015). Practicing Design Thinking also impacts individuals’ mindsets and attitudes towards their profession and has the potential to foster motivation, ownership, and engagement—thereby indirectly contributing to elements of Digital Transformation within organizations. For instance, Design Thinking has been linked to higher levels of empowerment in teams. One can also identify indirect connections on the group and process level, where the integration of Design Thinking has the potential to shape corporate culture via fostering experimentation, iteration, failure, and facilitating knowledge through collaboration. Another example from research for this mode is given by Appleyard et al. (2020), who linked Design Thinking to a higher level of trust and collaboration in cross-functional teams. Team trust and collaboration have, on the other hand, been identified as crucial elements affected by and affecting Digital Transformation (Hanelt et al., 2020; Vial, 2019). On a structural level, an example for Design Thinking to indirectly contribute to a firm’s Digital Transformation is via the facilitation of structural agility, for instance, in the course of Digital Innovation Units (Nambisan et al., 2017). Structural effects can also be identified when looking at inertia and resistance to transformation endeavors that are assumed to be limited by the effects of integrating Design Thinking (Magistretti, Pham, et al., 2021). Building on the connection between Design Thinking and Dynamic Capabilities presented in the previous sub-chapter, Mode 3 describes an indirect, long-termoriented effect of integrating Design Thinking that contributes to a firm’s Digital Transformation via building and sustaining Dynamic Capabilities. A strong link can be observed between Design Thinking and sensing and seizing opportunities as well as reconfiguring the innovation approach (Carlgren et al., 2014; Liedtka, 2020; Magistretti, Pham, et al., 2021). 5.2 Integrative Framework The primary goal of this conceptual work was to analyze how Design Thinking can contribute to an organization’s Digital Transformation. Hence, in this sub-chapter, we present our attempt of integrating the previously analyzed perspectives into a
72 C. Marx single framework that can guide researchers and practitioners. Beyond looking at possible modes in which Design Thinking might contribute to an organization’s Digital Transformation, the integration of a holistic perspective on Digital Transformation in combination with the powerful link with Dynamic Capabilities and the practice-oriented conceptualization of Design Thinking elements holds promising potential. Figure 3 shows our conceptualization of Design Thinking in organizations via its elements dependent on the diffusion and depth of its integration, and how it is linked via Dynamic Capabilities to Digital Transformation dimensions including contextual conditions, mechanisms, and outcomes. The lower part of the framework is adapted from Hanelt et al. (2020) and shows those dimensions of Digital Transformation at the corresponding level that are affected directly or indirectly by Design Thinking elements. Design Thinking affects sensing contextual conditions of Digital Transformations: Elements of Design Thinking such as empathy with the user and the inclusion of diverse perspectives as part of user-centeredness and involvement can be linked to sensing contextual conditions inside and outside the organization. Affected dimensions of Digital Transformation include awareness of the top management team towards topics, trends, opportunities, and challenges associated with Digital Transformation and sensing and interpreting digital consumer demands. Design Thinking affects acceleration and alignment mechanisms of Digital Transformation: Within the area of Digital Transformation mechanisms, innovating and integrating have been presented as relevant types of agentic behavior that have the potential to develop and align the required new organizational elements via constant unfreezing (Hanelt et al., 2020, p. 1178)). Our analysis proposes that Design Thinking elements have the potential to affect both types. First, it can be directly linked to creating digital innovations and more indirectly to mobilize the workforce towards welcoming the Digital Transformation of the organization. Further, we can see a linkage between Design Thinking and mindset shifts which fosters overcoming biases and potential inertial forces regarding the transformation. Secondly, one can connect elements of Design Thinking, such as interdisciplinary collaboration and the tolerance of ambiguity and failure with the mechanisms of unlocking and learning—hence integration. Agile and collaborative structures as embraced by Design Thinking further promote physical-digital harmonization and technological flexibility as part of the general alignment mechanism. With a view to this framework, we propose that Design Thinking affects the acceleration and alignment mechanisms of Digital Transformation. Design Thinking affects the outcomes of Digital Transformation within and outside the organization: Elements of Design Thinking can also be linked to changes in the organizational setup, spillovers of those changes to the individual and market level as well as changes in the economics of the firm. Agile organizational structures, customer-focused business models, and smart customized products are examples of Digital Transformation outcomes that are closely related to all elements of Design Thinking. Depending on the depth and diffusion of the Design Thinking integration also ecosystem-oriented and embedded organizational design can be partially
Fig. 3 An integrative framework of how Design Thinking contributes to Digital Transformation Design Thinking for Digital Transformation: Reconciling Theory and Practice 73
74 C. Marx fostered by the use of Design Thinking. Additionally, we propose a linkage between Design Thinking elements and spillovers as Digital Transformation outcomes. One of those spillover outcomes is the digitalization of the individual. Design Thinking can be used to identify or anticipate these effects and potential changes in expectations. Similarly, Design Thinking is closely related to general paradigms of customer centricity and connected markets, which have been identified as relevant spillover outcomes of Digital Transformation. Also on the economic level, one can draw a line between Design Thinking elements and Digital Transformation outcomes as reflected in improved firm performance via new forms of value creation or improved service quality. 6 Conclusion Organizations are increasingly confronted both internally and externally with novel challenges, deeply entangled with Digital Transformation. In this chapter, we shed light on how the integration of Design Thinking can enhance the value of digital technologies towards a more human-centric Digital Transformation. In particular, this study proposes three modes of design-enabled Digital Transformation and integrates theoretical with practical perspectives in the presented conceptual framework. The given examples of how to interpret the framework show that while Dynamic Capabilities can be seen as a generally strong link between Design Thinking and Digital Transformation, a variety of Design Thinking elements have the potential to affect every dimension of Digital Transformation in a multitude of ways. The holistic nature of the framework including the variety of possible combinations permits a nuanced investigation of specific, context-dependent cause–effect relationships. It can be used by researchers as a foundation for deriving and testing hypotheses on the specific contributions Design Thinking makes to Digital Transformation dimensions or to analyze empirically observed relationships from realworld cases. Concerning academic debate, this chapter enriches the understanding of Digital Transformation by unshadowing the value that Design Thinking might play in it. Practitioners can utilize this framework for analyzing, communicating, and steering how their organizations aim to contribute to their Digital Transformation with the aid of Design Thinking. References Acklin, C. (2010). Design-driven innovation process model. Design Management Journal, 5(1), 50–60. Appleyard, M. M., Enders, A. H., & Velazquez, H. (2020). Regaining R&D Leadership: The role of design thinking and creative forbearance. California Management Review, 62(2), 12–29.
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Experiences of Facilitating Virtual Design Thinking: Theoretical Reflections and Practical Implications Selina Mayer, Martin Schwemmle, Claudia Nicolai, and Ulrich Weinberg Abstract To deliver excellent virtual education experiences, design thinking educators adapt along all three “P”s: of People, Place, and Process. This book chapter provides the theoretical foundations for delivering virtual education experiences and, relying on both relevant streams of research and the authors’ own expertise, derives six areas of action—(Digital) Engagement, Embodied Cognition, Safe Space, Atmosphere, Random Inspiration, and Managing Workshops. The theories linked to these areas also inspire questions for further research and, together with the specific suggestions provided, give practitioners a rich resource for enhancing their virtual DT education. 1 Introduction Over the last years, Design Thinking (DT) has been on the rise at multiple institutions and universities to teach an innovative approach to problem solving to students and professionals (Dunne & Martin, 2006; Withell & Haigh, 2013). The Global Design Thinking Alliance (GDTA), a network of 22 internationally recognized academic institutions with the mission of developing DT education worldwide, derives elements of DT’s core principles and the mindset behind DT from what they call the big three “P”s: People, Place, and Process (GDTA, n.d.). “People” refers to the focus on multidisciplinary teams, fostering collaboration with broad perspectives. “Place” addresses the flexible innovation spaces, referring to materials as well as communication, collaboration, prototyping, and sharing opportunities through visualizations and other means. And finally, the “Process” offers guiding Selina Mayer and Martin Schwemmle contributed equally. S. Mayer (*) · M. Schwemmle · C. Nicolai · U. Weinberg HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany e-mail: Selina.Mayer@hpi.de; martin.schwemmle@hpi.de; claudia.nicolai@hpi.de; uli.weinberg@hpi.de © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_5 79
80 S. Mayer et al. steps to tackle complex problems in an iterative manner. In spring 2020, the Covid epidemic disrupted DT education, which had to be massively transformed from physical spaces to virtual settings. In this chapter, we explore how the three core elements of the DT, People, Place, and Process, should be adopted to the virtual setting. First, we describe the Education Experience Model, a model that provides insights on experiences that should be considered when teaching over distance. We also give an overview about the three Ps and how they might differ in different teaching settings, such as teaching face-to-face or virtually. Second, drawing on this model and our own experience within student and professional education of facilitating a large spectrum of different DT programs virtually in the last 18 month, we identified six fields of action—two for each of the three Ps—that help fostering DT education in a virtual environment. We describe these fields of action in detail and rely on a diverse set of research streams from psychology, business administration, or economics to explain their effects. Last, we provide examples from our facilitation experience, giving practitioners specific actions that can help them leverage the opportunities given by these six fields of action. As the future of DT education should include physical and virtual spaces as well as potential hybrid combinations (HyFlex; Beatty, 2014), it is important to understand the challenges and opportunities in the different contexts. Our chapter systematically uses two models to analyze the differences between offline and virtual DT education. For researchers, we provide an overview of the diverse set of research streams that might be of interest to deeper understand the specifics of designing DT offers and facilitating DT activities in virtual settings. For DT practitioners, we offer six specific areas of action, giving an understanding of how these areas come into play and we provide specific behaviors or actions that practitioners can engage in to foster the virtual facilitation of DT. 1.1 The Education Experience Model To understand the transformational changes induced by virtual DT facilitation, we rely on the “Education Experience Model” by Garrison et al., (2000), which was developed in the context of (early) distant education. As shown in Fig. 1, this framework postulates three main components of an online learning experience: cognitive presence, social presence, and teaching presence. To deliver these three presences and, as a result, achieve a good online education experience, DT education needs to adapt all three Ps. We therefore begin by introducing the elements of the Education Experience Model and the changes they undergo through digital education. Afterwards, we introduce the theories and strategies along the three Ps that subsequently make it possible to achieve such changes. Cognitive presence denotes the most basic element necessary for educational success and refers to learners’ ability to create meaning through communication. While the communication between members of an analog DT team usually occurs
Experiences of Facilitating Virtual Design Thinking:. . . 81 Fig. 1 Education Experience Model (Garrison et al., 2000, p. 88) naturally and is also fostered by physical closeness between learners and educators’ interventions, a virtual environment needs new ways to ensure cognitive presence. Furthermore, it is more difficult for participants to be present in online than in offline trainings, since looking at a screen creates fatigue and technology offers manifold distractions, such as trying out new software functions or answering e-mails. Social presence refers to the component of feeling comfortable and is defined as the “ability of participants [. . .] to project themselves socially and emotionally” into the community, in other words, to bring themselves into the learning environment as human beings with needs and desires and not only as learners. The absence of social presence can easily turn collaborative online learning into a simple mode of lecturing or ex-cathedra teaching (Schrage, 1995). In addition, emotions are more difficult to read as the perception of others is reduced to their faces in a virtual setting and facial expression is quite limited in range as many people focus on content on their screens in parallel to trying to look at other people. Hence, facilitating DT in a virtual context requires, in particular, embedding human elements and emotions that contrast with the more technology-driven virtual teaching environment. Teaching presence involves the two functions: (1) designing the educational experience, including the selection of content, the design of learning activities, and the development of an assessment, and (2) facilitating the learning experience. Building on social and cognitive presence, it is teaching presence that provides
82 S. Mayer et al. and delivers the actual learning experience. While virtual DT education might not tremendously change the teaching content, it has severe implications for its e-pedagogical design and facilitation. Together, cognitive, social, and teaching presence create the educational experience. Transitioning from offline to online education, hence, requires the virtualization of the entire experience, which means its three main components need to be carefully aligned. In particular, adapting the teaching presence without reconsidering cognitive and social presence can only deliver an incomplete online education experiences. In the next sections, we will therefore discuss strategies that ensure such an overall virtual DT education experience across a social, cognitive, and teaching presence. 1.2 Facilitating in Various Settings: People, Place, Process Many institutions around the world use the three Ps, People, Place, and Process, as grounding pillars for facilitating DT, for example, at the HPI School of Design Thinking in Potsdam (HPI D-School, n.d.) or the Hasso Plattner School of Design Thinking at the University of Cape Town (UCT d-school, n.d.). The first P, People, is deeply anchored in the core of DT. Research identifies interdisciplinary collaboration as one of the key attributes to characterize DT (Micheli et al., 2019). DT education therefore emphasizes bringing together people from various backgrounds and with a broad set of expertise, to form diverse, multidisciplinary teams that allow for the emergence of different perspectives on complex challenges. Fostering collaboration between team members is central to teaching DT. Setting up heterogeneous teams is equally possible in the face-to-face setting as in a virtual setting—or, in fact, might be even easier in the virtual context. Since international participation is not restricted by physical attendance, a larger audience can be reached with virtual learning programs. Yet, we have experienced differences in the communication, interactions and therefore interpersonal relationships when it comes to virtual facilitation. While virtual facilitation might allow more diversity and interdisciplinarity, it raises certain challenges concerning the social presence as described in Sect. 1.1. For instance, the following key questions arose: How do learners experience empathy? And How do people engage with each other in a virtual setting? In Sect. 2, we focus on literature of team engagement and embodiment to offer theoretical grounding for these two fields of action when fostering DT facilitation in a virtual setting. The second P, Place, refers to the creative spatial conditions DT often relies on to foster collaboration and creativity in teams. This includes movable furniture or material for quick prototyping as well as spaces designed for team work and for gathering to share learnings in larger groups. Investigating the influence of space on DT has found a wide interest in the research community (Overmyer & Carlson, 2019; Schwemmle et al., 2018; Schwemmle et al., 2021). As the physical space is clearly different in a virtual context, we were curious as to how DT facilitation can
Experiences of Facilitating Virtual Design Thinking:. . . 83 create a positive learning context in the virtual setting by offering a “safe space” as well as the right atmosphere—both online and offline. The Education Experience Model emphasizes the importance of setting climate, and incorporating the social and teaching presence. In Sect. 3, we rely on research on psychological safety as well as creativity research as a theoretical grounding for these two fields of action. The last P, Process, refers to the underlying structure DT, i.e. the DT process (Liedtka, 2011). While there is no clear definition or agreement on what DT exactly is (Micheli et al., 2019), most academic and professional teaching institutions rely on process steps. For instance, d.school Stanford uses a five-step process and the HPI School of Design Thinking uses a six-step process (What, n.d.b). While the process steps remain the same in a virtual facilitating context, Process also refers to surrounding conditions and activities such as engaging in warm-ups to break the ice between team members (Carlgren et al., 2016), using timeboxing to allow for speed and multiple iteration cycles (Efeoglu et al., 2013), or supporting small teams via expert coachings to enable learning in novices (Liedtka, 2020). We therefore wanted to understand how much process and structure is needed in virtual facilitation and how this structure might be different from a physical setting. In Sect. 3, we investigate how random encounters also fit a structured process as to what is important for managing workshops online. To sum it up, the next three sections follow the structure of People, Place, and Process and, for each P, present two fields of action that we derived from our experience in facilitating DT in a virtual setting. As displayed in Fig. 2, the six fields of action are (Digital) Engagement, Embodiment, Safe Space, Atmosphere, Random Inspiration, and Managing Workshops. While they are not part of the chapter structure, cognitive, social, and teaching presence underlie all of these fields of action, as they are necessary for the creation of successful educational experiences. 2 People 2.1 (Digital) Engagement One foundation of DT is the collaboration in diverse teams. Yet, to leverage the different perspectives, participants need to actively engage in the teamwork. In a virtual setting, devices and screens can act as a natural barrier to a feeling of involvement with the other people “present,” making the engagement of learners an important field of action in virtual DT education. Prior research identified five main drivers of engagement in virtual teams: trust, cultural intelligence, formal and informal communication, individual maturity, and technology (Shaik & Makhecha, 2019). These drivers cover a broad spectrum of topics, creating an overlap with the following sections. Trust, for example, also plays a role in creating psychological safety and a safe space for interaction and learning, which we elaborate on in Sect. 3.1. Similarly, informal communication and the
84 S. Mayer et al. Fig. 2 Six fields of action to foster Design Thinking facilitation in a virtual context creation of networks through random encounters are detailed in Sect. 4.1. These overlaps, however, mirror the strong interwovenness of the different elements of virtual education, as we have emphasized in the introduction. In this section, we want to elaborate on aspects of technology and its consequences for communication. As mentioned in the beginning, technology can hinder aspects of our normal communication, but it also allows new channels to be created. For example, being “alone” in front of a screen and muting the microphone often creates a high threshold to engage in discussions in a plenum setting. Therefore, addressing participants more directly than in an offline setting can help bridge these barriers in a virtual setting. One easy trick can be simply naming participants and asking directly for their opinion. While this might feel like being called on by the teacher in school, it raises cognitive presence, as people tend to be more attentive if there is a chance of being called on spontaneously. Furthermore, in a face-to-face setting, we often rely on non-verbal cues to engage others in a discussion, for example, by relying on eye contact. Due to the virtual setting, it is not possible to
Experiences of Facilitating Virtual Design Thinking:. . . 85 address individuals simply through eye contact; we therefore have to resort to verbal contact. Another hurdle to engagement for the facilitator is the lack of feedback, especially when faced with a larger group of learners. With larger groups, all the learners are often not visible at the same time due to technological limitations. One way to get easy feedback, for example, if participants have completed a certain task or template, is to ask for low threshold reactions. Most online communication tools allow some type of reactions, referring to icons demonstrating applause, thumbs up, or smiles, therefore offering a low threshold to engage with this new channel of communication. In DT, we often plan for sharing rounds, where each team presents their current results in a short time frame. In a physical space, these are often followed by applause, while in a virtual environment presentations are followed by silence. Introducing means of engagement explicitly (e.g., using the emojicon reactions, waving your real hands, or unmuting all together and then clapping loudly) can help to keep large audiences engaged. It can even be a source of fun for the group to try out different emoticons or clapping rhythms together. 2.2 Embodied Cognition Embodied cognition is a relatively new stream in research. It posits that individuals acquire, interpret, remember, and express valuable information with their entire body, and not only with their brains (Barsalou, 2008; Harquail & Wilcox King, 2010). As a consequence, they are also able to recall and reenact perceptual experiences. For example, that eating a piece of cake leads to a variety of sensual perceptions, including the visual appearance, texture, smell, and feel in the mouth. These perceptions can be mentally simulated, later, for instance, when a person simply sees the image of a cake (Shen et al., 2016). Hence, especially referring to this element of recalling, embodied cognition also plays an important role in learning and education (Shapiro & Stolz, 2019). DT relies on a lot of elements that support embodied cognition, such as working with visuals, becoming aware of one’s own body in warm-ups, and working with material to create haptic experiences during prototyping. Because a lot of these elements no longer work in virtual settings, facilitating DT virtually should in particular focus on recreating such embodied experiences. On the one hand, visual expression should be emphasized; on the other hand, new ways of individual embodied experience need to be created. To make use of embodied cognition, virtual expression should be a particular focus of virtual education. To this end, establishing a set-up where all participants have their cameras switched on is a basic requirement, which also fosters the cognitive and social presence. A very simple way of increasing embodied cognition is using gestures to underline the spoken word. Moreover, a more metaphorical language and the use of symbols and images in presentations or on boards fosters embodied cognition.
86 S. Mayer et al. There are many ways how educators can bring in learners’ own bodies into the learning experience. To begin with, warm-ups are a fantastic way of activating the entire body. Hence, virtual facilitators should refrain from the temptation of using technological-heavy warm-ups and create experiences that make learners aware of and feel their bodies. Being a passionate singer, I (Martin) have made great experiences with warm-up exercises from singing, as they involve body relaxation as well as breathing and feeling vibrations on the skin while humming. Used as interventions, such exercises can also re-activate learners during longer sessions in front of the screen. Dancing, stretching, and guided relaxation activities provide similar effects and also help learners reconnect with their senses. Second, not all virtual education needs to take place in front of the screen. Seeing fellow learners as heads in black tiles makes us easily forget that they (and we) all have an entire body. Hence, encouraging learners to get up, move, or even go outside during a virtual education experience will help rediscover the entire body. This can easily be done with individual reflection or ideation sessions, but is also possible for many other activities during a DT workshop. Third, online education usually reduces the tactile experience to mouse and keyboard. Thus, facilitators should include elements, where learners work with pen, paper, and other materials and then upload pictures to virtual whiteboards. Examples include prototyping (certainly), but also the understand or synthesis phase. Not only will such activities inhibit fatigue in front of the screen, but they also make it easier for learners to be visual since hand drawings are much easier made on paper than graphical objects on a board. Finally, physical props can enhance embodied cognition in virtual teaching. For instance, educators can ask students to prepare objects that refer to the challenge they are working on (like a three-dimensional mood board) and actively involve them in the workshop. For a challenge in the field of healthy nutrition, they might prepare a plate of fruit that students (/participants) smell, touch, and eat during the workshop. 3 Place 3.1 Safe Space Psychological safety refers to the individual’s belief, that a certain context, such as a workplace or a classroom, is safe for taking risks without interpersonal consequences (Edmondson, 1999). It is one key factor to fostering teamwork and team learning (Edmondson, 1999; Edmondson & Lei, 2014). One meta-analytic review noted factors that were influential antecedents for psychological safety. These included peer support, learning orientation, interdependence, and role clarity (Frazier et al., 2017). If a space feels psychologically safe, individuals experience a higher satisfaction and engage more actively in learning behaviors, such as seeking and sharing information, and reflecting and experimenting (Frazier et al., 2017). One famous example for the influence of psychological safety is a study from Google, which sought to determine factors that predict high performance teams. The
Experiences of Facilitating Virtual Design Thinking:. . . 87 number one distinguishing factor identified was psychological safety, identifying “high performance” teams which are less likely to leave the organization, more likely to build on the ideas of others, generate more revenue, and are evaluated as more effective by their leaders (Rozovsky, 2015). Establishing rituals can support an open failure culture and encourage a constructive way to deal with mistakes and negative feedback. Organizations, for example, establish regular meetings such as failure Fridays in order to share learnings generated by mistakes (Mayer et al., 2021). As DT embraces uncertainty and encourages making mistakes early on, following the “normal” DT program naturally already fosters the creation of psychological safety. However, we have experienced that it is far more difficult to establish psychological safety in a virtual environment than in a physical DT studio space. Nevertheless, in order to leverage peer support as one influencing factor for psychological safety, it is important to provide “space” for shorter peer exchanges. While DT already works with team check-ins and team check-outs, it can be helpful in virtual education to allow time for smaller exchanges in groups of twos and threes. Furthermore, facilitators can engage in one-on-one settings, so that the individual learner has a direct contact to the teacher. Often, these settings allow to emphasize cultural aspects, such as being open to making mistakes and not seeing that as failure, but rather as learning opportunities. 3.2 Atmosphere Creative climate research has investigated a variety of organizational factors that foster (or inhibit) creativity (Amabile et al., 1996; Newman et al., 2020). In a similar vein, a rich stream of literature has identified elements that create a positive atmosphere in service environments, such as stores and restaurants (Bitner, 1992; Brocato et al., 2014). Among these factors are leadership, experience, and education, but also very specific elements, including temperature, air quality, music, and personal artifacts. While the former elements are more general and overlap with other fields of action in this chapter, in this section on atmosphere, we especially focus on the latter ones. Applied to educational settings, educators need to ensure an atmosphere that is conducive to learning and that especially fosters concentration and well-being, which, in turn, fosters learners’ social and cognitive presence. In offline teaching, most elements can be determined or influenced by the educator, for instance, choosing the right space or preparing the space accordingly. Virtual DT facilitation, however, turns the creation of a good atmosphere for learning into a more complex task, as there are two different levels of space: The first space is the common virtual space, where educators and learners meet, such as the video conferencing tool, the virtual whiteboard, or a combination of both simultaneously. The second space is the physical space where each learner (and the educator herself/himself) takes part in online education. Successfully creating a good learning atmosphere thus means to influence the atmosphere in both the virtual and the physical spaces, as well as their integration.
88 3.2.1 S. Mayer et al. Atmosphere in Learners’ Physical Space Educators have only a very limited influence on their learners’ actual physical surrounding. However, there are various ways of how they can make the most out of the limited influence they have. First, and especially in longer formats, educators can invest some time to talk about the physical learning environment and encourage learners to try out different spots. For instance, learners could attend inputs sitting at their desks, then move to the dining table during group work, and, finally, move to the couch for reflections or feedback sessions. In doing so, they increase learners’ awareness for their spatial environment. Second, and extending this awareness building, educators can include the physical environment and bring it to the virtual space. For instance, learners can be asked to avoid using artificial backgrounds or show a 360-degree view of where they are as part of an introduction or check-in. Such activities allow all learners at least to see where their fellow students are, which helps to foster social presence. Third, educators can encourage students to use breaks to get some fresh air, by opening windows or even going outside. We have also had good experiences with asking learners to go outside for a specific part of the workshop. For instance, they might get inspiration when taking a walk during an ideation session or while discussing team issues. Fourth, especially with longer projects, learners could prepare their space with specific elements that relate to their DT experience. For instance, they might choose an iconic object that refers to the challenge they are working on. Or, every team member has the same object on their desk that creates and communicates the teams' identity (e.g., imagine a team that calls itself “Team Cactus” and each member putting a cactus on their desk to not only remind themselves of their team mates, but also to show the other learners that they belong to this team). 3.2.2 Atmosphere in the Virtual Space The two major atmospherical elements educators should influence in the virtual space are music and esthetics. Music has a huge effect on individuals. For instance, students who listened to classical music during a lecture showed better academic performance compared to a group that listened to no music (Dosseville et al., 2012). Other research shows that music influences the perception of the atmosphere in banks and bars (North et al., 2000). Moreover, playing music can give spaces with a negative atmosphere, such as basement room without windows, a more pleasant atmosphere (Ehret et al., 2021). Taken together, using music deliberately during workshops can advance both the overall atmosphere and learning outcomes. A detailed analysis of which kind of music best suits which DT phase would go beyond the boundaries of this book chapter. However, overall, more reflective phases will benefit from calmer music, ideation and prototyping from more energizing music, and easy listening music similar to the background music in cafés can create a welcoming atmosphere in breaks or before the workshop starts. Educators
Experiences of Facilitating Virtual Design Thinking:. . . 89 Fig. 3 Digital whiteboard design for a workshop with three teams (designed by Félix Deraed) should align the choice of music to both their own and their target groups' preferences. The majority of videoconferencing tools allows playing background music directly from the computer, so that no complex technological set-ups are required. The second atmospherical factor refers to the esthetics and design of virtual elements. For most DT education experiences, this might in particular refer to the design of digital whiteboards and other collaboration tools. On the one hand, prestructured boards with headlines, colors, and other visuals create a positive atmosphere, as they symbolize that somebody has prepared for the education experience. On the other hand, other than physical whiteboards, virtual whiteboards make it possible to create whole roadmaps. For instance, the structure of the entire workshop can be visualized along different whiteboards. Figure 3 shows an example for such a board design that especially illustrates plenary phases and team work in three parallel teams (design by Félix Deraed). Figure 4 shows the whiteboard
90 S. Mayer et al. Fig. 4 Digital whiteboard design for a Design Thinking education program at the HPI School of Design Thinking (designed by Anja Harnisch and Maria Aragon) structure of a Design Thinking Program at the HPI School of Design Thinking. It not only contains boards where teams work together, but also provides an interactive (i.e., clickable) structure for the entire semester (design by Anja Harnisch and Maria Aragon). Preparing such whiteboard ecosystems almost creates an own virtual DT space with its specific atmosphere. 3.2.3 Linking the Atmosphere in Physical and Virtual Spaces As already mentioned, successful virtual facilitation integrates the atmosphere of both physical and virtual spaces. As an illustration, the most beautiful digital whiteboard alone will not be able to create a good atmosphere, if the learner sits in a dark, small room. To link these two spheres, educators can encourage participants to upload pictures of their real physical environment into the online space or try to have certain visual elements from the virtual space also present in their physical environment. This could be an object, such as a plant, or a little paper artifact that is sent to learners in advance. 4 Process 4.1 Random Inspiration DT is often labeled as a creative approach to problem solving that relies on inspiration. Inspiration can be defined as a “motivational state that compels individuals to bring ideas into fruition” (Oleynick et al., 2014, p. 1). Facilitating DT often follows the six phases of the DT process, offering a natural structure to teaching DT in a rather linear sequence of methods within the phases. We observed that the
Experiences of Facilitating Virtual Design Thinking:. . . 91 transfer of this structure to the virtual world can result in a meticulous structuring, focusing purely on the content of the phases, decreasing learners’ internal motivation and therefore reducing inspiration. During face-to-face programs, learners often draw inspiration from random encounters with peers, for example, the classic meeting at the coffee machine during a break. In a virtual setting, these chances are limited or non-existent. We can draw from research to understand how meeting people by chance or allowing some free time to exchange thoughts can be beneficial in two main ways. First, drawing from the economics of social networks, meeting people at random is the starting point for creating (social) networks (Jackson & Rogers, 2007). Researchers found the random meeting factor to be significant for building friendships and lasting relationships, aside from an online community (Jackson & Rogers, 2007). Therefore, in order to allow participants in online education formats to leverage the social contacts, it is important to provide them with opportunities for random encounters. Second, research from cognitive psychology shows that taskand goal-related thinking is different to mind-wandering, dreaming, and spontaneous thoughts. Yet the latter three are significantly related to creativity, for example, mind-wandering being positively correlated to creative achievement (Agnoli et al., 2018; Tan et al., 2015). Therefore, allowing random encounters and times with no specific task can be very beneficial to online facilitation, fostering the development of social networks as well as creativity. From our experience, a little bit of process is still helpful here, since a random meeting in front of the coffee machine, the building, or at reception does not happen in a virtual context. We therefore set up random “blind dates,” bringing random groups of two or three participants together for coffee breaks or just for networking sessions, in particular at the beginning and during program activities. These can be accompanied by an open question to start conversions in the groups, but participants are explicitly allowed to turn to whatever topic of interest they like, to allow spontaneous thoughts and mind-wandering for the individual. As food and drinks play an important role in the physical setting to bring people together in a more relaxed context, it is also possible to use food-delivery services during online education. Doing so, participants get an individual meal, but have the chance to share the “same” lunch or dinner experience. 4.2 Managing Workshops DT Education not only includes teaching and coaching, but also a lot of organizational tasks to create an optimal educational experience. While virtual education makes some aspects easier, such as the preparation of physical spaces, there are a variety of new challenges that need to be managed. As the Education Experience Model implies, communication, the tools allowing it, and the facilitation of the education experience play important roles. Focusing on these elements, we share our experiences in four fields.
92 4.2.1 S. Mayer et al. Technology Usage and Training Unstable Internet connections, old software versions, and non-working technology can be severe barriers or obstacles in DT online education. To this end, educators should try to reduce or eliminate such obstacles early on. In our experience, already the invitation to workshops should clearly indicate the tools used (including the readiness to switch on the camera). This allows participants to prepare accordingly and, for instance, update their software in advance. Depending on the specific workshop, we explicitly recommend not dialing in from mobile devices, as this might cause issues with breakout sessions or other features. Moreover, we provide the data to dial into meetings via phone in addition to the link, so that learners who have trouble with their Internet connection have an easy workaround. Doing so is especially relevant if learners dial-in from regions or countries with a low bandwidth. For learners who are not experienced in virtual education, using videoconferencing tools and digital whiteboards might be a barrier for participation. To this end, we offer tech checks before the official workshop starts and provide a 15-minute introduction to the digital whiteboard so that every learner knows basic skills, such as navigating on the board as well as writing on and moving post-its. In longer academic programs and also in professional contexts, a digital hotline or concierge can be always approachable for learners in solving technical issues. 4.2.2 Communication with Various Stakeholders In workshop settings with various stakeholders, such as coaches or project partners, communicating with and integrating these partners needs some extra effort in virtual settings. In our experience, opening up a communication channel with all coaches that is independent from videoconferencing is beneficial, as group chat functions become problematic during breakout rooms. We usually use Slack or a mobile messenger. Concerning project partners or other external guests, such as interview partners or testers, educators need to ensure a proper welcome and introduction. While they can rather easily observe what is going on in physical workshops, an introduction to the technology and, maybe, a cheat sheet with the relevant links and functions might be beneficial in digital education. 4.2.3 Timing One huge advantage of virtual workshops is timing. Educators can centrally time the entire workshop using timers on boards or closing breakout rooms at a certain time. However, especially in professional education, learners are also more time sensitive, as they usually schedule follow-up meeting in breaks or directly after the workshop. Hence, educators should communicate the timing of beginning, ending, and breaks,
Experiences of Facilitating Virtual Design Thinking:. . . 93 and stick to these schedules. Doing so provides learners the opportunities to include their other work-related tasks and be more present during workshop times. 4.2.4 Avoid Over-Structuring the Learning Experience The majority of suggestions made in this section and the entire book chapter refers to clear structure and procedures. For instance, boards are designed in advance, making spontaneous changes harder than in physical workshops. Similarly, random encounters must be planned and facilitated (please refer to the previous Sect. 4.1). In light of all these highly structured elements and the boundaries given by technology, educators should be aware of structure “overkill,” getting in the way of the playfulness and creativity in DT education. For instance, planning buffer times in agendas allows for spontaneous interventions or preparing two alternative board structures gives coaches and teams the freedom to select what is best suited for a particular application. Not only will such room for creativity and flexibility benefit the participants, it will also make educating more fun for educators, as it goes beyond cooking after the recipe. 5 Discussion This chapter introduced the Education Experience model to highlight the changes that occur when moving from offline to online education. Using the three Ps of DT, People, Place, and Process, as a structure, we introduced six areas of action for DT educators in the virtual world. The theories linked to these areas inspire further research questions and give practitioners a rich resource for further reading. Further research regarding “People” might, for instance, deal with the engagement concept and analyze potential additional factors that foster engagement online. In this regard, relying on online tools and gamification could offer a variety of new opportunities for increasing learners’ engagement. Similarly, further research might transfer the concept of embodied cognition to online settings and develop a taxonomy of how to include its advantages in online education. An assessment of different ways of digital embodies cognition could indicate superior approaches and context factors that need to be considered. In the field of “Place,” researchers might combine extant work about psychological safety, trust, and data security to better understand the additional opportunities and obstacles for creating psychological safety online. Moreover, a deeper investigation of hybrid spaces and how to best combine digital and physical education, is a promising field for further research. Lastly, in the field of “Process” the question of how to plan for random encounters in online education, the outcomes and intervening factors provides a field for many theoretical and empirical investigations. In general, we expect that in the near future, researchers and practitioners will need to find criteria that help suggest if certain education experiences are better
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Accessibility of Linked-Node Diagrams on Collaborative Whiteboards for Screen Reader Users: Challenges and Opportunities Danyang Fan, Kate Glazko, and Sean Follmer Abstract Online whiteboards leverage our spatial thinking abilities to support rich, collaborative, and interactive design. However, these tools are often exclusionary to people who are blind. We ran a series of user studies with university students to better understand the accessibility of current tools and evaluated several existing audio and haptic approaches to inform design guidelines and future directions. We observed how current interfaces do not provide access to the graph generation process, provide incomplete access connections and spatial relationships, and can lead to users feeling uncertain or misinterpreting information in the graph. By exploring existing solutions, we identify and discuss the importance of reflecting the diagram structure within the navigation scheme and provide a spatial overview whereby users can reference their exploration. We summarize a series of recommendations to inform the investigation of future interactions to improve the accessibility of online whiteboards. 1 Introduction Designers collaborate using a variety of media. Increasingly, we see the use of online whiteboard software to allow users to collaboratively organize, and share, and reason through information. Many of these tools allow for rich remote collaboration and leverage our spatial thinking abilities. A large challenge is accessibility, and these tools often exclude people who are blind and visually impaired (Coombs, 2010). As Design moves to focus on the importance of inclusion and accessibility, we need Blind Designers. COVID-19 encouraged institutions to explore online methods of learning. Online whiteboard platforms have often been adopted by instructors to engage students in collaborative and interactive design (Metscher et al., 2021). These tools can be used to construct a variety of spatial diagrams, such as mind maps, flow charts, D. Fan · K. Glazko · S. Follmer (*) Department Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA e-mail: danfan17@stanford.edu; glazko@stanford.edu; sfollmer@stanford.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_6 97
98 D. Fan et al. hierarchical trees (Zaqoot & Oh, 2018). Such diagrams belong to a class of diagrams called “linked-node diagrams” (Burch et al., 2021), which are both spatial, and show connections between different pieces of information presented. In this work, we have sought to understand some of the accessibility gap when interacting with linked-node diagrams within online whiteboards. We ran a series of user studies with university students to better understand the accessibility of current tools, and we observed many challenges to understanding the information conveyed on them. These challenges include lack of access to the graph generation process, incomplete access to connections and spatial relationships, and uncertain feelings or misinterpretation of information on the graph. Researchers and students then collaboratively evaluated several existing audio and haptic approaches to inform design guidelines and future directions that might improve user’s access and understanding of linked-node diagrams. We discuss the importance of elucidating the construction process, reflecting the diagram structure within the navigation structure, and providing a spatial overview to which users can reference their exploration. We conclude with a summary of challenges as well as design guidelines and research opportunities to address these challenges. 2 Background and Related Work 2.1 Collaborative Design Tools Designers often engage in co-located spatial collaboration, traditionally using paper, post-it notes, and whiteboards. Researchers in the field of Computer Supported Collaborative Work have investigated techniques to support spatial collaboration at a distance, often drawing on collaborative design as a task domain. Some researchers have sought to replicate the affordances of these traditional collaboration tools for remote collaboration by investigating the use of synchronized digital whiteboards (Everitt et al., 2003; Tang & Minneman, 1991a). Alternatively, another line of research has investigated synchronous collaborative drawing and diagramming through video conferencing tools (Ishii, 1990; Ishii & Kobayashi, 1992; Tang & Minneman, 1991b; Wellner & Freeman, 1993). More recently, collaborative tools such as Miro (n.d.) and Mural (n.d.) have gained popularity, providing pervasive remote collaboration for spatial layout and design tasks, such as brainstorming or diagramming. However, such research and tools have not thoroughly considered accessibility for blind and visually impaired designers and participants. 2.2 Accessible Spatial User Interfaces for the Blind Touch-based interfaces, compared to other non-visual interfaces, often provide direct access to spatial information (O’Modhrain et al., 2015). Particularly, tactile
Accessibility of Linked-Node Diagrams on Collaborative Whiteboards. . . 99 graphics, which are images with raised surfaces, are commonly used to convey spatial information to people who are blind and visually impaired (Sheppard & Aldrich, 2001), and are well suited for technical users to comprehend graphical and spatial images (Gardner, 2002). However, these graphics are static and are not practical in collaborative environments in which spatial information is constantly changing. There are many efforts to produce refreshable touch-based interfaces to support dynamically changing content (O’Modhrain et al., 2015). Studies have explored electrovibration (Xu et al., 2011), modulating electrostatic force (Winfield et al., 2007), lateral skin vibration (Lévesque et al., 2005), vibrotactile feedback (Giudice et al., 2012; Linvill & Bliss, 1966; Palani, 2013), actuated pin arrays (Siu et al., 2019), and force displays (Magnusson & Rassmus-Gröhn, 2005), which provide haptic feedback as users actively move their hands and/or fingers to spatially interact with presented information. While these dynamically refreshable interfaces provide compelling ways to convey spatial information, they require specialized hardware, are limited in spatial resolution, and are often prohibitively expensive (Gorlewicz et al., 2018; O’Modhrain et al., 2015). Audio-based solutions that convey information through natural language or through sound can deliver high levels of detail and are often supported in ubiquitous computing devices such as smartphones and laptops (Billah et al., 2017; Sawe et al., 2020). However, they are often not perceived spatially and constrain the perception of information in less flexible sequences. Several works have investigated speech-based interfaces to support linked-node diagrams. These works explored hierarchical and connection-oriented navigation schemes (Bennett, 2002; Brown et al., 2003), but were not able to determine how well participants were able to internalize and form mental models of explore graphics. Metatla et al. developed a series of interactions that support the modification construction of hierarchical diagrams using an audio-keyboard interface (Metatla et al., 2008). Horstmann et. al. provided additional options for users to interact with hierarchical diagrams spatially through the use of joysticks and spatial audio to support spatial understanding (Horstmann et al., 2004). They found that participants were able to build mental representations using added spatial features. These studies provided several recommendations, which include supporting both hierarchical and connection-based browsing (Brown et al., 2003), helping users keep track of orientation (Horstmann et al., 2004) and providing summary overviews (Brown et al., 2004). How these findings might translate into collaborative whiteboard contexts, and how to best leverage spatial interactions, are still research gaps to be explored.
100 D. Fan et al. 3 User Study Design To better understand how haptic and audio systems might improve a user’s access to spatial information in the online whiteboard context, we ran a user study consisting of three parts: (1) an informal interview, (2) an evaluation of online whiteboards, and (3), an exploration of other spatial access methods. Three university students who are blind and use screen readers participated in these studies. The studies took roughly 60 min and participants were compensated $35 for their time. Participants were between 18 and 20 years old, and all reported to have high understandings of Braille, tactile graphics and familiarity with quantitative data (4–5 on a 5-point Likert scale). 3.1 Informal Interview The informal interview was designed to better understand the problem setting. Researchers asked participants to share contexts in which they may have encountered online whiteboards, if there were any challenges with their encounter, and if participants relied on any strategies to better understand any information presented. 3.2 Online Whiteboard Evaluation The goal of the evaluation was to identify specific gaps and challenges that participants come across when consuming information through online whiteboards. Participants were provided a sample whiteboard of a flow chart and hierarchical diagram to explore using a screen reader with which they were familiar. The sample flow chart depicted a product development workflow, while the hierarchical diagram depicted the business objectives of a makeshift online video-streaming business (Fig. 1), and was explored through Miro (n.d.) and Mural (n.d.), two commercial online whiteboard platforms. Both platforms have accessibility statements indicating efforts to improve the accessibility of their interfaces. Before participants began exploring, researchers gave an overview of the graph context. As participants explored the graph, researchers asked participants to try to gain as much information as they could from the exploration, and to discuss their thoughts and strategies through a think-aloud protocol.
Accessibility of Linked-Node Diagrams on Collaborative Whiteboards. . . 101 Fig. 1 Study participants explored a sample flow chart and hierarchical diagram hosted on collaborative whiteboard services Miro (a), and Mural (b) 3.3 Exploration of Other Access Methods Participants explored an additional audio-keyboard representation and a tactile graphic depiction of the same linked-node diagrams to identify and discuss potential methods for improving online whiteboards. In the audio-keyboard representation, the navigation of the diagram was meant to reflect the structure of the diagram, which was similar to the connection-based scheme used by Bennett (2002). Using this scheme, participants were able to transition from node to node based on their connections and retrieve spoken information using their keyboard. A list of keyboard interactions is provided in Table 1. The tactile graphic was constructed based on BANA guidelines (B. A. of North America, 2010) and is spatially similar to the visual graphic (Fig. 2). For this part of the study as well, researchers asked participants to try to gain as much information as they could and discuss their thoughts and strategies through a think-aloud. 3.4 Sequence of Exploration Mural, Miro, the audio-keyboard interface, and the tactile graphics were presented in a sequence through which the participants progressively gained information as they explored. Mural did not provide screen-reader access to the graphical Canvas, while textual elements in Miro were conveyed as a sequential list and did not show any
102 D. Fan et al. Table 1 Participants used the keyboard commands to navigate and retrieve information from the audio-keyboard interface Keyboard command “s” “d” “Down arrow” “Up arrow” “Enter” Interaction Articulates current node Articulates current node number out of total number of nodes Previews nodes that the current node is connected to. Repeated presses cycle through those connections Previews nodes that the current node is connected from. Repeated presses cycle through those connections Moves into previously previewed node Spoken sample “Currently at <node text>” “node id is <current node id> of <total number of nodes>” “Connects to <child node text>, <child node number> of <total number of children of current node>” “Connects to <parent node>, <parent node number> of <total number of parents of current node>” “Navigating to <previewed node’s text>” Fig. 2 Study participants explored tactile graphics which depicted the flow chart (a) and hierarchical diagram (b) represented by Fig. 1. connections. The audio-keyboard provided access to the textual elements as well as the connections, but did not provide any spatial information. The tactile graphic provided both the textual information, connections, and spatial context. As participants moved from one interface to the next, researchers also asked participants to highlight if they learned any additional information that they did not learn from their prior explorations to understand how having access to connections and spatial relationships might affect their comprehension of the diagrams.
Accessibility of Linked-Node Diagrams on Collaborative Whiteboards. . . 103 4 User Study Results 4.1 Informal Interview Participants described how they had heard of online whiteboards in educational settings, especially as courses migrated online during the COVID-19 pandemic (p1, p2), but they were never accessible. As a workaround, participants often asked course instructors and peers to provide verbal descriptions of the information (p3), which were described as difficult to understand if the diagrams were spatially complicated, and especially if the concepts were new and the purpose of the diagram was to illustrate novel concepts. One participant also requested accessible versions of graphics through their school’s local accessibility office, which were sometimes provided as a tactile graphic. While tactile graphics convey spatial concepts well, they show only the end-result, often several days after the lecture or activity. They do not capture the process in which the graphic was generated for in-class activities or explained for lectures (p1). As a result, participants often were not equally included in the generative or design processes involved with collaborative whiteboards. 4.2 Online Whiteboard Evaluation With Mural, participants were only able to toggle through the interactive menu items, but could not access the diagram canvas itself. Through Miro, the participants were able to explore nodes of the diagrams through their screen readers. However, while individual pieces of information were accessible, we observed many challenges with using the interface to understand the nodes. First, participants were unable to tell whether a spoken piece of information was a part of the diagram, a detail about the diagram, or a menu item, which led to uncertainty and confusion (p2, p3). One participant recommended using tags to identify the type of information that is described (p3), similar to when accessing the web. For example, nodes in the graph should be tagged as a graph, and menu items can have a different type of tag. Second, participants only had access to the nodes as a sequential list, but did not reflect connections and spatial relationships of those nodes. While participants were able to make loose inferences about connections based on the node content, such an exercise often causes confusion (p3), and is mentally taxing (p1). Participant 3 recommended that the navigation structure reflects the connected structure of the diagram. Third, additional efforts that were required to gain little additional information about the graph made exploration cumbersome. For example, users had the option to open additional pop-up windows that provide detailed information about each node, such as its color and shape. While one participant appreciated efforts to make this information accessible (p3), the execution of a series of keyboard events or minimal information—necessitating user memorization—made quick exploration to understand broader pictures difficult (p1).
104 4.3 D. Fan et al. Exploration of Other Access Methods The audio-keyboard interface was an example of an implementation in which the navigation structure reflected the connections of the diagram. All participants were able to learn the keyboard instructions within 10 min and to explore all parts of the sample diagrams. Participants using the interface were able to articulate features such as connections to and from each node (p1, p2, p3), as well as broader structural features, such as cycles within the flow chart (p3), the number of layers in the hierarchical diagram (p1), and the complicated nature of a graph (p3). For understanding broader structures, one participant appreciated information about the total number of nodes and the number of connections each node had that was provided by the interface (p1). While the hierarchy did not convey any spatial relationships, participants also described forming spatial mental models based on information they explored (p1, p2, p3). For example, p2 describes how they assumed that the nodes are conveyed top-down, while p1 and p3 used finger-drawing to internalize the spatial structure. Interestingly, two participants, while later exploring the tactile graphics, articulated having formed hypothesized spatial mental models that pretty well resembled the tactile graphic (p2, p3). One significant challenge participants described when interacting with the audiokeyboard interface was keeping track of the large structure and where they were within that structure. Participants described having to dedicate effort keeping track of their mental mapping, which relied on a lot of memorization (p1). For the interface, participants recommended providing information about the spatial structure in addition to the connections. This was suggested both in the form of a general structure, as if they were exploring a tree or a circle, as well as in the form of particular locations they were at in space if the graph were to be laid out onto a piece of paper (p3). Contributing to this challenge of keeping track of explored information was the necessity to explore the hierarchy sequentially. This took time (p3) and made revisiting information difficult (p1). Participants used systematic approaches to understand the entire structure (p1, p2, p3), which often involved going down each branch in the order the items were presented. The interface, which enumerated the connections to and from each node provided a way to keep track. The tactile graphic, providing information about the connections, was spatially displayed, and enabled multi-hand strategies that all participants leveraged. Participants stated that they were able to quickly gain information about the broader structure (p1, p2, p3), as well as how individual nodes were situated within that structure (p1, p2). All participants alternated between reading specific pieces of information on the Braille labels, and referencing the connective structures in the spatial vicinity.
Accessibility of Linked-Node Diagrams on Collaborative Whiteboards. . . 105 5 Discussion and Recommendations Based on the user studies, we identified several categories of design recommendations to inform the investigation of future interactions to improve the accessibility of online whiteboards. Improving the Accessibility of the Graph Generation Process Can Promote Participation and Understanding Prior efforts focus around improving the accessibility of published or final-form graphics. For generative and collaborative design activities, understanding the generation process is important for not only understanding the final graphic but being an active participant. From the informal interview, we discussed not just the importance of making the constructing of spatial diagrams accessible, but also how we perceived the diagrams as they are being constructed by others. Because the latter has not been as explored in research, we advocate increased research efforts to explore interactions to elucidate the process of gradual transformations spatial graphics undergo as it gradually becomes formed. Tagging Articulated Information Can Reduce Misunderstandings and Confusion With novel spoken interfaces, users can confuse articulated graphical nodes with textual nodes or menu options. Tagging all spoken components by its content type and relationship to the spatial graphic can clarify its purpose and provide a way for users to infer its function. Providing tags of Study participants recommended tagging schemes and interactions to follow html and pdf best practices (Reid & SnowWeaver, 2008) so that screen reader users can leverage their familiarity to more effectively navigate and explore the graph. Reducing Navigation Effort Can Improve Exploration Experience and Focus Attention on Understanding Broader Relationships Pop-up windows can be used to elaborate on nodes of the graphics without adding clutter. However, requiring screen reader users to perform several keyboard operations to access information introduces additional cognitive load and slows down an already sequential exploration process. Even in cases where little to no useful information is provided through them, screen reader users may still open up these windows in order not to miss out on potentially useful information. One participant recommended reducing the steps of operation for accessing useful information. This means not providing the possibility to open up additional content if little to no useful information is present (p1). Additionally, with the tactile graphic, all participants appreciated how quickly they could access and re-reference information which effectively offloads having to hold and retrieve information from memory to their perceptual system. Another participant recommended adding hotkeys and shortcuts that enable more flexible navigation (p3), though added features should be careful to not substantially increase the base level of complexity required to explore the graph.
106 D. Fan et al. Navigation Schemes That Reflect the Structure of the Spatial Graphic Provide Ways for Users to Form Structural and Spatial Understandings of the Graph Navigation schemes that reflect the connective structure of linked-node diagrams enable screen reader users to listen to details that resemble the information flow communicated by the graph. While prior studies on hierarchy-based audio-keyboard schemes did not indicate whether blind users could form accurate spatial models without spatial information (Bennett, 2002; Brown et al., 2003), our preliminary observations suggest this was possible for the types of diagrams used in the study and with participants who self-reported having high familiarity with tactile graphics and data manipulation. Consistent with previous studies are indications that understanding graphics through these interfaces takes significant cognitive load (Brown et al., 2003). While this work focused on the accessibility of linked-node diagrams, different types of navigation schemes may be well suited to different forms of spatial graphics, and there is opportunity to explore how different schemes might work best in different collaborative whiteboard contexts. Anchoring Exploration to a Spatial Overview Can Potentially Reduce Cognitive Load and Inform Exploration Whether exploring the nodes as a linear list in the form of Miro or as an interconnected hierarchy in the form of the audio-keyboard interface, participants described trying to form spatial mental models to internalize explored nodes (p1, p2, p3). Participants appreciated how the tactile graphic conveyed the overall structure right away, reducing the need to piece together details from a part-to-whole approach, and to potentially reform mental models as more information is explored. Anchoring exploration spatially to an overview can also help users better track how much there is to explore, and areas they have explored already. Using the tactile graphic, participants quickly alternated between reading specific pieces of information on the Braille labels, referencing the connective structures in the spatial vicinity, and exploring broader spatial structures within the graph, highlighting advantages of spatial haptic representation that enable flexible spatial exploration. For web-based keyboard access methods, we think there might also be an opportunity for naturallanguage summaries or sonified spatial summaries not just to provide broad spatial overviews before exploration, but also smaller overviews to anchor users’ exploration to spatial relationships within the exploration vicinity. 6 Conclusion Through our user studies, we explored the accessibility of online whiteboards with university students that use screen readers to access the web. We observed how current interfaces do not provide access to the graph generation process, provide incomplete access connections and spatial relationships, and can lead to users feeling uncertain or to misinterpreting information in the graph. By exploring several existing solutions, we identified potential recommendations on how the interfaces
Accessibility of Linked-Node Diagrams on Collaborative Whiteboards. . . 107 may be improved. Our initial results suggest that connection-based navigation schemes explored by prior work and web-exploration best practices could improve these collaborative whiteboard interfaces. Additionally, we identified the need and opportunity to investigate and better understand ways to elucidate the construction process and provide smaller overviews to spatially anchor users’ exploration to spatial relationships within the exploration vicinity. References B. A. of North America. (2010). Guidelines and standards for tactile graphics. Bennett, D. J. (2002). Effects of navigation and position on task when presenting diagrams to blind people using sound. In International conference on theory and application of diagrams (pp. 161–175). Springer. Billah, S. M., Ashok, V., Porter, D. E., & Ramakrishnan, I. (2017). Ubiquitous accessibility for people with visual impairments: Are we there yet? In Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 5862–5868). Brown, A., Pettifer, S., & Stevens, R. (2003). Evaluation of a non-visual molecule browser. ACM SIGACCESS Accessibility and Computing, 77–78, 40–47. Brown, A., Stevens, R., & Pettifer, S. (2004). Issues in the non-visual presentation of graph based diagrams. In Proceedings. Eighth international conference on information visualisation, 2004. IV 2004 (pp. 671–676). IEEE. Burch, M., Ten Brinke, K. B., Castella, A., Peters, G. K. S., Shteriyanov, V., & Vlasvinkel, R. (2021). Dynamic graph exploration by interactively linked node-link diagrams and matrix visualizations. Visual Computing for Industry, Biomedicine, and Art, 4(1), 1–14. Coombs, N. (2010). Making online teaching accessible: Inclusive course design for students with disabilities (Vol. 17). Wiley. Everitt, K. M., Klemmer, S. R., Lee, R., & Landay, J. A. (2003). Two worlds apart: Bridging the gap between physical and virtual media for distributed design collaboration. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 553–560). Gardner, J. A. (2002). Access by blind students and professionals to mainstream math and science. In International conference on computers for handicapped persons (pp. 502–507). Springer. Giudice, N. A., Palani, H. P., Brenner, E., & Kramer, K. M. (2012). Learning non-visual graphical information using a touch-based vibro-audio interface. In Proceedings of the 14th international ACM SIGACCESS conference on computers and accessibility (pp. 103–110). Gorlewicz, J. L., Tennison, J. L., Palani, H. P., & Giudice, N. A. (2018) The graphical access challenge for people with visual impairments: Positions and pathways forward. In Interactive multimedia-multimedia production and digital storytelling. IntechOpen. Horstmann, M., Lorenz, M., Watkowski, A., Ioannidis, G., Herzog, O., King, A., Evans, D. G., Hagen, C., Schlieder, C., Burn, A.-M., King, N., Petrie, H., Dijkstra, S., & Crombie, D. (2004). Automated interpretation and accessible presentation of technical diagrams for blind people. New Review of Hypermedia and Multimedia, 10(2), 141–163. Ishii, H. (1990). Teamworkstation: Towards a seamless shared workspace. In Proceedings of the 1990 ACM conference on computer-supported cooperative work (pp. 13–26). Ishii, H., & Kobayashi, M. (1992). Clearboard: A seamless medium for shared drawing and conversation with eye contact. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 525–532). Lévesque, V., Pasquero, J., Hayward, V., & Legault, M. (2005). Display of virtual braille dots by lateral skin deformation: Feasibility study. ACM Transactions on Applied Perception (TAP), 2(2), 132–149.
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Part II Fostering Innovation Behavior and Co-evolution
A Pattern Language of an Exploratory Programming Workspace Marcel Taeumel, Jens Lincke, Patrick Rein, and Robert Hirschfeld Abstract Software design and the underlying programming activities entail a great portion of exploration to better understand problem and solution spaces. There are programming tools and environments that support such exploratory programming practices exceptionally well. However, inexperienced programmers typically face a steep learning curve until they can reach the promised efficiency in such tools. They need a long time to study best practices firsthand in real projects. The tools in use might also need adjustments, given that modern programming languages are continually introducing new features or redesigning old ones. We want to apply the idea of patterns to capture traditional and modern practices of exploratory programming. In this chapter, we focus on the workspace tool, whose core ideas transcend many different programming communities such as the Smalltalk workspace, the Unix shell, and data-analysis notebooks. We extracted the essence into a novel pattern language around the conversations that programmers have with their environment. We believe that our work can help programmers to quickly understand and apply the idea of workspaces, as well as tool builders to increase the efficiency of their project team when facing exploratory challenges. 1 Introduction The exploratory mindset is an intensive form of user-to-software mediation where programmers are especially motivated to find a design that both works and inspires: “I know it when I see it.” Programming languages are expressive enough to unfold anything from unsurprising complexity to surprisingly beautiful simplicity. The latter is key. Programming tools play a crucial part in the exploratory journey to reach highquality software. All programming activities have complementary tool support: M. Taeumel (*) · J. Lincke · P. Rein · R. Hirschfeld Hasso Plattner Institute, University of Potsdam, Potsdam, Germany e-mail: marcel.taeumel@hpi.uni-potsdam.de; jens.lincke@hpi.uni-potsdam.de; patrick.rein@hpi.uni-potsdam.de; robert.hirschfeld@hpi.uni-potsdam.de © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_7 111
112 M. Taeumel et al. Fig. 1 Workspace-like tools support effective and efficient conversations between programmers and their environments. They can be found in many different domains such as (f.l.t.r.) Squeak/ Smalltalk (workspace), Ubuntu/Linux (shell), and Jupyter/IPython (notebooks) reading, writing, debugging, testing, deploying, and so on. Programmers can thrive within environments that offer a sense of liveness (Rein et al., 2018; Tanimoto, 2013) and short feedback cycles through tools. Mistakes can be corrected on short notice—with the next iteration likely more productive than the previous one. While an exploratory mindset expects certain things from tools and their user interfaces, good tools for exploration can even foster such an exploratory mindset. As illustrated in Fig. 1, exploratory tools have been around for a long time such as the Smalltalk workspace (Goldberg, 1983), the Unix shell (Raymond, 2004), and notebooks for scientific computing (Pimentel et al., 2015). However, it is not easy for inexperienced programmers to learn about and grow an exploratory mindset. Even if the appropriate tools are at hand, it can be challenging to use them effectively and efficiently. Many best practices are supported (or even promoted) through tools but not easily discovered during normal use. Without proper guidance, you may never leave the traditional path, that is, remaining pleased with the habits you acquired by chance, completing your tasks in a “good enough” fashion. Provided that you are a tool builder in a foreign environment, it can be challenging to pinpoint specific issues but rather to criticize everything that does not feel “like home.” For example, if you prefer the programming experience in a Smalltalk environment, you might be tempted to recreate its entirety for your JavaScript workflow. While there is the Lively system for modern web development (Ingalls et al., 2008; Lincke et al., 2017) in the spirit of Smalltalk, it took a great effort and many resources to reach its productivity level as of today. Instead of specific tool implementations, we are looking for a way to capture the essence of exploratory programming practices—covering both mindset and tools—and that is easily accessible for learners and practitioners—for tool users and tool builders. We apply the idea of patterns to capture the core of best practices for exploratory programming. Originally promoted for architectural design (Alexander, 1979), patterns have proven useful for software design (Gamma et al., 1995) as well as for guiding human behavior (Iba & Isaku, 2016). We like the generative aspects of the pattern form, which allows for variation while clearly separating the repetitive elements. For example, there is clearly an overlap between Smalltalk workspaces
A Pattern Language of an Exploratory Programming Workspace 113 and Unix shells, but it is not easy to see for a non-expert. The patterns in this chapter build upon our previous work. Our experience with tools for exploration stems from the Squeak/Smalltalk programming system (Ingalls et al., 1997), which we have been using in teaching and research for more than a decade. Our first take on patterns approached the concepts of enabling and controlling exploration (Taeumel et al., 2021). Our second take presented actions around direct window manipulation (Taeumel & Hirschfeld, 2021), which we collected during our work on the Vivide tool-building platform (Taeumel, 2020). In this chapter, we contribute a pattern language around the conversation style that happens between programmers and their environments using workspace-like tools: the exploratory programming workspace. The central pattern “Conversation in Context” describes the typical question/response cycle that programmers follow to learn more during their programming task such as the location of a bug or the best way to implement a new feature. The three main components of that interaction lead to the other patterns: “Elaborate Inquiry” and “Coach Your Environment” dive into questions, “Concept in Shards” and “Proxy Transport” talk about the context, “Simple Response” and “Tangible Response” address responses. The last pattern “Pause and Explore” embeds a single conversation into the bigger picture, because programmers must often manage multiple conversations at the same time. In Sect. 2, we describe the pattern form and agents we use in each pattern. We also give an overview of all pattern’s intents. In Sects. 3–10, we present the eight patterns in this pattern language. Finally in Sect. 11, we conclude this chapter with a discussion on pattern quality and possible next steps. 2 Pattern Form, Intents, and Agents In this section, we describe the elements (or form) of each pattern, give an overview of all intents in our pattern language, and elaborate on the agents whose characteristics and actions we try to capture in each pattern. It is all about you working on a project in an environment while continually switching between different interaction contexts. 2.1 The Pattern Form Building upon the original triad of context-problem-solution by Christopher Alexander (Alexander et al., 1977), we took inspiration from both patterns of objectoriented design (Gamma et al., 1995) and human behavior (Iba & Sakamoto, 2011) to shape the pattern form for this chapter: • Intent is a quick summary of the actions that are typically part of the pattern’s solution in the manner of “Do this, then that.”
114 M. Taeumel et al. • A Desire for Exploration motivates the pattern through a hands-on story. We typically refer to our experiences from the Squeak/Smalltalk system (Ingalls et al., 1997). • The Profile is a brief summary of the pattern’s context, problem, and solution. As in patterns on human actions (Iba & Sakamoto, 2011), it includes a list of forces that the pattern tries to resolve. • The Structure is a more detailed listing of what the programmer and its tools (or environment) must achieve to resolve the conflict (i.e., the forces). Less technical than object-oriented design patterns (Gamma et al., 1995), it, however, includes an abstract visual model as in Iba’s patterns (Iba & Sakamoto, 2011). • Consequences outline possible trade-offs that tool builders face when implementing tools for the particular pattern, which might eventually also affect programmers as tool users. • Related Patterns are the backbone in our pattern language. Without meaningful connections between our patterns, we would have a list of unrelated scenarios. Yet, our patterns all deal with the conversational style between programmers and their workspace-like tools. Note that we will use the term “context” differently throughout this chapter. We will not address a “problem context” but an “interaction context,” which is the knowledge base full of technical artifacts leading toward the solution. See below. 2.2 Overview of All Patterns Our pattern language includes eight patterns. The first and the last pattern frame the middle ones, which are organized in pairs dealing with questions, context, and responses, respectively. Each pattern’s intent is as follows: Conversation in Context (Sect. 3) Ask the environment one or more questions to better understand the context you are currently working with. Iteratively revise each question until you are satisfied with the environment’s response. In followup questions, refer to prior responses to make your wording (and the responses) more expressive and precise. Elaborate Inquiry (questions, Sect. 4) Decompose the topic of your conversation into parts. Then compose your central question from multiple sub-questions according to those parts. Let the environment manage the topic’s complexity to reduce your cognitive load and hence the risk of making mistakes. Coach Your Environment (questions, Sect. 5) Extend the environment’s knowledge by adding information to the current interaction context. Extend its language vocabulary by adding tools for reference in follow-up questions. Both kinds of additions can either come from your memory (and experience) or be loaded from external resources.
A Pattern Language of an Exploratory Programming Workspace 115 Concept in Shards (context, Sect. 6) Relevant information might be scattered all over the environment. Do not be satisfied with the first useful response. Keep asking questions to reduce guesswork and to gain more substantial insight. If necessary, import external knowledge from outside the environment. Proxy Transport (context, Sect. 7) Design a flexible proxy to integrate external information into your interaction context. Map the external format (e.g., file contents) onto the internal one (e.g., object fields). Use lazy loading and caching to avoid high memory consumption and slow access times. Simple Response (responses, Sect. 8) Every question has a simple response. Do not hesitate but ask away; embrace trial-and-error. A text will appear, a picture will render, a sound will play—tackle complexity with a combination of these. Tangible Response (responses, Sect. 9) Directly engage (e.g., click on) a response as it is represented on screen. The environment will respond to the “What is this?” question with more details. Return to the particular conversation later on or spawn new ones. Pause and Explore (Sect. 10) Suspend an ongoing conversation if you need to explore and understand details of a prior response. Start a new conversation to learn more about these details. Later, resume the original response and make use of your freshly gained knowledge to pose better questions. 2.3 You and Your Project We are aware that readers of this chapter might not necessarily identify themselves as programmers. Even though non-professional, end-user programming is widespread (Burnett & Myers, 2014; Ko et al., 2011), we think that exploratory programming affects devoted programmers more than hobbyists. The exploratory mindset covers more than just “getting things done;” it entails an intensive relationship with the computational medium. Therefore, each pattern’s “You” addresses the reader as if they were programmers who really want to dive into source code to make their software project not just working but simple and inspiring. The exploratory programmer—meaning you—might be working on a multitude of programming tasks where conversations—and thus our pattern language—can provide guidance. Besides hunting down bugs or designing new features, programmers also add commentary to source code or write tests for untested modules. They might also teach new team members about existing system components directly through the live system, which is more credible than talking about (dead) documentation. In all these cases, programmers engage with the programming environment and its tools to investigate and learn. Tool builders are programmers, too. While your project’s goal is mainly about new software for your customers, there is other software to be built or adapted on the side: the programming tools. In our pattern language, you will notice requirements for specific services that the programming environment has to provide. It is the job of tool builders to ensure that there are tools in the environment that comply.
116 M. Taeumel et al. If not, tool-using programmers cannot follow the particular pattern’s guidance. So, when a problem force claims that “It is easy to ...” or the pattern structure dictates that “The environment should ...,” tool builders have to take action. Note that every programmer can take on the role of a builder, even ad-hoc during a task. 2.4 The Environment This is your conversational partner on your exploratory journey: The environment is an active entity because it offers various ways of interaction. As a programmer, it helps you author program source code. As an explorer, it helps you find and connect relevant information. It is your tool shed because it offers many services through interactive tools. You can use keyboard, mouse, or touch input to express your goals. It will acknowledge your input and try to fulfill your expectations through text, graphics, or sound. The environment has one or more languages, which you must use during conversations. A language is not necessarily a programming language but sometimes just a graphical interface with clickable, labeled buttons. Then, you must click the right buttons to express your goals. In general, a language’s vocabulary is extensible so that you can teach new phrases for a more efficient communication. There are other environments, each with its own boundaries. For one thing, your environment might be embedded within a (parent) environment. Then again, that parent might embed more environments besides yours, which makes them siblings. Practical examples include operating systems, which host applications that may complement each other’s use cases. Being aware of other environments helps you overcome limitations in your knowledge and that of your environment. You can always learn from things outside your own boundaries. 2.5 The Interaction Context The environment has a knowledge base, which you will constantly probe and expand during conversations: the interaction context. The context is the passive counterpart to the active environment. It is separate from the language vocabulary and contains
A Pattern Language of an Exploratory Programming Workspace 117 all kinds of domain-specific information. Within the environment’s boundaries, information is represented in a common (transport) format. In a Smalltalk system, everything is an object. In a Unix-based system, everything is a file. Thus, Smalltalk workspaces talk about objects (and structured fields) while Unix shells talk about files (and byte streams). Accessing information from the interaction context is not as flexible as using your brain to think of something. The context is typically scoped to a particular tool. That is, the information from one conversation is not necessarily accessible in another one. Yet, there are means of combining such local scopes or even promoting them to being globally available in the entire environment. Note that you can typically refer to pieces of information in a (scoped) context by name. Examples include the “environment” in a Unix shell or the “bindings dictionary” in a Smalltalk workspace. To sum it up, your environment might have all the knowledge, but you have to find an appropriate context to access the information you are looking for. 3 Conversation in Context Ask the environment one or more questions to better understand the context you are currently working with. Iteratively revise each question until you are satisfied with the environment’s response. In follow-up questions, refer to prior responses to make your wording (and the responses) more expressive and precise. 3.1 A Desire for Exploration In exploratory programming, conversations with the computational medium are paramount. The environment represents a gateway to all digitally available information, which includes all kinds of software-related artifacts. Starting with a simple question, the environment can help you learn more about your current opportunities within your current task context. Even if your overall knowledge feels satisfactory, exploring responses to concrete questions can reduce the risks of relying on false hypotheses. If you are lucky, responses are provided without noticeable delay so that you can follow up and establish a conversation that feels live and direct. At the end of such an interaction, you will know more than before, and the environment might also have learned something about your context-specific inquiries. So, instead of asking yourself, you ask the environment. Provided that you are working within a task-specific context, you might want to ask something about a module’s interface or explore the structure of an artifact: Does the property X hold in this context? How many kinds of X objects are there? What are the fields of X? How does the service X work? Is this module X still functional? Responses can range from simple Booleans, numbers, or texts to deeply structured information.
118 M. Taeumel et al. Note that the actual representations of questions and responses are not captured in this pattern. We assume that you want to engage with the environment in a direct conversation to help you move forward in your programming task. There can be many reasons for why you would want to keep up a conversation, such as unclear wording in your prior question or confusing details in a prior response. Representations might be textual, visual, or even audible, which depends on your human– computer interface. Imagine that you read about the notion of DateAndTime in a piece of documentation. Now you want to learn more about it. By example, you reason like this: “Today is Friday. The environment should know that. Let’s ask it.” Visually, the conversation might have happened as follows: First, you found a space on screen where the environment could listen to your questions. Second, you posed several questions using the (programming) language that the environment could understand. In this case, simple text-based representations of both questions and responses drove the conversation successfully. Finally, you learned more about the interface of DateAndTime, which is knowledge that you can now apply in your task (and program). 3.2 The Profile You want to ask the environment a question about something you have on your mind, that is, within a context. You also know about the (textual or visual) language that the environment can understand, that is, evaluate to present a notable response. However, you cannot directly express your thoughts even though you have the feeling that the environment has all the information it needs to answer your questions, to help you move forward.
A Pattern Language of an Exploratory Programming Workspace 119 • It is easy to type, draw, or drop something using your input devices (e.g., keyboard, mouse, or touch screen). • It is difficult to find a space where (1) you can focus on phrasing the question and (2) the environment will (try to) respond. • It is difficult to find the right words for a question the first time. • The environment’s responses can be too technical or verbose, thus not comprehensible immediately. Therefore, you engage with the environment and establish a conversation to learn more about the current context. First, you locate a space where the environment waits for your direct input. Then you type, draw, or drop something that complies with the environment’s language. After posing your question, you make a gesture to let the environment evaluate your words and present a response. After your initial interaction, you can establish a conversation through (revised) follow-up questions to clarify misunderstandings or unpack complexity. Consequently, you can rely on the environment to maintain a history of prior questions and responses. On the one hand, you can easily revise your prior wording. On the other hand, you can precisely refer to selected pieces of information you just acquired. Your goal is to continue this conversation until you fully understand the environment’s response(s). 3.3 The Structure
120 M. Taeumel et al. The environment should provide the following services: • Screen space that is dedicated to direct user input (i.e., the questions) and the presentation of results (i.e., the responses). • Context that is accessible through the environment’s known language (and vocabulary) such as identifiers (or names) of relevant software artifacts. • History of prior questions that is automatically written with every new question posed. The history’s contents are directly accessible for the user in a way that allows for effortless revisions. • History of prior responses that is automatically written with every new response delivered. The history’s contents are directly accessible for the user in a way that allows for references selected pieces in follow-up questions. You should adhere to the following interaction (loop): (A) Pose a new question by typing, drawing, or dropping something into the dedicated space that the environment can understand. (B) Optionally, access one of the prior questions to repeat or revise its wording. (C) Optionally, access one of the prior responses to integrate references to its contents into your next question. (D) Finish your input with a dedicated gesture, let the environment evaluate your question and present a response, and then explore that response. 3.4 Consequences Direct conversations with the environment reduce the need for taking offline notes. Both histories of questions and responses document your thoughts and can thus help you remember them later in the process. Plus, the notes you would take outside the environment would be detached from what actually goes on in your project, challenging to synchronize. Being immersed within your environment, instead, you can easily build up trust in your knowledge and your decisions. A conversation can only be as effective as its representations for questions and responses. For example, if you cannot express your thoughts in text, you might wish to draw a shape instead. Yet, the environment’s language might not support a visual syntax. At worst, inappropriate representations can be misleading and thus timeconsuming. The environment’s response might be “rich” and interactive in the sense that it promotes you to a different tool (window) and experience. In a similar fashion, your questions might also be formed through an elaborate interaction using keyboard, mouse, or touch. That is, even though there are many text-based command-line interfaces following the “Conversation in Context” pattern, you might also be able to establish conversations through a graphical tool’s interactive widgets.
A Pattern Language of an Exploratory Programming Workspace 121 The environment benefits from an efficient window manager to help you organize your questions and responses on the screen. Screen space is limited, and you might struggle finding a “quiet place” where you can focus on your next conversation. Multiple other conversations might also be in progress at the same time. The integration of multiple tools (and windows) might become an issue. Otherwise, you have to repeat your query in every new conversation, risking mistakes on the way. The environment should respond as fast as possible; it should create a feeling of liveness during conversations. Otherwise, there is a chance of you losing interest in engaging with the environment in the first place. To avoid unnecessary workload in the environment, you finish your question with an extra gesture. Yet, unforeseen complications might increase the time before you can receive and read a response. Maybe you even want a pause button to be able to further explore the environment’s current progress to then cancel and revise your question. The history of prior questions and responses might consume many resources, thus possibly interfering with your other tasks. You might not be able to decide when to discard older information. You might not even know about the actual footprint of a question or response. So, the environment should probably figure out a way to discard deprecated information and compress redundant details to save resources. It might be able to inform you about the most demanding artifacts to help you make critical decisions. 3.5 Related Patterns This pattern is the umbrella for all other patterns in this chapter: • The questions in a conversation are covered in “Elaborate Inquiry” and “Coach Your Environment.” • The context that the environment needs to evaluate each question is covered in “Concept in Shards” and “Proxy Transport.” • The responses that the environment produces is covered in “Simple Response” and “Tangible Response.” The final pattern “Pause and Explore” complements the liveness and flow (that we strive for in each conversation) with the importance to manage conversations at a higher level and be able to reflect on every little detail.
122 M. Taeumel et al. 4 Elaborate Inquiry Decompose the topic of your conversation into parts. Then compose your central question from multiple sub-questions according to those parts. Let the environment manage the topic’s complexity to reduce your cognitive load and hence the risk of making mistakes. 4.1 A Desire for Exploration Simple questions are good conversation starters. You learn about the environment’s vocabulary and can easily browse the current interaction context. Soon you begin to wonder whether it is possible to combine selected pieces of information, that is, prior questions or responses. There are some operations that you could do in your head or on a piece of paper such as picking the longest entry from a longer list, sorting a shorter list alphabetically, or removing all odd numbers from a list. However, all of these operations are tedious and error-prone, especially if done repeatedly and manually. And your exploratory journey has just begun. Many interesting artifacts might lie ahead, waiting to be explored. Computers are generally good at sorting, filtering, and transforming millions of data points repeatedly and efficiently. You just have to apply the environment’s means of combination and abstraction. We assume that the environment’s language is either a general-purpose or domain-specific programming language that allows for combining simple expressions. Such a language does not have to be textual but usually is. Provided that you are working in a Unix shell, you can easily combine small (filter) programs through pipes: curl https://en.wikipedia.org/wiki/Unix \ | grep -o -P ’href="/wiki/.*?"’ | sort | uniq \ | sed -r ’s/href="(.+)"/http:\/\/en.wikipedia.org\1/g’ \ > urls.txt This program fetches a website, extracts URLs from its contents in a sorted and unique fashion, and finally writes everything into the urls.txt file. You can now use this “Elaborate Inquiry” to continue exploration at a higher level, that is, for extracting URLs from arbitrary websites. A language’s means of abstraction can help you wrap complex questions (i.e., the combinations) into modules (e.g., functions) which you can then refer to by name in follow-up questions. For example, think about the beginning of the Fibonacci sequence: 1, 1, 2, 3. You want to know the tenth number but not calculate it by hand. So, you begin a conversation and describe the underlying rule of the sequence, which is “Add the two prior numbers to get the next one.” Visually, the conversation might have happened as follows:
A Pattern Language of an Exploratory Programming Workspace 123 First, you wrote a little reminder about what you want to discuss here. Then you designed the underlying rule as a recursive function, which you labeled “fib.” You tried out the function multiple times until you got it right. Finally, you collected the sequence up to ten and found your definitive answer: 55. Given that function, you asked for the 100th number, but after waiting for a while you decided to cancel the computation without getting a response. 4.2 The Profile You are in the middle of a “Conversation in Context.” It turns out that the topic you are interested in has several aspects with rather complex relationships. You notice a substantial risk of making mistakes, relying on guesswork. However, you struggle with comprehending the environment’s responses because their complexity outranks your questions’ simplicity. Follow-up questions only increase your cognitive load because you start to combine facts in your head rather than letting the environment combine the facts for you. • • • • • It is easy to ask follow-up questions that refer to prior responses. Such follow-up questions benefit from a good understanding of prior responses. It is difficult to manually interpret complex responses. Mistakes in understanding can infect follow-up questions. It is difficult to express your complex thoughts using primitive vocabulary. Therefore, you adhere to a modular approach when (com-)posing your (followup) questions. At best, the final revision of your central question will satisfy your entire informational need. First, you decompose the topic of your conversation into—more or less isolated—parts. Second, you compose your question from sub-questions, each addressing one important part. The combination of sub-questions will naturally process the environment’s intermediate responses. Consequently, the manual, error-prone act of posing multiple follow-up question will become an automated, robust one. The environment will immediately tell you when you make a mistake. Eventually, you will feel like an author who can safely
124 M. Taeumel et al. prepare the script for a play, rather than a person who is trapped in the middle of a heated discussion. 4.3 The Structure Considering a common trichotomy for programming language design (Abelson et al., 1996, pp. 4–31), the environment’s language should have the following properties: • Primitive expressions that allow for posing simple questions and a stepwise unpacking of complex responses. • Means of combination that allow for composing multiple (sub-)questions to automatically integrate intermediate responses. • Means of abstraction that allow for encapsulating recurrent parts of the conversation to further reduce cognitive load and the risk of making mistakes. You should make use of the language’s properties as follows: 1. Begin your conversation with simple questions using only basic vocabulary. 2. Employ means of combination as soon as you need to refer to multiple (prior) responses in a single follow-up question. 3. Employ means of abstraction to avoid repetition and verbosity in your questions. 4. As you learn more about the topic of your conversation along the way, decompose important parts into sub-questions and compose follow-up questions from these. 5. Your final question should encompass everything you want to get out of the current conversation. 4.4 Consequences If you can manage to reduce the conversation to a single (or few) composite question (s), your cognitive load will be reduced, too, because the environment can process even millions of data points without making any mistake. Yet, you have to be sure to
A Pattern Language of an Exploratory Programming Workspace 125 ask the correct questions or you will be faced with millions of misleading results. Overall, an “Elaborate Inquiry” is more prone to errors than a couple of simple (r) questions. While the environment can be optimized to ensure quick response times for simple questions using only standard vocabulary, complex questions might take longer to process. Without taking extra care, your “Elaborate Inquiry” can easily include operations whose time-to-process grows exponentially. A progress indication can help you assess the impact of your question, but then you need to be able to pause or cancel the computation to fix your mistake. 4.5 Related Patterns Especially if you want to “Coach Your Environment,” following “Elaborate Inquiry” can help you author side effects. 5 Coach Your Environment Extend the environment’s knowledge by adding information to the current interaction context. Extend its language vocabulary by adding tools for reference in followup questions. Both kinds of additions can either come from your memory (and experience) or be loaded from external resources. 5.1 A Desire for Exploration Think about a conversation with another person. There, you can give instructions like “Imagine that you are visiting a foreign city.” You can keep on describing the situation like “Now your smartphone stops working.” And finally, you can ask questions like “How would you find the next good Italian restaurant?” The other person is able to learn about (imaginary) constraints to then reason within those constraints while answering questions. Your programming environment can do that, too. You just have to coach it. Your questions can be as expressive as the environment’s vocabulary. Thus, if you install new tools, that vocabulary can grow. For example, there are tools for static and dynamic analysis that allow for very specific questions about your system’s modular structure. The environment’s responses are only as informative as the data they are based on. Thus, if you load more information from external sources into the current interaction context, the same questions can yield more meaningful responses. For
126 M. Taeumel et al. example, if you ask questions about photographs (e.g., number of recognized faces), having more photos available will increase the responses’ quality. You can also coach your environment by writing prototypical programs. In object-oriented systems, programmers often explore the interfaces of objects. They instantiate classes, send messages to the instances, and observe the (side) effects. It is still a conversation with the environment, but with a focus on selected objects in particular. Side effects play an important role when “setting up the scene” before any meaningful question can be asked. Provided that you wanted to learn more about event handling, you prepared the scene as follows: First, you instantiated a Morph (i.e., a basic shape) and positioned it on the screen (here: the world). Then you configured an event handler to react on mouse clicks. The handler will either log information on the Transcript (here: event printString) or change the Morph to a random color (here: Color random). Clicking this prototypical button is still part of the conversation about understanding event handling. Every click is a question, the change of color a response. You taught the environment how to do this. 5.2 The Profile You are in the middle of a “Conversation in Context.” You repeatedly question the quality of the environment’s responses. You get the feeling that the environment’s knowledge about the current topic is too limited. However, you cannot think of a better way to phrase your questions so that the environment might provide a satisfactory response. • • • • It is difficult to know everything about a topic. It is easy to learn something new during a conversation. Your questions must use the vocabulary from the environment’s language. The quality of responses depends on the environment’s knowledge (i.e., current interaction context). • Both language/vocabulary and knowledge/context can be extended during conversations.
A Pattern Language of an Exploratory Programming Workspace 127 Therefore, you tell the environment to remember new things so that it can react differently to your follow-up questions. Technically speaking, your interactions with the environment should have side effects. You can add information to the current context by writing down what you know in a primitive form (e.g., text, numbers, records). You can also tell the environment to load external information from outside its boundaries (e.g., host file system, network storage, Internet/Web content). Considering the environment’s language, you can add new vocabulary like you would in an “Elaborate Inquiry” using, for example, labels (or names) for new classes/methods or the information you just loaded into the interaction context. Note that you can also load external tools into the environment, which usually bring along additional vocabulary as well. This extra vocabulary can help you make your follow-up questions more precise. 5.3 The Structure The environment should provide the following services: • Extensible Context, which is the common representation of knowledge in the environment (e.g., files, objects, tables); thus, it should remain open for ad-hoc additions during your exploratory journey. • Extensible Vocabulary, which is your primary source of “building blocks” for posing (or revising) questions; thus, it should remain open for ad-hoc additions during the conversations with the environment. • Interface for Imports, which allows for loading both machine-readable and directly/manually entered information from outside the environment. You are responsible for coaching your environment in time: • Type or draw the facts you already know directly from your memory to then build upon that knowledge in follow-up questions. • Investigate the world outside the environment to learn about relevant external information. • Be precise: do not load everything you find directly into your environment but consider the relevance of your current “Concept in Shards.” • Be thoughtful: construct a “Proxy Transport” for data that is hardly reachable or memory-heavy.
128 5.4 M. Taeumel et al. Consequences If you are not careful, you can break the environment’s current context. For example, you can get punished for your coaching if you overwrite existing knowledge with useless (or wrong) facts. Follow-up questions will then yield misleading results. You can also tamper with the language vocabulary in the sense that once meaningful questions cannot be processed anymore. Fortunately, many environments support “undoing” your latest mistakes to then continue the conversation in a fruitful manner. The state of having too limited knowledge can quickly turn into an oversupply of information. It can then be difficult to narrow down a question to get a response that you can actually understand. Some external sources might be of poor quality, which will further increase your cognitive effort because you will constantly have to scrutinize the value of the environment’s responses. Having no information on a topic is easy to check, an oversupply, however, time-consuming to explore. Your questions might head into the wrong direction, which might be why the environment seems to have no valuable responses for you. Instead of loading new external information, you might want to backtrack and double-check the point where your environment became less helpful. Maybe you made a mistake earlier that needs to be corrected. 5.5 Related Patterns Both “Coach Your Environment” and “Elaborate Inquiry” are close friends. 6 Concept in Shards Relevant information might be scattered all over the environment. Do not be satisfied with the first useful response. Keep asking questions to reduce guesswork and to gain more substantial insight. If necessary, import external knowledge from outside the environment. 6.1 A Desire for Exploration The symbolic debugger is a conversational tool that manages to connect pieces of information that can be otherwise scattered across modular boundaries (e.g., packages, classes, methods). Debuggers interrupt the running system and offer a conversation about the system’s dynamic control flow—a snapshot that entails many
A Pattern Language of an Exploratory Programming Workspace 129 concrete details about abstract code. While details can be overwhelming at times, programmers have the chance to explore first-hand, reliable facts if they manage to persevere. Here is a typical interface of a debugger in an object-oriented environment: However, it can be tempting to prematurely jump to conclusions. Exploring the control flow step-by-step is not without efforts. Programmers have to collect and assess the pieces they find. Thus, the conversation might become cumbersome, depending on how the debugging tool helps you manage your findings. Sometimes, you can only go forward and not backward, which is problematic when exploring systems that have many (stateful) side effects. Sometimes, you cannot restart (or repeat) the conversation if the circumstances (i.e., the system state) are difficult to recreate. Therefore, you should reflect on the value of every debugging situation that you encounter. Do not give up too soon. 6.2 The Profile You are in the middle of a “Conversation in Context.” You just got the first response that has information you are looking for. However, you notice that several important details of the conversation’s topic are still unclear or missing. You wonder whether you should start to “Coach Your Environment” about what you know from your own experience. But then you remember: • The environment’s knowledge (i.e., current interaction context) encompasses many (software) artifacts. • A concept (or topic) might reach across artifact (or module) boundaries. • It is easy to ask follow-up questions. • It is possible to combine multiple responses in an “Elaborate Inquiry.” Therefore, you keep on having this conversation to collect more details. The concept you are talking about lies “in shards” virtually in front of you. You just have
130 M. Taeumel et al. to pick up each individual piece to appreciate its value. Do not stop at the first piece but reason over a comprehensive amount of information. While you might not be able to collect all shards of a concept, the relevant fraction will bring you further. Note that there might be relevant knowledge located outside your environment. Therefore, you might have to “Coach Your Environment,” after all, in how to access the external knowledge. 6.3 The Structure The environment should provide the following services: • Means of Collection that allow you to manage “the relevant whole” you have identified so far. • Means of Comparison that allow you to quickly determine the added value for each new piece you find. • Means of Loading that allow you to load external information from outside the environment. You should design your exploratory journey as follows: 1. Stay nosy: keep asking questions and do not be satisfied with the first promising response. 2. Finish the conversation when it becomes tricky to find anything new and relevant, yet do not give up too early. 3. Do not worry: you can often bring up the same topic later in a different conversation. 6.4 Consequences When collecting pieces of information, you rely on efficient window management since each piece might come with a dedicated view that cannot be embedded into the conversation directly. When you then “Pause and Explore” such views, the extra
A Pattern Language of an Exploratory Programming Workspace 131 conversations could derail your train of thought. You have to be extra careful not to lose focus. When comparing pieces of information, it can be challenging to assess the quality of similar pieces coming from different (external) sources. If you accumulate too much similar information in your conversation’s interaction context, follow-up questions can become tricky to pose or their responses cumbersome to handle. When loading external information into the environment, you can face consequences similar to the ones of “Coach You Environment.” An oversupply of information can noticeably increase your cognitive effort. 6.5 Related Patterns While you “Coach Your Environment” to reach a shard outside the environment, a “Proxy Transport” can help you reduce memory consumption within the environment. An “Elaborate Inquiry” can help you connect all relationships in a compact form. 7 Proxy Transport Design a flexible proxy to integrate external information into your interaction context. Map the external format (e.g., file contents) onto the internal one (e.g., object fields). Use lazy loading and caching to avoid high memory consumption and slow access times. 7.1 A Desire for Exploration The environment’s knowledge is limited. You can surely talk about all the code artifacts that you have created. After all, code management is among the primary features of the environment’s tools. However, there is much information only indirectly related to the software system under construction: tickets in a bug tracker, emails in your inbox, or documents in a forum. Chances are that your environment does not manage all these artifacts itself but that other environments take care of it. There are two main reasons why you should not just copy external knowledge into your environment. First, its actuality can quickly be compromised if outside tools keep on updating the information without informing your copy. Second, your resources and your environment’s resources are limited. It can take a while to copy data from external environments, which can conflict with the time you estimated for your current task. At some point, your local machine might be out of memory, or the tools need what seems like an eternity to provide meaningful results. Consequently,
132 M. Taeumel et al. you have to make external information accessible to be selectively loaded on-the-fly and cached efficiently. Think about a collection of documents persisted as PDF files on your disk. During the last months, you collected several files and also added much commentary to their contents. Now you want to explore and process this information from within your environment. You have to map the files and its structured contents to objects and fields, which your environment can understand. You might also want to edit selected commentary but keep everything stored in external files to let external tools work on those, too. Eventually, you create a Report proxy that is able to read and write PDF files: You can now have conversations in the same way as you would about artifacts stored inside the environment. Yet, if the external files get moved without notice, the flow in your conversation will break. You have to make the trade-off between availability and efficiency. 7.2 The Profile You are in the middle of a “Conversation in Context.” You want to “Coach Your Environment” to load external information, maybe because your central “Concept in Shards” pointed to a shard outside the environment. However, you realize that the external information sources you selected are rather demanding in terms of access time and/or memory consumption. • It is easy to load external information into the current interaction context. • The environment relies on the context’s common format (e.g., objects, files, tables). • It is easy to map an external format (e.g., files) onto the context’s format (e.g., objects). • It is difficult to find a good mapping, meaning, one that fits your conversation’s needs (e.g., file’s contents to object’s fields).
A Pattern Language of an Exploratory Programming Workspace 133 Therefore, you “Coach Your Environment” to make that external information available through proxies as part of the current interaction context. A proxy is an informational artifact that has special properties: • It is transparent, which makes it look like normal information stored within the environment. • It provides lazy loading, which only loads the pieces required to process a specific question to avoid high memory consumption. • It provides caching, which stores pieces required for one question to be used in another one to avoid slow access/response times. • It remains adaptable so that you can revise its mapping rules as you revise your questions. 7.3 The Structure The environment should provide the following services: • Means for “Proxification,” which should be a convenient way for you to “Coach Your Environment” about the useful but costly information outside. • Interface for Imports, which is constantly accessed “behind the curtains” when working with the proxy artifact in conversations. Examples include file handles, database sessions, and network sockets. • Means for Debugging, which is needed when a proxy artifact disturbs the flow in your conversation. The quality of external resources (availability/reliability) is always hard to control from within the environment. The proxy abstraction might “leak.” You should work with external information the same way you do with internal information: • “Coach Your Environment” to load external information but consider its costs (e.g., access time, memory consumption). • Stay focused: try to ignore the special characteristics of proxies in your conversations, even if you know about it.
134 M. Taeumel et al. • Be flexible: if a proxy “leaks,” plug it and quickly return to your conversational flow. • If you misjudged the costs, turn the proxy into a fully loaded artifact of the current interaction context. 7.4 Consequences The biggest issue with proxies is a leaky abstraction in the middle of a conversation. For example, if you talk about an artifact that you did not know was a proxy, a sudden increase of response times—because an external resource became unavailable—can break the entire flow of that conversation. You can then either choose to become a tool builder to fix it or you can exclude that artifact from your conversation and risk guesswork. The second biggest issue is “proxification,” because in many environments you have to construct a proxy transport for external artifacts yourself. It is typically not a single button click but involves actual programming effort. Depending on the quality of the external source, you might risk not having a caching strategy to be able to quickly return to your conversation. However, a leaking proxy might bring you back to the construction site more quickly than you want. Eventually, programmers often try to bring external information closer to the environment if it cannot be loaded entirely into the environment. For example, information from the Web can be cached in a database on your computer. Your environment can then access that (still external) database without any noticeable lag of a slow network connection. 7.5 Related Patterns You will have to “Coach Your Environment” to set up the proxies for external information, which can itself be part of your conversation’s central “Concept in Shards.” Note that the Proxy pattern is common in object-oriented software design (Gamma et al., 1995), which provides implementation guidelines for tool builders. 8 Simple Response Every question has a simple response. Do not hesitate but ask away; embrace trialand-error. A text will appear, a picture will render, a sound will play—tackle complexity with a combination of these.
A Pattern Language of an Exploratory Programming Workspace 8.1 135 A Desire for Exploration Every screen cluttered with a multitude of windows, each one filled with plenty of information, is made of simpler, comprehensible elements. Not just the windows (or views) themselves, but also their contents are made of smaller pieces. Color, shape, animation, and interaction help users to distinguish a single piece from its surroundings. We know what buttons look like and that we can click on them. We know that textual labels in a list view can be selected. We know that such labels represent abstract handles to more structured information. Think of your address book as an example with all its details about your friends. Of course, at some point, we had to learn about these elements, but now we know and expect certain things from a graphical interface. During conversations, the environment will give you responses that look rather simple, even if they entail more complexity. In object-oriented systems, for example, every object has a print-string, which is a textual representation of its most prominent (and identifying) features. That print-string is used in many tools to provide you with familiarity during exploration. You will read it and might think: “Ah! I know this object from before.” Therefore, simple responses will keep you oriented and focused during conversations. However, simple responses can be misleading. If you forgot to “Coach Your Environment” about the identifying features of a new kind of domain object, the environment can only present generic features. Take the following list of fruit objects as an example. On the left, there is only a generic representation, which is difficult to explore. In the middle, the kind of fruit got exposed, which made you realize that a Tomato might be in the wrong class. On the right, you see a more visual take on each object’s features: Consequently, it is your responsibility to “Coach Your Environment” about an appropriate representation of informational artifacts throughout the conversation. Only then can you find your path through cluttered screens and be successful on your exploratory journey.
136 M. Taeumel et al. Remember the Visual Information Seeking Mantra (Shneiderman, 1996): overview first, zoom and filter, details on demand. In the end, you browse and explore through a multitude of simple responses. Complex structures are only composites. 8.2 The Profile You are about to start a “Conversation in Context.” A couple of possible first questions begin to form in your mind. However, you cannot decide on a good first question. You fear that the environment’s response might overwhelm you with details you are not yet ready for. • • • • It is difficult to get a satisfactory response on the first try. The environment will not punish you for bad questions. It is easy to revise your wording in follow-up questions. The environment will provide some response. Therefore, you do ask away your first question and wait for the response. The environment will present a piece of information from the current interaction context in a simple form. All artifacts have some kind of textual label, a name if you will. Such names help you identify and compare different pieces of information. Naturally, texts will be represented as themselves and so will pictures. Sounds are audible through your speakers. All these simple forms help you assess the quality of your question. Note that if your question does not make sense to the environment, it will complain in a simple manner, too. Usually, you will be instructed to change your wording or explain the unknown vocabulary. Embrace simplicity; fail early to move on quickly. 8.3 The Structure
A Pattern Language of an Exploratory Programming Workspace 137 The environment should provide the following services: • Acknowledge any user input, even if malformed. • Keep it simple: show text, render pictures, play sound or music. • Wrap complex data structures into simple representations for a pleasant first encounter with the user. You should embrace the fact that the environment always listens: • Embrace trial-and-error during conversations. • Try asking for alternative representations. • Ask for more details when struggling with unclear (or shallow) representations. 8.4 Consequences In object-oriented systems, objects with no real-world counterpart can be difficult to represent in a meaningful, simple way. For example, people have names and photographs have colors. Even network sockets have addresses. All these properties can simply be shown on screen to identify the objects behind them. However, purely artificial objects such as observers (Gamma et al., 1995, p. 293) and mediators (Gamma et al., 1995, p. 273) merely have their class name and maybe a location in memory to distinguish individual instances. During conversations, the simple/ generic representation of such artificial objects can be cumbersome to use (e.g., anObserver(1234) and anObserver(5678)). Users might have accessibility issues with specific on-screen contents due to diminished vision or hearing. Especially an environment that is crowded with information—and multiple ongoing conversations—can be challenging for all kinds of users. The legibility of text might suffer, some colors in pictures might be hard to see, a specific sound might not be audible in a noisy setting. You might want to change the representation of some artifacts during a conversation, but only for that conversation. For example, while the name of a person might not matter its favorite food does. You therefore “Coach Your Environment” to represent people differently. However, such changes must be scoped to the task at hand so as not to interfere with other conversations or your generic perspective on the domain artifact. We designed the Vivide environment (Taeumel, 2020) for exactly that purpose. 8.5 Related Patterns Every “Simple Response” has a non-simple counterpart with a more technical structure. Thus, each response should be a “Tangible Response” since its simple representation usually hides many details that you might want to explore.
138 M. Taeumel et al. 9 Tangible Response Directly engage (e.g., click on) a response as it is represented on screen. The environment will respond to the “What is this?” question with more details. Return to the particular conversation later on or spawn new ones. 9.1 A Desire for Exploration During conversations, you and the environment will fill the screen with (visual) information. Your questions take space, and so do the environment’s responses. Therefore, you have to keep track of the meaning of all those visuals. Yet, occasionally, the environment will show you something that you do not quite understand (or have already forgotten about). Then, the follow-up question is simple: “What is that?” You can use touch or mouse input to point at something; you can also use the keyboard to move a cursor to indicate curiosity. In any case, you want to look “behind the pixels” on screen. A textual label might hide details about an important artifact. A graphical composite might be connected to data as well. In the Web, documents offer clickable (hyper-)links. We have gotten used to hovering over and clicking on anything colorful and suspicious. Yet, your programming environment can offer more tangibility than plain web browsers do. It can help you deconstruct and explore everything you see. The following examples indicate tangible interactions during conversations: On the left, you told the environment to create and show a button that is now sitting on the screen, waiting to be clicked on. In the middle, you discovered a special gesture to open a halo (Maloney & Smith, 1995) around a window to take a look at its composite structure. On the right, you found a link in a text field that you can click on to explore more information. Overall, your environment can help you explore and understand everything it knows and shows.
A Pattern Language of an Exploratory Programming Workspace 9.2 139 The Profile You are in the middle of a “Conversation in Context.” You just got an interesting “Simple Response” and want to learn more about its details. However, you cannot figure out how to ask the environment about details without knowing what lies behind that simple form of the latest response. You are not sure whether there is any vocabulary to express your intent. • It is easy to touch or click on something that is visible on screen. • It is more difficult (but possible) to indirectly reference prior responses in a follow-up question. • A response can be a (generic) question on its own: what is this? Therefore, you directly engage (i.e., click/touch/activate) with the response as it is represented on screen (Hutchins et al., 1985). The environment will treat this interaction as a generic “What is this?” question, whose response will be more detailed and informative, yet keeping it simple in its parts. You then “Pause and Explore” until you are ready for your next follow-up question. You might already be satisfied and finish the conversation. You might also have many new different questions and thus spawn several new conversations. Note that the “Conversation in Context” maintains a history of prior responses so that you can engage with any of them later on as well. 9.3 The Structure The environment should provide the following services: • Provide visual cues that foster the direct engagement with an on-screen representation (e.g., text color). • Help the user organize exploration paths for deeply structured artifacts. • Keep track of any sideline conversations that emerge during exploration. You should hover over, click on, or touch visual cues: 1. Treat any response as a tangible entity that you can engage with. 2. Effectively work with a multitude of simple forms but do not forget to probe details so as not to rely on guesswork.
140 9.4 M. Taeumel et al. Consequences Direct manipulation for exploration needs dedicated gestures such as mouse clicks. Yet, graphical, interactive objects already need many gestures for normal interactions. Thus, for tool builders, the design challenge is to find a trade-off between extra/hidden/overlapping gestures to integrate exploratory and normal use. A special exploration mode can help to temporarily free reserved gestures from such objects. Visual clutter affects the size of click targets, which can lead to input errors. It might be easier to indirectly reference a prior response in a follow-up question than to interact with it, even if it is visible on screen. Any “Conversation in Context” should reserve enough screen space for you to compose your question. Even if graphical objects get occluded, you can rely on programmatic references such as variable names. 9.5 Related Patterns The direct inspection of a response leads to “Pause and Explore” and thus another conversation about the response’s details. 10 Pause and Explore Suspend an ongoing conversation if you need to explore and understand details of a prior response. Start a new conversation to learn more about these details. Later, resume the original response and make use of your freshly gained knowledge to pose better questions. 10.1 A Desire for Exploration A single conversation will probably not satisfy all the information needs that arise during an exploratory programming task. Chances are that you hit a block in one conversation that can only be resolved by approaching the topic from a different perspective in another conversation. The environment has several specialized tools that each offer their own conversational interactions. Therefore, you keep on jumping between tools to collect all their insights. The environment’s window manager helps you organize screen contents and thus all open conversations:
A Pattern Language of an Exploratory Programming Workspace 141 Note that, in the process, you can only focus on a single conversation at a time (Miyata & Norman, 1986). Also, resuming suspended conversations takes extra time (Parnin & Rugaber, 2011). On the bright side, you can ask very precise questions through specialized tools, whose constraints allow them to also provide very specific responses. After all, your exploratory journey benefits from a rich tool set and your ability to select and integrate appropriate tools as you go. 10.2 The Profile You are in the middle of a “Conversation in Context.” You just exploited a “Tangible Response” and the environment presented many new details you now want to dive into because they seem to be a crucial part of your current conversation. You proceed to start another “Conversation in Context.” However, you get the feeling that the new information becomes outdated while you are studying it. You feel in a hurry to get back to your original conversation. But then you remember: • The flow in a conversation will stagnate if you cannot understand crucial information. The risk of mistakes through guesswork will rise. • Every sample in a pool of information is valuable; pieces allow for extrapolating to the whole concept. • It is easy to suspend and later resume an ongoing conversation. • It is easy to spawn new (sub-)conversations to learn more about crucial aspects of/for other conversations. Therefore, you take a deep breath and dive into the new information you have at hand. You start a new “Conversation in Context” to ask different questions. You suspend the original conversation in the sense that it will be easy for you to resume it later on. The environment does not bother; it can wait and help you shift focus to your new topic on interest. Note that highly dynamic (and live) systems exhibit quickly changing interaction contexts during conversations. Every response might only capture a single snapshot in time for you to explore. It might be difficult to generalize from a few snapshots.
142 M. Taeumel et al. Luckily, it is possible to represent such liveness as steady frames (Hancock, 2003), which can serve as a more comprehensive topic to talk about with your environment. 10.3 The Structure The environment should provide the following services: • Manage to have multiple ongoing conversations at the same time. • Keep track of topic(s) per conversation. • Support switching focus between conversations; suspension and resumption should feel effortless. You should acquire the ability to work with exploratory conversations at a higher level: • Start a new conversation if you want to dive into the details of a prior response in an ongoing conversation. • Close (or archive) conversations that you see finished. New conversations can always tackle the same topic(s). 10.4 Consequences Efficient conversation management relies on a good window manager, just like when collecting your “Concept in Shards.” Too many (overlapping) windows will clutter the screen and thus affect click targets of a “Tangible Response.” Overall, you might lose your focus and forget about which conversation was spawned because of which other conversation. You might want to merge two conversations if you realize they lead you to complementary insights. Technically speaking, you might want to merge two (scoped) interaction contexts to then continue your conversation based on a comprehensive knowledge base.
A Pattern Language of an Exploratory Programming Workspace 10.5 143 Related Patterns Every time you take a break from one conversation to explore data in the context of another one, you will find you way back to a “Conversation in Context” and with it all patterns that come along. 11 Conclusion We presented the pattern language of an exploratory programming workspace, which captures the conversational style of workspace-like tools such as Smalltalk workspaces, Unix shells, and scientific notebooks for computation. Each pattern in the language offers guidance for programmers as tool users and tool builders. We are aware that there might not be a single implementation out there that covers each and every pattern perfectly. Looking at the consequences we discussed earlier, there can be many practical trade-offs involved in your favorite programming environment. For example, direct manipulation of a “Tangible Response” might be limited in a Unix shell but rather rich in Squeak’s Morphic (Taeumel & Hirschfeld, 2016). Programmers have to cope with such limitations on a daily basis. The impact of a tool builder can be crucial to the success of a project. At best, there are dedicated programmers that have the resources to continuously evolve the tools their colleagues are using. Sometimes, however, it can be beneficial to just realize an idea yourself directly during the programming task without having to talk about it. There are environments that support evolving the tools directly during use such as Squeak/Smalltalk (Ingalls et al., 1997) and Lively/JavaScript (Lincke et al., 2017). Our pattern language can help programmers focus during such construction efforts. In the future, we want to connect this chapter’s patterns to more different real-world projects, tools, and environments. Following traditional patterns of object-oriented design (Gamma et al., 1995), implementation sketches might help tool builders to better understand common pitfalls early on. We think that this pattern language will grow since we already identified more aspects to talk about such as “Surprise Response” and “Conversations Re-used.” Change is omnipresent in the lifetime of a pattern as it gets applied and revised along the way. Acknowledgements We gratefully acknowledge the financial support of the HPI Research School on Service-oriented Systems Engineering (www.hpi.de/en/research/research-schools) and the Hasso Plattner Design Thinking Research Program (www.hpi.de/en/dtrp).
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Practice-to-Research: Translating Company Phenomena into Empirical Research Innovation Behavior in Turbulent Times Lena Mayer, Katharina Hölzle, Karen von Schmieden, Reem Refaie, Hanadi Traifeh, and Christoph Meinel Abstract Doing relevant and rigorous research often leads to a research-to-practice and practice-to-research gap, which both involved parties need to actively address and bridge. All parties involved face the challenges presented in the steps of identifying practice- and science-relevant research questions. This begins with the translation of these questions into a practice-feasible, but also empirically-based, research design and is followed by the retranslation of observations into scientific language to assess and interpret the observed phenomena. In this chapter, we report on our research approach to move from innovation practitioners’ narratives to a quantitative study design. We translate what we have learned from employees about innovation activities, behaviors, and structures, into a study design to measure employees’ innovative behaviors overall in a large quantitative study. The resulting empirical study assesses the link between employees’ Job Insecurity and Innovation Behavior as well as three assumed moderating effects (Organizational Support, Participative Decision-Making, Job Autonomy). L. Mayer (*) · K. Hölzle · K. von Schmieden · R. Refaie · H. Traifeh HPI School of Design Thinking, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany e-mail: Lena.Mayer@hpi.de; Katharina.Hoelzle@hpi.de; Karen.Schmieden@hpi.de; Reem.AbouRefaie@hpi.de; hanadi.traifeh@hpi.de C. Meinel Hasso Plattner Institute and Digital Engineering Faculty, University of Potsdam, Potsdam, Germany e-mail: Christoph.Meinel@hpi.de © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_8 147
148 L. Mayer et al. 1 Introduction The basis for this chapter is the lead author’s doctoral research project with an industry partner,1 which we have kept anonymous for reasons of confidentiality. The company’s headquarters is located in Germany. It employs more than 110,000 people worldwide, with approximately 39,000 employees working at the headquarters itself. We describe how employees shared stories and observations with the research group and how these narratives led to the empirical design of a survey to assess employees’ innovation behavior during turbulent times. Shortly after the start of the project, the corporation was going through a fundamental reorganization which was set to last for the succeeding 3 years. Next to planned labor shortage, the new organizational strategy came with newly formed teams. Responsibilities were additionally shifted from governance units to business units, thereby providing more autonomy to business units enabling them to act and adapt faster. One of the complex challenges of conducting research in industry is to match scientific research gaps and questions with organizational interests. While industry partners are looking for direct practical and hands-on implications of the results, researchers have to follow strict scientific standards to get valid and reliable results to add value to an existing body of literature. Another dilemma emerges when researchers want to study a specific condition, derived from an academic standpoint and research gap, which is not or only partially reflected in the organizational context. This dilemma was the impetus for this research project. While the experience was troubling at first, it provided an opportunity to seek a more practiceembedded research topic and to observe and listen to organizational relevant topics with an unbiased perspective. We describe this approach of identifying company phenomena, matching them with suitable explanations in the literature, deriving a research gap, defining constructs, and the subsequent study design. In this way we believe industry research scholars can make a two-fold contribution—both to the scientific community and by offering applicable organizational results. 2 Conversations and Narratives: Study Inspiration In the context of the above described reorganization, we started to talk to a number of colleagues in the organization’s innovation landscape. Our intention was to gain an understanding and feeling for what topics were en vogue, what bothered employees, and what they reported from previous and current innovation projects to identify a starting point for our research endeavor. 1 The corresponding analysis reported in our empirical paper was accepted at the 28th Innovation and Product Management Conference (access via MayerMayer et al., 2021).
Practice-to-Research: Translating Company Phenomena into Empirical Research 149 The final inspiration for this study came from a conversation with an employee who worked in the internal consultancy department of the organization. He told us about his experiences accompanying two independent innovation project teams. During the exploration phase2, one of the teams (Team A), which today is an internal startup, finished their exploratory activities in around eight months. The members of the other team (Team B) belonged to a subsidiary company. This team completed the exploration phase in approximately two months. Surprisingly, Team B was highly motivated, although these employees were not sure whether or not they would keep their jobs—the subsidiary company had announced plans to cut staff. This made us wonder if the pressure and potential of losing jobs urged employees to create results faster. From a psychological standpoint, we were, however, not satisfied with looking at the phenomena of putting employees under pressure. Hence, we reversed the question and opted to examine whether organizational factors could foster employees’ innovation behavior, so they can exhibit their full potential. Walking away from the conversation, we wondered: In moments of crisis, how do employees feel? And how does this influence their innovativeness? AHA-Moment: The team that had to deal with the fear of job loss was more motivated and it finished the innovation exploration phase earlier than the other team. We considered this story within the overall context of an extensive restructuring. Such a reorganization is accompanied by constant change, agitation, upheaval, and loss, which can lead to feelings of insecurity for individuals (e.g., Keim et al., 2014). At the same time, innovation research suggests that a safe working environment is crucial for employees to engage in open conversations, generate innovative ideas, and allow for new thing to happen unintentionally (e.g., Anderson et al., 2014). We captured this narrative in 2019 and started to deep dive into the literature to set up a study design. At the beginning of 2020, we were close to completing our survey design. This time marked the starting weeks of the Covid-19 spread in Europe. Hence, in addition to the internal structural alterations in the company, everyone faced the economic and personal effects of the global Covid-19 pandemic. This combination of events reinforced the experience of uncertain times and the fit of our research topic. During such globally unstable times, the topic of job security is of particular relevance and it has been discussed in relevant research trend publications, such as the “Innovation and Covid-19: Impulses for the Future of Innovation”3 report by the German research institute Fraunhofer (Bauer et al., 2021). The authors state that the pandemic has led to a heightened insecurity in companies and markets, which could lead to innovation budget cuts. They conclude that companies should try to prevent this situation by further increasing their competitive ability in reaction to these challenges. 2 Innovation processes within the company are commonly divided into three phases: Exploration/ Discovery/Ideation, Incubation/Prototype, and Acceleration/Grow. 3 Translated from the German report title.
150 L. Mayer et al. Furthermore, employee insecurity has also become an emphasized focus point for important industry bodies: In 2020, the German trade union IG Metall (representing the metal industry) centered “job security” as their core topic for their collective bargaining (retrieved from the German newspaper SZ online on November, 4, 2021 www.sueddeutsche.de). The appearance of our research topic in public and contemporary research literature assured us that it was not only relevant for our partner company, but that the phenomena were experienced throughout society and needed to be examined in more detail. 3 The Translation Process The internal employee’s story was our ignition point to dive into literature on creative and innovative behavior, organizational behavior and crisis. Our first impulse was to look for a construct and measure that captures the extent to which employees feel that their jobs are safe and secure, using the keyword search “job security.” Quickly, it became obvious that the inverted construct “job insecurity” (JI)4 was represented more prominently in the existing literature and only a few papers examined “job security” as a variable (e.g., Probst & Lawler, 2006; Davy et al., 1997). Employees are central for organizations. Their innovative behaviors are vital for the innovation performance of an organization (see, e.g., Strobl et al., 2020). Organizations should therefore take measures to stimulate the innovation willingness of employees and promote their innovation behavior. If employees go through a profound internal reorganization while experiencing the threat of a global pandemic, this can create a feeling of unpredictability and lack of control (Keim et al., 2014). If on top of that, innovation is expected from employees, a challenging situation arises for both them and their employer. Figure 1 depicts how we translated the narrative and the organization’s situation into scientific and quantifiable terms. In summary, we derived the following research questions: In moments of crisis, – how do employees perceive their job security? – how does this influence their innovation behavior? – which environmental factors could be fostering individual innovation behavior? 4 Two major conceptualizations for JI exist in the literature: quantitative vs. qualitative JI, and cognitive vs. affective JI. We decided for the more recent differentiation between quantitative and qualitative JI (e.g., see review by Shoss, 2017, p. 1913). Quantitative JI captures to what extent employees fear losing their job, and qualitative JI to what extent they are afraid of losing important job characteristics (e.g., change of location).
Practice-to-Research: Translating Company Phenomena into Empirical Research 151 Fig. 1 The organizational context and factors observed We chose the organizational levering factors based on observations and stories from colleagues and based on theory (outlined in subchapter 4, Moderator search). The most prominent IB scales come from Lukes & Stephan (2017) and De Jong & Den Hartog (2010). From these, we decided to work with the Innovation Behavior Inventory (Lukes & Stephan, 2017), because their subscales best represent our assessment intention and data collection approach in the organization. Also, the scale is an individual level measurement. In comparison, De Jong & Den Hartog (2010) designed their Innovative Work Behavior scale to assess employee–supervisor dyads. For our project we set the focus on employees and their personal perceptions. Hence, IB supervisor ratings did not fit into our planning. With the two main variables (JI and IB) in place, we screened the literature for empirical papers on their relationship. The research on the direct link was scarce. Available research includes papers by Farzaneh & Boyer, 2019; Niesen et al., 2018; Roll et al., 2015; De Spiegelaere et al., 2014. The hypothesized negative effect for the direct JI-IB link was not significant in some studies (e.g., Farzaneh & Boyer, 2019; Roll et al., 2015). Other scholars found partial significant negative effects on subscale level between JI and IB (e.g., Wang et al., 2019, Niesen et al., 2018; De Spiegelaere et al., 2014). Next to these inconsistent findings on the JI-IB link, we also noticed that none of the researchers had conducted a complete assessment of JI and IB. For example, Niesen et al. (2018) found a significant negative effect between quantitative JI and idea generation (as one IB subdimension), as well as between qualitative JI and idea generation. However, Farzaneh and Boyer (2019) could not confirm a direct negative relationship between JI and IB as hypothesized. For a literature review on the JI-IB link, we refer to Mayer & Farzaneh (in press). We also assumed that the inconsistent findings could be due to boundary conditions. Hence, we decided to add moderators to our study model. We describe the process of choosing suitable moderators for our company context in the next paragraph.
152 L. Mayer et al. In summary, we identified three research gaps: • Inconsistent findings for the direction of the relationship between Job Insecurity (JI) and Innovation Behavior (IB) • Only partial assessments of either JI or IB in current literature • No moderator studies to this point (only mediator studies) Next to the independent, dependent, and moderator variables we wanted to include a personality measure to control for possible personal characteristics that influence employees' innovative behaviors. Personality traits are most commonly assessed with a BIG 5 scale. Therefore, we wanted to add a short-version of the traditional BIG 5 scale (Muck et al., 2007). However, we encountered another issue. The company side stated that the items of the personality trait questionnaire touch on very private characteristics and these bear too great a similarity to the annual employee HR assessment. Our contact partner summarized that this doubling poll might be disturbing to employees and raise the question of why personal traits are assessed to capture innovation topics. Consequently, we dropped the personality assessment aspect from our survey. 4 Moderator Search It was important to us to pick moderator variables that were suitable and relevant for the company context. We first collected an extensive list of possible moderators from the literature5, and then discussed and verified them with our contact at the company.6 The conversation helped us to identify crucial factors that our contact experienced as potential levers for employees in the innovation landscape. As factors hindering innovation aspects, the contact listed “no time, no leadership by results, little appreciation from leadership and little rewarding, no customer validation, no training/how-to/coaching, company is not market-based.” He also named some supportive characteristics, all referring to the employee themselves: “intrinsic motivation, working closely with the customer, hierarchy aversive people, those providing ecosystems and networks, experience with internal projects (based on long history of know-how).” Besides this, he mentioned “many degrees of freedom (e.g. budget)” as beneficial for employees’ innovative activities. In other conversations, employees told us about feeling micromanaged, not having the freedom to get work done but feeling instead like jour-fixes and meetings prevented them from engaging in their “real tasks.” Another narrative referred to the board and middle management members. One employee described them “sitting For example, based on the Shoss’ (2017) integrative review and her “Conceptual Model of Antecedents and Outcomes of Job Insecurity.” 6 We did this in an open discussion asking which organizational factors he perceives as hindering and which ones as supportive for employees in the innovation scene. 5
Practice-to-Research: Translating Company Phenomena into Empirical Research 153 Fig. 2 Hypothesized model behind an internal information filter,” where they could feel safe. He specified that these filters are to be understood as time-fixed meetings with auditioned content. He claimed that this prevents dynamics as well as honest conversations or transparency about current and future states. Nonetheless, “behind that filter,” board and middle management members are highly alert and aware that changes are necessary for the company. From these narratives and moderator suggestions in the literature (for theoretical literature reviews, see Shoss, 2017, and De Witte, 2005; organizational relevance for innovation climate is put forward in Anderson et al., 2014, and Crossan & Apaydin, 2010), we added three moderators to our study design: Organizational Support, Participative Decision-Making, and Job Autonomy (see Fig. 2; short definitions are summarized in 5 Final Study Design & Assessment). We presumed that each moderator serves as a psychological contract variable that alters the direction of the JI-IB link (see details in Mayer et al., 2021). 5 Defining Hypotheses In our quantitative study, we examined the impact of perceived Job Insecurity on employees' Innovation Behaviors, assuming a negative effect (insecurity leading to less IB). We also assessed the contextual factors of Organizational Support, Participative Decision-Making, and Job Autonomy for potential mitigating effects. We formulated the following hypotheses. For more details on how we derived them from the existing literature, we refer to our empirical paper with all company results (Mayer et al., 2021). • H1: Job Insecurity (JI) is negatively related to Innovation Behavior (IB). • H2: Organizational Support (OS) moderates the negative relationship between JI and IB such that the relationship becomes weaker as OS increases. • H3: Participative Decision-Making (PDM) moderates the negative relationship between JI and IB such that the relationship becomes weaker as PDM increases.
154 L. Mayer et al. • H4: Job Autonomy (JA) moderates the negative relationship between JI and IB such that the relationship becomes weaker as JA increases. 6 Final Study Design For our company partner, the survey story was crucial. It was necessary to communicate transparently with employees and to distinguish our survey from the company’s own employee survey conducted by HR. The introductory story is shown in Fig. 3. Our contact also emphasized that we need to create a win–win situation for colleagues to participate in the survey. If they take the time to fill in the survey and provide their data, we need to consider what they can get and expect from us in return. In response, we added a pdf file containing creative tips to start online meetings as a gift—this came at a time when many employees had just shifted to remote work and were new to facilitating online meetings. Fig. 3 Screenshot of the introduction to the survey
Practice-to-Research: Translating Company Phenomena into Empirical Research 6.1 155 Choosing Robust Scales We chose existing validated scales per variable to get robust results and aimed for good construct scale reliabilities. For most constructs, we tried to choose prominent, well-established measures. All measures in our questionnaire captured self-reported ratings. In order to counteract common method bias, we applied three recommendations from Podsakoff et al. (2003). First, we informed participants that taking part in the survey is completely voluntary, that all data will be aggregated and thereby anonymously analyzed, and that there are no right or wrong responses. Second, we guaranteed for proximal separation between the dependent variable (IB) and the independent variable (JI) in our survey. IB was placed as the first itemset, and JI as the last itemset within the questionnaire. Third, the introduction ensured psychological separation. 6.2 Final Survey Design A 5-point Likert response scale accompanied all measures (except for the control variables). In total, seven persons contributed to the translation (from English into German and back) of the scales. Four of these contributors work in innovation research. Additionally, we wanted to reassure that the item batteries, derived from the literature, would resonate with the target audience of employees within the organization. Hence, we engaged in a conversation with a senior head of innovation and an innovation inhouse consultant to capture their feedback and accommodate it where possible. The final measures are listed below. Job Insecurity (JI). Due to the incomplete assessment of JI in previous JI-IB studies, we proceeded with measuring Qualitative as well as Quantitative JI. Quantitative JI measures the level of fear that employees experience at the prospect of losing their job. We applied De Witte’s (2000) Job Insecurity Scale (JIS), which has been validated in several languages (see Vander Elst et al., 2014). Also, the scale yields scientific proof for good construct and criterion validity, as well as good reliability. In particular, Vander Elst et al. (2014) even advise practitioners to use the short scale for screening purposes. In accordance with our company partner, we included the following two items: “Chances are, I will soon lose my job,” and “I feel insecure about the future of my job.” The qualitative dimension captures to what extent employees fear losing elements of the job, such as fear of being passed up for a promotion in the near future. We chose the scale Multidimensional Qualitative Job Insecurity Scale (MQJIS) by Brondino et al. (2020) as a suitable one. The scale is composed of four dimensions with eight items in total.
156 L. Mayer et al. The four dimensions are Social Relationships, Employment Conditions, Working Conditions, and Work Content. An example item is “I worry I might get another supervisor in the future” (from the subdimension Social Relationships). Innovation Behavior (IB). Employees rated their Innovation Behaviors on five subscales (16 items in total) of Lukes and Stephan’s (2017) Innovation Behavior Inventory. We assessed the subscales Idea Generation, Idea Search, Idea Starting Activities, Involving Others, and Overcoming Obstacles. Example items are “I am interested in how things are done elsewhere in order to use acquired ideas in my own work” (Idea Search subscale), and “I try to involve key decision makers in the implementation of an idea” (Involving others subscale). Organizational Support (OS). OS evaluates employees’ perception of their organization paying attention to their well-being and aiding in their professional goals. We added six items from the well-established Perceived Organizational Support scale by Eisenberger et al. (2020, p. 104, originally consisting of ten items). An example item reads, “The organization strongly considers my goals and values.” Participative Decision-Making (PDM). We applied Bordia et al.’s (2004) four item scale to rate employees’ Participative Decision-Making. PDM captures the extent to which employees perceive to be actively involved in decisions made by the company. For example, one item states “I actively participate in decision-making regarding things that affect me at work.” Job Autonomy (JA). Job Autonomy measures how freely and autonomously employees can act and reflect in their jobs. We used eight items from Ramamoorthy et al.’s (2005) perceived Job Autonomy scale. Example items are “I choose the methods to carry out my work,” and “I often review my work objectives.” Control Variables. Our choice of control variables for the survey roots on what we found in other JI-outcome papers and which were relevant for our company context (e.g., Brondino et al., 2020; Niesen et al., 2018). We included altogether six control variables in the questionnaire: Age Group, Gender, Functional Role, Disciplinary Responsibility, Type of Contract, and Organizational Tenure. 6.3 Additional Remark During our discussion sessions before the company assessment, as well as afterwards when presenting results, we always had to make clear that we are talking about “subjective, personal” Job Insecurity. Company members and researchers at conference meetings regularly voiced their concerns about measuring how secure employees feel about their jobs in a corporation. They argued that the company has solid contractual structures, providing high security and protecting against gratuitous job loss. The construct we measured, perceived subjective JI. This does not refer to written contracts that formalize mutual responsibilities and duties. Following psychological contract theory, subjective JI applies to psychological contracts in the shape of unwritten mutual expectation between employee and employer (see Shoss & Probst, 2012).
Practice-to-Research: Translating Company Phenomena into Empirical Research 157 7 Conclusion In this chapter, we have elaborated our embedded research approach to find inspiration from narratives within an organization and to translate them into an empirical study design. Going back and forth between literature and organizational voices defined and shaped our study. Furthermore, we discussed in close loops with our company partner. Thereby, we could adapt to company jargon, foresee potential survey acceptance issues participants might have, and integrate valuable variables into the company context. With this approach, we believe we could counter the research-to-practice gap while gaining a unique data access, which otherwise would not have been possible. Making sure that our observations from practice were constantly aligned with research, we derive valuable insights and results on innovative behavior in times of crisis also for companies beyond the study's organization context. Acknowledgements We thank the HPI-Stanford Design Thinking Program and our company partner for enabling this study and research collaboration. We want to convey our thanks to Dr. Robert Rose for his extensive advice and contribution to the survey creation as well as to all translation helpers. Many thanks to Dr. Sharon Nemeth for copyediting and language support. References Anderson, N., Potočnik, K., & Zhou, J. (2014). Innovation and creativity in organizations: A stateof-the-science review, prospective commentary, and guiding framework. Journal of Management, 40(5), 1297–1333. https://doi.org/10.1177/0149206314527128 Bauer, W., Edler, J., Lauster, M., Martin, A., Morszeck, T. H., Posselt, T., et al. (2021). Innovation und COVID-19: Impulse für die Zukunft der Innovation. Update 2021. Fraunhofer-Verbund Innovationsforschung. Bordia, P., Hobman, E., Jones, E., Gallois, C., & Callan, V. J. (2004). Uncertainty during organizational change: Types, consequences, and management strategies. Journal of Business and Psychology, 18(4), 507–532. Brondino, M., Bazzoli, A., Vander Elst, T., De Witte, H., & Pasini, M. (2020). Validation and measurement invariance of the multidimensional qualitative job insecurity scale. Quality & Quantity, 1–18. https://doi.org/10.1007/s11135-020-00966-y Crossan, M. M., & Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: A systematic review of the literature. Journal of Management Studies, 47(6), 1154–1191. https://doi.org/10.1111/j.1467-6486.2009.00880.x Davy, J. A., Kinicki, A. J., & Scheck, C. L. (1997). A test of job security’s direct and mediated effects on withdrawal cognitions. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 18(4), 323–349. De Jong, J., & Den Hartog, D. (2010). Measuring innovative work behaviour. Creativity and innovation management, 19(1), 23–36. De Spiegelaere, S., Van Gyes, G., De Witte, H., Niesen, W., & Van Hootegem, G. (2014). On the relation of job insecurity, job autonomy, innovative work behaviour and the mediating effect of work engagement. Creativity and Innovation Management, 23(3), 318–330. https://doi.org/10. 1111/caim.12079
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Timely State Exposure for the Coevolution of Mental Models and Dynamic Systems Parastoo Abtahi, Sidney Q. Hough, Jackie Junrui Yang, Sean Follmer, and James A. Landay Abstract The computational systems we interact with are increasingly intelligent and dynamic, as they learn from user interactions and are updated over time. Principles of good design highlight the importance of understanding user mental models and providing feedback to expose the internal state of the system. In the context of intelligent systems situated in the real world, this notion of feedback is highly temporal and requires a deep investigation of when feedback should be provided to users. In this chapter, we first motivate the need for timely state exposure in the design of such dynamic systems. We then lay out the design space of timing strategies for providing feedback, highlighting the frequency of state exposure and the information necessary for each strategy. We report on a preliminary exploration of this design space using our news article recommender system. We find that one feasible approach may be to provide feedback only after failures when the system behavior is incorrect. Finally, we discuss open challenges that need to be studied in future work, including methods for effective evaluation of user mental models over time, anticipation of breakdowns, and detection of system failures. 1 Introduction Every day we interact with computational systems that adapt over time. For example, smartphones learn from usage patterns and optimize their charging rate to improve the lifespan of the battery.1 When users are typing on a smartphone, 1 https://support.apple.com/en-us/HT210512. P. Abtahi (*) · S. Q. Hough · J. J. Yang · J. A. Landay Computer Science Department, Stanford University, Stanford, CA, USA e-mail: parastoo@stanford.edu; shough@stanford.edu; jackiey@stanford.edu; landay@stanford.edu S. Follmer Mechanical Engineering Department, Stanford University, Stanford, CA, USA e-mail: sfollmer@stanford.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_9 161
162 P. Abtahi et al. predictive text algorithms provide suggestions based on prior user activities to make typing on a small keyboard more efficient.2 Another category of everyday technology that learns and adapts over time is recommender systems (e.g., Spotify music, YouTube videos). Recommender systems are also an integral part of social media platforms and news websites and play an important role in the information we have access to and the media we consume. Recommender systems learn from user activity patterns and recommend posts and news articles that a particular user is likely to be interested in, such that they can increase engagement with their platform. Internet of Things (IoT) devices in smart homes are another set of dynamic systems. These devices evolve over time as new user data arrives; however, due to a lack of timely feedback, users’ mental model of the system remains largely static. For example, the Nest Learning Thermostat learns by observing users’ interactions and identifying patterns. After a week of use, the thermostat learns users’ temperature preferences and automatically schedules heating and cooling throughout the day. It treats weekdays and weekends as separate entities and over time becomes less sensitive to everyday changes. These design decisions as well as the learned patterns are not effectively communicated to users. The dynamic nature of the system and the constant change in behavior make understanding and interacting with the system challenging for users. Particularly, when the system makes incorrect decisions, by cooling or heating the space to an undesirable temperature, users feel frustrated because they do not understand the learned pattern and cannot correct it (Pernice, 2015). The growing gap between a dynamic system’s state and the user mental model leads to expectation violations and system incorrigibility. It is necessary to expose the system state to users at different times throughout long-term interactions to enable user mental models and dynamic systems to co-evolve and to ensure users are well-positioned to handle system errors in cases of failure. This raises the question: when and how often should we communicate system changes to users? Perhaps the simplest strategy is to expose the system state at all times. This approach is not feasible, because the system may change often, both globally through software updates and locally as the system learns by observing implicit and explicit interaction patterns. Moreover, the strategy of constant state exposures does not scale to a large number of dynamic systems, which is inevitable given the ubiquity of intelligent systems because they will all interrupt users simultaneously and compete for their attention. In this chapter, we explore state exposure timing strategies that enable users to develop an up-to-date mental model of a system that continuously learns and evolves over time (see Fig. 1). We begin by providing a brief background on mental models and discuss how this next generation of computing systems challenges our traditional notions of good design and the user-centered processes we have established for generating them. We then lay out the design space of timing strategies for state exposure and report on our preliminary exploration using an article recommender 2 https://support.apple.com/en-us/HT207525.
Timely State Exposure for the Coevolution of Mental Models and Dynamic Systems 163 Fig. 1 From left to right showing the coevolution of a dynamic system (A0–A4) and the user’s mental model (MM0–MM1) over time system. Finally, we highlight the need for new design methodologies to support the real-time evaluation of user mental models and provide timely feedback. 2 Background Mental models are “the mechanisms whereby humans generate descriptions of system purpose and form, explanations of system functioning and observed system states, and predictions of future system states” (Rouse & Morris, 1986). Understanding mental models is a critical design activity that has been carefully studied in the human–computer interaction and the human factors literatures (Carroll & Olson, 1988). To improve usability, as designers we evaluate users’ mental models at the beginning of the design process to create products that are congruent with those models and we strive to clearly present our own conceptual models to users in our designs, as shown in Fig. 2 (Norman, 2013). However, emerging intelligent systems, such as the Nest Learning Thermostat, are probabilistic and dynamic. Due to their probabilistic nature, traditional design methodologies that assume a deterministic user flow are no longer applicable. As designers, we are challenged with the task of predicting how the system will behave over time and we need to design for a potentially limitless state space, as the system adapts. They are also dynamic, meaning that the system behavior changes over time both globally through software updates and locally through learned patterns from user input. Therefore, it is necessary to develop new methodologies that evaluate users’ mental models in the long-term and provide continuous feedback to communicate the state of the system to users. In other words, we need new strategies to bridge the gulf of evaluation and execution in these systems. A relevant area of research that has emerged at the intersection of artificial intelligence and human–computer interaction is explainable and interpretable machine learning. While there is an overlap between these topics and the discussions in this chapter, our exploration is applicable to a broader set of systems that learn and
164 P. Abtahi et al. Fig. 2 Top: designers communicate their conceptual model of the system to users through the system image, and users develop a mental model of how the system works through their interactions with the system (Norman, 2013). Bottom: the open challenges that remain in understanding user mental models and the role of designers in emerging dynamic systems that change and adapt over time, highlighting the need for new design methodologies adapt over time using a range of techniques, from simple heuristics to complex machine learning. Moreover, most prior research in this space has focused on studying mental models after explicit user interactions with the system. This means proposing strategies that an intelligent agent can use to explain a particular behavior or decision (Gero et al., 2020; Kulesza et al., 2013). Our exploration is focused on the timing of state exposure in dynamic systems, not only after explicit user interactions, but also as the system behavior changes over time. The distinction between implicit and explicit interactions (Ju, 2015) is particularly important in our discussion of timing strategies. When users are explicitly interacting with a system (top left quadrant in Fig. 3) they expect feedback from the system and this instance of interaction may be the appropriate time to provide an explanation. For example, when the user clicks a button on a 2D graphical user interface, they expect their input to be utilized in some way and the effects of their input can be communicated then. However, when the system learns over time from implicit commands (bottom left quadrant in Fig. 3) it becomes less clear when the changes in the system can be communicated to users, particularly when the system performs a silent action (bottom right quadrant in Fig. 3). For an example of such a system we return to the Nest Learning Thermostat, where the thermostat silently adjusts the room’s temperature based on the learned patterns. In this chapter, we explore the design space of when and how frequently dynamic systems can expose their dynamic state to users to engender more accurate mental models over time as they learn from both explicit and implicit interactions. In the remainder of this chapter, we refer to failures as instances where the system behavior is incorrect and breakdowns as instances where the user mental model is incongruent with the system, resulting in a system behavior that is correct
Timely State Exposure for the Coevolution of Mental Models and Dynamic Systems 165 Fig. 3 The implicit interaction framework (Ju, 2015) is particularly relevant in the discussion of timing strategies for state exposure in dynamic systems (or helpful), but unexpected. We also refer to instances where the system exposes its state to the user as a form of feedback (shown as pink arrows in Fig. 2), and direct interactions that the user has with the system as a form of user input (shown as yellow arrows in Fig. 2). 3 Design Space of Timing Strategies As we argued in the introduction, never exposing the user to the internal changes in the system results in inconsistencies between user mental models and system behavior and ultimately leads to expectation violations. On the other hand, exposing the internal state at all times is not feasible. Another approach is to provide feedback during explicit interactions, which does not apply to systems that implicitly interact with users, as described in Sect. 2. Providing feedback whenever there is a system update, even if users are not explicitly interacting with the system at that instant, gives the users a chance to keep up with system changes over time. However with this strategy, if system behavior changes often, users will be frequently interrupted. To mitigate this interruption overload, we can draw on prior research tackling similar problems in mobile phone notifications. Using concepts from context-aware computing literature (Abowd et al., 1999) we can identify relevant parameters (e.g., activity, location) and present notifications at opportune times (Fischer et al., 2011; Pejovic & Musolesi, 2014), such as when users are experiencing less cognitive load and are able to attend to the information (Adamczyk & Bailey, 2004).
166 P. Abtahi et al. In mobile phone applications, adaptive notifications have been largely successful at delivering information to users when is least disruptive (Okoshi et al., 2017). However, in the broader context of dynamic systems that are becoming more ubiquitous in our everyday life these strategies may not scale, as many devices will be competing for opportune moments and our attention. Therefore, there is a need for exploration of strategies that require less frequent state exposures. One such strategy is state exposures after mental model breakdowns, which requires the system to keep track of the user mental model and predict when a foreground action will be unexpected by the user. Another strategy is after system failures, which require the system to predict failures from the user’s response, and ideally without requiring additional explicit input from the user. Finally, we may allow users to explicitly ask the system for state exposures when needed. However, these instances may be infrequent, as to prevent user mental models to effectively co-evolve with the system over time. The following table summarizes these strategies by describing the extra information that the system needs, the additional user input required, and the frequency of state exposure on a scale: always, very often, sometimes, rarely, and never. When During explicit interactions After system updates Opportune times Upon breakdowns Upon system failures After user queries System information needed None User input required None Exposure frequency Very often None The user’s context The user’s mental model Failure detection None None None Ideally none Ideally none request Very often Sometimes Sometimes Rarely Rarely 4 Article Recommender System To begin exploring this design space, we built an online machine learning system that recommended articles from the New York Times (NYT).3 We queried the NYT API for articles published in 2020, collecting the titles, abstracts, and the news desks responsible for them. For a given article, the news desk took on one of the following values: “foreign,” “world,” “business,” “business day,” “US”, “national,” “arts,” “arts and leisure, “book review,” “books,” “movies,” “television,” “sports,” “science,” “climate,” “fashion and style,” “style,” “styles,” “technology,” “dining,” “food,” “Washington,” “politics,” and “culture.” We needed to strike a balance between system explain ability, accuracy, and learning time. In general, the more complex a model, the less explainable it is and the 3 https://www.nytimes.com/.
Timely State Exposure for the Coevolution of Mental Models and Dynamic Systems 167 longer it takes to train; however, it is more expressive and thus performs better (e.g., better predicts user preferences). After some experimentation with smaller systems, we settled on fine-tuning a large language model (BERT) to classify articles into topics using their news desks (Devlin et al., 2018), and adding a simple reinforcement learning layer (UCB1) to learn over time which topics and therefore which articles users preferred to read (Auer et al., 2002). At each time step, the algorithm selected a topic and then sampled an article uniformly at random from the set of articles classified into that topic to present to the user. The user’s indicated preference for this article was then used to update the recommender system. 5 Generating Explanations To expose the internal system state, we used a standard explainable AI (XAI) technique known as SHAP (SHapley Additive exPlanations). SHAP allocates credit to various parameters of the input according to the extent that they contributed to the output of a function (Lundberg & Lee, 2017). Our function was our end-to-end learning system: the inputs were the tokens comprising the title and abstract of an article, and the output was a prediction of whether or not the user would read the article. A technical note about the reinforcement learning component of our system is that we did not use the calculated UCB1 values, which determine whether or not to recommend an article to the user, because these values capture a notion of “exploration” that does not reflect the agent’s true expected reward from a given topic (i.e., their interest in that topic). Instead, we used internal parameters of the agent that stored the empirical past reward of recommending articles of a given topic. After calculating SHAP values for each token, we highlighted the top quartile of tokens as our explanation, in addition to revealing the topic of the article. This was done based on the intuition that knowing which words and phrases were the most relevant to a given decision better enables the user to predict a future decision if the decision is conditioned on a similar set of words and phrases. 6 User Interface Design We built a web application with a Flask4 backend and React5 frontend, to display recommendations to the user and gather user input, as shown in Fig. 4. We showed the user the title and abstract of a New York Times article and asked the user (1) whether or not they would be interested in reading the article, and (2) whether or not they think the system believes they would read the article (see Sect. 7). Then, 4 5 https://www.fullstackpython.com/flask.html. https://reactjs.org/.
168 P. Abtahi et al. Fig. 4 New York Times article recommender system. Left showing the user interface for collecting user input (i.e., their prediction of system behavior). Right showing the user interface for state exposure, including the system behavior and the explanation (i.e., SHAP values and article topic) conditionally, we generated and displayed an explanation for the model’s belief by highlighting words according to their importance, as described in Sect. 5. After every ten recommendations, we gave the user a more complete “quiz” where they were tasked with predicting system behavior on a wider variety of example articles (see Sect. 7). User responses to this “quiz” were for evaluation purposes only and did not affect the learning of the recommender system. 7 Evaluating User Mental Models In order to evaluate the accuracy of the user’s mental model, we continuously sampled for the system’s “forward simulatability.” In machine learning literature, “forward simulatability” is used to evaluate the interpretability of systems, as the
Timely State Exposure for the Coevolution of Mental Models and Dynamic Systems 169 user’s ability to correctly predict the model’s behavior signals that the user understands why a model makes certain predictions (Hase & Bansal, 2020). We asked the user to simulate the system in two settings: after each article recommendation and in intermittent quizzes. The former allowed us to capture the user’s representation of a system at every step as the model learned and the system evolved over time. This approach also allowed us to observe the accuracy of the user’s mental model with regard to the most relevant content (i.e., the articles that would be recommended to the user in a real-world setting). The latter gave us a higher-level overview of the user’s understanding of the recommender system’s behavior across the entire search space. While the first approach is useful, it is a biased sample in that the articles that are recommended to users are more likely to be articles that the system believes the user is interested in reading. On the other hand, the quizzes evaluate the user’s ability to simulate system behavior over all possible topics. 8 Frequency of State Exposures For different cohorts, we varied the timing of explanations to understand how state exposure affects the development of user mental models. For our preliminary exploration, we chose three different explanation frequencies: 1. Never: In our control group, users never received explanations. 2. Always: In the second group, users always received explanations. While this is an unachievable standard in practice, we wanted to understand how well-timed explanations improve the accuracy of user mental models relative to this ideal. 3. Upon failures: Users received explanations when the system mis-predicted whether or not they would be interested in reading an article. We expected this frequency to be both economical and scalable in practice. First, explanations for cases in which the system did make the right decision are less useful, because while user expectations may not align with system behavior, there is no need for correction or intervention and as such, it is less critical for the user to have an accurate mental model. The cases in which the system fails are those for which the user needs an explanation so that they can adjust their own behavior or expectations to predict or even prevent failures in the future. Second, system failures should become much less frequent than system successes over time (or else the system is fundamentally unusable), so users will not be overwhelmed with information as they receive explanations with this strategy.
170 P. Abtahi et al. Fig. 5 User prediction errors based on user input after every article shown for each condition 9 Preliminary Results We conducted a pilot study with three volunteers (2 male, 1 female, ages 14, 19, and 52), each randomly assigned to one of the three conditions described above. Each participant used our NYT article recommender system, shown in Fig. 4, for roughly half an hour. For each article, they predicted system behavior (i.e., whether or not the system thinks they will be interested in that article) and provided ground truth data (i.e., whether or not they were interested in the article). After interacting with each article, for some articles (the frequency of which was determined by the state exposure strategy in the condition they were assigned to), they were provided with an explanation in the form of words that were highlighted based on SHAP values. Finally, after interacting with ten articles, participants completed a more comprehensive, intermittent survey described in Sect. 6. The results for prediction errors after interacting with each article are summarized in Fig. 5 and for prediction errors in the intermittent surveys are summarized in Fig. 6. Due to the limited number of participants, we cannot perform statistical analyses on the data; however, the stratification points toward the following trends: 1. User mental models ossify. Without any state exposure throughout long-term interaction, user mental models cease to develop along with the changes in the dynamic system. As a result, error rates may not improve over time unlike other system exposure strategies. 2. System simulatability may be a function of frequency of explanations. More frequent system exposures may be inversely correlated with user error in predicting system behavior.
Timely State Exposure for the Coevolution of Mental Models and Dynamic Systems 171 Fig. 6 User prediction errors in intermittent quizzes are shown for each condition, over a sliding window of size 10 and balanced over news desks 3. Infrequent, but timely, state exposures may be competitive with constant state exposures. For example, exposing the state only after failures may sufficiently improve the accuracy of the user’s mental model as compared to always exposing the dynamic system’s state, which is neither economical nor scalable. While these results are very preliminary, they provide a road map for future work that can explore the design space of state exposure timing strategies discussed in Sect. 3. Conducting longitudinal studies with a larger population on crowdsourcing platforms such as Amazon Mechanical Turk will allow us to study the effects of each timing strategy in more detail. In the future, we hope to use this study design to further explore the design space as well as adaptive explanation strategies that may change over time based on various factors, such as system uncertainty and error rate. 10 Open Challenges Many challenges remain in the exploration of the design space of timely state exposure for dynamic systems, which can be summarized by discussing the following four research questions:
172 10.1 P. Abtahi et al. How Can We Continuously Evaluate User Mental Models Over Time? In our work, we asked users for their prediction of the system’s behavior at each timestep as a mechanism for evaluating their mental model. This approach has two main limitations. First, it is not practical for real-world systems, because asking users to pause and predict system outcomes results in significant friction and delay during their interactions with the system. Moreover, while correctly predicting system behavior signals that users might have a correct mental model, it does not paint a complete and accurate picture of their mental model of the system. In other words, even though a user might correctly predict what the system state will be, they may not be able to describe why they believe this to be the case. A range of other techniques have been presented by prior research for evaluating user mental models, each with its own strengths and limitations. For example, verbal protocols have been utilized by asking users to “think aloud” as they perform a task (Rouse & Morris, 1986). Another approach is co-experimentation in which two people are asked to collaboratively complete a task and the communication of knowledge between them is used to elicit users’ mental models (Sasse, 1991). While these techniques provide information about what users are thinking, similar to forward simulatability techniques, they offer little information about how they are thinking. Particularly, if mental model representations are pictorial, it would be difficult for users to accurately verbalize them (Rouse & Morris, 1986). But more importantly, these techniques add a similar layer of friction that make them impractical for deployment in situ and throughout long-term interactions with a system. Therefore, effectively and continuously evaluating user mental models of a dynamic system without introducing significant friction during interactions remains an open challenge. Can we evaluate user mental models without intervention and by observing and analyzing their implicit and explicit interaction with the system? 10.2 How Can We Predict Mental Model Breakdowns? As system designers, we have access to the internal state and the inner workings of a dynamic system, however complex they may be. From this vantage point, once we have an understanding of (or even an approximation of) the user’s mental model over time, we might be able to predict when there will be a breakdown (i.e., when the system might behave in a way that is unexpected to the user). Predicting such instances offers an opportunity for infrequent state exposure that (1) reassures users the system behavior is correct and (2) provides a timely explanation to help user mental models co-evolve with the dynamic system. While such a strategy may be effective, the mechanics of predicting mental model breakdowns remains an open challenge. Can we articulate user mental models and the current state of a dynamic system with similar representations? How can we
Timely State Exposure for the Coevolution of Mental Models and Dynamic Systems 173 compare these two representations? Through this comparison, can we predict, with high certainty, when there will be a mismatch between the user’s mental model and the ground truth (i.e., the current state of a dynamic system)? 10.3 How Can We Detect System Failures? Another strategy that requires further exploration is state exposures after system failures. Through our initial explorations, we found that such timely and infrequent feedback may be comparable to continuous feedback, in terms of facilitating user mental model updates as the system changes over time. Dynamic systems are optimized to prevent failures, nonetheless at times they may have incorrect behaviors. In practice, it may be challenging to detect when such system failure occurs. How might we detect system failures from implicit or explicit user interactions that follow? For example, if the user ignores a particular news article recommendation instead of reading that article, we may conclude that the system suggested an incorrect article (i.e., an article that the user was not interested in). How might we detect failures from user’s emotional responses to the system behavior? For example, by analyzing the user’s response to a voice assistant, including their language or tone, we may be able to detect negative emotions such as frustration and anger that may point to a system failure (Cuadra et al., 2021). We may also be able to analyze user responses indirectly, without access to video or audio during interactions. For example, prior research has shown that we may be able to detect that users are stressed indirectly from the motions of their computer mouse (Sun et al., 2014). 10.4 How Should We Present State Exposures? We used word highlighting based on SHAP values for state exposure. The highlighting provided an explanation of which words were important in classifying an article into a particular topic category, and the topic label communicated to the user that the system believes they are interested in articles within that category. While this technique may be appropriate for a user evaluation of a news article recommender system, it may not be practical for real-world recommender systems and does not generalize to other types of dynamic systems, such as those that are not text-based. This raises the research question: how might we provide feedback about the state of a dynamic system? Once we identify a suitable state exposure timing strategy and have the mechanisms by which to do so (i.e., are able to predict mental model breakdowns or detect system failures), what is the best medium to communicate this information (e.g., visually, textually, verbally, etc.) and at what level of abstraction?
174 11 P. Abtahi et al. Conclusion In this chapter, we laid out the design space of state exposure timing strategies that can be utilized to ensure user mental models of dynamic systems evolve over time as the system learns and adapts. We implemented a news article recommender system that learns from users’ reading preferences and suggests relevant articles that they are likely to be interested in. For the state exposure strategy we highlighted words based on their importance in the system’s decision-making process and utilized the “forward simulatability” technique to evaluate the user’s mental model accuracy of our dynamic recommender system. We then explored parts of the presented design space with the system that we implemented. Our preliminary findings motivated further exploration of infrequent, but timely state exposures, such as after breakdowns or upon system failures. Finally, we discussed the open challenges that require further study for effective utilization of these infrequent state exposure strategies, including predicting when user mental models might break down and detecting when the system may have failed. A deeper exploration of these topics will reveal new design methodologies that do not assume a deterministic user flow, yet facilitate adaptive feedback enabling user mental models and dynamic systems to co-evolve over time. Acknowledgements The following icons from the Noun Project have been modified and used in this chapter under the Creative Commons License: • Computer by N.Style from the Noun Project. Source link: • Website by N.Style from the Noun Project. Source link: • Conference by N.Style from the Noun Project. Source link: • smart camera by N.Style from the Noun Project. Source link: • Courier by N.Style from the Noun Project. Source link: https://thenounproject.com/icon/2946578/ https://thenounproject.com/icon/2614749/ https://thenounproject.com/icon/2855612/ https://thenounproject.com/icon/2986408/ https://thenounproject.com/icon/3340375/ References Abowd, G. D., et al. (1999). Towards a better understanding of context and context-awareness. In International symposium on handheld and ubiquitous computing (pp. 304–307). Springer. Adamczyk, P. D., & Bailey, B. P. (2004). If not now, when? The effects of interruption at different moments within task execution. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 271–278). Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem. Machine learning, 47(2), 235–256. Carroll, J. M., & Olson, J. R. (1988). Mental models in human- computer interaction. Handbook of human-computer interaction, 45–65.
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Designing for Value Creation: Principles, Methods, and Case Insights from Embedding Designing-as-Performance in Digital Health Education and Research Jonathan Antonio Edelman, Babajide Alamu Owoyele, Joaquin Santuber, and Stefan Konigorski Abstract Designing for Value Creation in healthcare entails engaging stakeholders in both academia and industry. Therefore, education and training on value creation for students and professionals are central to designing better healthcare systems. In this chapter, we explore the state of digital health design education, particularly how value creation can be rigorously explored and implemented. A Designing-as-Performance (MEDGI + PretoVids) approach augments current healthcare innovation approaches in increasing customer engagement, reducing risk, and improving profit. Here we review the literature on current Design Thinking (DT) in healthcare and posit Designing for Value Creation as a way forward based on our 3+ years of action research in this emerging field. Our insights are relevant for design educators, medical practitioners, and industry actors looking to leverage Design for Value Creation in healthcare in the digital transformation era. 1 Introduction Price is what you pay; value is what you get.—Warren Buffet1 Every system is perfectly designed to achieve exactly the results that it gets.—Don Berwick, MD Healthcare is transitioning from care within hospital walls into broader digital and physical spaces outside the direct reach of nurses, doctors, and insurance companies. Apps, platforms, and digital infrastructure bring telemedicine opportunities and risks as smartwatches and phone apps serve diverse communities from the old to the very 1 https://tommccallum.com/2019/04/23/price-is-what-you-pay-value-is-what-you-get/ J. A. Edelman (*) Stanford, California, USA B. A. Owoyele · J. Santuber · S. Konigorski Hasso Plattner Institute for Digital Engineering, Potsdam, Germany e-mail: Babajide.Owoyele@hpi.de; Joaquin.Santuber@hpi.de; stefan.konigorski@hpi.de © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_10 177
178 J. A. Edelman et al. young. Likewise, the role of healthcare providers, practitioners, and patients is also in transition with the advent of self-management apps and personalized medicine. Beyond just innovation for profit, the healthcare industry and individual healthcare practitioners adopt Design (design modes of doing and thinking) to explore problems and solutions. Beyond fancy prototypes and esthetically sound user interfaces for smartphone applications, Design asks the types of questions that lead to a richer understanding of people (doctors, nurses, patients, family) and thus has radical implications for creating whole ecosystems of care offerings. Design also allows for deeper explorations of what people value in healthcare, their pain and pleasure, and why they value what they value. The design approach is even more relevant in medicine in recent times, considering COVID-19. This pandemic has forced many nations and organizations to be innovative with their products, services, and systems. This increase in an adoption of Design doing and thinking is evident in a way that it is relevant for both healthcare practice and academia. Figure 1 shows the annual growth rate of literature related to Design, design thinking in the medical context. Much of the literature employing Design is related to understanding health behavior, and rightly so, many studies focus on the human element in healthcare, particularly related to behavior change through mobile applications. Many studies leverage design to evaluate and configure procedures for randomized controlled trials, while others leverage qualitative questionnaires to solve problems combined with surveys and interviews. See Fig. 2 for top keywords in current digital health literature. Digital Health and Design are thus seen as two sides of a coin that can help reduce risk and improve treatment outcomes in healthcare, as well as improve profits or reduce costs, and improve patient outcomes. Digital health offers society a way in which patients do not have to get sick by leveraging smart devices and machine learning algorithms to understand genotype and phenotype patterns, i.e., through personalized medicine and preventive care approaches. In parallel, design and design thinking education facilitates better outcomes of research and innovation in healthcare contexts; specifically, by generating innovative healthcare outcomes. Although Design is being adopted in healthcare, we see a gap in current design approaches in healthcare contexts. First, we see a lack of application of shared models across diverse medical teams in regard to explicitly designing for value creation. Second, we observe that most approaches adopting Design do not draw on robust and rigorous methods and principles that have been vetted in a real-life medical context, beyond the anecdotal approaches to brainstorming and using sticky notes to facilitate “ideation” sessions. We believe that these current approaches may not deliver credible results in high performing contexts such as healthcare. To address this opportunity, we proceed in the following sections of the chapter as follows: 1. First, we employ bibliometric analysis to map the literature on digital health and Design in journals, books, and conference proceedings. 2. Then, using visual semantic networks as sensemaking tools, we determine and show that healthcare is a distinct cluster in current design literature.
Fig. 1 Annual scientific growth annual growth rate: 49.9% from 1999 to 2021 (SCOPUS Database) Designing for Value Creation: Principles, Methods, and Case Insights. . . 179
180 J. A. Edelman et al. Fig. 2 Wordcloud digital health-design literature from 1999 to 2021 (SCOPUS Database) 3. Finally, we further zoom in on some key digital health design literature to situate our contribution in this chapter. We aim to show the landscape and then showcase our approach to Designing for Value Creation (D4VC) in healthcare. Using the acronym D4VC from here on, we show why and how Design can mediate and facilitate the creation of Value for “all” actors involved, not just patients. D4VC is about improving user engagement, reducing risk, and reducing costs. In our approach D4VC, we depart from the dogmatic notions in design thinking in healthcare approaches that focus too much on the patient while ignoring other actors/agents/nodes, e.g. doctors, algorithms, families, radiologists. Yes, we assume that patients deserve better experiences. But we believe this is also the case with doctors, health professionals, managers, and other non-patient actors. Patients are central in healthcare. Doctors save lives. There are high stakes for all stakeholders in the network of healthcare. We need humble and high stakes design paradigms and methods that are rigorous and robust through and through for multiactor value creation. Our guiding questions: • What characterizes the state of value creation research in design methods and practice in digital healthcare and medicine from a bibliometric perspective? • How can digital health design education benefit from action research to embedding Designing-as-Performance in its r multi-actor practices? We have explored these questions using action research, having focused on educational contexts in which we have been embedded for the past 3 years at the Digital Health Center at the Hasso Plattner Institute, Potsdam. We have engaged both students and industry to iterate and finetune our principles and methods in a
Designing for Value Creation: Principles, Methods, and Case Insights. . . 181 reflexive mode of action research. Our work is far from complete but may offer some insights into how Design may be embedded in Digital health. Our bibliometric overview is also not exhaustive as we rely on titles, abstracts, and keywords available from Scopus to generate these visualizations. Nevertheless, we hope they inspire others to situate themselves in the emerging digital health design field and perhaps drive research attention toward connecting the unconnected dots shown throughout this chapter. Finally, we want to state that this work is not a systematic literature review, although we invite others to explore or search phrases for such tasks. The following sections proceed as follows. First, we provide a high-level bibliometric overview, i.e. a thematic mapping of the digital health and design thinking field, discussing what topics (keywords) have been addressed in healthcare using Design and design thinking. Next, we zoom in on digital health design education to present our motivation for entering what we perceive to be uncharted territory. Here we posit how Designing-as-Performance(D-a-P) principles and methods can make design approaches more robust, rigorous, and reflexive in healthcare contexts. Finally, to demonstrate the value of our approach, we discuss 3 cases where we iteratively developed and embedded MEDGI and PretoVids, as two fundamental approaches to designing/exploring products, services, and systems in digital health education contexts. Again, we emphasize that our goal is to ensure robust value creation for multi-actor healthcare innovation. The final section summarizes our work, reflecting on insights drawn from our engagement at the Digital Health Center and with patient/user-facing partners. We end the chapter with selected quotes from our participants over the years. Finally, we suggest future emerging fields of Designing possibilities for Digital Health teams, particularly implications for doing truly transdisciplinary work. 2 Mapping Design, Design Thinking, and Digital Health Interface Design in its many forms has been growing its application in the medical field. Design comes in different flavors, such as a human-centered approach (Bhattacharyya et al., 2019) to place-based innovation (Vechakul et al., 2015). Many of these experiences have been studied and analyzed in academic contexts, particularly focusing on user-centered development (Chokshi & Mann, 2018). Looking at scholarly outputs on the application of Design in medicine and health serves as a proxy to illustrate the current state of the practice and application of Design in digital health. This section presents the bibliometric review of the research on the interface of Design, design thinking, and digital health. See appendix with visual Mapping of DT and Design in Healthcare
182 2.1 J. A. Edelman et al. Bibliometric Review Query Note on methodology: This bibliometric analysis was conducted in September 2021. We conducted two (2) searches using the SCOPUS database, searching for the following queries and results. Search 1: ("digital health*" AND "design") OR ("digital health*" AND "innovation") 1555 document results from SCOPUS. Article (880), Review (305), Conference Paper (241), Book Chapter(41), Note (33), Conference Review (18), Editorial (18), Letter (7), Erratum (6), Book (4) Search 2 : TITLE-ABS-KEY ( ( "digital health*" AND "design thinking*" ) ( "digital health*" AND "innovation*" ) ) OR ( "digital health*" AND "usercentered design" ) OR ( "digital health*" AND "human centered design*" ) AND ( LIMIT-TO ( SRCTYPE , "j" ) ) Figure 3 illustrates the keywords (top bigrams) in the title of the literature in the intersection of design, design thinking, and digital health. 2.2 Zooming in on Digital Health Design-Oriented Education Design has become increasingly popular in Information Technology, business and, more recently, healthcare to solve wicked problems and frame them well (Dorst, 2011). Design is also crucial to developing interventions that change health behavior (smoking, eating habits, alcohol intake) and has been adopted by academia and industry to provide better alternatives to wellness and self-care (Mummah et al., 2016). Several authors have highlighted how design and design thinking can enhance effectiveness and efficiency in healthcare education contexts by carefully considering human patient and user needs (Altman et al., 2018; Boydell et al., 2021; Chan, 2018). Research suggests that although more attention is now being given to those equally important other persons involved (doctors, radiologists, nurses), as reflected in work by Muinga et al. (2021); von Thienen et al. (2015), there is still a lot of uncharted territory in Digital Health design education. Recently, attention has been paid to medical “people/users” because doctors are people too, and they deserve beautiful experiences in their work, as do researchers engaged in providing studies to improve the care of society. Beyond problem-solving and beautiful user interfaces, the role of design education in healthcare is a very hot topic as it promises inclusion (Almendra, 2013), understanding implicit bias (Zeidan et al., 2019), supporting multidisciplinarity and more radical approaches to need analysis (Gardner et al., 2019; Woods et al., 2018).
Designing for Value Creation: Principles, Methods, and Case Insights. . . 183 Fig. 3 Top bigrams from titles of literature on digital health and design Design is also valuable for reflecting on the opportunities and challenges of digital technology, e.g. Artificial intelligence (Ala-Kitula et al., 2018; Belkacem et al., 2020; Ferreira et al., 2020; Liyao, 2021). For our research at the Digital Health Center, we see that Design is unequivocally important in medical education as others have suggested (Badwan et al., 2018; Chao & Chao, 2018), especially with approaches that leverage digital tools, platforms, and infrastructure. Da Silva et al. (2020) have developed a cardiopulmonary resuscitation prototype for health education contexts, while other authors emphasize the Value of Design Thinking principles in bringing circularity and openness to medical innovation (Albala et al., 2018; Buckley et al., 2021; Deitte & Omary, 2019; Gottlieb et al., 2017; Hong et al., 2021). In collaborating with educational actors, Industry actors are also adopting design thinking approaches to explore and exploit digital health solutions for improving consumer care and usability (Bell, 2003; Janz et al., 2016; Saidi et al., 2019). Related to digital health design education, DT has been applied in IoT new product development (Kalyazina et al., 2019) for developing educational, social media videos on inflammatory bowel disease (Khalil et al., 2020). Furthermore, in terms of mapping digital health design, McLaughlin et al. (2019) recently did a qualitative review of design thinking frameworks in health profession
184 J. A. Edelman et al. Fig. 4 Digital healthcare conceptual structure based on author keywords search education. They conclude that although the potential value of design thinking to healthcare is high, many questions remain because of healthcare systems’ complexity and multi-actor nature. Therefore, teaching medical students design thinking is valuable but will not suffice to deliver value that is useful and appreciated by users, doctors, payers, and other practitioners in healthcare. As shown in Fig. 4, our mapping of the literature on digital health landscape appears to generate 8 clusters, depicted in the variants colored. 2.3 The Gap/Opportunity in Digital Health Design Education: Value Creation by Design In his book, the reflective practitioner, Donald Schön emphasizes rigor as a pinnacle of professionals engaged in practice and action (Schön, 1987). For him, there is value in professional education to move beyond analytic techniques, which have been traditional in operations research, and more into the active, synthetic skill of “designing a desirable future and inventing ways of bringing it about” (i.e., methods). Following a thematic analysis of the Digital Health Education landscape, the authors suggest that value creation in patient care can be more successful in terms of reduced risk, learning outcomes, citizen engagement when some degree of design-
Designing for Value Creation: Principles, Methods, and Case Insights. . . 185 Fig. 5 Top keywords in digital health and design literature based entrepreneurship is added (Niccum et al., 2017; Sawyer, 2020). To contribute to current digital health design education from these suggestions, we see immense opportunity for what we propose, designing for value creation. As shown in the conceptual structure map below, we see the need to integrate the clusters (i.e., bringing design thinking to be a form of design-based entrepreneurship that prioritizes value and its creation for multiple actors). The complexity in healthcare is that Value is different for every stakeholder in healthcare. Researchers prioritize standards, publishing well-designed scientific research and systematic procedures; while practitioners value patient care, providers and payers value healthcare delivery, and leadership comes to the fore. Patients want to enjoy products and services that lead to wellness and are open to ways to prevent getting sick in the first place. Digital health design education does not explicitly align all these-sometimes-conflicting values in every innovation artifact, product, service, or system (Fig. 5). Beyond rigor and robust methods in design thinking, it is crucial that r designing in digital health be based on empirical insights drawn from watching teams, enjoying insights from advanced design theory and method. So, following Schön’s strong position on the use of robust methods, we reflexively asked ourselves and the designers we have worked with some reflexive questions that are valuable for embedding design in healthcare. For example: What is my design process? Why am I using these design methods? How can I get better?
186 J. A. Edelman et al. What are the ways that busy doctors could get into Design? What are the fundamentals that should be practiced in care design settings, like in sports or dance? What kinds of exercises can be useful in boosting affective, cognitive, and skill-based outcomes, particularly in the time-constrained and high stakes domain of health care? How can we design for a multi-actor healthcare setting where value creation for all actors is the priority? In the following section, we present the answers to some of these questions. It is very much an ongoing work, and our cases reflect our initial experiences and objectives as they have evolved. 3 Action Research Cases: Designing for Value Creation Our definition of action research follows that adopted by a disciplined process of inquiry conducted by and for those taking action. Here our primary reason for engaging in action research is to assist the “actor,” i.e. the course participant (digital health students, medical doctors) in improving and refining their Design and skilled actions. Action research has been well applied to improve action in educational and learning contexts. (Bilorusky, 2021; Rowell et al., 2017) Sagor’s quote inspires us: “Perhaps even more important is the fact that action research helps educators be more effective at what they care about most—their teaching and the development of their students. Seeing students grow is probably the greatest joy educators can experience. When teachers have convincing evidence that their work has made a real difference in their student’s lives, the countless hours and endless efforts of teaching seem worthwhile” (Sagor, 2000). We select action research because it is appropriate for making iterative and developmental interventions in educational settings (Fig. 6). 3.1 Embedding Designing-as-Performance (D-a-P) Principles and Methods into Health Contexts To instantiate designing for value creation, we have two foundational elements to the approach: The first element encompasses the Designing-as-Performance principles. The second and complementary element encompasses the collection of methods. In the past years (Edelman et al., 2020; Edelman, Owoyele, et al., 2021; Edelman, Santuber, et al., 2021), our research group has developed strong stances to address Design for digital health and radical innovation. These stances encompass principles,
Designing for Value Creation: Principles, Methods, and Case Insights. . . Fig. 6 Overview of D-a-P 3 principles (blue) and 2 methods (red) for designing for value creation (D4VC) in digital health multi-actor settings 187 Increased Customer/User Engagement MEDGI-Shared team based model for performing Design PRETOVIDS -low cost video pretotyping D4VC Reduced Risk/Improved Safety Reduced Cost/Improving Profit models, and methods developed and applied under the Designing-as-Performance approach to Design. Specifically, we have dedicated book chapters and articles for an in-depth explanation. In the following section, we briefly introduce Performative Patterns, MEDGI, and the PretoVids, yet we suggest consultancy the previously published chapters for more detailed elaboration. The fundamental premise of the Designing-as-Performance (DaP) approach is that designing is a performative act. Design sessions are a performance of a corpus of behaviors with mediating objects and narratives (Edelman et al., 2020). A primary research aim for our group has been to understand the characteristics and mechanisms of high performing design teams. We have taken a very grounded approach to understanding performance through a wide lens of other performative disciplines that engage several performers working together to reach a goal. Observations of sports, music, dance, and surgical teams have yielded data and analysis of what constitutes highly effective performance practice. A survey of research concerning how teams work in situ offers plenty of useful insights into the characteristics and mechanisms of high performing teams in nearly any domain. Furthermore, examining the teaching methods that cultivate high-performance yields insights into how teams can be taught to perform well and what constitutes team performance itself (Edelman, 2011). Whether in sports or music, training consists of an integrated curriculum of theory, practice, and cognitive work. No single one of these stands on its own, and no single one leads to great results. Instead, theory, practice, and psychology reinforce one another and make each other possible. All the encouragement and positive mindsets in the world cannot make an Olympic athlete without a thorough
188 J. A. Edelman et al. understanding of body mechanics and correct practice based on mastery of fundamentals (Edelman, Owoyele, et al., 2021). 3.1.1 D-a-P Principles (3): Performative Patterns for Reducing Risks and Costs, Increasing Profits, and Improving User Engagement The fundamental premise of the Designing-as-Performance approach is that designing is a performative act, and that design sessions are a performance of a corpus of behaviors often with mediating objects. We call these behaviors Performative Patterns. Performative Patterns constitute team interactions, both micro and macro interactions, that have been distilled from observing high-performance teams at work. Performative Patterns can be articulated and taught through training routines comprised of relevant theory (frameworks) and repeated practice of well-crafted drills and exercises. Furthermore, Performative Patterns serve as shared models, or event schemas (both mental models and interactive models) that enable design teams to perform well. It is important to keep in mind that Performative Patterns are models, none of which are complete in themselves. Each Performative Pattern represents a different point of view, a different look at the behaviors of high-performance teams. These behaviors have been deconstructed into understandable, repeatable chunks so that they can be practiced separately, like passing or shooting in a ball game, or like scales and chord progressions in music. Neither passing nor shooting alone will win games; neither scales nor chord progressions alone constitute a great performance, but the mastery of each is essential for excellence. When Performative Patterns are practiced separately and reintegrated into a whole, they promise to bring design teams to new levels of expertise. One value of the D-a-P approach is that design methods which are empirically proven can be applied to create value in healthcare contexts in three ways, namely design methods that explicitly focus on reducing risk, increasing profit, and boosting patient/practitioner/payer engagement. In this regard, the performative patterns are building blocks that are put to work toward creating value in the form of the following principles: 1. Increasing profit/reducing cost 2. Reducing risk/increasing safety 3. Increasing user engagement 3.1.2 D-a-P Method A: MEDGI Following our elaboration of the three principles for design-based value creation, this section describes what characterizes how design methods are used to operationalize these principles. This section presents MEDGI as a method for health-centered value creation.
Designing for Value Creation: Principles, Methods, and Case Insights. . . 189 M-E-D-G-I is an acronym that stands for Mapping, Educing, Disrupting, Gestalting, and Integrating and serves as a primary performative pattern based on work by Edelman and our research group (Edelman, Owoyele, et al., 2021). MEDGI is both a macro and a micro pattern in that it describes both long-term project development arcs and moment to moment development team interactions. MEDGI Re-Design Method originates from 10+ years of research at Stanford, Imperial College, Politecnico de Milano, and HPI into how small design teams create new concepts (Edelman, Owoyele, et al., 2021). MEDGI was extracted by watching highperformance teams in action and analyzing their interactions through video analysis and behavior coding. MEDGI affords that digital health design teams can now move an existing object-interaction to a state of potentiality and then reform it into a new object-interaction(Edelman et al., 2020). On the macro level, Mapping activity is about creating maps of “current objectinteractions in healthcare and their accompanying narratives on a time and space map” (Edelman et al., 2020). Mapping is a process for creating a shared representation of the current state of digital health affairs. Digital Health Mapping therefore ensures that the representation of the state of affairs in healthcare contexts is both externalized and somewhat persistent. A digital health design map further allows team members to point to and to refer to specific care-related points in time and space that they can then address and explore further. Digital health maps can be sketches of health products, services, or systems. Digital health mapping is driven by the fundamental question: what is (and is not) here? An example of Mapping is a journey map of endometriosis care, in Fig. 7. Educing refers to identifying and highlighting what works and what does not work in medical contexts, and pain and pleasure points in the experience of actors in healthcare. On a macro level, Educing often means encoding the map problem and success areas, literally identifying and highlighting them for the digital health team Fig. 7 Sample of mapping in digital health education. Source DHDL Course
190 J. A. Edelman et al. Fig. 8 Sample of MEDGI exercise on N-of-1 App redesign. Source DHDL HPI to see. In digital health, Educing refers to enacting what works and what doesn’t work, pain and pleasure points, on an experiential level. For example, the notion that a diabetes management app does not allow a user to log in to see their own data is an example of an educed insight. The “pain” is that users are not happy or satisfied with being locked out of their own data, even though they now have an app that lets them, e.g. monitor their sugar intake. Here again, as in each step of MEDGI, the question drives the digital health enquiry but also acknowledges what works. In digital health taking stock of what works is as important as taking stock of what does not. Disrupting in digital health design follows Mapping and Educing with a proposal to the change to the state of affairs in an object-interaction, and take the form of questions like, “what happens if...?” (Edelman, Owoyele, et al., 2021). For example, a digital health design team would disrupt by asking, “What are some of the things that could happen if we could hear diabetes intake via earbuds?” or “What happens if we made the diabetes syringe a sensor that calculates the blood insulin state too?” In digital health Disrupting, our experience is that there is little or no explicit notion of a solution; the goal is more “towards exposing the potentiality of an object-interaction” (Edelman, Owoyele, et al., 2021) (Fig. 8). Gestalting follows the previous three steps and implies “roughing-in” or “sketching” a novel object-interaction. Gestalting is seen as a general picture of the field of health possibilities that could result from the Disruption previously proposed. Here the digital health design team could sketch the diabetes syringe that measures insulin with labels of how it could look. Here some level of details are carefully articulated. In the spirit of rigor, the digital health design team may bring in a product designer to Gestalt the syringe. If the solution is an app that gives data
Designing for Value Creation: Principles, Methods, and Case Insights. . . Fig. 9 MEDGI loop (Edelman, Owoyele, et al., 2021) 191 Mapping Integrating Gestalting Educing Disrupting access to diabetes patients, the digital health design team may sketch the interface using pencil and paper to give form to the concept. Integrating is what typically follows a well-executed digital health design process, here we say “the new state of affairs comes together, the crystallized in a new articulation” (Edelman, Owoyele, et al., 2021). Integrating is about giving specifications for manufacturing and distributing the solution so that users (e.g., doctors, patients, and family members) can truly enjoy the solution. With Integrating, the value is for radically highlighting a very compelling user experience and interaction, as well as systems integration for the digital health product service or system. The focus is on digital health app features like buttons and adjustors, payment interfaces and giving the solution a novel name. Unlike Gestalting, in which the question is about how does it look roughly, and how does it work roughly, Integrating tends toward more questions like “How does it really work excellently? Is it esthetically pleasing, and will users boast about this product, service, or system to others?” (Fig. 9). 3.1.3 D-a-P Method B: PretoVids How can new digital product development teams proto-type new digital products without writing a line of code or spending a dime on software development to learn what customers love? In recent work we presented PretoVids (see Fig. 10), a digital and practical, research-based method for structured, evidence-based prototyping (Edelman, Santuber, et al., 2021). PretoVids is a new prototyping technique that is genuinely relevant for showcasing digital media’s role in design-as-performance in concept development. We discovered early on that for digital health contexts “the traditional prototyping methods do not provide an agile heuristic for digital product development teams” (Edelman, Santuber, et al., 2021).
192 J. A. Edelman et al. Fig. 10 PretoVids principles (Edelman, Santuber, et al., 2021). Furthermore, critically looking at literature on prototyping approaches in healthcare revealed videos as a robust tool for agile prototyping in designing for healthcare. In the context of new healthcare product development, PretoVids has been employed to reduce the many risks of building fully digital projects. It thus saves precious time for busy doctors when they are engaged in design sessions, and affords easier communication of value proposition. Moreover, videos PretoVids allow rapidly sharing and learning, in particular design projects, because these videos may be handed off to professional video designers in later design phases. In 5–7 steps, digital health design teams can perform an iterative process to develop new healthcare product/service/system concepts without spending a penny on software development or writing a single line of code. Please see previous research on PretoVids for further insights to its workings (Edelman, Santuber, et al., 2021). 4 Cases: Designing for Value Creation in Digital Health with MEDGI and PretoVids This section introduces the cases in which we applied the Design for Value Creation methods (MEDGI and PretoVids) to real projects with partners and medical doctors as well as master students engaged in designing for digital health. We conducted the cases provided in the following section in the context of the Digital Health Design Lab (DHDL), at the HPI Digital Health Center. 4.1 DHDL Course: Value Creation at the Hasso Plattner Institute Digital Health Center The Digital Health Design Lab is a hands-on studio course about shaping innovative products, services, and systems. This course is part of the Hasso Plattner Institute’s Digital Health Center and offered at a multidisciplinary international master program. Our course website describes the course as follows: “Through richly
Designing for Value Creation: Principles, Methods, and Case Insights. . . 193 Fig. 11 Participants of the Digital Health Design Lab at HPI, using tracing paper to map the “what’s there” of an existing medical device (shape, buttons, screen, etc.. . .) illustrated presentations, relevant theory, and hands on exercises, master students gain insight and experience with the foundations of Design in the context of digital health, as well as understanding and practicing effective team interaction and communication” (DHDL, Hasso Plattner Institute, 2020). Participants are supported in exploring, developing, and communicating a professional vision of their contribution to the field of Digital Health. This is “done through development of individual and group project work relevant to their focus in Digital Health. Participants are expected either to make significant progress on their Digital Health projects during the semester, or be very well prepared for their digital health projects in the upcoming semester” (DHDL, Hasso Plattner Institute, 2020) (Fig. 11). 4.1.1 Learning Goals These goals are set in order that course participants become familiarized with the principles and practices of human-centered Design in the context of Digital Health. 4.1.2 Conveyed Competencies • Students acquire knowledge-related competencies relating to “how Design is an essential element in creating rich and engaging products, services and user experiences; how to make radical, relevant and rigorous innovation in the context of products, services, systems; and how to cultivate a culture of innovation” (DHDL, Hasso Plattner Institute, 2020). • Students also deepen their methodological competencies such as case study creation and analysis; synthesis; prototyping and testing; video prototyping for ideation and communication; design theory and methodology; workshop creation; presentation techniques. To augment academic work and prepare students
194 J. A. Edelman et al. for real-life engagements, the course provides opportunities for boosting social competencies: group work, discussions, and structured critiques. The course offers lectures, group discussion, participant presentations, hands-on exercises, and project work. This hands-on course requires active participation and engagement from attendees. Participants are expected to contribute verbally and materially to each session of the course, sharing insights, questions and their project work on a regular basis. 4.1.3 Charite: HPI Digital Health Design Lab To further iterate the Designing for Value Creation in health care scenarios, we collaborated with Charité Berlin to engage medical practitioners in exploring how digital health design education might augment value creation in the Charité’s education curriculum. Project Goal: Exploring D4VC in Charité Medical Students Education The mini hackathon took place entirely online as part of a 3-week module “Digital Health” at the Charité University. Third-year medical students (sixth semester) and students from HPI’s Digital Health Design Lab (DHDL) worked together in teams of three to four people. The teams identified a challenge (see Table 1), developed ideas to solve that problem, created a prototype, and presented their solutions in the form of a short video (PretoVid). The goal for the medical team was to learn basic methods of designing in healthcare and to understand the underlying principles. Table 1 Summary of challenges addressed during the Mini-Hackathon Challenge Studying medicine during the pandemic Increase patient safety on normal wards Improving Outpatient Care for patients living with a chronic condition Description Online education, no “student life,” and a new, unfamiliar city but no way to get to know it—starting medical school in 2020/21 is challenging. How can we improve the first-year medical student experience in the context of the COVID-19 pandemic? On normal wards, there is often no possibility to connect patients to hemodynamic monitoring. The nursing staff is not trained to work with such a system and the infrastructure is not sufficient to install it. As a result, dangerous situations can be overlooked and therapeutic measures can be initiated too late How can we improve outpatient care and patient experience? (e.g., communication with health workers physician, understanding treatment plan, remotely monitoring vital signs). Special focus on patients with chronic conditions
Designing for Value Creation: Principles, Methods, and Case Insights. . . 195 Fig. 12 Miro layout-hackathon prepared by the teaching team for the participants One positive externality of doing this workshop online is that participants become familiar and confident using online collaboration tools. One remote collaboration tool we used for the mini-hackathon was Miro Boards (see Fig. 12). These digital boards provide a shared space for participants to write notes, upload images, sketch their maps, and display new user interfaces. • 08.02.2021/10.30-11.15: Team building with preparation for individual small group work • 08.02.2021/11.30-12.15: MEDGI for Digital Health—more than just an app • 16.02.2021/09:30-11:45: Problem Solving • 22.02.2021/10:45-13:00: Prototyping • 22.02.2021/14:30-17:00: Flexible working session with mentors • 23.02.2021/9:30-12:15: Presentation Skills/Prototyping
196 J. A. Edelman et al. • 23.02.2021/13:00-17:00: Flexible working session with mentors • 25.02.2021/10:30-11:15 Uhr: Evaluation Charité Mini-Hackathon 4.2 4.2.1 Zooming in on Digital Health Design-Oriented Education StudyU: DHDL Project Goal: To Explore Pain and Pleasure Points in Current N-of-1 Trial Research App and then Redesign This project focused on redesigning StudyU as a research platform. In this regard, the DHDL team worked with the researchers from the Machine Learning chair at the HPI Digital Health Center. By using D-a-P Principles and MEDGI, students and scientist conducted user research to improve the user experience of StudyU. StudyU is a digital platform for designing and conducting innovative digital N-of-1 trials. Currently, N-of-1 trials are the gold standard study design to evaluate individual treatment effects and derive personalized treatment strategies. In engaging with researchers at the Digital Health Center, we wanted to explore the role of D4VC in leverage digital tools in initiating a new era of N-of-1 trials in terms of scale and scope. The challenge that fully-functional platforms are not yet available (Konigorski et al., 2020). The StudyU platform is open source and includes both the StudyU Designer and StudyU App. In the DHDL class we critically applied MEDGI and PretoVids to the redesign of the StudyU Designer, a platform where “scientists are given a collaborative web application to digitally specify, publish, and conduct N-of-1 trials” (Konigorski et al., 2020). The StudyU App is a mobile application with innovative user-centric elements for participants to partake in the published trials and assess the effects of different interventions on their health. Thereby, the StudyU platform allows clinicians and researchers worldwide to easily design and conduct digital N-of-1 trials in a safe manner. The vision for StudyU is that it can change the landscape of personalized treatments both for patients and healthy individuals, democratize and personalize evidence generation for self-optimization and medicine, and can be integrated in clinical practice. For personalizing health interventions and evaluating individuallevel treatment effects, N-of-1 trials are the gold standard Design. StudyU is developed as an open-source, free, and easy-to-use platform called StudyU for performing digital N-of-1 trials. The application also contains a study designer that enables researchers to collaboratively design and publish N-of-1 trials, as well as an app that allows participants to take part in these trials. By leveraging Designing-as-Performance methods students in the DHDL contributed to its refinement whereby StudyU can contribute to open, transparent, and reproducible medical research linked to clinical care. Figure 13 depicts the redesign process in Miro.
Designing for Value Creation: Principles, Methods, and Case Insights. . . 197 Fig. 13 StudyU redesign (Miro) 4.2.2 Sleepfull Project (DHDL Winter Semester 2021 in Collaboration with StudyU)2 Project Goal: To Design a PretoVids Concept for N-of-1 Study Design Platform StudyU on the Topic of Insomnia Studies suggest that 10–30% of all adults suffer from chronic insomnia with a higher prevalence in older people and pregnant women (Bhaskar et al., 2016). Insomnia and sleeping problems in general have an impact not only on a person’s mental, but also physical health. By addressing sleep conditions with N-of-1 trials, Sleepfull has the potential of helping 0.5–1.5 billion people in the age group between 15 and 653 (Worldometer, 2021). StudyU allows us to provide this large group of people with a professional 2 The following paragraphs are adapted from the DHDL participants’ reports. Team Members: Dennis Kipping, Marius Michaelis, Larissa Röhrig, Denise Schmidt, Julius Severin 3 Population Ages 15–64 (% of Total Population), n.d., World Population Clock: 7.8 Billion People (2021)—Worldometer, n.d.
198 J. A. Edelman et al. N-of-1 trial without needing to leave the house. Sleep is something very personal, which results in insomnia being a very diverse condition. Thus, N-of-1 trials have an immense potential in finding the best treatment for everyone’s personal insomnia. For a first iteration of the Sleepfull N-of-1 trial the following interventions were chosen: • Limiting screen time prior to bedtime: The usage of electronic devices before sleep can have impact on the production of melatonin which results in an increased alertness and thereby interferes with sleep (Pacheco, 2017) • Restricting alcohol, caffeine, and tobacco consumption: The intake of certain substances has a major impact on sleep. Not only on the duration, but mostly on its regularity, which may result in a long but non-restoring sleep (Spadola et al., 2019) • Exercise: There is strong evidence that exercise helps in falling asleep. Nevertheless, timing matters and therefore there should be at least 2 hours between exercise and bed time (John Hopkins Medicine, 2021) • Meditation: Studies suggest that mediation, and in particular mindfulness may improve sleep quality (Pacheco, 2020) • Consistent sleep schedule: Following a consistent sleep schedule is especially helpful when it comes to sleep-onset insomnia (Fry, 2018) • Colored light exposure: Studies suggest that exposure to different light intensities and wavelengths can affect the circadian rhythm and thereby adapt the sleep– wake cycle (Tähkämö et al., 2019) The opportunities the Sleepfull team leveraged (for example, new technologies or new ways of collecting data or new medical knowledge or new insights into human behavior) were many. Furthermore, the Sleepfull project leveraged from different existing and ready-to-use technologies. Those easily accessible technologies were, on the one hand, used to improve the outcome and adherence of the trial, but, on the other hand, also to improve future trials by giving researchers the data they need. More specifically, Sleepfull combined telemedicine with N-of-1 trials approach. Insomnia is a disease that does not require instant treatment and interventions. Therefore, the threshold for people to seek professional help is higher than with other conditions. When a patient signs up for the Sleepfull N-of-1 trial, it offers the patient to connect them with a suitable practitioner. Through video consultation, the patient can then discuss the upcoming trial. Telemedicine also allows the patient to be monitored throughout the trial and to discuss the outcome of a trial with an expert and make sure that the right steps are taken. The use of sensors allows holistic monitoring of the patient. It allows Sleepfull to personalize interventions based on the current needs of the user. By donating the data to research, a patient has the chance of not only improving the N-of-1 trial but also of help researchers to understand insomnia better. The average person spends around 26 years of their lives sleeping. Sleep plays a crucial part for our health as it allows the body and the brain to slow down and recover. At the beginning of the class, the whole Sleepfull team had sleep or a sleeprelated topic as one of the things they love. Many people take sleep as a given and do
Designing for Value Creation: Principles, Methods, and Case Insights. . . 199 not give more attention to it. But what happens if restful sleep, which is often taken for granted, becomes a problem? During the research for the N-of-1, the team members learned about the complex multitude of factors that can influence the sleep quality and how different the impact on the personal sleep of a person can be. In the development of the N-of-1 study they, therefore, included manifold evidence-based interventions to cover different perspectives of possible causes for insomnia and other sleep conditions. Sleep is as diverse as people and therefore, insomnia and its causes are as well. This makes it a perfect case for N-of-1 trials and gives patients a professional framework to try out different interventions and thereby learn about themselves and their personal insomnia. In looking at the literature and practice we found a significant gap regarding an understanding of the “user,” as understood as a synonym for the patient in healthcare contexts. While we agree that healthcare systems should serve patients better, there is a whole spectrum of users that remain underserved. Moreover, not only the quality of the whole healthcare system improves when focusing on other stakeholders, but also the value that creates the new product, service, or systems is higher. For instance, in our projects, we have defined the value created for patients, doctor, and scientists. This broader scope enables synergies of value to be created, while the same data entry serves the patient goals, the doctor, and researchers well. Figures 14 and 15 show examples of sketches of Sleepfull’s patient-facing interfaces. These have been designed for making patient participation in an N-of-1 study easy, pleasurable, and meaningful. Here, comprehensibility of a time extensive and somewhat complex process is key for patient compliance and the success of the study. Patients are able to see where they are in the process, where they have been, and what the next step is for their N-of-1 study. Fig. 14 Sleepfull PretoVids frames
200 J. A. Edelman et al. Fig. 15 (a) Sleepfull PretoVids frames from patient view. (b) Sleepfull PretoVids frames from doctor view Figure 15 shows a sketch of a doctor-facing interface. Doctors have to track and manage many patient cases. The Sleepfull Patient Overview page enables doctors to get a clear view of a cohort of patients and their progress. If a patient needs special attention, it is clearly indicated on the page. The doctor can easily follow a link to obtain necessary information about the patient, and contact them if need be (Fig. 16).
Designing for Value Creation: Principles, Methods, and Case Insights. . . 201 Fig. 16 Sleepfull PretoVids frames from researcher view 5 Reflections and Insights for Design Educators and Practitioners in Digital Health Contexts Redesigning our current healthcare products, services, and systems into digital offerings requires multiple perspectives. Together with the technical and medical capabilities, novel digital health solutions need to cover financial, regulatory, policy, governance, legal, and the like. However, there is more to great design than technical requirements. In our daily lives, both analog and digital experience have been designed to capture our attention and emotions. People—doctors, patients, and researchers—are watching exciting movies on Netflix, meeting interesting people on Tinder, listening to captivating music on Spotify, going to live theater, eating in well-appointed restaurants with amazing menus, attending engaging sports competitions, calling cars with Uber, and getting directions with Google Maps. The attention of doctors, patients, and researchers is constantly being captured by ingeniously designed analog and digital experiences that are addictive. In respect to the user experience of doctors and researchers, digital health design has not shown an equivalent level of concern for user engagement. Too much of doctors’ time has been allocated for painful and tedious data entry—precious time that has been stolen from quality time with their families and from caring time for their patients. The Designing-as-Performance approach invites designers to think broadly about value creation in its many forms: technical, financial, experiential, and existential. It enables designers to consider that researchers are people too, and that their experience at work saving lives can be as fabulous as going to dinner and a show. Designing-as-Performance challenges and empowers digital health designers to ask questions like:
202 J. A. Edelman et al. “What happens if . . . a digital health patient record was as enjoyable for doctors and administrators as the home page of Netflix?” “What happens if . . . segmenting and annotating brain MRIs was as engaging for researchers as a first-rate video game?” “What happens if . . . polygenic risk scores were as understandable and navigable for doctors as Google Maps?” A well-structured approach, with a strong theoretical foundation, as found in D-aP, can ease the adoption of Design in education and research. In the three action research cases we have presented in this chapter, a clear, yet deep, approach to designing has been key to the success of those projects. With a strong emphasis on hands-on and learning-by-doing approaches, the theoretical aspect of the methods used in Design are often lost. In the experiences we have made, a concise and coherent theoretical explanation of the “why” of what is done helps participants to make sense of things and be more confident when executing their tasks. The Designing-as-Performance approach enables teaching teams and facilitators to leverage decades of research on design and design practices. Because of this, practitioners do not need to make up theory because sound design theory is provided by teaching teams and facilitators. Because D-a-P is fundamentally transdisciplinary, teachers and practitioners can readily draw from different design traditions and increase team performance and the impact of their work. Moreover, different approaches to designing are complementary and are not exclusive. D-a-P draws on work in human–computer interaction, product design, graphic design, industrial design, service design, experience design, fashion design, digital/web design, architectural design, mathematics, musical composition, visual and fine arts, opera, pop culture... the list goes on. This rich diversity of sources helps digital health designers to find multiple references that match and enhance to their experiences, and consequently enriches their sensemaking processes. These rigorous approaches to Design are immersed, embedded in a continuum of techniques, methods, approaches, knowledge creation, and ultimately world views. Grounding digital health design practices in mature fields of design practice can give our work as designers a muchneeded depth and rigor. Acknowledgements This research was fully supported by the Hasso Plattner Design Thinking Research Program and the Digital Health Center. Collaboration opportunity with Charite Berlin is also acknowledged. Particularly the collaboration with Charite Berlin Researchers Dr Med Lina Moosch, Ulrike Anders, Dr Med Akira-Sebastian Poncette, and Dr Med Daniel Pach. References Ala-Kitula, A., Talvitie-Lamberg, K., Tyrvainen, P., Silvennoinen, M., Tinetti F. G., Tran Q. -N., Deligiannidis L., Yang M.Q., Yang M. Q., & Arabnia H. R. (2018). Developing solutions for healthcare—Deploying artificial intelligence to an evolving target. In Proceedings—2017
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Part III Problematizing Design Thinking as a Concept
Different Concepts of Human Needs and Their Relation to Innovation Outcomes Julia von Thienen, Constantin Hartmann, and Christoph Meinel Abstract While design thinking has been described in different ways, there is widespread agreement about two characteristics of the approach: Design thinking involves a “focus on needs” and works towards “radical innovation”. However, some authors have argued that these two characteristics actually antagonize each other. According to their assessment, a focus on needs reduces the innovation potential of projects, rather than fostering new breakthrough solutions. What is the logic of these arguments and is design thinking in trouble? The purpose of this chapter is to shed further light on the concept of needs in design thinking. We review need theories by three authors—John Arnold, Abraham Maslow and Robert McKim—who have prominently shaped design thinking theory and practices from the 1950s onwards. In each case, we summarize the author’s basic statements and trace relations to present-day methodologies of working with human needs. The chapter highlights notable agreement among all discussants concerning favourable approaches to foster radical innovation. We further emphasize the importance of distinguishing between narrow versus wide accounts of needs, where design projects with narrow accounts stick closely to user statements that are often highly contextbound, while projects with wider accounts include re-framing and visionary contextualisation. Design thinking education as offered at the d.school in Stanford and the D-School at Potsdam involves a wide account of human needs. In this context, two important skills in order to move from need assessments to worthwhile, radical innovation are the abilities to uncover need hierarchies from contextdependent desires stated by users to basic human needs, and to identify conflicts in need hierarchies that call for different and better solutions in society. J. von Thienen (*) · C. Hartmann Hasso Plattner Institute, Potsdam University, Potsdam, Germany e-mail: Julia.vonThienen@hpi.de; Constantin.Hartmann@hpi.de C. Meinel Hasso Plattner Institute and Digital Engineering Faculty, University of Potsdam, Potsdam, Germany e-mail: Christoph.Meinel@hpi.de © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_11 209
210 J. von Thienen et al. 1 Introduction Human needs are a key concept in design thinking. This focus is already emphasized by Meinel and Leifer (2011) in the first Springer book on design thinking research. Design thinkers are said to endorse a “human-centric point of view” (Meinel & Leifer, 2011, p. xv). They work to solve problems “in ways that satisfy human needs” (ibid.). The mindset of a design thinker is to “focus on human values” (d. school, 2010, preface). While “design thinking” is interpreted differently across institutions and by different authors (Lindberg, 2013; Schmiedgen et al., 2016), there is widespread agreement about the importance of a focus on needs. “Although the concept has different interpretations, Design Thinking is usually characterized by [. . .] a human centered perspective, where innovators build empathy with users” (Verganti et al., 2019, p. 1). In all scientifically grounded endeavours, a thorough explication of key concepts is part of the expected academic rigour. In this sense, a careful discussion of what a focus on needs means in more precise terms is certainly desirable. Conceptual clarifications also help to develop nuanced observations and analyses. For instance, when design thinkers work with human needs, what potential measures quantify the sophistication of their approach? How about measures such as the number of stakeholders the team is in touch with, the frequency of interactions with stakeholders, the diversity of stakeholders that get involved in a project (cf. Royalty & Roth, 2016), the design thinker’s versatility in need analyses as via Why-HowLaddering (d.school, 2010; von Thienen et al., 2012), or other measures? Yet, a thorough discussion of human needs is important in design thinking for reasons far beyond ordinary scientific diligence. Noteworthy claims have been made, according to which the focus on needs would only help to improve details in existing solutions (incremental innovation), while being ill-suited for finding novel, worthwhile solution avenues (radical innovation). When design thinking aspires to both—a focus on needs and radical innovation—such claims are of key importance and call for careful consideration. Clearly, whether or not a focus on needs hinders or facilitates radical innovation depends essentially on what a “focus on needs” means. It is therefore key to explicate this meaning in careful ways. This chapter begins by reviewing claims in the literature about the design thinking focus on needs as advancing incremental rather than radical innovation (Sect. 2). In this context, we also trace the particular understanding of a focus on needs that underlies this line of argumentation. We then review three influential need theories that inform practices at educational facilities for design thinking, such as the d.school at Stanford and Potsdam’s D-School. These theories have been set forth by design thinking pioneer John E. Arnold (Sect. 3), Abraham Maslow (Sect. 4), and Robert H. McKim (Sect. 5). In each section, we also trace the legacy, which the theory leaves in terms of shaping present-day design thinking practices. How each need concept impacts the prospects of radical innovation is another important line of discussion. The chapter closes by highlighting the diversity of human need concepts across authors and communities (Sect. 6). Overall, we look at how conceptual
Different Concepts of Human Needs and Their Relation to Innovation Outcomes 211 decisions concerning the focus on needs impact reasonable outcome expectations in design thinking innovation projects. The chapter closes with an outlook to a new development in design thinking, namely a comprehensive framework of human needs to analyse product risks and benefits in systematic ways. 2 Does a Focus on Needs Foster Incremental Innovation Only? Next to the focus on needs, a second design thinking characteristic highlighted in many introductions is the advancement of worthwhile innovation. Indeed, design thinking is often introduced as an approach that enables innovation at the high-end spectrum of creative achievement. The approach is expected to bring about solutions that are more than just slightly new and valuable. Rather, breakthrough solutions are said to emerge, or creative quantum-leaps, if you will. In this sense, David Kelley characterizes design thinking as “a method for how to come up with ideas. These are not just ideas, but breakthrough ideas that are new to the world” (Kelley & Camacho, 2016, p. 88, emphasis added). For Tim Brown, design thinking is an “approach to innovation that is powerful, effective, and broadly accessible [. . .] to generate breakthrough ideas” (2009, p. 3, emphasis added). Christoph Meinel and Larry Leifer (2011) also describe an intricate connection between design thinking and worthwhile, large-scale innovation, positioning that “great innovators and leaders need to be great design thinkers” (p. xiii, emphasis added). However, dark clouds loom at the horizon. Does the human-centric perspective of focusing on needs really harmonize with innovation aspirations as design thinkers like to believe? Several authors caution us not to assume this carelessly. Indeed, might there be reasons to believe the opposite? Could it be the case that humancentred design focussed on needs hinders creative quantum-leaps and breakthrough innovation rather than being helpful? In this sense, Donald Norman and Roberto Verganti (2014) provide a critical discussion. [Human-Centred Design] starts with extensive design research to determine user needs. However, this process unwittingly restricts the potential solutions to incremental innovations because, by its very nature, it focuses on things people already know about [. . .]. Not only do the users of products have difficulty envisioning radical new meanings because of their total immersion in the current context and cultural paradigm, but the more that design researchers immerse themselves in the existing context, the more they, too, are trapped in the current paradigms. (Norman & Verganti, 2014, p. 89) According to the authors, Human-Centred Design (HCD) plays a prominent role in incremental innovation, but it is not a driver of radical innovation. Instead, (i) technological change and (ii) a change in the meanings of products or technologies for people are discussed as major sources of radical innovation. As always in science, conclusions about relationships depend on how you understand key terms. Norman and Verganti (2014) write about Human-Centred
212 J. von Thienen et al. Design as a relatively well-defined and deliberately applied framework, which invokes “an iterative cycle of investigation—usually characterized by observations, an ideation phase, and rapid prototype and testing” (p. 78). Importantly, the process involves a “careful analysis of a person’s or even a society’s needs” (p. 79). There is often a “formal analysis of needs” (p. 79, emphasis added). Moreover, HCD is understood as exploring “people’s current meanings assigned to products and aims at detecting existing meanings” (p. 92, emphasis added). According to this understanding, HCD is strongly focused on exploring the status quo. This does not seem to facilitate radical innovation projects, since these require leaps away from the status quo. HCD does not appear to provide helpful directions, as to which leaps away from the status quo could be promising. Based on a comprehensive review of famous historical and contemporary case examples of radical innovation, the authors conclude that good directives for breakthrough innovation typically emerge from sources other than (formal) need analyses. Instead, important impulses can come, for instance, from the “dreams” (p. 95) and “inner vision” (ibid.) of highly creative individuals. Another source of inspiration can be “subtle and unspoken” (p. 90) socio-cultural dynamics that have not previously entered people’s consciousness, and therefore have not yet been expressed as specific needs. “The invention of the mini-skirt in the 1960s is an example: It was not simply a different skirt, but a radically new symbol of women’s freedom that signalled a radical change in society” (ibid.). When the pursuit of inner visions or subtle socio-cultural dynamics are construed as different from need-focused approaches in HCD, the concept of “needs” appears to be understood in a rather narrow sense: something users are aware of, i.e. rather concrete concerns that users can readily explicate or acknowledge. In a similar vein, researchers from the MIT Media Lab find a focus on needs to be limiting for innovation projects. More could be achieved with a vision-driven design approach, rather than by a need-driven procedure. Looking back through the history of HCI [Human Computer Interaction], we see that quantum leaps have rarely resulted from studies on users’ needs or market research; they have come from the passion and dreams of visionaries such as Douglas Engelbart [inventor of the computer mouse]. We believe that vision-driven design is critical in fostering quantum leaps, and it complements needs-driven and technology-driven design by looking beyond current-day limits. (Ishii et al., 2012, p. 49f.) This discussion is well in line with Norman and Verganti’s description of “inner visions” as important sources of inspiration for radical innovation. As a subtle difference, Norman and Verganti highlight the potential of technologies to change and thus bring about radical innovation. By contrast, Ishii et al. (2012) view technologies as associated with the status quo. Thus conceived, technologies seem to provide little directives for the development of radically different solutions for the future. Another aspect brought up by Ishii et al. (2012) concerns the endurance of solutions. In this regard, too, the authors find vision-driven design more favourable than need-driven or technology-driven design.
Different Concepts of Human Needs and Their Relation to Innovation Outcomes 213 The reason why we focus on the vision-driven approach is its life span. We know that technologies become obsolete in about one year, users’ needs change quickly, and applications become obsolete in about 10 years. However, we believe the strong vision can last beyond our lifespan. (Ishii et al., 2012, p. 50) Following such a vision-driven approach, the authors suggest a new way for people to interact with the digital world: “Radical Atoms is a vision for the future of human-material interactions, in which all digital information has physical manifestation so that we can interact directly with it” (p. 38). Inspiration for this vision emerges from careful observations and analyses of how humans interact with physical objects: “Humans have evolved a heightened ability to sense and manipulate the physical world, yet the digital world takes little advantage of our capacity for hand-eye coordination” (p. 38). 3 John Arnold’s Need-Based Innovation Theory: Or— Focussing on Basic Human Needs Instead of Transient User Needs Much older and more fundamental than the concept of “user needs” is the concept of “human needs” in design thinking traditions. This outlook was prominently introduced by John E. Arnold, who laid important groundworks for design thinking at Stanford Engineering and used the term “design thinking” already (Arnold, 1959/ 2016; Clancey, 2016; von Thienen et al., 2017). John Arnold introduced need-based theories of creativity and innovation. To Arnold, creative work is generally “concerned with how the basic needs of man can be better satisfied” (1959/2016, p. 63). This focus on needs provides an essential evaluative dimension for creative performance. By his definition, every successful creative project yields a novel solution that “better solves some need of mankind” (p. 66). Analogously, he takes for granted the goal that “our innovations better satisfy some need of man” (p. 67, emphasis in original). According to this theoretical framework, human needs are a key factor in all innovation projects, whether or not a design thinking approach is explicitly pursued. However, the explicit endorsement of a focus on needs in present-day design thinking can likely be of great service, according to Arnold’s theories. It will be much easier to better satisfy human needs when the innovator is actually aware of relevant needs and tracks the impact of potential new solutions carefully. Notably, at Arnold’s time, there is not yet any clear differentiation between innovator versus user. The creative person can very well work to satisfy their own, personal needs. However, for Arnold and his successors at Stanford the great art of innovation is to not make improvements regarding contingent, arbitrary needs, but to satisfy basic needs. These are shared by many people. Like Norman and Verganti (2014), Arnold analyses many different creative projects to better understand the emergence of radical innovation. Amongst other
214 J. von Thienen et al. case studies, he treats the development of instant colour pictures pioneered by Edwin Land from Polaroid, the invention of Freon as a refrigerant by Thomas Migley, the invention of disposable safety razors by King Gillette and many more. In his analysis, Arnold invokes a major distinction between “organized” versus “inspired” creativity approaches as avenues towards innovation. Organized approaches typically follow “a logical, orderly, step-by-step type of problem solving technique” (Arnold, 1959/2016, p. 73). Examples can be: a structured form of marked research, a systematic trial-and-error approach or comprehensive analyses and re-combinations. By contrast, projects that pursue an inspired approach do not follow a well-structured process. A prominent example of an inspired approach is what Arnold calls the “big dream”. The big dream approach [. . .] is carried out by asking yourself the biggest question you possibly can, by dreaming the biggest dream that you possibly can, by sort of soaring off into space with a grand idea, and then expending every possible effort to answer this big question, to make this big dream come true, to get some tangible tie between your flight into space and solid reality. (Arnold, 1959/2016, p. 76) This approach resembles the pursuit of an “inner vision” described by Norman and Verganti (2014) or “vision-driven design” set forth by Ishii et al. (2012). In accordance with these authors, Arnold finds the big dream approach most promising for purposes of radical innovation. Similar to Norman and Verganti (2014), Arnold believes that any well-structured procedure (like a structured HCD process) is not a promising avenue towards radical innovation. Inspired [. . .] approaches [. . .] are those closely associated with the art of creativity rather than the science. Big leaps in knowledge are apt to occur using these approaches, as compared with the slow but steady step-by step advancement made using organized techniques. (Arnold, CE, p. 73) At the same time, for Arnold inspired approaches do not contrast with a focus on needs, human-centred design or needs-driven design. Quite to the contrary: According to Arnold, dreaming up new inner visions or meanings can be at least as human-centred and focused on needs as market research. In fact, inspired approaches are said to have a greater innovation potential than organized approaches, and innovation is understood by Arnold as a move towards better addressing human needs. Thus, dreams and visions can be even better approaches to satisfying important human needs than organized market research (whether or not market research is concerned with user needs). All in all, Arnold construes needbased design much more widely than Norman and Verganti (2014) or Ishii et al. (2012). Regarding the two approaches Arnold distinguishes—inspired versus organized—he credits both with important benefits. Inspired approaches are considered most apt for an exploration of radically new, innovative solutions. Organized approaches are credited with being especially helpful in the development of highly sophisticated and effective outcomes, with reliable step-by-step progress. Again, this analysis resembles that of Norman and Verganti (2014), who describe structured
Different Concepts of Human Needs and Their Relation to Innovation Outcomes 215 HCD-processes as “a form of hill-climbing, [..] [which] is only suited for incremental innovation” (p. 79). In Arnold’s descriptions of organized versus inspired creativity approaches, a combination of both seems ideal. Present-day design thinking in the StanfordPotsdam tradition attempts exactly such a balance and symbiosis of inspired as well as organized approaches (von Thienen et al., 2017, 2019; Meinel & von Thienen, 2021). Yet, methodologically, how can human-centred design that focuses on needs overcome the traps of current cultural paradigms? How can it have an impact beyond the innovators life-time even though current user needs might become obsolete within just a few years? From the beginning onwards, in the Stanford-Potsdam design thinking tradition, human needs have been understood as hierarchically organized: from concrete to abstract. Concrete needs can be specific for individuals and/or situations. They are highly context dependent. Abstract needs are culture-independent, common-human and assumed to be so enduring as to not have changed much since humanity’s prehistory. A re-current example John Arnold discusses is mobility. In modern times, this topic often surfaces in terms of traffic concerns. People may experience, for instance, the very concrete needs of “finding a parking space” or “reaching one’s destination quickly despite being stuck in a traffic jam”. When these needs are considered on more abstract levels, human needs come into focus that are important for most people, such as mobility in general: “Man must be kept mobile—yet not be overly frustrated” (Arnold, 1959/2016, p. 94). Moving up even further on the ladder of abstraction, mobility seems to be just one possible solution to satisfy an even more basic need of mankind, namely our desire to communicate: “Man must be able to communicate freely with other men and machines” (p. 94). Up to the present, in this design thinking tradition, needs are analysed hierarchically. “Why-How-Laddering” is a prominent and sophisticated methodology to this end (d.school, 2010; von Thienen et al., 2012). Presently, the method is often used to make sense of insights from empathy research with users. Notably, however, creators can use this method just as well to reflect on personal visions developed without user research, in order to better understand and advance one’s innovation project. Why-How-Laddering works by starting with concrete needs (often: conscious intentions, preferences, desires) and asking why the matter would be important. The answer will again be formulated as a need statement, typically in verb-form. For instance, in user research it may be observed that users like to buy baking mixtures that require the addition of fresh eggs. Of course, egg powder could easily be including in the baking mixture, entailing an even easier baking procedure for customers. When asked about their preferences, users may reply that they want to add something fresh to the product. Why? Some users may say that they want to use less eggs than the recipe foresees, to reduce cholesterol. Others may reply more generally that they want to participate in the creation of the product, to control what ingredients go into it, to ensure ingredients are of good quality and not
216 J. von Thienen et al. harmful. Why? Continuing with the inquiry, eventually a need will be reached that is so abstract as to be shared by almost all humans, like “to be healthy”. This can be the top of the hierarchy, if no further, even more abstract need seems to figure in the background. To clarify branches and details of the need hierarchy, one can also start with a more abstract need and ask “how” the end would be pursued. For instance: “How do you try to stay healthy?” “I try to nurture good organisms in my body”, which the person may try to achieve by eating yoghurt. Concrete needs surface as “solutions” to satisfy more abstract, basic needs. A visualization of this food example is provided by the d.school (2010), showcasing the typical branch-structure of a need hierarchy (Fig. 1). According to this understanding and methodology, creative developments are by no means confined to current-day limits when a project focuses on needs. This view is a fundamental understanding in Stanford-Potsdam design thinking traditions up to the present. In the same vein, Larry Leifer and Christoph Meinel, directors of the Stanford-Potsdam Design Thinking Research Program, emphasize in an introduction to design thinking: “The human needs that we seek to satisfy have been with us for millennia” (Meinel & Leifer, 2011, p. xv). Here, we may think back to needs discussed before: “to be mobile”, “to communicate”, “to be healthy”. Such needs are unlikely to change in the next 100, 200 or even 1000 years. Fig. 1 In the Stanford-Potsdam design thinking tradition, needs are analysed hierarchically, from concrete and context-dependent to abstract, common-human and enduring (image reprinted from d. school, 2010, p. 20, CC BY-NC-SA)
Different Concepts of Human Needs and Their Relation to Innovation Outcomes 217 Here, again, an important difference surfaces between the Stanford-Potsdam design thinking tradition in comparison to the discussions of Norman and Verganti (2014) or Ishii et al. (2012). In the Stanford-Potsdam tradition, needs are construed in a wide sense: from concrete to abstract, from readily explicated desires and observed behaviours to remote, underlying concerns. Based on such a wide understanding of needs and hierarchical need analyses, radical innovation can easily be understood to emerge from a focus on needs in innovation projects. By fluently moving up and down the ladder of abstraction in need hierarchies, radically new solutions come into sight. In John Arnold’s traffic example, starting with concrete needs of finding a parking lot and bypassing a traffic jam, the elaborated need hierarchy leads to quite radical creative perspectives. In his case discussion, the abstract need “to communicate” is suggestive not so much of incremental, but rather of radical innovation projects. Based on his need analysis, Arnold reaches the questions: How can we better use public transportation? Can subways be used to carry freight & keep some trucks out of city? What new systems of public transportation can I dream up? [. . .] What means of communication can we substitute for face-to-face meetings? (E.g., closed circuit TV) What are some of the wildest approaches? (E.g., disposable cars) (Arnold, 1959/2016, p. 94) Thus, differences in how we construe “needs” have major implications for emerging views about the relationship of need-based design and prospects of incremental versus radical innovation. 4 Abraham Maslow’s Need Theory: Or—Design Thinking to Satisfy the Designer’s Basic Needs Along with John Arnold’s need-based creativity and innovation theory, another outlook on needs and creativity authored by Abraham Maslow found its way into Stanford-Potsdam design thinking traditions. John Arnold was a passionate collaborator. He worked and taught together with world-renowned theoreticians, including people like Abraham Maslow, who contributed a guest lecture in Arnold’s Creative Engineering Seminar at Stanford (Clancey, 2016) and also wrote a guest essay for the course manuscript (1959/2016). According to Maslow, basic human needs exhibit a structure that can be visualized by a pyramid (Fig. 2). “There are at least five sets of goals, which we may call basic needs. These are briefly physiological, safety, love, esteem, and self-actualization” (1943, p. 394). Moreover, according to Maslow’s theory, needs on lower hierarchy levels must be satisfied to some degree before needs on upper hierarchy levels emerge.
218 J. von Thienen et al. Selfactualization Esteem Love Safety Physiological Fig. 2 Maslow’s hierarchy of basic human needs depicted in a pyramid It is quite true that man lives by bread alone—when there is no bread. But what happens to man’s desires when there is plenty of bread and when his belly is chronically filled? At once other (and “higher”) needs emerge and these, rather than physiological hungers, dominate the organism. And when these in turn are satisfied, again new (and still “higher”) needs emerge and so on. (Maslow, 1943, p. 375) At the highest level of the need hierarchy, humans pursue self-actualization. Here, creativity is said to figure essentially. Maslow explains self-actualization as follows: This tendency might be phrased as the desire to become more and more what one is, to become everything that one is capable of becoming. The specific form that these needs will take will of course vary greatly from person to person. In one individual it may take the form of the desire to be an ideal mother [. . .] and in still another it may be expressed in painting pictures or in inventions. [. . .] [In] people who have any capacities for creation it will take this form. (Maslow, 1943, p. 382f.) Among all three need theories discussed here, in Maslow’s account the focus on needs remains most remote from “user needs”. Good creators, innovators or designers are not described as individuals who excel at finding and satisfying user needs. Rather, they are described as excelling in the pursuit of their own basic needs, especially the need of self-actualization: People do something they find worthwhile, creatively. Maslow’s theory of needs was never fully endorsed in the Stanford-Potsdam design thinking tradition. In particular, it is not customary to look for five different basic needs (physiological, safety, love, esteem and self-actualization) when making sense of insights from user research. However, Maslow’s theory did have impetus. It certainly contributed to a lasting ideal of people achieving self-actualization by means of worthwhile, creative projects. In that sense, design thinking skills appear
Different Concepts of Human Needs and Their Relation to Innovation Outcomes 219 most serviceable to help people make the best out of their lives and tap their full creative potential. The concern for self-actualization has been enduring. Design thinking is not only invoked to advance worthwhile, new products or services. Rather, there is also a strong interest in how design thinking helps any human in satisfying their own needs, so as to live a fulfilled life. In line with Maslow’s need theory, creativity is highlighted as essential in this endeavour. In this sense, Bill Burnett, Executive Director of Stanford’s Design Program, and Stanford lecturer Dave Evans (2017) have authored Designing your life. How to build a well lived, joyful life. Here, they write about design thinking independent of product development and user needs. You’re going to see the benefits of design thinking in your own life. Design doesn’t just work for creating cool stuff like computers and Ferraris; it works in creating a cool life. You can use design thinking to create a life that is meaningful, joyful, and fulfilling. [. . .] A welldesigned life is a life that is generative—it is constantly creative, productive, changing, evolving, and there is always the possibility of surprise. (Burnett & Evans, 2017, p. xvi, emphasis in original) In related terms, the HPI D-School offers Wayfinder education. Wayfinder is a newly developed program by HPI D-School [. . .] in the area of Design Thinking: for self-leading and designing your own well-lived life and career. [. . .] The program is based on the "Designing Your Life" Concept and has been extended and further developed by the HPI D-School. Wayfinder has four major focus areas: 1. Empathy and Self-Awareness: Understanding one's own values and attitudes. 2. Exploring: Shaping career and personal life with purpose and energy. 3. Prototyping: Making good choices and exploring options. 4. Iterate: Learning forward in a strong network. (D-School, 2021, emphasis in original) Similarly, Bernard Roth (2015), academic director of Stanford’s d.school has written The achievement habit. Stop wishing, start doing and take command of your life. Here too, design thinking is not applied to improve product developments by means of better addressing user needs. Rather, the aim is to help readers better address their own important needs. Many who have taken my [design thinking] course over the years credit it with helping them achieve significant personal and professional successes in their lives [. . .]. It’s empowering to realize you have more control than you ever knew over what you achieve in life. When you are not happy with an aspect of your life, you can change it! Really, you can. (Roth, 2015, p. 4) This fundamental concern with the design thinker’s private life satisfaction shared by many influential educators would not be explicable if design thinking were all about satisfying user needs. In the Stanford-Potsdam tradition, design thinking is considered a very valuable approach for addressing basic human needs—including everyone’s personal concerns. However, even when design thinking is applied to deliver product or service innovation, Maslow’s need theory and topos of “self-actualisation through creativity” help to clarify an important link: There is a connection between the innovator’s needs and user needs. Humans are social animals, for whom relations to others are
220 J. von Thienen et al. essential (Tomasello, 1999; WHO, 2004; Plank et al., 2021). In this sense, we can also appreciate how it is more rewarding for creators themselves to deliver solutions others care deeply about (because the users’ key needs get satisfied) than to deliver solutions people hardly care about (because barely anything changes regarding the users’ key needs). From this perspective, there seems to be a serendipitous evolutionary alignment of creators/innovators being better able to pursue their own basic need of self-actualization when they endorse a design thinking approach, compared to following other product development strategies that disregard user needs. As empathic humans, creative designers themselves will be happier when the creative process leads to design outcomes audiences are deeply grateful for. 5 Robert H. McKim’s Need-Based Design Theory: Or— Need-Centred Design as Culture Therapy Robert H. McKim is another design thinking pioneer (McKim, 1959/2016, 1972; von Thienen et al., 2019, 2021). Next to Maslow, McKim was also a guest lecturer in John Arnold’s Creative Engineering Seminar at Stanford. McKim contributed an essay to the course manuscript where he spelled out a design theory based on human needs. Like Arnold, who construes all innovation in terms of satisfying human needs, McKim construes all design activities in terms of satisfying human needs. To McKim, design is a “response to human physical, intellectual, and emotional needs—human needs which are partially formed and modified by the natural and cultural environment” (1959/2016, p. 200). McKim differentiates three domains of basic need: the physical, emotional and intellectual. To McKim, good design is highly respectful of basic needs in all three domains. Physical needs include bodily well-being and the ability to achieve action goals. This covers needs such as staying “alive, fed, and sheltered” (p. 198). There is also a concern for “physical comfort and sensory well-being” (p. 217). Moreover, people need to be physically capable of performing the actions and achieving the ends to which they aspire. Often this has to do with the use of the human senses. It is very unfavourable to make product users reliant on one sense modality only, such as the visual sense channel. Every evening thousands of Americans climb into their automobiles, reach for the headlight knob, turn instead its identical twin, the windshield wiper knob, or perhaps its triplet, the cigarette lighter. It is not difficult to find examples of “Chinese puzzles” in our everyday design world. Unfortunately these puzzles are not fun; they are frustrating. (McKim, 1959/ 2016, p. 204) Such an automobile design could be much improved by mindfully addressing further sensory channels, such as “touch”.
Different Concepts of Human Needs and Their Relation to Innovation Outcomes 221 The headlight—windshield wiper—puzzler [. . .] could easily be minimized in several ways. (1) Coding the knobs by shape or texture so that their differences would be tactually clear—day or night. The confusion that arises with these controls usually takes place when it is dark. (2) Positioning the knobs according to their respective functions—the windshield wiper knob near the wipers, the headlight knob near the ignition key for handy use when starting up at night. (McKim, 1959/2016, p. 204f., emphasis in original) Notably, McKim’s need-based design approach is human-centric in a straightforward way. This is especially easy to understand in the realm of physical needs. For instance, humans cannot see well at night in the dark, which then results in unsatisfied orientation needs. However, other species have different perceptual abilities—like bats who can orient themselves well in the dark. Similarly, humans have different temperature needs than polar bears or desert foxes. Being mindful of physical needs implies being mindful of the organism for whom one designs, and humans typically design for human needs. Moving on to the second category of basic needs, according to McKim, important emotional needs of humans include the experience of positive or situationappropriate emotions and living out personal motives. Amongst other lines of discussion, McKim highlights how designers can help users experience joy by bearing in mind “the delight which sensory stimuli such as color, shapes, rhythmic patterns, and textures can bring to the emotions” (p. 212). Intellectual needs cover objectives such as “understanding something”. Users need to understand the purpose of tools or the content of messages quickly. Users need to be able to operate tools with minimal intellectual effort. Sometimes it also matters to users that designs accord to philosophical ideals. “Understanding nature” is another example of an intellectual need. As McKim admits, in real life experienced needs often span several categories— physical, emotional and/or intellectual. The distinction is proposed, especially for designers, to be mindful of human needs in a comprehensive way when developing novel solutions. Each need category should be screened carefully and systematically, to see if a given design already addresses all relevant basic needs well. Notably, the design of Radical Atoms proposed by Ishii et al. (2012) would figure very favourably in McKim’s framework, precisely because it seems to address basic human needs so well. In terms of physical needs, Radical Atoms address more than one human sensory channel. The approach is not only visual, but also tactile. In terms of emotional needs, Radical Atoms have a much greater potential compared to Graphical User Interfaces in terms of delighting users via carefully chosen sensory stimuli, such as shapes, rhythmic patterns and textures. Radical Atoms also have a huge potential to address people’s intellectual needs, because the material can rapidly communicate all kinds of information in most flexible ways. In all three need categories—physical, emotional and intellectual—McKim expects basic, enduring and less basic, contemporary needs. The designer should do their best to satisfy basic needs, while the contemporary needs can sometimes even be morally unjust. In the latter case, designers should abstain from addressing these not-basic needs. For instance, an automobile customer with the basic need of
222 J. von Thienen et al. feeling socially recognized may develop the contemporary need to drive an expensive, fast car furiously. A designer who straightforwardly tried to satisfy the customer’s contemporary need by providing a respectively designed automobile would endanger the driver’s basic needs for physical safety, and also that of others in the driver’s vicinity. In light of McKim’s framework, human-centred design or a focus on needs implies critical re-assessments of the society’s current need hierarchies. In particular, contemporary needs pursued with a high priority in a culture at the expense of more basic needs should be corrected through better, often radically different designs. For instance, some car models in the 1950s were associated with high rates of deadly accidents. In these and other cases, need hierarchies informing contemporary designs should be critically re-thought. Both the society’s need-hierarchies and products emerging from them should be radically re-designed. In a modern society such as our own, the cultural environment probably has a more decisive effect upon human needs than does the natural environment. It often causes seemingly irrational needs for design which appear absurd to the people of other cultures. It causes fashions and styles in design. It sometimes frustrates the satisfaction of important human needs. [. . .] A cultural environment which frustrates the healthy satisfaction of human needs is, in my opinion, a culture which is in for a change. (McKim, 1959/2016, p. 200) This is where human-centred, radical innovation emerges in McKim’s framework. Good designers should not stop their assessments when faced with concrete desires expressed by customers, who may actually provide a very coherent image of what they jointly think they “need”. Instead, a thorough need analysis should take place. The analysis shall explore need hierarchies systematically. These will often be characteristic of a particular user group and culture domain. Good design means to critically re-assess given need hierarchies, to ensure that basic needs get satisfied comprehensively. By contrast, more concrete desires or contemporary needs will often be less helpful as design objectives. Sometimes they will even be harmful, as contemporary needs can be morally dubious or detrimental to the pursuit of basic human needs. Good human-centred design rethinks culture domains and looks behind seemingly important deliverables. Good design is ready to provide radically different, more human-centred solutions than those commonly sought and provided in product or service design. Clearly we badly need the designer who understands and is capable of responding to the needs of the whole man. [. . .] He must understand man’s physical needs, needs not only for power over his environment but needs for physical comfort and sensory well-being. He must understand man’s intellectual needs, needs for minimizing needless problem solving in design as well as visual needs for knowledge and order. The designer who designs for the whole man will also understand man’s emotional needs for designs which satisfy civilized motivations and which delight the emotions through the senses. This designer must have the fortitude to exert his influence on the current cultural environment which is depriving us all of basic human needs. (McKim, 1959/2016, p. 217) A key concept in McKim’s design theory is the description of “conflicts” in societal need hierarchies, where culture-specific contemporary needs clash with the
Different Concepts of Human Needs and Their Relation to Innovation Outcomes 223 satisfaction of basic human needs. According to McKim’s analysis, such dysfunctional tensions in need hierarchies occur often. They are the rule rather than the exception. Until today, conflicts between people’s needs are considered a great opportunity for the development of new and radically better solutions in Stanford-Potsdam design thinking traditions. Trainees learn to be on the lookout for such conflicts. “Identify needs directly out of the user traits you noted, or from contradictions between two traits—such as a disconnect between what one says and what one does [. . .]. One way to identify the seeds of insights is to capture ‘tensions’ and ‘contradictions’ as you work” (d.school, 2010, p. 15). Thus, even when a design team is much concerned with the status quo of needs in a field, the identification of need conflicts can be a promising source of inspiration for radically new and better solutions. 6 Reflection and Outlook The question as to whether a design thinking focus on needs facilitates or antagonizes radical innovation looks like an empirical one. We, too, believe that empirical research is important and can help to elucidate a comprehensive picture. In this chapter, however, we have started with conceptual clarifications, because the way in which we conceptualize needs, and their role in design thinking, largely predetermines relations to innovation. For instance, it makes a huge difference whether needs are understood in a narrow sense, as being strongly linked to user testimonials in a status quo situation, or whether the term is used in a wide sense, so as to cover remote, abstract, cross-cultural and enduring human concerns. For conceptual reasons, discussions of design thinking with a narrow account of needs link the approach to incremental innovation, while discussions with a wide account of needs link design thinking to radical “breakthrough” innovation. As a key difference, in wide accounts the design thinking focus on needs does not relate uniquely to user needs, but to basic human needs. Design thinking in the Stanford-Potsdam tradition invokes a wide account of needs. The impact of conceptual decisions is also notable with regard to the categorization of projects. For instance, Ishii et al. (2012) categorize the project of Radical Atoms as vision-driven design, in contrast to needs-driven design. However, according to Robert McKim’s need-based design theory, Radical Atoms emerge as an example for good design that addresses human needs comprehensively, and in that sense is highly human-centric and need-focused. Beyond impactful differences between narrow versus wide need conceptions in inter-communal debates, also within the Stanford-Potsdam design thinking tradition varying ideas can be discerned. For instance, should we differentiate specific types of basic human need? John Arnold does not, Abraham Maslow and Robert McKim do. If we distinguish different types, how many categories of basic needs shall we invoke, and what is their content? McKim distinguishes three types of basic needs:
224 J. von Thienen et al. physical, emotional and intellectual. Maslow invokes five categories: physiological, safety, love, esteem and self-actualization. Another point of divergence between the theories concerns the role of designers. According to Maslow’s theory, people (users) naturally satisfy their more basic needs first. So, more basic needs should be satisfied most often in society. By contrast, McKim believes that cultures often have dysfunctional need hierarchies where contemporary needs antagonize the satisfaction of basic human needs. Consequently, McKim describes good designers as “culture therapists”. The task of a good designer is to provide novel (often radically different) solutions that allow people to satisfy their more basic needs at once, while culture-specific objectives can also be satisfied when they seem ethically sound. According to McKim’s theory, good designers rethink the status quo comprehensively, identify dysfunctional dynamics in given cultures, radically change solution approaches when appropriate, and thus exert profoundly new and positive influences on sub-cultures or even societies. Overall, one major takeaway of this chapter concerns the plurality of need concepts in design thinking. Conceptual decisions matter a lot, because they determine the methodology that gets implemented in a design thinking project, giving rise to very different outcome expectations, alternating between incremental versus radical innovation. Another important factor to address is the level of methodological skill and rigour invested by design thinkers in a project. To what extent a focus on needs facilitates or antagonizes innovation clearly hinges on the methodological proficiency of creators. How well can they make sense of needs in a field? In the need theories informing Stanford-Potsdam design thinking traditions, two skills have been highlighted as particulary important. Firstly, design thinkers need to be able to move fluently up and down the ladder of abstraction in hierarchical need analyses. Secondly, they also need to be able to identify dysfunctional need dynamics. Problematic need constellations can be diagnosed in a status quo culture when contemporary needs get satisfied at the expense of basic human needs, or when the satisfaction of one basic need conflicts with the satisfaction of another basic need. All such dysfunctional dynamics provide opportunities for breakthrough innovation via novel solutions that radically “change the game” for users, as people now can satisfy basic needs comprehensively without having to compromise in important need domains. As an outlook to future developments regarding human need conceptions in design thinking, we can point to a comprehensive framework of human needs that is currently being developed (Borchart, 2020; Borchart et al., 2021a, b; von Thienen, 2020). The framework is based on need theories from design thinking traditions, especially McKim’s conception, and amended by need theories from further sources (such as Deci & Ryan, 2000; Max-Neef, 2017), concepts of fundamental human rights in legal contexts, and policy frameworks such as the Sustainable Development Goals (United Nations, 2021; World Health Organization, 2021). The purpose of the framework is to support comprehensive reflections on product risks and benefits both during the design process, and afterwards as when legal decisions need to be taken about the admissibility of novel products. Used in design processes, these systematic
Different Concepts of Human Needs and Their Relation to Innovation Outcomes 225 reflections can help to create products that are respectful of a broad range of human needs, so as to close blind-spots and overcome potentially dysfunctional dynamics where products satisfy some needs at the expense of others. When risks and benefits have been identified in available products, this also helps to identify stakeholder groups—namely people who benefit from the introduction of a new product and those who may suffer a disadvantage. From the design point of view, a careful consideration of latter stakeholder groups can also lead to favourable product re-designs. This novel framework for comprehensive considerations of product risks and benefits based on human needs will be shared in an upcoming chapter in this book series. References Arnold, J. E. (2016). Creative engineering. In W. J. Clancey (Ed.), Creative engineering: Promoting innovation by thinking differently (pp. 59–150). Stanford digital repository. Retrieved from http://purl.stanford.edu/jb100vs5745 (Original manuscript 1959). Borchart, K.-P. (2020, Dec 3–4). Exploring ethical perspectives on digital engineering developments – Using design thinking templates for risk-benefit assessments. In Presentation at the research meeting Design thinking – Innovation, law and politics. Hasso Plattner Institute at the University of Potsdam. Borchart, K.-P., von Thienen, J., Molitorisová, A. (2021a, March 15–17). The needs-based design template. A framework for reflecting on product risks and benefits based on human needs. In Workshop at the Hasso Plattner design thinking research meeting. Stanford University [online]. Borchart, K.-P., von Thienen, J., Molitorisová, A. (2021b, March 30). Introducing the needs-based design template for assessments of product risks and benefits based on human needs. In Presentation for the Bundesamt für Verbraucherschutz und Lebensmittelsicherheit and the University of Bayreuth. Brown, T. (2009). Change by design: How design thinking transforms organizations and inspires innovation. Harper Collins. Burnett, B., & Evans, D. (2017). Designing your life: How to build a well-lived, joyful life. Knopf. Clancey, W. J. (2016). Introduction. In W. J. Clancey (Ed.), Creative engineering: Promoting innovation by thinking differently (pp. 6–53). Retrieved from http://purl.stanford.edu/jb100 vs5745 d.school. (2010). Bootcamp bootleg. Retrieved from https://hpi.de/fileadmin/user_upload/ fachgebiete/d-school/documents/01_GDTW-Files/bootcampbootleg2010.pdf Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. D-School. (2021) Wayfinder – Self and leadership development [website]. Retrieved from https:// hpi.de/en/school-of-design-thinking/for-students/wayfinder.html Ishii, H., Lakatos, D., Bonanni, L., & Labrune, J.-B. (2012). Radical atoms. Interactions, 19(1), 38–51. Kelley, D., & Camacho, M. (2016). David Kelley: From design to design thinking at Stanford and IDEO. Journal of Design, Economics, and Innovation, 2(1), 88–101. Lindberg, T. S. (2013). Design-thinking-Diskurse: Bestimmung, Themen, Entwicklungen [Dissertation]. Universität Potsdam, Germany. Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50, 370–396. Maslow, A. H. (2016). Emotional blocks to creativity. In W. J. Clancey (Ed.), Creative engineering: Promoting innovation by thinking differently (pp. 188–197). Stanford Digital Repository. http:// purl.stanford.edu/jb100vs5745 (Original manuscript 1959).
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Facets of Human-Centered Design: The Evolution of Designing by, with, and for People Jan Auernhammer, Matteo Zallio, Lawrence Domingo, and Larry Leifer “[. . .] We have developed speed, but we have shut ourselves in. Machinery that gives us abundance, has left us in want. Our knowledge has made us cynical. Our cleverness, hard and unkind. We think too much and feel too little. More than machinery, we need humanity. More than cleverness, we need kindness and gentleness. Without these qualities, life will be violent, and all will be lost. [. . .] You, the people, have the power—the power to create machines. The power to create happiness! You, the people, have the power to make this life free and beautiful, to make this life a wonderful adventure [. . .].” —Charlie Chaplin 1940 in The Great Dictator. Abstract This book chapter outlines different facets of Human-Centered Design, which evolved over half a century. These facets have different foundational influences that lead to design by, with, and for people. Designing for people, including Ergonomics and Human Factors and Interactions Design, originated from early developments in experimental psychology. Similarly, designing for people with specific needs emerged from developments in medicine and rehabilitation, which resulted in design approaches, such as Universal Design and Inclusive Design. Designing with people, including Participatory Design, developed from communal architecture. Designing by people is grounded in the psychology of creativity, resulting in design approaches, such as Creative Engineering and Design Thinking. Early developments in social psychology developed over time into Social Design and Design by Society. These approaches emerged as designers responded to sociomaterial and socio-economic challenges with new Human-Centered Design approaches. This book chapter aims to raise awareness of the contextual evolution J. Auernhammer (*) · L. Domingo · L. Leifer ME Design Group, Center for Design Research, Stanford University, Stanford, CA, USA e-mail: jan.auernhammer@stanford.edu M. Zallio Inclusive Design Group, Engineering Design Center, University of Cambridge, Cambridge, UK © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_12 227
228 J. Auernhammer et al. of different Human-Centered Design approaches and the need to continuously respond creatively to these challenges with new design solutions and adequate design approaches. 1 Introduction Over the last century, Design has evolved from form-giving to the design and development of technology, interactions, experiences, and organizations (Archer, 1965; Auernhammer 2020; Buchanan, 2015; Fulton Suri, 2003; Moggridge, 2007). These developments emerged as new challenges required new design approaches. For example, Archer (1965, p. 57) expressed: “[. . .] there has been a world-wide shift in emphasis from the sculptural to the technological. Ways have had to be found to incorporate knowledge of ergonomics, cybernetics, marketing, and management science into design thinking. As with most technology, there has been a trend towards the adoption of a systems approach as distinct from an artifact approach.” A similar shift is the development of Human-centered Design that emphasizes people and the living world rather than artifacts and systems. Over the last halfcentury, various contextual challenges and developments have resulted in approaches, such as Ergonomics and Human Factors, Participatory Design, Inclusive Design, Creative Engineering, and Social Design (e.g., Arnold, 1959; Carlsson et al., 1978; Chapanis et al., 1949; Clarkson et al., 2013; Rittel, 1987). Early Experimental Psychologists developed approaches to assessing the psychological fitness for operating airplanes (Benary et al., 1919; Koonce, 1984). These psychological developments changed from assessing people’s qualifications to designing technology for people, resulting in Ergonomics and Human Factors (Christensen, 1962; Edholm & Murrell, 1973). A similar development for designing for specific needs of people resulted from developments in Medicine and Rehabilitation (Rusk & Taylor, 1953). Including non-designers in the design project in Communal Architecture resulted from the opportunities provided by insights from various stakeholders, such as urban dwellers and craftspeople (Rudofsky, 1964). Humanistic Psychology developments of creativity and human values influenced humanistic and creative design practices (Auernhammer & Roth, 2021; Christensen, 1976; Maslow, 1954, 1956). Similarly, developments in Social Psychology resulted in dialectic design approaches to resolve social tensions (Lewin, 1936, 1946, 1947; Rittel, 1987). Figure 1 outlines the evolution of these diverse Human-centered Design approaches. 2 Experimental Psychology in Design One of the first professional practices that considered people’s behavioral capabilities and limitations was aviation psychology (Koonce, 1984). In the 1910s, psychologists examined people’s abilities to identify their suitability for operating
229 Fig. 1 Several diverse evolutions of Human-centered Design Facets of Human-Centered Design: The Evolution of Designing by, with,. . .
230 J. Auernhammer et al. airplanes (Benary et al., 1919). These developments shifted from assuring people’s fitness to use technology to designing technology suitable for people. 2.1 Ergonomics and Human Factors As early as the 1920s, laboratories focused on studying people’s behavior when using designed solutions, and one of the first Human Factor laboratories in the USA was the Bell Telephone Laboratories (Christensen, 1976). Researchers and designers utilized experimental psychology to design for people’s physiological and psychological abilities in defense-related systems (e.g., Fitts, 1946; 1947a, b; Fitts & Jones, 1947; Flanagan, 1947; Loucks, 1944; McFarland, 1946; McGehee, 1943; Weitz, 1944a, b). These experimental psychology approaches were utilized to design artifacts for everyday life activities (Chapanis, 1951; Chapanis et al., 1949). In 1949, these developments resulted in the establishment of the Ergonomics research society in the United Kingdom and, in 1957, the Human Factors society in the USA (Christensen, 1976; Edholm & Murrell, 1973). These societies brought together interdisciplinary researchers, who started developing new methodologies to investigate psychological and physiological aspects within the interaction between people and machines and work environments (Chapanis, 1965; Craik, 1947, 1966; McCormick, 1957; Murrell, 1965a, b; Rodger, 1959; Taylor, 1957; Taylor & Garvey, 1959; Woodson, 1954). The fundamental doctrine of Ergonomics and Human Factors was to design solutions that allowed people to accomplish a specific task in the way it meets the characteristics of those who use it. Le Corbusier (1948, 1955) and Henry Dreyfuss (1960) developed an anthropometry of the human body to support designers in designing for people’s physiology. Over the decades, Ergonomics and Human Factors advanced as a systematic and interdisciplinary approach, designing for people and society (Bennett et al., 1963; Christensen, 1962; Cumming & Corkindale, 1969; Grether, 1968; Hanson, 1983; Van Cott & Kinkade, 1972). Many interdisciplinary scholars developed practices, tools, methods, models, and theories to design for people’s psychological and physiological attributes (e.g., Alexander, 1986; Bailey, 1982; Chapanis, 1996; Proctor & Van Zandt, 1994; Reason, 1990; Rouse & Boff, 1987; Stanton et al., 2005; Wickens, 1984; Woodson, 1981). These Human-centered Design approaches developed further through new technological advancements, such as computer systems. 2.2 Human–Computer Interaction In the 1970s, the design approach and community in Human-Computing Interaction emerged under various names, such as human or user engineering and cognitive systems engineering (Bennett, 1976; Brooks, 1977; Card et al., 1980, 1983; Hansen, 1972; Hollnagel & Woods, 1983; Norman, 1982, 1986). For example, Engelbart
Facets of Human-Centered Design: The Evolution of Designing by, with,. . . 231 (1962), interested in developing a means to support people in complex problemsolving, developed a design approach to “augmenting human intellect,” resulting in developments of personal computing. He and his team presented technology, such as the computer mouse, command input, video conferencing, and word processing, to augment people’s abilities at the “Mother of all Demos” (Engelbart & English, 1968). The focus changed from analog to interactive digital systems (Hansen, 1972). New psychological approaches were required to design for people’s interactions with digital systems with a focus on storing or retrieving information (Sackman, 1970). In the 1970s, designers at Stanford Research Institute and Xerox PARC introduced psychological research to examine and design human interaction with computers, resulting in the field of Human–Computer Interaction (Card et al., 1983; Cooper et al., 2014). In the late 1970s, the ACM community became more concerned about human interaction with computer systems to develop peopleorientated systems (Borman, 1996). In 1982, the Computer-Human Interaction group (ACM SIGCHI) was established, focusing on cognitive and psychological aspects of people when interacting with digital systems (Card et al., 1983; Clancey, 1997; Foulds & Joyce, 1998; Hollnagel & Woods, 1983; Norman, 1982, 1986; Suchman, 1983, 1985; Winograd & Flores, 1986; Woods & Roth, 1988). 2.3 Interaction Design The approaches developed for digital system design found their way back to the industrial design-side integrating objects, media, and software under the term Interaction Design (Houde & Hill, 1997; Moggridge, 2007, 2010; Norman, 1988; Winograd, 1996). Cooper et al. (2007) expressed that Bill Moggridge and Bill Verplank, who worked on the first laptop computer, coined the term Interaction Design in the 1980s. Several decades later, in 2005, the Interaction Design Association was incorporated (Cooper et al., 2007). Designers and scholars focused on non-utilitarian aspects of design, such as pleasure, emotions, and experiences (e.g., Buchenau & Fulton Suri, 2000; Fulton Suri, 2003; Green & Jordan, 2002; Hassenzahl, 2004, 2018; Houde & Hill, 1997; Jordan, 2000; Norman, 2007). The focus expanded to designing for embodied cognitive approaches and implicit interactions (Ju, 2008; Kirsh, 2013; Klemmer et al., 2006). 3 Medicine and Rehabilitation in Design Another Human-Centered Design approach emerged from developments in Medicine and Rehabilitation and the concern of people with diverse needs (Rusk & Taylor, 1953).
232 3.1 J. Auernhammer et al. Design for People with Disabilities In the 1950s, institutions, such as the Institute of Physical Medicine and Rehabilitation in New York, started developing a coordinated approach for the dissemination of information concerning self-help devices that might aid disabled persons in the performance of the daily activities of life and work (Rusk & Taylor, 1953). This approach advanced and became popular based on the argument that despite scientific and medical technological advancements, problems increase for people with disabilities (Nugent, 1961). This movement resulted in the first building standard that addressed disabilities, the ANSI 117.1–1961, and the first federal law requiring accessibility in government buildings, the Architectural Barriers Act (American National Standards Institute, 1980; Farber, 1975). Designers started evaluating existing design solutions concerning people’s specific needs and abilities and utilized these insights to redesign solutions for people with disabilities and toward a barrier-free environment (Goldsmith, 1963; Morgan, 1976; Sommer, 1972; Steinfeld, 1979). The human values and concern for people with different needs resulted in Designing for People with Disabilities. In 1978, Larry Leifer created an interdisciplinary design program and practice at Stanford’s Rehabilitation Engineering Research and Development Center at the Palo Alto VA (Burgar et al., 2000). In 1972, scholars at the University of California, Berkeley, established the Center for Independent Living (Zukas, 1975). This center was a grassroots organization that created environments designed for independence and empowering people to perform their daily routines. The emphasis shifted from disability to independent living, without discrimination of ethnicity, gender, age, and immigrant status (Lifchez, 1987; Lifchez & Winslow, 1979). Scholars argued that such collaborative design between designers and people with disabilities leads to essential attitudes for designing for people (Finkelstein, 1975). 3.2 Transgenerational Design The increased attention from institutions and the growth of public interest resulted in a focus on diverse groups. Design approaches matching the needs of people with physical and sensory disabilities resulted in Transgenerational Design (Pirkl & Babic, 1988). Designers considered the broadest spectrum of individuals, such as young, old, able, and disabled people, without disadvantaging a specific group (Pirkl, 1994).
Facets of Human-Centered Design: The Evolution of Designing by, with,. . . 3.3 233 Universal Design Shortly afterwards the 1990 American with Disabilities Act was established with the emphasis on considering all people in the design process (Americans with Disabilities Act (ADA) of 1990; Lebovich, 1993). This act resulted in the emergence of Universal Design (Mace et al., 1991). The Disabilities Act and Universal Design profoundly contributed to reducing discrimination against people with disabilities in all areas of public life. Designers are obligated to consider the entire life span of people, including temporary disability and future use, when designing spaces and products (Mace et al., 1991; Story et al., 1998). Universal Design became the precursor of a new wave of approaches in which products must be universally accommodating and cater conveniently to all people (Goldsmith, 2000). The spectrum of specific needs became the new prism through which postmodernity examines and defines itself (Davis, 2002). 3.4 Design for All In Ireland, the European Institute for Design and Disability was founded to foster the practice of designing for people of all abilities, which resulted in the Design for All (Coleman et al., 2003). In 2004, the institute passed “The Stockholm Declaration,” which emphasized the design for human diversity, social inclusion, and equality (Bendixen & Benktzon, 2015). 3.5 Inclusive Design In a similar disposition, the core value proposition of Inclusive Design optimizes the design and development of solutions for individuals with specific needs (Clarkson et al., 2003; Coleman et al., 2007). The design approaches that emphasize design for all people focus on accommodating and empowering all people. Commercial design and design for disability can inspire each other through a more playful, creative approach (Pullin, 2009). Many designers and design scholars have developed practices and methods to create inclusive solutions, systems, and environments (Guffey, 2017; Null, 2013; Steinfeld & Maisel, 2012).
234 J. Auernhammer et al. 4 Humanistic Psychology in Design The evolution with the emphasis on the designers’ values, attitudes, abilities, and activities to design for people emerged in the 1950s. Christensen (1976) suggested that the emphasis on human values reflects the perceived movement up Maslow’s (1954) scale toward self-fulfillment. Noticeably, humanistic psychology has had a decisive effect on the development of design education and practice (Auernhammer & Roth, 2021). 4.1 Humanistic and Creative Design Designers, including John Arnold (1959) and Bob McKim (1959), developed a humanistic and creative design approach. Both collaborated with psychologists, such as Abraham Maslow and J.P. Guilford. Insights from gestalt and creativity psychology, including Wertheimer (1945), Duncker (1945), Maslow (1954, 1962), Guilford (1950, 1959), and Rogers (1954), informed the creative design practices. This approach focuses on creatively satisfying people’s physical, intellectual, and emotional needs (Adams, 1974; Arnold, 1959; Fuller, 1957; McKim, 1959, 1980). These creative practices aimed to design for impact in the real world (Papanek, 1973). Such design works with conviction and enthusiasm in the intersection of designers’ and clients’ interests and profound considerations for society (Eames & Eames, 2015). 4.2 Design Thinking These design practices based on liberal arts, incorporating science, art, and humanities, developed into Design Thinking (Buchanan, 1992; Cross, 2011; Lawson, 1972, 1980; Rowe, 1987). From a structuralist perspective, psychological theories of productive thinking resulted in developments in design science and design cognition (Eastman, 1970; Selz, 1922; Simon, 1969, 1981). Similarly, many design scholars have built on the work by Gestaltists, including Wertheimer’s (1945) Productive Thinking to develop insights and approaches in design thinking (Goldschmidt, 1991; Lawson, 1972, 1980; McKim, 1980; Schön, 1963). In 1991, the research workshop in Design Thinking focused on design cognition and computational modeling of the design process, establishing the Design Thinking Research Symposium series (Cross, 2018). Many design scholars studied and developed design thinking models and strategies (Dorst, 2015; Dym et al., 2005; Eastman et al., 2001; Eris, 2003; Faste, 1994; Gero, 1996; Goldschmidt, 1991; Jung, 2011; Lawson, 1980; Minneman, 1991; Plattner et al., 2011; Rowe, 1987; Schön, 1983; Tang & Leifer, 1988; Valkenburg & Dorst, 1998).
Facets of Human-Centered Design: The Evolution of Designing by, with,. . . 235 5 Communal Practices in Design In the 1960s, another movement emerged that focused on designing with people as a source of inspiration and democratization in design (Rudofsky, 1964). 5.1 Design Participation Early ideas of involving people in design resulted from the argument that architects and designers got out of touch with people’s needs, and there is an untapped source of inspiration from the practical knowledge of the untutored builders and urban dwellers (Rudofsky, 1964; Turner & Fichter, 1972). Design emerges from different people in society, and participation in design allows tapping into this source of emerging perspectives and ideas. A similar idea was developed in the 1960s in the Netherlands based on Habraken’s (1972) “support and infill” concept, incorporating different stakeholders (Carp, 1986). In 1971, these ideas were brought together in The Design Research Society conference with the primary theme Design Participation (Cross, 1972). 5.2 Cooperative Design In Scandinavia, projects such as NJMF, DEMOS, DUE, and UTOPIA emphasized the participation of people in design activities (Carlsson et al., 1978; Ehn & Kyng, 1987; DUE Project Group, 1979; Howard, 1989; Nygaard, 1979; Nygaard & Terje Bergo, 1975; “The DEMOS Project: A Short Presentation,” 1978). These projects, in collaboration with workers unions, emerged as computer mainframe systems impacting the work environment. These projects led to Cooperative Design, in which designers collaborated with non-designers to develop computerized tools and systems in the workplace (Sundblad, 2011). These developments gave rise to the term Human-centered Design (Cooley, 1980, pp. 76–77). Cooley (1980, p. 77) emphasized that people have to decide to fight for the “right to be the architects of the future, or allow a tiny minority to reduce [them] to bee-like responses.” This design approach considers the broader socio-economic and socio-technical context and its impact on people through participation. 5.3 Participatory Design The collaborative design approaches popularized under the term Participatory Design. In 1990, the international Participatory Design research community
236 J. Auernhammer et al. gathered at the first Participatory Design Conference (Bødker et al., 1995; Robertson & Simonsen, 2012). Participatory Design democratizes the design practices and embraces the politics involved in a design project (Björgvinsson et al., 2010; Kensing & Blomberg, 1998). However, there are several challenges in providing the conditions for designing with people, such as considering who is participating, the time frame of continuous participation, power-structures involved in decisionmaking, compensations for participation, and social dynamic where no social community exists and no consensus seems to be possible (Bjögvinsson et al., 2012; Bødker, 1996; Robertson & Simonsen, 2012). Several designers and scholars developed techniques and practices for designing with people (e.g., Bjerknes et al., 1987; Greenbaum & Kyng, 1991; Schuler & Namioka, 1993). Participation in information system design became common practice (Bannon et al., 2018; Bodker et al., 2009; Carroll & Rosson, 2007; Smith et al., 2017). 6 Social Psychology in Design Research in Social Psychology established action research practices to resolve societal tensions (Lewin, 1936, 1946, 1947). This development provided a social practice to resolve social tensions that emerged in the interactions of people with the artificial, cultural, and natural environment. 6.1 Social Design The practices developed in social psychology evolved into action science and organizational learning, emphasizing the conflict between the individual and the designed organizational system (Argyris, 1957, 1970; Argyris et al., 1985; Argyris & Schön, 1989, 1992, 1996). Resolving social tensions requires dialogue and action. In architecture and urban planning, similar ideas emerged, requiring dialectic reasoning to tame wicked problems inherent in a pluralistic society (Rittel, 1987; Rittel & Webber, 1973). In a similar vein, social informatics focuses on designing information systems and technology to enable social systems, such as organizations (Kling, 1973, 1977; Kling & Scacchi, 1980, 1982; Kling & Star, 1998). Sociopolitical dynamics influence the design practices and projects, and the design outcome impacts society (Frascara, 2002; Margolin, 2002; Margolin & Margolin, 2002). Designing is a socially constructed and political effort that requires many diverse groups who do not have the economic or political means to generate a formal design demand (Manzini, 2015; Margolin, 2002; Rittel & Webber, 1973; Whiteley, 1997).
Facets of Human-Centered Design: The Evolution of Designing by, with,. . . 6.2 237 Design as Social Collaboration From a bird’s-eye view, design solutions are created collaboratively by, with, and for many people. This is a design culture in which many people contribute results in open, free, distributed, and shared innovation (von Hippel, 1988, 2005, 2016). In management, the term co-design emerged with the emphasis on co-creating value between customers and organizations. The meaning and creation of value shifted from a product and a firm-centric approach to personalized consumer experiences (Prahalad, 2004; Prahalad & Ramaswamy, 2004). Co-design is a network of informed, empowered, and active people who co-design valuable solutions with organizations, resulting in entire distributed communities of people co-designing solutions. It is also a force for social and political change (Heller & Vienne, 2003). Such Social Collaboration of designing is an ongoing social activity that requires people with attitudes, values, attributes, and abilities within a supportive environment, allowing design for various needs and tensions collaboratively (Auernhammer & Roth, 2021). 7 Conclusion Different cultural, socio-economical, and other contextual developments in specific periods led to the evolution of diverse Human-centered Design approaches. Experimental psychology informed designers in the practices to design for the physiological and psychological needs of people. This approach developed further into Human–Computer Interactions and Interaction Design. New developments in experimental psychology and new technological advancements, such as the personal computer, resulting in new Human-centered Design approaches. Today, global pandemics and climate changes require designers to respond with new design approaches. For example, collaboration in design is impacted by remote settings and the use of technology, influencing the abilities of design teams to respond to these challenges. Designers need to develop new design approaches to tackle the many challenges, such as inequality in society and ecological sustainability, creatively. This book chapter illustrated the interrelations between emergent situations and developments of design approaches and highlights the importance of developing new design approaches to tackle the many societal, economic, and ecological challenges of today and tomorrow. References Adams, J. L. (1974). Conceptual blockbusting: A guide to better ideas. Stanford Alumni Association. Alexander, D. C. (1986). The practice and management of industrial ergonomics. Prentice-Hall.
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Decades of Alumni: Designing a Study on the Long-Term Impact of Design Education Sheri D. Sheppard, Helen L. Chen, George Toye, Timo Bunk, Nada Elfiki, Felix Kempf, J. L. Lamprecht, and Micah Lande Abstract Design is an essential part of engineering practice and engineering education. As such, our research aims to examine the longer-term impact of engineering design education on graduates’ career paths and their practical utilization of design, post-graduation. We have focused our attention on several decades of alumni from two specific graduate course sequences, Project-Based Engineering Design Innovation & Development (ME310) and Smart Product Design (ME218), in order to gain a deeper understanding of how particular course elements and strategies are directly linked to what alumni retain and take away from their education. These course sequences represent two possible Mechanical Engineering depth areas that leverage a project-based learning approach to allow students to dive deeply into designing and building functional systems of some engineering complexity. In this chapter, we describe a multifaceted and mixed methods research effort that considers decades of graduates from ME310 and ME218 at Stanford University. The qualitative interviews and quantitative survey studies were designed to establish a deeper understanding of the longer-term impact of education on career plans and pathways (particularly as related to engineering innovation and entrepreneurship) and to also demonstrate the need to take a “bigger view” of graduates’ feedback on courses and on their formal education more generally. The analyses of this rich dataset are already bearing fruit by allowing us to identify specific curricular “features” that inspire innovative and entrepreneurial actions. We are also seeing how ME310 and ME218 graduates have built careers in a variety of professions, are scattered around the globe, and do not follow a singular career pathway or even dominant industry sector. Some stay highly technical throughout their post-graduate work, whereas others turn to less technical roles immediately after graduation. S. D. Sheppard (*) · H. L. Chen · G. Toye · T. Bunk · N. Elfiki · F. Kempf · J. L. Lamprecht · M. Lande Department of Mechanical Engineering, Stanford School of Engineering, Stanford, CA, USA e-mail: sheppard@stanford.edu; hlchen@stanford.edu; toye@stanford.edu; felix.kempf@kcl.ac.uk; johannes.lamprecht@tum.de; Micah.Lande@sdsmt.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_13 247
248 S. D. Sheppard et al. 1 Introduction For more than 50 years, design has been a strong component of mechanical engineering education at Stanford University. Within the Department of Mechanical Engineering, the Design Group offers a robust set of undergraduate and graduate courses and curricula to support mechanical engineering design learning opportunities. The founding of the Hasso Plattner Institute of Design (d.school) in 2005 marked an evolution of design methodology, popularized as design thinking, beyond just the disciplinary bounds of traditional engineering. This methodology also served as an extension to the long tradition of project-based learning in support of engineering design. The creation of engineering design-centered courses started at Stanford in the early 1960s and showed a steady growth throughout the subsequent decades. Today there are over 100 design courses offered each year and many include an emphasis on project-based pedagogies (Arnold, 1953; Adams, 1974; Auernhammer & Roth, 2021). In this research we examine the longer-term impact of engineering design courses on graduates’ career paths and their practical utilization of design, post-graduation. We have focused our attention on several decades of alumni from two specific graduate course sequences, Project-Based Engineering Design Innovation & Development (ME310) and Smart Product Design (ME218), in order to gain a deeper understanding of how particular course elements and strategies are directly linked to what alumni retain and take away from their education. These course sequences represent two possible Mechanical Engineering depth areas that leverage a projectbased learning approach to allow students to dive deeply into designing and building functional systems of some engineering complexity. Both ME310 and ME218 share similar pedagogical approaches of a productfocus on learning (Larson et al., 2020) and a type of Maker-based pedagogy (Lande et al., 2017). Students have some autonomy to explore and direct their design challenges through the effects of prototyping (Schrage, 1996; Gerber & Carroll, 2012), scaffolded by activities, labs, and milestones (Lande, 2016). Students not only engage in the application of a mechanical engineering design process but are mentored in the ingenuity of developing a technology into a product with regular engagement with a supportive teaching team of instructors and teaching assistants as design process and technical coaches (Reich et al., 2009; Strong et al., 2019). This high degree of interaction between the students and teaching team translates into a healthy network of course alumni who participate on the periphery as coaches or in quarterly events, presentations, or end-of-year activities. The specific content of ME310 and ME218 differs in significant ways. ME310 is focused on team-based design processes for innovative product development with industry-sponsored projects. Since the mid-2000s, there has also been an element of distributed collaboration with a number of academic partners around the world (Carleton & Leifer, 2009). In contrast, ME218 has emphasized the combined engineering disciplines in mechatronics and employed projects as a means for
Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 249 students to learn how to integrate these technologies into discrete functioning systems to achieve operating design goals (Brunhaver et al., 2012; Carryer, 1999). This chapter summarizes the mixed methods research efforts and analyses designed to learn more about the impact of ME310 and ME218 on their graduates. This work addresses the following research questions: • RQ1: What career paths have graduates from immersive engineering design courses pursued? • RQ2: What are the alumni’s attitudes and perspectives on the various components of these types of design courses? • RQ3: How might course experiences influence graduates’ career paths, especially as related to being innovative and/or entrepreneurial? • RQ4: What can we learn from listening to alumni about the effectiveness of education more generally? What improvements are suggested for engineering design education? 2 Background Engineering programs and higher education institutions serve important roles in preparing graduates to join a rapidly evolving workforce and changing environment in fields both within and beyond engineering (Atman et al., 2010; National Academy of Engineering, 2018; Sheppard et al., 2014; Gilmartin et al., 2017). Decisionmaking about career pathways is iterative and often repeated across one’s professional trajectory, from the first position that is taken after graduation to retirement. New career interests and motivations, emerging technologies, and unanticipated personal and professional opportunities and challenges may also contribute to these choices. In order to respond to the needs of their students and prepare them for these unpredictable career pathways, educators have made corresponding changes to their curricula and pedagogies within the fields of design education and engineering education through project-based design engineering courses. These courses operationalize key elements of the design thinking philosophy and practices that underpin the graduate curriculum offered by the Design Group within the Department of Mechanical Engineering at Stanford (Auernhammer & Roth, 2021). Over the last 2 years, we have examined how these intensive course-based design experiences contribute to and facilitate intentions related to entrepreneurship and innovation in former students. This research draws on prior work in three areas: Academic and Professional Pathways of Engineering Students and Alumni The individual and environmental factors that contribute to a student’s experience in engineering education has been a fundamental area of research for engineering educators. From 2003 to 2010, the Center for the Advancement of Engineering Education addressed the components of skills, identity, education, and workplace
250 S. D. Sheppard et al. through studies of students, faculty, and early career engineers (Atman et al., 2010, Sheppard et al., 2010). About a decade later, the National Academy of Engineering (2018) highlighted the diversity in the skills that engineers use across occupations and industries, the need for engineers with diverse backgrounds and life experiences, and the ability of the engineering community to convey the value of engineering education to diverse stakeholders. Employing Qualitative, Quantitative, and Mixed Methods Approaches The transition from a pipeline to pathways model recognizes that there is no one singular student experience (Atman et al., 2010) and more expansive research methods must be used to effectively gain insights into the perspectives of engineering students and alumni (Sheppard et al., 2014). The National Academy of Engineering (2018) found that the limitations of national survey-based datasets contributed to data gaps that impede the field’s understanding of educational and career pathways. As a result, triangulation among datasets informed by surveys as well as interviews can strengthen the credibility and generalizability of research findings. Connections to Entrepreneurship and Innovation Outcomes Gilmartin et al. (2017) documented the findings from the Engineering Majors Survey, an instrument designed to investigate undergraduate engineering students’ decisions and plans for the post-graduate transitions into the workplace and to gain a more nuanced understanding of the academic and professional learning experiences as well as the individual characteristics that contribute to entrepreneurial and innovation interests and goals. Barth et al. (2020) provide another model of examining the innovative behaviors and outcomes of early career engineers. The findings from Bunk (2021), Elfiki (2021), and Sheppard et al. (2021a, b) advance the research literature and the methodologies used to explore how graduate-level curriculum incorporating both design and engineering components contribute to entrepreneurial and innovative actions and career interests of alumni. 3 Study Design and Methods 3.1 Overview of the Research Design The goal of this research is to investigate the longer-term impacts of educational experiences. As a result, we focused on several decades of alumni who completed specific engineering design course sequences: Project-Based Engineering Design Innovation & Development (ME310) and Smart Product Design (ME218). The emphasis on course completion contrasts with alumni studies that are at the level of a program (often for continuous improvement processes such as ABET, formally known as the Accreditation Board for Engineering and Technology) or administered by the school or university, where perspectives are gathered as part of alumni relations, institutional accreditation, and development. Our unique approach allows
Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 251 us not only to collect enough data for some interesting quantitative data analyses, but also to explore how careers change over time (in a cross-sectional manner) and conduct comparative analyses between the two course sequences. Brief Description of ME310 Project-Based Engineering Design Innovation & Development, with its emphasis on industry-sponsored engineering design projects, was first taught in the Stanford Mechanical Engineering department in 1967. Programmatically, the ME310 A/B/C course sequence is a graduate-level, academic year-long, multi-disciplinary, project-based learning, design engineering, student experience. In the 1990s, ME310 updated its emphasis toward providing an integrative journey of team-based innovative product design development processes (going from concept explorations to tested prototypes), grounded in real-world contexts through its industry-sponsored project framework. Team projects with students from across a growing network of international academic partners reflect the increasing interest in and adoption of globally distributed teamwork in industry. Annually, ME310’s project portfolio spans a varying multiplicity of product and service domains, where design challenges often include considerations for human psychology, economics, and business models (Carleton, 2019; Carleton & Leifer, 2009; Sheppard et al., 2021b). Brief Description of ME218 This Smart Product Design course sequence originated in the 1970s as a single course for graduate students to design and build electromechanical systems (also known as “mechatronics”) and has now evolved to cover continuously advancing technologies and topics in software/firmware and electromechanical and electronic hardware design. The ME218 A/B/C/D course series offered in Mechanical Engineering is a graduate-level, depth sequence option that spans four academic quarters where students incrementally acquire greater technical knowledge and master more advanced design, build, and debugging skills through a series of hands-on laboratory assignments and projects. ME218A emphasizes fundamentals, ME218B applications, and ME218C practice. In ME218D, student teams dedicate their efforts to industry-sponsored projects. Crafting a study approach to answer our research questions was not without challenges, since the instructors and curricular topics for ME310 and ME218 have changed over the decades—although we believe that their core fundamentals have remained largely intact. In addition, there have been generational variations given the changing profile of graduate students admitted to Stanford’s Mechanical Engineering program as represented by the research on Baby Boomers, Gen X, Gen Y/Millennials, and now Gen Z (Pew Research Center, 2015). The picture is further complicated by the fluctuating job market, especially as engineering work continues to evolve and expand. Figure 1 and Table 1 provide an overview of the various datasets that were collected and that constitute our overall research design. This mixed methods study weaves together quantitative data collected through surveys with semistructured interviews that draw out more details about alumni’s course experiences and subsequent career choices. The research design, funded by the Hasso Plattner
252 S. D. Sheppard et al. Fig. 1 Overview of the ME310 and ME218 alumni research design Table 1 Summary of the ME310 and ME218 instruments, participants, relevant research questions, and researchers Research instrument ME310 Survey: deployed July 2020 ME310 Interviews: conducted in November 2020 ME218 Interviews: conducted December 2020– January 2021 ME218 Survey: deployed August– September 2021 Participants ME310 Alumni from 1992 to 2018. Survey sent to 734 ME310 alumni. 41% response rate. Alumni identified from ME310 Survey Responses, based on Entrepreneurial and Innovation SelfEfficacy Measures. 39 interviews conducted out of 75 invitations. ME218 alumni who are or who have been successful entrepreneurs. Interview candidates were identified from recommendations from E. Carryer, LinkedIn profiles and ME218 public archive, based on founder activity. 19 interviews conducted out of 26 invitations. ME218 Alumni from 1987 to 2019. Survey sent to 1735 alumni. 33.8% response rate. Research questions/goal RQ1, RQ2, RQ3, RQ4 Lead researchers Toye, Chen, Kempf, Elfiki, Sheppard RQ3, RQ4, with a particular focus on identifying entrepreneurs and intrapreneurs Elfiki RQ3, RQ4, with a particular focus on identifying successful founders Bunk RQ1, RQ2, RQ3, RQ4, plus distinguishing how/if the emersive experiences in ME310 and ME218 differ from one another. Toye, Chen, Kempf, Lande, Bunk, Lamprecht, Sheppard Design Thinking Research Program (HPDTRP), evolved over the course of 2 years. The first year facilitated the design and development of the ME310 Alumni Survey, and the second year expanded the work to include alumni from ME218 which allowed for comparative analyses.
Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 3.2 253 ME310 and ME218 Alumni Survey Instruments and Respondents Both the ME310 and ME218 Alumni Survey instruments share common sections on Education (degrees earned), Career Pathways (post-course occupations, current/ most recent job, future plans), Attitudes and Skills (self-efficacy and innovative behaviors) and Demographics. Self-efficacy, the belief in one’s ability to perform a specific task or action (Bandura, 1986), and its relationship to Social Cognitive Career Theory (Lent et al., 1994; Lent & Brown, 2006) serve as the theoretical foundation informing our alumni research particularly as it relates to engineering, innovation (Barth et al., 2020; Gilmartin et al., 2017; Schar et al., 2017a, b), entrepreneurship (De Noble et al., 1999), and design thinking (Schar, 2020). Table 2 describes the three self-efficacy measures included in the survey instruments that were particularly relevant to the ME310 and ME218 interviews, along with a key behavioral measure on innovation. These items concentrate on the attitudes and skills demonstrated on the job, and build on prior research findings from the Engineering Majors Survey (Gilmartin et al., 2017; Schar et al., 2017a, b) and De Noble et al. (1999), who focused on innovative and entrepreneurial activities and behaviors. ME310 and ME218 survey participants were also asked how often they engaged in various activities and behaviors listed in Table 3 as a strategy to build a more nuanced picture of individuals’ day-to-day work. Survey Items Tailored to the Course Experience Each survey also includes survey questions that focused on the attributes of the course experience, outcomes, and takeaways. Sheppard et al. (2021a, b) provide more details about the rationale and development of these measures. Some of the ME310 and ME218 survey questions focused on recalling and assessing the impact of course-specific core design strategies and skills, whereas other questions were designed to prompt reflection on their course projects. These Table 2 Descriptions of key self-efficacy and innovative behavior measures Self-efficacy measures: Innovation (ISE.5) [5 items] Engineering Task (ETSE) [4 items] Entrepreneurial (ESE) [10 items] Behavior measure: Innovative Behavior (IB) [6 items] Confidence in one’s ability to: Five-point Likert scale: from “Not confident” (0) to “Extremely confident” (4) Innovate, i.e. to engage in specific behaviors that characterize innovative people. Perform integral technical engineering “tasks” such as “analyzing the operation or functional performance of a complete system.” Pursue a new venture opportunity, representing two dimensions related to developing “new product and market opportunities” and “coping with unexpected challenges.” Description: Five-point Likert scale: from “Never” (0) to “Very often” (4) Individual behaviors that contribute to a collective innovation process focusing on idea generation, coalition building, idea realization, and transfer/diffusion.
254 S. D. Sheppard et al. Table 3 Work behaviors in current job General work behaviors • Presenting your work to senior managers or leaders • Working on something that generates interest and feedback from others in your organization • Working in areas or domains that are unfamiliar to you • Cross-functional collaboration (working with different business units, etc.) • Working on ambiguous, ill-defined problem • Leading or directing others • Documentation • Ethical questions or considerations Entrepreneurial, innovative, and engineering work behaviors • Searching out new technologies, processes, techniques, and/or product ideasa • Generating creative ideasa • Promoting and championing ideas to othersa • Investigating and securing resources needed to implement new ideasa • Developing adequate plans and schedules for the implementation of new ideasa • Selling a product or service in the marketplacea • Designing a new product or project to meet specified requirementsb • Analyzing the operation or functional performance of a complete systemb • Discovering new ways to improve existing productsc • Seeing new market opportunities for new products and servicesc • Creating products that fulfill customers’ unmet needsc Note: Respondents were asked to indicate their frequency of engagement in the activities listed above on a five-point Likert scale including: Never, Rarely, Sometimes, Often, Very Often a These six items are part of the Innovative Behavior construct described in Table 2 (Barth et al., 2020) b These two items relate to engineering task behaviors (Schar, Gilmartin, Rieken, et al., 2017b) c These three items relate to entrepreneurial behaviors (De Noble et al., 1999) strategies and skills varied between the two courses. Furthermore, because of ME310’s emphasis on the social-dynamics of design, additional questions related to the team and classmate interactions and performance and the durability of teammate relationships, both locally and globally, were included. In contrast, a series of questions on the ME218 survey related to the pervasiveness of continued activities related to software and electronics design, writing interface firmware, and designing mechatronic systems. These differences are summarized in Table 4. Key Survey Deployment Details Details of the deployment of the ME310 Alumni Survey are provided in Sheppard et al. (2021a, b). Many of the same procedures were followed with the deployment of the ME218 Alumni Survey (e.g., using Qualtrics, working with the Institutional Research office to identify names of students who had completed the course over the 25 year period, and then with the Alumni Relations group within the School of Engineering to send out email invitations to the identified alumni). For the ME218 alumni, a commemorative digital poster representing a collage of course photos was commissioned and sent out to all survey participants. In addition, 19 survey participants were randomly selected in a drawing to receive a gift card to the Stanford Bookstore and/or an invitation to a virtual meal with the current ME218 instructor
Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 255 Table 4 ME310 and ME218 course experience survey items ME310 course-specific questions • What ME310 course skills do you remember from ME310? (e.g., design process, public communications, collaboration, project and team management) • Which of these skills have you subsequently found valuable in your life/career? • What ME310 design process strategies do you remember from ME310? (e.g., challenging assumptions, building quick prototypes, taking risks with radical design ideas) • Which of these strategies have you subsequently found valuable in your life/career? • Did you work on a project with a global partner team? • Since ME310, how many social connections have you maintained (e.g., with your project’s Stanford teammates, your project’s global teammates, etc.)? ME218 course-specific questions • What ME218 course skills do you remember from ME218? (e.g., collaboration, project planning, development process) • Which of these skills have you subsequently found valuable in your life/career? • Clusters of questions about experiences and activities related to: -Designing and building electric circuits -Developing software applications (coding) -Writing sensor/actuator interface firmware -Designing/building integrated mechatronic systems (per chronological contexts: at entry to ME218, subsequently in first and current jobs, and episodically on personal projects.) and fellow alumni. It is important to note that the success of both surveys relied heavily on leveraging the social capital of class alumni to participate. Survey Respondents The demographics of the survey respondents among ME310 and ME218 alumni who had been invited to participate in the survey are shown in Table 5. These demographics largely mirror those of the larger population in the graduate program in Mechanical Engineering at Stanford, where there has been a slow and steady rise in female enrollment over the years. Noteworthy is the underrepresentation of those who self-identified as American Indian, Black or African American, or Hispanic. The ME310 and ME218 survey datasets represent a unique and heterogeneous network of alumni who graduated from Stanford between 1993 and 2017 for ME310 and 1988 to 2019 for ME218. Despite the extraordinary course histories and multigenerational survey audience, the survey respondents are evenly distributed across year groups and clusters as visualized in Fig. 2a. An even distribution of survey participants across year groups allows us to gain robust and valuable insights into how these courses were perceived over the years. Figure 2b shows the response rate across the years. The response rate is defined as people with registered email address divided by the number of responses. Note: In the ME218 dataset we have 12 missing values regarding the academic year of enrollment.
256 S. D. Sheppard et al. Table 5 ME310 and ME218 course alumni survey respondents ME310 Alumni (N ¼ 267)a Respondents Percent (a) By Gender Females 58 Males 208 Missing 1 Total 267 (b) By Race/Ethnicity (mark all that apply) American Indian or Alaska Native 2 Asian or Asian American 110 Black or African American 10 Hispanic or Latino/a 22 Native Hawaiian or Pacific Islander 1 White 135 Other 4 I prefer not to answer 5 Missing 1 ME218 Alumni (N ¼ 503)a Respondents Percent 21.7% 77.9% 0.4% 100.0% 95 402 6 503 18.9% 79.9% 1.2% 100.0% 0.7% 41.2% 3.7% 8.2% 0.4% 50.6% 1.5% 1.9% 0.4% 1 152 16 42 2 290 6 19 3 0.2% 30.2% 3.2% 8.3% 0.4% 57.7% 1.2% 3.8% 0.6% Note: These two survey samples may include individuals who completed both ME310 and ME218. At the time of writing, we have not separated that group out a The authors decided to consider only complete (defined as 95% of survey progress) survey submissions leading to an effective sample size of 267 for the ME310 dataset and 503 for the ME218 dataset. The rationale behind this decision is due to the high number of incomplete survey responses within the ME218 dataset, i.e., 14.3%. To ensure consistency and comparability the 95% criterion was also applied to the ME310 dataset leading to a loss of 2% of the survey responses 3.3 Interview Studies Both interview studies described below employed a multiple case research strategy for theory building (within the case of the ME310 or ME218 course) so as to focus on further understanding the dynamics that are present within a single context (Eisenhardt, 1989). The fundamental source of data stems from semi-structured interviews, as these are an effective and efficient technique of gathering information-rich data from numerous and highly knowledgeable informants who view the focal phenomena from diverse perspectives (Eisenhardt & Graebner, 2007). Selection and Recruitment for the ME310 Interview Study on Entrepreneurs and Intrapreneurs, and Development of Interview Protocol A theoretical sampling approach was taken to identify which ME310 survey participants to interview based on the following criteria: 1. Moderate to high average Entrepreneurial Self-Efficacy (ESE) and Innovation Self-Efficacy (ISE) scores (see Table 2) 2. Current position: entrepreneur or intrapreneur
Fig. 2 (a) Enrollments and invitations sent to ME310 and ME218 alumni, by year of class enrollments Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 257
Fig. 2 (continued) (b) ME310 and ME218 alumni survey response rates by year enrolled in course 258 S. D. Sheppard et al.
Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 259 3. Moderate to high average entrepreneurial and innovation work behaviors (see Table 3) 4. Survey-proportionate gender and generational balance A four-section structured interview protocol with semi-structured questions was developed. The first section asked for the interviewee’s definition of and perceived differences between innovative and entrepreneurial behavior to ensure that the interviewer and interviewee were aligned on the scope of the topic. The focus of the second and third sections was on their learning experiences during their professional and academic years (including the ME310 course) in relation to the correlated Innovation Self-Efficacy (ISE) and Entrepreneurial Self-Efficacy (ESE) items. Lastly, the interviewees were encouraged to reflect back on what they wish they had learned earlier during their study years. In doing so, they were prompted to design their own hypothetical course and describe how they would foster innovative and entrepreneurial engineering leaders. In total, 39 ME310 alumni interviews were conducted, with men accounting for 67% and women representing 33% of those interviewed. Of this group, 49% identified as founders and 46% as intrapreneurs at the time of the interviews. More details on the study procedures can be found in Elfiki (2021). ME218 Interview Study on Successful Founders A theoretical sampling approach was also used to identify and invite alumni for an interview. However, because these interviews were conducted before the ME218 alumni survey, a selection strategy was needed that did not rely on survey data. The task became one of the identifying successful company founders who had taken ME218 through means such as referrals from the current course instructor, searches on LinkedIn, and in the publicly available ME218 archive. The following selection criteria were used to identify 132 ME218 alumni for possible interviewing: 1. 2. 3. 4. 5. Being a ME218 alumnus while Dr. Edward Carryer was teaching ME218 Holding a co-founder or former co-founder position Contributing to gender balance Contributing to generational balance Contributing to variation in industry The interview protocol began with a philosophical question prompting the interviewee about their general motivation and interest in entrepreneurial activities. Interviewees were asked about the influence of their educational experiences and ME218 specifically as the means to explore how courses and other related learning fostered entrepreneurship, provided valuable skills, and increased Entrepreneurial Self-Efficacy. Additional questions explored how education could be improved to prepare students for an entrepreneurial career. These questions also focused on interviewees’ ideas for improving education and areas they felt were missing in their education. Interviewees were also prompted to share their definitions of success and the pathways they took to achieve success and navigate challenges and failures, particularly in the startup environment, thereby generating advice for new founders. Finally, interviewees were asked to describe something they would teach to current
260 S. D. Sheppard et al. students in a lecture and how they would demonstrate their acquired skills to students with entrepreneurial interests. In total, interviews were conducted with 19 individuals from a variety of industries. Men accounted for 89% and women represented 11% of those interviewed. More details on the study procedures can be found in Bunk (2021). 4 Results Our study design allowed us to undertake a variety of analyses to better understand the longer-term impact of immersive engineering design experiences like ME310 or ME218 on the career paths and entrepreneurial and innovative interests of alumni. Opportunities for mixed methods, comparative approaches, and the triangulation of findings with the quantitative and qualitative datasets are represented. We have amassed a significant and varied collective dataset on alumni’s course and career experiences and additional analyses and findings will be forthcoming, as outlined in Sect. 5. 4.1 Overall Picture of ME310 and ME218 Alumni Table 6 shows that some 36.7% of ME310 course alumni were working in mediumto-large firms (49% when missing responses are excluded), and 15.0% were working in small firms (20% without missing responses). This is in contrast to 49.1% of ME218 alumni who reported working in medium-to-large firms, and 23.5% working in small firms. These results suggest that ME218 course alumni may be more drawn to smaller organizations than ME310 course alumni. Another 13.1% (17.6% without missing responses) of ME310 alumni are founders of a for-profit organization, compared with 11.9% of ME218 alumni; both numbers are significantly higher than the 4% number reported in the National Academy of Engineering (2018) report on academic and career pathways of engineering graduates. We also see that 7.6% of ME310 alumni (excluding missing data) and 6.4% of ME218 alumni identify as being a faculty member at a university or college. These numbers are in the range reported for individuals who choose to pursue a PhD degree after earning a Master’s degree, then going on to careers in academia (National Academy of Engineering, 2018). Both alumni surveys asked respondents to classify their current or most recent job and their first job after completing ME310 or ME218 from a list of 16 job functions. Respondents were asked to “mark all that apply.” As shown in Fig. 3, the three job functions that saw notable increases in respondent involvement between the first post-ME310 (or ME218) job and their current job were Marketing/Public Relations, Sales, and Technical Management (suggesting migration into these functions). The
Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 261 Table 6 Organizational role best aligned with current or most recent job Organizational role (current or most recent job) Employee for a medium- or large-size business Employee for a small business or start-up company Founder/co-founder of your own for-profit organization Faculty member or educational professional in a college or university Employee for a non-profit organization (excluding a school or college/university) Employee for the government, military, or public agency (excluding a school or college/ university) Founder/co-founder of your own non-profit organization Teacher or educational professional in a K-12 school Missing Total ME310 Alumni Respondents Percent 98 36.7% ME218 Alumni Respondents Percent 247 49.1% 40 15.0% 118 23.5% 35 13.1% 60 11.9% 15 5.6% 32 6.4% 4 1.5% 9 1.8% 4 1.5% 20 4.0% 1 0.4% 2 0.4% 1 0.4% 1 0.2% 69 267 25.8% 100.0% 14 503 2.8% 100.0% Fig. 3 (a) ME310 alumni respondents reporting various job functions of their current job and first post-ME310 job (N ¼ 267). (b) ME218 alumni respondents reporting various job functions of their current job and first post-ME218 job (N ¼ 503)
262 S. D. Sheppard et al. Fig. 3 (continued) Table 7 Engineering and non-engineering classifications of first position after course and current position Position type: Engineering First position after course Current position ME310 Alumni Respondents 220 104 Percent 82.4% 39.0% ME218 Alumni Respondents 452 353 Percent 89.9% 70.2% three job functions that saw notable decreases in respondent involvement were Design, R&D, and Production/Manufacturing (suggesting migration out of these functions). We also asked ME310 and ME218 alumni to classify their first and current jobs as engineering or non-engineering. As shown in Table 7, some 90% of ME218 alumni described their first job as “engineering”; this number decreased to 70% when classifying their current job. For ME310 alumni, some 82% identified their first job as “engineering”; this dropped to 39% when describing their current job.
Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 4.2 263 Dissemination of Findings to Date The findings from the ME310 and ME218 alumni interviews are documented in Elfiki’s (2021) and Bunk’s (2021) master’s theses, respectively. Elfiki (2021) identified four main categories (Mastery by Doing, Connection to Real Life, Interdisciplinary Exposure, Supportive Environment) that have the potential to enhance Entrepreneurial Self-Efficacy and Innovation Self-Efficacy. Bunk (2021) examined other factors that influenced ME218 alumni’s decisions to start companies and further aspects crucial for their entrepreneurial success including Relevant Learnings for the Entrepreneurial Path (important lessons learned by founders during their education beyond their ME218 experience), and Success Factors, Entrepreneurial Motivation Factors, and Confidence for Entrepreneurship, which described elements that have the potential to inspire more students to consider an entrepreneurial pathway. Sheppard et al. (2021b) provide details on the design of the ME310 survey, including logistical considerations for survey deployment and participant incentives. Cronbach’s Alpha scores, based on the data, for the self-efficacy measures of Innovation Self-Efficacy (α ¼ 0.72), Engineering Self-Efficacy (α ¼ 0.85), Entrepreneurial Self-Efficacy (α ¼ 0.87), and Design Thinking Self-Efficacy (α ¼ 0.75) were reported, along with the actual calculated values of these measures based on the weighted average of associated items. Sheppard et al. (2021a) was shared results at the annual meeting of the American Society of Engineering Education in July 2021. In this conference paper and presentation, variations across the self-efficacy measures among the ME310 alumni were investigated. Through comparative analyses of the ME310 survey data, we looked at how leadership roles, employment in various types of organizations, and job functions might result in differences in self-efficacy and/or innovative behaviors. We found that greater Entrepreneurial Self-Efficacy and Innovative Behaviors were exhibited by those who reported more leadership responsibilities and/or who were currently a founder. Furthermore, those whose current job involved R&D and design, and whose first job had involved one or both of these functions exhibited greater Engineering Self-Efficacy, but no greater Innovation Self-Efficacy or Entrepreneurial Self-Efficacy than any of the other groups we considered. Those whose first and current jobs did not include any design and/or R&D functions exhibited the lowest innovative behavior. As part of our efforts to gain insights into the ME310 course experience for the teaching team, we experimented with new approaches of data analysis including a “word salad” question where we presented survey participants with a list of 18 words and asked them to mark all that were applicable to describing their overall ME310 design journey. Using the R programming language to analyze the data, the word cloud in Fig. 4 summarizes how frequently an individual word was selected by the ME310 survey participants. The majority of the words identified by 301 respondents (84.1%) were deemed positive (e.g., engaging, enjoyable, inspiring) rather than negative (15.9%, e.g., painful, frustrating, tiring). These results were included in a
264 S. D. Sheppard et al. Fig. 4 Word cloud summarizing word descriptors report to the ME310 teaching team. This same survey item was also included in the ME218 alumni survey and we also expect to share the corresponding findings with the course instructor. The spring and fall 2021 Hasso Plattner Design Thinking Research Program (HPDTRP) community meetings served as important touchpoints for our project with the workshop format prompting synthesis with the design educator audience in mind. The multi-disciplinary community of HPDTRP with diverse expertise— engineering, design, medicine, science, and the humanities—served as a critical sounding board for how we continue to frame and communicate our findings to a broader community of designers and educators. The feedback and suggestions we received have directly informed the development of the framework in Fig. 5 which situates the intensive project-based design courses within a broader context of the pedagogical and curricular components identified in our research. We also shared sample findings from the ME218 survey at the fall 2021 ME218 alumni BBQ, a fall gathering that has been hosted for the last 10 years by the firm 219 Design. The ME218 research team wanted to thank the alumni for their participation by providing some preliminary survey insights at the annual ME218 alumni gathering. In order to present the data in a memorable and creative way, the team used Kahoot!, a game-based learning platform, to create a poll with seven questions from the actual survey. For example, participants could each pick one out of four options to guess how many survey respondents are currently founders or co-founders of their own organizations. After each question, the quiz responses were compared with the actual survey data and people with the correct answers received
Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 265 Fig. 5 Emerging framework for the design, implementation, and assessment of project-based design engineering courses. (a) The blue triangle highlights the Course Elements of Design Thinking, Engineering, and Innovation & Entrepreneurship, with affiliated courses (indicated by the dots) in hypothesized locations; (b) the red triangle identifies the Teaching Strategy dimensions and their hypothesized alignment with the Course Elements points. At the end of the poll, the winner with the highest number of points was announced followed by a short Q&A regarding the actual survey results. 5 Discussion and Next Steps 5.1 Discussion In this chapter, we have outlined a multifaceted research effort that considers decades of graduates from two engineering design course sequences at Stanford University. These studies were designed to establish a deeper understanding of the longer-term impact of education on career plans and pathways (particularly as related to engineering innovation and entrepreneurship) and to also demonstrate the need to take a “bigger view” of graduates’ feedback on courses and on their formal education more generally. The analyses of this rich dataset are already bearing fruit by allowing us to identify specific curricular “features” that inspire innovative and entrepreneurial actions. We are also seeing how ME310 and ME218 graduates have built careers in a variety of professions, are scattered around the globe, and do not follow a singular career pathway or even dominant industry sector. Some stay highly technical throughout their post-graduate work, whereas others enter less technical roles immediately after graduation. This research design has not been without challenges. For example, we worked to create research instruments that asked questions that would be relevant to someone who had taken ME310 or ME218 just a few years ago (say 2018) or many years ago
266 S. D. Sheppard et al. (say 1992)—we recognize these are generations of engineers are very different. We were also challenged by wanting the data collection experience for the alumni (whether through a survey or interview) to be a positive interaction since the user experience is critical to good design. At the time of writing, we are in the midst of several lines of inquiry with the datasets, as outlined below. 5.2 Next Steps in our Research Here are brief descriptions of several analyses that are underway: • Lent’s Social Cognitive Career Theory (SCCT) links learning to actions through self-efficacy development (Lent et al., 1994; Lent & Brown, 2006). Our ME310 interviews allows us to identify features in the ME310 curriculum that have been impactful in the development of entrepreneurial and innovation self-efficacy. Our ME310 survey data allows us to statistically model innovative actions and behaviors in terms of self-efficacy and other aspects of the workplace environment. This is a unique opportunity to utilize the SCCT framework in connecting higher education experiences to longer-term professional activities. • In combination, we have 58 interviews of ME310 (N ¼ 39) and ME218 (N ¼ 19) alumni focused on features of these courses that promote innovative and entrepreneurial ambitions, skills, and interests. The two interview studies have also identified additional skills that founders and intrapreneurs have found to be essential to their career success. We are now examining the combined set of 58 interviews to: (1) develop a more comprehensive list of innovation- and entrepreneurial-promoting course features and other essential skills; (2) evaluate and contextualize these course features in the literature on innovator and entrepreneur characteristics; and (3) interpret and disseminate the findings to diverse audiences including innovation and entrepreneurship researchers, engineering educators, and aspiring innovators and founders. • We added a few additional questions on the ME218 survey asking alumni where they have been working during the COVID-19 pandemic and how satisfied they were with their arrangements. This set of questions, plus additional items on the survey, allows us to identify a pool of ME218 alumni to interview in order to better understand what effective remote teamwork among engineers looks like. We are particularly interested in developing models of collaboration and leadership approaches. Lastly, we recently submitted two paper abstracts for consideration for the 2022 American Society of Engineering Education Annual Conference; one teases out differences and similarities in design education approaches (using ME310 and ME218 as examples of Project-Based Learning) and the other highlights curriculum features in the ME218 course that inspire and support founders.
Decades of Alumni: Designing a Study on the Long-Term Impact of. . . 267 6 Parting Remarks We hope that this chapter inspires... • design educators to reach out to their alumni through a variety of means to see how their education is serving them, and how it might be improved; • design and educational researchers to create study plans that probe both the shortterm and long-term impacts of design education on careers; and • today’s engineering students to recognize that learning that requires iteration, creativity, reflection, and hard work is likely to be the learning that “sticks,” and sometimes the most important part of education are the human relationships that get built. Acknowledgments We have an extended team to say “thanks” to: Professors Larry Leifer and Mark Cutkosky, Dr. Ed Carryer, Crystal Pennywell, Gosia Wojciechowska, Elizabeth Mattson, Mark Schar, Hung Pham, Elizabeth and Lucy Higgins, Nicole Esther Salazar, Katie Toye, NiclasAlexander Mauss, Lawrence Domingo, and Jan Auernhammer, and colleagues in the Stanford Designing Education Lab. We could not have completed this study without the help of Alumni Relations and Student Engagement in the School of Engineering—in particular Drea Sullivan and Catherine McMillan. We are very grateful for the keen editing-eyes of Tammy Liaw and Sharon Nemeth, and the organizational talents of Jill Grinager in bringing the Springer volume into existence each year. We are also grateful to Dr. Claudia Liebethal and Prof. Helmut Schönenberger from UnternehmerTUM who helped recruit dynamite researchers to join the team. Finally, we are appreciative to those who participated in the pilot phases of survey design, the many ME310 and ME218 graduates who completed these surveys, and those who agreed to be interviewed—they represent the essential ingredients of this research. Finally, we acknowledge the generous support of this work from the Hasso Plattner Design Thinking Research Program. References Adams, J. L. (1974). Invention and innovation in the university. In F. Essers & J. Rabinow (Eds.), The public need and the role of the inventor (pp. 37–46). U.S. Department of Commerce, National Bureau of Standards. Retrieved from https://www.govinfo.gov/content/pkg/ GOVPUB-C13-ae7b48f209a10cf3a889a6d57de047f9/pdf/GOVPUB-C13-ae7b48f209a10cf3 a889a6d57de047f9.pdf Arnold, J. E. (1953). Case study: Arcturus IV, creative Engineering Laboratory. Creative Engineering Laboratory, Mechanical Engineering Department, Massachusetts Institute of Technology. Retrieved from https://archive.org/details/casestudyarcturu01john Atman, C. J., Sheppard, S. D., Turns, J., Adams, R. S., Fleming, L. N., Stevens, R., Streveler, R. A., Smith, K. A., Miller, R. L., Leifer, L. J., Yasuhara, K., & Lund, D. (2010). Enabling engineering student success: The final report for the Center for the advancement of engineering education. Morgan & Claypool Publishers. Auernhammer, J., & Roth, B. (2021). The origin and evolution of Stanford University’s design thinking: From product design to design thinking in innovation management. Journal of Product Innovation Management, 00, 1–22. https://doi.org/10.1111/jpim.12594 Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
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Different Types of Productive Thinking in Design: From Rational to Social Design Thinking Jan Auernhammer and Bernard Roth Abstract This book chapter outlines the theory of Productive Thinking in Design. This theory is the psychological foundation of today’s Design Thinking. Productive Thinking incorporates the psychological processes of finding a need, problem, or structural tension and determining a means that satisfies the need and harmonizes the tension. These psychological processes are driven by forces and factors, including attitudes, attributes, and human values. In this article, we outline five different types of Productive Thinking and discuss them in the context of Design. These are (1) Rational, (2) Situational, (3) Experimental, (4) Dialectic, and (5) Counterproductive. Each type of Productive Thinking is dependent on the situational context for which a design solution needs to be created. For example, a situation in which a solution-method can be determined directly requires Rational Productive Thinking, while unintelligible, ambiguous, and emerging situations require Experimental or Dialectic Productive Thinking. We emphasize that it is essential to cultivate a Productive Culture in which individuals can freely, creatively, confidently, competently, and collaboratively design for a harmonious ecological and social whole. 1 Introduction Our research on the evolution of design at Stanford University identifies foundational theories of today’s Design Thinking (Auernhammer & Roth, 2021). The psychological theories in Productive Thinking explain how people determine new solution-methods that resolve grasped problem-situations and structural tensions and satisfy human needs in everyday life. Various design scholars and practitioners have developed design practices and strategies to educate and support people in thinking and collaborating productively (e.g., Arnheim, 2004, 2009; Arnold, 1959; Brown, 2009; McKim, 1972; Schön, 1983). Productive Thinking incorporates thought processes, driven by psychological factors and forces, in everyday problem finding J. Auernhammer (*) · B. Roth Mechanical Engineering Design Group, Stanford University, Stanford, CA, USA e-mail: jan.auernhammer@stanford.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_14 271
272 J. Auernhammer and B. Roth and solving and meaning creation. These thought processes are foundational to creative accomplishments in science, art, and design (Duncker, 1945; Guilford, 1950; Selz, 1922; Wertheimer, 1945). Productive Thinking occurs in mathematics, physics, music, painting, design, and sports, to name a few (Arnheim, 2009; Arnold, 1959; Pólya, 1957; Selz, 1922; Wertheimer, 1945). Questions regarding Productive Thinking are still being investigated in contemporary research in psychology and neuroscience (Kounios & Beeman, 2009, 2014; Mayer, 1995; Wertheimer, 1996). In Design, Productive Thinking has been reconceptualized into Design Thinking over the decades. For example, Eastman (1970) and Simon (1969, 1981) reconceptualized Selz’s (1922) Productive Thinking through de Groot’s (1965) work in Thoughts and Choice in Chess as “design” and “design cognition.” Similarly, Dorst (2019) writes that there are many parallels of design models going back to de Groot (1969). Others, such as Lawson (1972, 1980) and Goldschmidt (1991), refer to Wertheimer’s (1945) Productive Thinking as theoretical explanations of how designers think. Schön’s (1963, 1992a, b) work on the displacement of concepts, design education, and reflective practices incorporates Wertheimer’s (1945) Productive Thinking and Dewey’s (1938) Theory of Inquiry. Similarly, Rowe (1987), in Design Thinking, outlines theoretical positions, including the Würzburg School and Gestalt Movement. Selz (1922) was part of the Würzburg School (structuralism) while, e.g., Wertheimer (1945), Koffka (1935), Köhler (1925); Duncker (1945), and Arnheim (1954, 1969) were Gestaltists (humanism). Arnold’s (1959) and McKim’s (1972) work incorporate various aspects of Productive Thinking by Wertheimer (1945), Duncker (1945), and Guilford (1950), as well as Visual Thinking by Arnheim (1954, 1969). McKim (1972, p. vii) expressed that John Arnold influenced him through his “pioneering efforts in educating Productive Thinking.” Productive Thinking theories are the psychological foundations of Design Thinking. For example, Selz (1922) outlined that directly determining a solution-method of a task requires grasping a purpose-means relationship (Zweckmittelverbinding). Simon (1969) reconceptualized Selz’s (1922) work as a means-end analysis, providing the basis for his Science of the Artificial. It also comes as no surprise that design researchers identified that designers determine solutions by building “bridges” between problems (purpose) and solutions (means) in conceptual design tasks (Dorst & Cross, 2001). The Productive Thinking theories influenced many fields, including artificial intelligence, creativity, design research, entrepreneurship, innovation management, organization studies, philosophy of science, and human–computer interactions, to name a few (e.g., Card et al., 1983; Guilford, 1950; March & Simon, 1958; Newell & Simon, 1956; Popper, 2002; Sarasvathy, 2001; Schön, 1992a; Simon, 1957). In that sense, Design Thinking provides a renaissance of Productive Thinking (i.e., human creativity) in diverse fields and disciplines. This book chapter outlines the Psychology of Productive Thinking and discusses different Productive Thinking types in Design.
Different Types of Productive Thinking in Design: From Rational to Social. . . 273 2 Psychology of Productive Thinking Productive Thinking incorporates the psychological processes of how human beings and other primates grasp problems, needs, and structural tensions within everyday life and determine a solution-method that satisfies the needs and harmonizes situations (Duncker, 1935, 1945; Koffka, 1925; Köhler, 1925; Selz, 1922; Wertheimer, 1945). Every productive thought process starts with an intent or felt and experienced structural tension (i.e., a need) in everyday life (Dewey, 1938; Duncker, 1935, 1945; Selz, 1922; Wertheimer, 1945). In a simplified manner, Duncker (1945, p. 1) describes this moment of need as follows: A problem [need] arises when a living creature has a goal [intent or task] but does not know how this goal is to be reached. Whenever one cannot go from the given situation to the desired situation simply by action, then there has to be recourse to [productive] thinking. (By action we here understand the performance of obvious operations.) Such thinking has the task of devising some action which may mediate between the existing and the desired situations. Thus the [design] “solution” of a practical problem must fulfill two demands: in the first place, its realization must bring about the goal [or harmonious] situation, and in the second place one must be able to arrive at it from the given situation simply through action. However, people often do not recognize a moment of tension as our “automatic system” finds a seemingly appropriate conclusion or explanation (Kahneman, 2011). Effortful attention in the here-and-now (i.e., direct experience) is essential in grasping the moment of need, triggering productive thought processes. Additionally, in real-life (as opposed to laboratory settings), tasks and intents are often unclear, situations are ambiguous and changing, and diverse people have different intents and needs that interact, resulting in need-tensions (Lewin, 1936, 1946; Wertheimer, 1945). Therefore, different types of Productive Thinking are required depending on the situation. Selz (1922) and Duncker (1945) outlined three different types in relation to distinct situations. Selz (1922) described (1) direct, (2) reproductive, and (3) coincidental Productive Thinking. Similarly, Duncker (1945) outlined Productive Thinking as (1) totally intelligible, (2) partially intelligible, and (3) unintelligible depending on the situation. These different types are discussed in detail in Sect. 3. In Design, Productive Thinking is the thought process in which an individual grasps an emerging human need in everyday life and determines and creates a tangible design (e.g., an artifact, instrument, and/or environment) that produces a situation that satisfies the need. “[D]esign is, after all, the response to a human need” (McKim, 1959). Such needs are produced by the natural, cultural, and artificial environment (Gibson, 2014; Maslow, 1954; McKim, 1959). A human need can always be satisfied in new and interesting ways with no definitive solution (Arnold, 1959). Great design solutions produce situations in everyday life in which people’s profound needs are satisfied, have emotional value, are meaningful, practical, and useful, are usable without high cognitive load, and produce as little as possible consequential structural tensions (e.g., environmental damage) (Krippendorff, 2006; McKim, 1959; Norman, 1988; Rams, 1995). Identifying profound cultural and
274 J. Auernhammer and B. Roth environmental needs within a living world is an essential part of Productive Thinking in Design. In Wertheimer’s (1945, p. 123) words: [. . .] the function of thinking is not just solving an actual problem but discovering, envisioning, going into deeper questions. Often in great discovery, the most important thing is that a certain question is found. Envisaging, putting the productive question is often more important, often a greater achievement than a solution of a set question [. . .]. Problem or need-finding is essential in creative accomplishments in science, art, and design (Auernhammer & Roth, 2021; Getzels, 1980; Getzels & Csikszentmihalyi, 1976; McKim, 1982; Wertheimer, 1945). Finding profound needs and determining solution-methods for harmonizing structural tensions are dependent on psychological forces and factors. Wertheimer (1945, p. 199) expressed that various conditions, forces, and factors may determine the structure of both the problem-situation (i.e., grasped tension) and the solution-situation (i.e., felt harmony). Often inertia of habits, attitudes, and psychological tendencies determine the grasping of situations and direction of thought process (Wertheimer, 1945). Such psychological tendencies can lead to premature closure in problem finding and solving. In the case of premature closure, individuals fall victim to a seductive simplification (Wertheimer, 1945). Roth (2015, pp. 63–75) shows how a problem-reframing technique can overcome fixation, lead to divergent thinking, and more meaningful solutions. Over the decades, many studies have identified how psychological forces, factors, and principles, such as affect, motivation, needs, attitudes, attributes, behavior, and environments, influence the productive or creative processes of perception, imagination, and expression (e.g., Amabile, 1996; Bruner, 1957, 1994; Csikszentmihalyi, 2014; Deci & Ryan, 2000; Guilford, 1950; Hoover & Feldhusen, 1994; Runco, 2004; Ryan & Deci, 2000; Wise, 1987). These findings are based on different theoretical conceptions of Productive Thinking. 2.1 Psychological Position in Productive Thinking Since the early research in Productive Thinking, different psychological positions have been debated. Two main positions are a structuralist (mechanistic) position, e.g., Selz (1922) and a Gestalt (humanistic) position, e.g., Wertheimer (1920, 1945), Koffka (1925), and Duncker (1945). These positions have expanded over the decades. Selz’s (1922) position was advanced in chess research by De Groot (1965) and from there into information-processing theory by Simon and colleagues (Simon, 1981). The Gestaltists position was advanced by scholars, such as Maslow (1943, 1954, 1956). From the 1960s onward, mainstream psychology favored the structuralist position (Bruner et al., 1986; Mandler & Mandler, 1964; Simon, 1981). In design, the distinction between these two positions can be found in the difference between Simon (1969) and Schön (1992a). McKim (1980) expressed the difference as “left-hemisphere,” e.g., Newell and Simon (1972), and “right-hemisphere,” e.g., his Experiences in Visual Thinking.
Different Types of Productive Thinking in Design: From Rational to Social. . . 275 This distinction has existed since the early developments of the theories in Productive Thinking. The first distinction is the understanding of the task and situation. Through introspection, Selz (1922) investigated Productive Thinking, in which the tasks represent the situation. As the situation is equal to the given task, it is possible to determine a solution-method by grasping a purpose-means relationship, as there is a very close relationship between a given task and a solution-method. In contrast, Koffka (1927) expressed that such Productive Thinking is enforced by an artificial stimulus and effected by alien and arbitrary factors rather than psychological processes and forces that represent the nature of real-life situations. He argues that the coincidental constellation of the circumstances brings about the effect, not a property inherent in the causal process (Koffka, 1927). This position emphasizes that the task and solution are not “assigned” to one another because the solution is not triggered by the task-situation but emerges from it. Psychological forces and principles reshape the phenomenal fields (i.e., the perceived situation) to reduce tensions, and with each new insight emerges something new, and something new enables new possibilities (Koffka, 1927). Secondly, Selz’s (1922) position has been labeled a “machine theory” (Benary, 1923). The stimulus, reflexes, and operational results can be determined as an abstracted schema (i.e., purpose-means or if-then relationships). Selz (1922) expressed that it is secondary for his theory if the insight occurred from anticipation as long as a purpose-means (if-then) relationship can be determined retrospectively. Koffka (1927) expressed that this position does not allow for identifying, e.g., if the operational result actually emerged from the anticipation of a purpose-means (if-then) relationship. Individuals are aware of and can anticipate circumstances without a conscious abstraction of an if-then schema, and this is the most critical distinction between Productive Thinking from a structuralist and Gestalt position (Koffka, 1927). From a Gestalt position, Productive Thinking is grasping a “structural tension” through direct experience, which sets forth a motivation toward a “harmonious structure” (Wertheimer, 1945). Like Wertheimer (1945), Dewey (1938), from his functionalist position, described the activities of inquiry as resolving an indeterminate (i.e., confusing or conflicting) situation and into a determinate situation, which is an experimental and social activity. Thirdly, the two positions differ in the understanding of novelty. In Selz’s (1922) Productive Thinking, novelty is the determination of a new solution-method by grasping a purpose-means relationship. In contrast, Koffka (1927) expresses that transformation processes in the perceptual field (e.g., Rubin’s Vase: Perceivable as a vase and two faces) create new environmental conditions. In such cases, the “abstraction of circumstances” is a truly productive (i.e., creative) process as something novel is created, which was not in psychological existence before. In design, this distinction is present in Simon’s (1969) interpretation of means-end analysis (structuralist) and Schön’s (1983) interpretation that the “situations talks back” (Gestalt and functionalism). Selz’s (1922) position influenced the “cognitive revolution” of machine models of human thinking, replacing behaviorism as the predominant position (Mandler, 2002; Simon, 1981). Many scholars debated aspects inherent in the distinctions of
276 J. Auernhammer and B. Roth the different positions over the decades (e.g., Clancey, 1993; Csikszentmihalyi, 1988; Greeno & Moore, 1993; Schön, 1992a; Simon, 1988; Vera & Simon, 1993). For example, Neisser (1963) criticized the machine position in human psychology, and Bruner et al. (1986) expressed that psychology is now in the shackles of “computational models.” The distinction between the two positions is particularly evident in research on color vision, as it is an ecologically embedded activity rather than a form of information-processing (Koffka, 1912; Varela & Thompson, 1990). The distinction in psychological position matters in the conception of the different types of Productive Thinking, particularly in situations that are “unintelligible.” 3 Different Types of Productive Thinking in Design Different situations require and lead to distinct types of Productive Thinking. The different types of Productive or Design Thinking are (1) Rational, (2) Situational, (3) Experimental, (4) Dialectic, and (5) Counterproductive. Figure 1 shows the five different types of Productive Thinking. 3.1 Rational Productive Thinking The first type is Rational Productive Thinking. In this type, a solution-method can be directly determined by abstracting the means-purpose or if-then relationship from the given task, and the situation is totally intelligible (Duncker, 1935, 1945; Selz, 1922). The purpose-means or if-then relationship is inherent in the nature of the situation, such as mathematical problems (e.g., winning a chess game). In a given situation, Rational Productive Thinking is essential in which the if-then relationship is grasped directly through analysis (constitutional co-constrained) and synthesis (non-constitutional co-constrained). The if-then relationship can be Fig. 1 Different types of productive thinking
Different Types of Productive Thinking in Design: From Rational to Social. . . 277 directly determined if b is already co-constrained in a and can be analytically explicated from a and if the situation is paradigmatically constructed for inspection to grasp a new perspective synthetically (Duncker, 1945). In the direct determination of a solution-method, a “moments of insight” through synthesis incorporates greater neural brain activity over the temporal lobes of both cerebral hemispheres (i.e., around the ears) and over the mid-frontal cortex, in comparison to when a solutionmethod is determined analytically, which incorporates posterior (visual) cortex neural activity (Kounios & Beeman, 2009). In design, Rational Productive Thinking developed further in Simon’s (1969) means-end analysis (Simon, 1981, 2017). This means-purpose relationship developed further into problem–solution coevolution (Dorst, 2019; Dorst & Cross, 2001; Maher & Poon, 1996; Yu et al., 2015). Like Duncker’s (1945) analysis and synthesis, various if-then statements (i.e., problem-solution reframing) can be determined to identify a solution-method (what-how) in relation to an outcome (purpose) (Dorst, 2011, 2015). Such reframing is possible in a stable situation (e.g., conceptual design). This situation permits determining different means-purpose relationships directly. Such thinking in design often following step-by-step design process models include (1) grasping the problem (purpose) within the given situation, (2) determining analytically or synthetically a means, and (3) evaluating the purpose-means relationship (Archer, 1965; Burton et al., 2015; Pahl et al., 1996; Pólya, 1957; Simon, 1969; Wallas, 1926). Such Rational Productive Thinking is possible when a stable situation can be abstracted into a symbolic system, in which ontologies, such as Function-Structure-Behavior, allow directly determining a solution-method (Gero, 1990). This type works in symbolic systems, virtual worlds, or conceptual design in which the situation is stable. 3.2 Situational Productive Thinking The second type is Situational Productive Thinking, which makes it possible to determine solution-methods in a more fluid world. Selz (1922) describes this type of productive thinking as reproductive, in which an observed and abstracted purposemeans relationship is (re-)applied to a new situation. It is partially intelligible (Duncker, 1945). A given situation does not allow directly determining a solution-method as the situation is contextually unique. In such a situation, identifying the common aspects of a similar situation (e.g., from memory and observing similar situations) and comparing it to situations that are different reveals a relatively stable aspect of the situations, making it possible to determine “∎ leads to b.” However, this insight does not provide why this purpose-means relationship occurs. Identifying why a certain behavior a leads to a specific outcome b (“a leads to b”) allows determining a solution-method (Duncker, 1945). This abstracted purpose-means relationship can be reproduced in a comparable situation to determine the solution-method in the new context.
278 J. Auernhammer and B. Roth In design, expert designers recall their repertoire of past purpose-means relationships from memory to generate or determine a solution for a similar situation (Schön, 1983). This type of Productive Thinking applies in situations when designing for usability and human interactions with everyday things (Norman, 1988). Observing that people are not able to open a door effortlessly identifies “∎ leads to b.” Identifying and abstracting why this behavior or interaction occurs allows grasping the purpose-means relationship. The door signaled through its design (e.g., a handle) to pull the door, while pushing it would have been the correct behavior. Grasping the relationship that design a led to behavior b in a relatively stable situation allows designers to make appropriate changes. Situational Productive Thinking is contextdependent and is based on a stable world on an abstract level, making it possible to utilize expertise (e.g., recalling means from memory) in new situations. Similarly, expert intuition is learned by recognizing familiar elements (abstracted “∎ leads to b”) in a new situation and acting accordingly in an appropriate manner without exactly knowing why. Such Situational Productive Thinking based on experience is, however, inadequate in an unintelligible situation, when the situation is novel and emerging. 3.3 Experimental Productive Thinking The third type is Experimental Productive Thinking, which is coincidental (Selz, 1922) and unintelligible (Duncker, 1945). This type is required when it is not possible to determine directly or reproductively (i.e., from memory) an if-then or purpose-means relationship. A solution-method is determined through anticipation, resonance, and recentering within an emerging situation. It is what Louis Pasteur expressed as “chance favors the prepared mind,” and Arnold (1959) expressed as serendipity in creative design. Three different modes of Productive Thinking in an ambiguous and emerging situation allow identifying a solution-method. These are anticipation (Selz, 1922), resonance (Duncker, 1945), and recentering (Wertheimer, 1945). 3.3.1 Anticipation Selz (1922) describes that an arbitrarily prepared reaction is the anticipation of a certain behavior (V) to occur in relation to a later occurring opportunity to react (R). d ) leads to certain behavior, and when the anticiEnvisioning this relationship (RγV pated or desired behavior (V) actually occurs in response to R without further ado, a solution-method can be determined. This anticipation is not pure trial-and-error, as the anticipation provides a direction (i.e., prediction) toward a promising solution-method. Successful reproduction or reoccurrence of the anticipated behavior–reaction relationship in ambiguous situations increases the certainty of
Different Types of Productive Thinking in Design: From Rational to Social. . . 279 the solution-method (Selz, 1922). Such anticipation of a specific purpose-means relationship often reduces the ambiguous situation to specific aspects and stability. In design, it is problematic when planned (i.e., anticipated) conceptions are implemented without experimentation and evaluation. In such a case, the emergence of the situation is disregarded, as the anticipated solution-reaction may not emerge from the situation (Koffka, 1927). 3.3.2 Resonance In ambiguous situations, a solution-method is sought through the resonance effect of a signal (Duncker, 1945). A solution can be sought out through the correspondence between the characteristic or property anticipated (i.e., imagined) or signaled (i.e., observed) and that which is inherent in what is sought. It is the search for “something like” (e.g., a metaphor) that could provide a solution-method to an ambiguous situation. Duncker (1945) described a mode of “search” for “something like” that solves the problem or satisfies the need. He outlined strategies, including reformulating and reinterpreting the problem or goal, critical analysis of the situation (what makes it difficult to solve), and learning from unsuccessful attempts, which provide a resonance that triggers the productive thought process (Duncker, 1945). The new resonance allows finding objects and operations (e.g., within visual periphery or from memory), resulting in grasping a new means-to-the-goal (Duncker, 1945). The means-to-the-goal (i.e., a concept) is sought, which is not necessarily the same as determining an actual working solution-method. Determining a working solution-method requires reproduction and evaluation (e.g., prototyping and testing). However, in conceptual design, ambiguous and multifaceted problem-situations are rephrased (i.e., reframed), leading to reducing the whole (i.e., inherent complexity) to a specific aspect for which a solution is identified and later evaluated in action. Such one-sided solutions may not resolve the tensions inherent in the situational whole. Designers reframe or reinterpret the ambiguous situation to a specific problem-part (a specific frame). For example, Schön (1983) outlines, in an educational setting, that reframing (e.g., “Can I solve the problem I have set?” or “Do I like what I get when I solve the problem?”) allows finding new solution directions. However, reducing the situation to satisfy the problem-frame set by the designers will not necessarily resolve the actual tension that exists in the “whole” situation. 3.3.3 Recentering Wertheimer (1945) outlines Productive Thinking as grasping and harmonizing structural tension through direct experience. Wertheimer (1945, p. 197) expressed this thought process as follows: S1 is the situation in which the actual thought process starts, and then, after a number of steps, S2 in which the process ends, the problem is solved. [. . .] S1, compared to S2, is structurally incomplete, involves a gap or a structural trouble, whereas S2 is in these respects
280 J. Auernhammer and B. Roth structurally better, the gap is filled adequately, the structural trouble has disappeared: it is sensibly complete as against S1. In ambiguous situations, it is not possible to determine a purpose-means (i.e., if-then or problem-solution) relationship. It is rather a process of harmonizing structural tensions. For example, a piano player plays a set of tones while assessing the harmony. The structural tension in the played set of tones triggers a direction toward improvement that continues until the player plays and grasps a harmonious melody. This harmony is a Gestalt quality that individuals directly experience (von Ehrenfels, 2014). Creating a harmonious esthetic and emotional Gestalt is the process of creating meaning (Krippendorff, 2006; Wertheimer, 1996). Determining a harmonious whole follows different psychological and productive thought processes. The grasped structural trouble of a problem-situation triggers certain strains, stresses, tensions in the thinker and yields vectors, psychological forces (i.e., motivation) in the direction of improving the situation and changing it accordingly (Wertheimer, 1945). It requires recentering to discover, envision, go into more profound questions, and understand the whole situation (Arnheim, 2009; McKim, 1980; Wertheimer, 1945). Recentering is the change from, e.g., understanding a situation centered on the “ego” (i.e., one-sided) to grasp the whole situation from diverse centers (i.e., different needs, experiences, and points of view) (McKim, 1980; Wertheimer, 1945). Recentering allows perceiving the situation as a completely new situation. It is not reframing the purpose-means relationship within the situation as the new understanding of the task emerging from the situation, changing the situation entirely (Koffka, 1927; Wertheimer, 1945). Visual thinking supports perceiving centers, recentering, envisioning, and enacting (i.e., mock-up building) a new whole (Arnheim, 1969; McKim, 1980). Through these embodied psychological activities, the whole situation dynamically evolves through recentering as grasping a new whole provides new inherent tensions, resulting in envisioning a new possibility for a harmonious situation. The inherent Gestalt quality can be directly experienced through the enactment of this new situation. Individuals’ attitudes and values play an essential role in productive recentering (Arnheim, 2009; McKim, 1959; Wertheimer, 1945). Generally speaking, there are two different attitudes deeply rooted in human nature that determine the “centers,” which are (1) self-centered (centricity) and (2) self-actualized (eccentricity) (Arnheim, 2009). Self-centered Productive Thinking is driven by ego (e.g., own needs or perspective). Psychologically, in a self-centered or centric tendency, things are understood as being directed toward or away from the self, and actions are controlled by one’s own needs, wishes, pleasures, and fears (Arnheim, 2009). Productive Thinking is essentially about harmonizing structural tensions for oneself or one’s own social group. Such self-centric Productive Thinking can occur when designers are “gifting” people (Dell’Era et al., 2020; Verganti, 2009). Papanek (1973), a student of John E. Arnold at MIT, expressed the self-centered tendency in design as a movement coming from the arts. Designers express themselves egocentrically at the expense of spectators and/or consumers, and many design statements have become highly individualistic and autotherapeutic (Papanek, 1973). Papanek (1973) compares this
Different Types of Productive Thinking in Design: From Rational to Social. . . 281 self-centric Productive Thinking in Design to a community of intellectual elites in Hermann Hesse’s Magister Ludi, who reduced all knowledge to a unified framework, lost all contact with the outside world (to others), and just engaged in playing a glass bead game. The self-centered design fulfills designers’ needs, e.g., impressing peers intellectually, with little real-world contribution (Papanek, 1973). Selfcentered companies (e.g., Facebook, as revealed by a recent whistleblower) produce designs that generate societal tension favoring their own interests. Self-actualized A different mode of Productive Thinking is based on selfactualization. Self-actualized people accept themselves and others as they are (Maslow, 1954). A self-actualized human being is reaching out to others beyond the ego. Human meaning in life is in the relationship and care for others (Frankl, 1992). Arnheim (2009, p. ix) expressed this relationship of self-awareness and relevance to others as “composition,” as follows: The task in life of trying to find the proper ratio between the demands of the self and the power and needs of outer entities was also the task of composition [or invention]. This psychological relevance justified the concern with the formalities of composition. Far from being limited from playing with pleasant shapes, artistic form turned out to be as indispensable to human self-awareness as the subject matter of art and indeed as the intellectual investigations of philosophy and science. Similarly, Eames and Eames (2015) expressed that great design happens in the intersection of designers’ interest (i.e., motivation), clients’ wishes (i.e., socioeconomic complexity), and social concerns (i.e., profound concerns of the living world). Wertheimer (1945) provides an example of two boys playing badminton for self-actualized Productive Thinking in social settings. The older boy outperforms, the younger, making it an unpleasant game for the younger boy. An insight of “let’s see how long we can keep the ball going between us” changed the rules of the game, making it a more enjoyable experience and harmonious Gestalt and social situation (Wertheimer, 1945). In such Productive Thinking, individuals’ attitudes are becoming imperative as changes of centering from self to others are characteristics of profoundly important accomplishments in human beings and society (Wertheimer, 1945, p. 170). Attitudes that are ego-based harmonize for one’s own satisfaction (e.g., winning), while self-actualized attitudes result in psychological processes of Productive Thinking that harmonize for togetherness. Maslow (1954) expressed that self-actualization requires overcoming primary and ego needs. This perspective provides a humanistic perspective in Productive Thinking in which the human experience matters (e.g., in playing chess), in contrast to the fixed means-end conception of design. This attitude inherent in self-actualization is essential when designing for people (McKim, 1959). Designers are not neutral in situations, as beliefs and values influence every step of the design (Rittel, 1987). Translating structural tensions in people’s lives into a harmonious and valuable experience requires qualities, such as attitudes, human values, and need sensibility (Auernhammer & Roth, 2021). For example, need-finding is a deliberate effort of recentering to others. It aims to understand structural tensions from diverse points of view through awareness and
282 J. Auernhammer and B. Roth direct experience within the situation. Grasping and envisioning a new and valuable Gestalt of the situation allows creating and prototyping concrete artifacts and actions that change the situation as a whole. This new situation can be directly experienced and assessed for its structural tension or harmony. Designing a harmonious situation includes considering needs, meaning, usefulness, and usability. In self-actualized Productive Thinking, designers translate the structural tension in people’s lives (i.e., needs) into a new tangible and valuable design that changes the whole situation (McKim, 1959). Such humanistic Productive Thinking is essential in social situations. However, the complexity increases when designing for a pluralistic society, making the situation essentially a “wicked problem” (Rittel & Webber, 1973). 3.4 Dialectic Productive Thinking The fourth type is Dialectic Productive Thinking. This type of Productive Thinking is required as it is not possible to grasp and experience all felt tensions of diverse groups inherent in pluralistic society. Such social situations cannot be grasped and resolved as a purpose-means relationship as there is no stable state (Schön, 1973). In social situations inherent in a pluralistic society, many needs of diverse groups produce emerging and ever-changing situations of need-tensions (Lewin, 1936, 1946). These situations are “wicked problems” inherent in a pluralistic society (Rittel & Webber, 1973). In such situations, social tension cannot be comprehended before solving it, as each situation is viewed from many diverse perspectives, and there is no correct perspective (Rittel, 1982; Rittel & Webber, 1973). Rittel (1982) emphasizes that tensions in social situations have no final solution as they can always be resolved in new and different ways. Every social tension is a symptom of another “solution” (e.g., one’s group harmonic situation is another’s group social tension) with no time limits to the consequences, making testing a solution impossible as each trial changes the situation (Rittel, 1982). Each social intervention results in unique societal consequences, making the problem solvers (i.e., designers) responsible for what they are doing (Rittel, 1982). Such social situations as a whole can only be grasped indirectly by identifying the inherent value-tension within the social interactions of diverse groups. A social Gestalt (e.g., need-tension or harmonious situation) can be directly experienced in the many everyday social interactions of diverse groups and within a culture (Lewin, 1943, 1946). Resolving these social tensions requires a productive dialogue (Lewin, 1936, 1943, 1946, 1947; Rittel, 1987). Lewin (1936) stated that Dialectic Productive Thinking requires the conditions of a high level of fluidity within a social situation, which is an essential dynamic property of a situation in which social change is possible. Such Dialectic Productive Thinking is required to destruct the forces maintaining the “old equilibrium” (e.g., social tensions) and establishing or liberating forces toward a “new equilibrium” (e.g., social harmony) (Lewin, 1943). Such a fluid social situation is a space for
Different Types of Productive Thinking in Design: From Rational to Social. . . 283 collaborative experimentation and Dialectic Productive Thinking. It is a psychologically safe and free space (Freiraum) for new collaborative creativity and experimental social action (Auernhammer, 2012, 2020; Auernhammer & Hall, 2014; Edmondson & Lei, 2014; Rogers, 1954). It is imperative to create the fluidity necessary for change and take steps to bring about the permanence of the new situation through self-regulation (Lewin, 1943). In this productive discourse, it is vital to indicate political and general moral and ethical attitudes or value-tension inherent in the productive dialogue. Otherwise, one cannot comprehend the inhered social complexity because of all the implicit deontic assumptions involved (Rittel, 1987). In other words, Dialectic Productive Thinking is a social process of freeing diverse groups of people from social tensions and empowering them to have agency for translating their value-tensions into a productive dialogue, resulting ideally in a Gestalt that is harmonious for all diverse groups. In design, Dialectic Productive Thinking is often facilitated through participatory design practice, engaging specific groups that are debating through socio-material interactions new design objects, resolving social tension in relation to a specific design task (Bjögvinsson et al., 2012; Cooley, 1980; Ehn & Kyng, 1987). In urban planning, diverse groups, such as urban dwellers, crafters, and architects, come together in the early stages of the design project (Rudofsky, 1964). This Dialectic Productive Thinking is a dialogue between diverse groups in which the resulting design is later tried out in real-life settings, with often a focus on use and usability. Dialectic Productive Thinking in Design is a political discourse (Margolin, 2002). In a living and healthy society, this productive dialogue among diverse groups is an ongoing activity of grasping social tensions and resolving them into more harmonious social situations. A different productive discourse is the ongoing translation of need-tensions into harmonious social situations. It builds on self-actualized Productive Thinking. Such Productive Thinking requires grasping how one’s own and others’ needs generate a need-tensions field between diverse groups and translating the social field into a harmonious situation (Lewin, 1946). For example, Michele Wells shows in Theater for Humanity how theater provides a psychologically safe space, allowing enacting experiences and interactions of social tensions between different communities (Wells, n.d.). Social need-tensions are experienced directly, and the whole is grasped by enacting the tension from different centers. These enacted experiences allow a continuous recentering of the whole (Wertheimer, 1945). Such enacted productive dialogue makes the value-tensions explicit and experiential, triggering psychological forces to collaboratively harmonize the Gestalt of the social situation (Lewin, 1943, 1946). Social “design” is guiding the embodied productive dialogue of grasping the inherent value-tensions in social interactions by directly experiencing them, exploring new situations collaboratively, and translating the need-tensions into a concrete harmonious social situation. Recreating the harmonious Gestalt in an everyday social situation may require various means, artifacts, and activities as well as human attitudes and values to resolve need-tensions inherent in a pluralistic society and produce situations valuable for the diverse groups in society. Such Dialectic
284 J. Auernhammer and B. Roth Productive Thinking is essential for social innovation (Manzini, 2015; Margolin, 2002). 3.5 Counterproductive Thinking The last type of Productive Thinking is Counterproductive Thinking. Much of everyday thinking is reproductive in which individuals unconsciously and habitually accomplish their everyday tasks and satisfy their needs. However, our thinking and behavior can also be counterproductive. Several situations result in counterproductive thinking. Depending on the situation, different Productive Thinking types can be counterproductive. Continuously emerging and highly context-dependent situations, the so-called wicked problems, cannot be approached through rational if-then relationship determination (Rittel & Webber, 1973). In situations where there is a loss of a stable state, people react with “resistance” and “defense mechanisms” (Schön, 1973). Such reactions can lead to counterproductive thinking and behavior in ambiguous situations. Tolerating ambiguity and openness to experiences are essential attributes in Productive Thinking (Rogers, 1954). Similarly, approaching situations that are totally or partially intelligible (i.e., simple problems) do not require Dialectic Productive Thinking. For example, designing the interactions of people with a door allows determining if-then relationships (Norman, 1988). Such situations do not require a productive dialogue. The situation determines the required type of Productive Thinking. The overemphasis of one type of Productive Thinking over others can become counterproductive. For example, the Design Methods Movement overemphasized Rational Productive Thinking, and shortly after, one of its leading members, Christopher Jones (1977), expressed how his view has changed on the movement and that he dislikes the machine language, the behaviorism, and the continual attempt to fix the whole of life into a logical framework. Maslow (1954) expressed this counterproductive thinking as mean-centering (e.g., methods) rather than problem-centering (e.g., situational circumstances). Purely Rational Productive Thinking can be counterproductive when designing for human experiences. For example, treating people like an if-then problem (e.g., as simulated in agent-based models) reduces human relationships and experiences, resulting in structural tensions as people are stereotyped. Productive Thinking can be used to produce structural tensions in people’s lives, which are highly dependent on human attitudes and values (McKim, 1959; Wertheimer, 1945). Providing knowingly undetermined, false, or not working “solutions” (e.g., stereotypes and fake news) for ego needs (e.g., political gain, user engagement, and financial success) creates or increases structural tensions (e.g., fear or depression) in people’s lives. Such Productive Thinking in Design can be highly counterproductive for living beings. Not all design is good design and glorifying “design” is counterproductive. Therefore, it is imperative to develop a
Different Types of Productive Thinking in Design: From Rational to Social. . . 285 Productive Culture, including human values, attitudes, attributes, beyond simple “methodologies” and cookbook patterns (McKim 1980; Auernhammer & Roth, 2021). 4 Developing a Productive Culture The different types of Productive Thinking provide different psychological processes, factors, and strategies to determine means and resolve tensions. There is a need for many individuals to contribute creatively and productively to resolve the many emergent social and environmental tensions. Such collective Productive Thinking requires a Productive Culture. A Productive Culture is not merely one where many individuals create and determine valuable solutions. It is about a critical mass of people collaborating, accepting, supporting, and helping each other productively. Productive thought processes are determined and influenced by psychological forces and factors that emerge in everyday interactions with people and the environment (e.g., how we treat each other). A Productive Culture is when a critical mass of people inherently hold attributes, attitudes, values, and abilities, such as care for others, confidence in creative ability, and collaborative mindsets (Auernhammer & Roth, 2021). Since the beginning of the Design Division (today Design Group) and Joint Product Design program at Stanford University in the 1950s and 60s, the main aim has been to educate a humanistic and creative philosophy of design (Adams, 1974a, b; Arnold, 1959, 1962a, b; Auernhammer et al., 2022; Auernhammer & Roth, 2021; McKim, 1959, 1972). Therefore, a particular focus has been placed on experiencing and educating self-actualized Productive Thinking by centering and recentering on the many people involved in the problem-situation. Exercises and practices, such as need-finding and personal design statements, focus on understanding one’s own needs and others’ tensions, with the aim of developing attributes, attitudes, values, and abilities (Auernhammer & Roth, 2021). Experiencing human values and feeling someone else’s tension aims to develop need sensitivities. With a self-centric attitude, the same design practices are enacted to design for one’s own needs (e.g., power-hunger), potentially causing structural tensions in people’s lives (McKim, 1959). As Wertheimer (1945) and McKim (1959) emphasized, attitudes and human values are imperative in Productive Thinking. Developing a Productive Culture requires fluency and flexibility in Productive Thinking. Technical problems require more Rational Productive Thinking, while human needs require Experimental Productive Thinking. Educating different types of Productive Thinking develops fluency and flexibility in thinking. In self-actualized Productive Thinking, it is essential that design decisions are directed and informed by care for and engagement with other living beings, who are impacted by the design solution in their everyday life. Too often, design choices are directed by seniority. Developing a Productive Culture requires exploring and understanding the whole situation collaboratively. It is a non-hierarchical culture. Such a culture can be tremendously freeing when people support and help one another, and thereby producing psychological safety
286 J. Auernhammer and B. Roth and freedom for creativity (Arnold, 1959; Rogers, 1954). In such a culture, designers can create, build, break, and learn from mistakes in a safe environment (e.g., Design Loft and Product Realization Lab culture). Individuals build creative confidence through countless projects (Kelley & Kelley, 2013). To enable such an educational environment, John E. Arnold redeveloped the student shop (today Product Realization Lab) program in the late 1950s to put creativity and design skills into practice (Beach, 1974). Such a productive environment allows people to explore and develop solutions that contribute to society. A Productive Culture is established through a critical mass of self-actualized, confident, capable, and considerate individuals. This culture enables Productive Thinking in Design, and it holds the potential to resolve the continuous emergent tensions in the real world collaboratively and productively. Acknowledgment The first author would like to thank Larry Leifer, Bernie Roth, and the wider design community at Stanford for cultivating this self-actualized behavior. It is tremendously freeing when people support and help one another. References Adams, J. L. (1974a). Conceptual blockbusting: A guide to better ideas. Stanford Alumni Association. Adams, J. L. (1974b). Invention and innovation in the University Paper presented at the the public need and the role of the inventor. Monterey. Amabile, T. M. (1996). Creativity in context: Update to the social psychology of creativity. Avalon Publishing. Archer, B. L. (1965). Systematic method for designers. Council of Industrial Design. Arnheim, R. (1954). Art and visual perception. University of California. Arnheim, R. (1969). Visual thinking. University of California Press. Arnheim, R. (2004). Visual thinking. University of California Press. Arnheim, R. (2009). The power of the center – A study of composition in the visual art. University of California Press. Arnold, J. E. (1959). Creative engineering seminar, 1959. Stanford, University. Arnold, J. E. (1962a). Education for innovation. In S. J. Parnes & H. F. Harding (Eds.), A source book for creative thinking. Charles Scribner’s Sons. Arnold, J. E. (1962b). Useful creative techniques. In S. J. Parnes & H. F. Harding (Eds.), A source book for creative thinking. Charles Scribner’s Sons. Auernhammer, J. M. (2012). Autopoietic organisation of knowledge, creativity and innovation: A case study of the automotive manufacturer Daimler AG. Edinburgh Napier University. Retrieved from https://ethos.bl.uk/OrderDetails.do?did¼1&uin¼uk.bl.ethos.580699 Auernhammer, J. M. (2020). Design research in innovation management: A pragmatic and humancentered approach. R&D Management, 50(3), 412–428. https://doi.org/10.1111/radm.12409 Auernhammer, J. M., & Hall, H. (2014). Organizational culture in knowledge creation, creativity and innovation: Towards the Freiraum model. Journal of Information Science, 40(2), 154–166. Auernhammer, J. M., & Roth, B. (2021). The origin and evolution of Stanford University’s design thinking: From product design to design thinking in innovation management. Journal of Product Innovation Management, 00. https://doi.org/10.1111/jpim.12594 Auernhammer, J. M., Leifer, L., Meinel, C., & Roth, B. (2022). A humanistic and creative philosophy of design. In C. Meinel & L. Leifer (Eds.), Design thinking research: Achieving real innovation. Cham, CH.
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The Cultural Construction of Creative Problem-Solving: A Critical Reflection on Creative Design Thinking, Teaching, and Learning Xiao Ge, Chunchen Xu, Nanami Furue, Daigo Misaki, Cinoo Lee, and Hazel Rose Markus Abstract While people around the world constantly come up with ingenious ideas to solve problems, the expressions of their ingenuity and their underlying motivations and experiences may vary greatly across cultures. Currently, the role of culture is often overlooked in research and practice aimed at understanding and promoting creativity. The lack of understanding of cultural variations in creative processes hinders cross-cultural collaboration in problem-solving and innovation. We challenge the unexamined American perspectives of creativity through a systematic analysis of how ideas, policies, norms, practices, and individual tendencies around creative problem-solving are shaped in American and East Asian cultural contexts, using the culture cycle framework. We share initial findings from several pilot studies that challenge the popular view that only agentic change-makers are seen as creative problem solvers. In the context of design, designers are culturally shaped shapers who are motivated to solve problems in creative ways that resonate with their cultural values. Our research seeks to empower designers from non-Western societies. We urge design educators and practitioners to explicitly incorporate culturally varied ideas about creative problem-solving into their design processes. Our ultimate goal is to ground the theories and practices of design thinking in cultural contexts around the world. X. Ge (*) Center for Design Research, Stanford University, Stanford, CA, USA e-mail: xiaog@stanford.edu C. Xu · C. Lee · H. R. Markus Stanford SPARQ, Stanford University, Stanford, CA, USA N. Furue School of Management, Tokyo University of Science, Tokyo, Japan D. Misaki Faculty of Engineering, Kogakuin University, Tokyo, Japan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_15 291
292 X. Ge et al. 1 Introduction As soon as winter begins, locals in Harbin, a city in the northeast part of China, cannot wait to stock up piles of napa cabbage on their porches and balconies. As the temperature quickly falls below zero Celsius in the dry winter, the perfect conditions are created to keep the cabbage fresh and tasty for several months. Stews with cabbage, glass noodles, pork, and tofu make this perfect dish throughout the dark winter. For people in northeast China, the tradition of eating this dish in winter dates back to thousands of years ago during the Tang Dynasty. To Harbiners, storing stacks of cabbage is not just a casual tradition, it is a cultural practice that bridges millions of families and connects the young with the old and the present with the past. In Harbin, such connection to the past and to people is a prevalent element in everyday life, manifesting itself in both material and symbolic cultures. In San Francisco, by contrast, the experience of food is an ever-changing fashion. People seek diverse dining experiences—from unique menus to unconventional dining occasions. Chez Panisse, a popular high-end restaurant, for instance, is famous for its distinct culture and cuisine, whose menu “changes everyday” (Chesbrough et al., 2014). High-tech companies in Silicon Valley are venturing into new territories to reimagine food production and consumption (e.g., Dance, 2017). In San Francisco, people discover novel sensations and constantly seek to break with tradition. These food practices in San Francisco would be considered creative in the USA, because creativity in the USA has been represented and exercised as “defying the crowd” (Sternberg & Lubart, 1995) and “breaking tradition” (Lubart, 1999). As a consequence of reinforcing such cultural ideas in practice, creative idea generation is widely perceived as serving to instigate change: coming up with new ideas to change the status quo, stand apart from the past, assert control over their surrounding environment, as well as establish their uniqueness from other people. If we were to adopt such a theory of creativity, Harbin chefs who do not seek changes in their food tradition would be seen as less creative. The role of culture, however, is not explicitly discussed either in deriving the theory or in promoting certain creative practices. For example, the amplification of radical change and transformation as a goal and attitude is widely observed in various settings of design education and innovation practice regardless of the participants’ cultural backgrounds. Creative idea generation is widely understood as “a structured way of breaking out of structure,” as Tim Brown, chair of IDEO, famously quoted in his book Change by Design (Brown, 2009). “Enable change in Japan through design and creativity,” is the mission of IDEO Tokyo (IDEO, 2021). Culture is too often overlooked, such that the interpretation of East Asian behaviors and practices becomes rather a projection of American ideas. This in turn leads to misunderstanding and misjudging East Asian creativity. Online discussions of “Can Asians be creative?” and popular book titles such as Can Asians Think? (Mahbubani, 2002, cited in Morris & Leung, 2010) provide a glimpse of such stigma.
The Cultural Construction of Creative Problem-Solving: A. . . 293 Does creative behavior have to be associated with changing, breaking, and seeking freedom? Instead of advocating change and disruption, many places in East Asia promote connection and preservation. The contrast between food practices in San Francisco and in Harbin is an example of the broad cultural difference. While American society tends to be centered on instigating agentic change-making in the environment, East Asians place importance on preserving continuity with good practices in the socio-physical environment, and adjusting the self to changes that occur in the environment. Therefore, the predominant cultural value in East Asia may activate a different creative process that is associated with returning to, preserving, sustaining, and connecting. People who exemplify such a different creative process are less talked about or known in the USA. In design thinking, teaching, and learning, when our design educators and managers celebrate some American cultural values and restrict others, either consciously or not, this puts people with different values and tendencies at a disadvantage. With the salient power dynamics between educators and students, managers and junior employees, this means alienation, misjudgment, and disconnection. If these cultural differences are ignored, valuable mindsets and practices that originated in the USA, such as design thinking, cannot expand to East Asia. To address this issue, we examine how crucial differences between American and East Asian cultural values affect why and how people come up with ideas for problem-solving. In Sect. 2, we review prior cultural and cross-cultural perspectives on creative problem-solving in design. In Sect. 3, we apply the culture cycle framework (Markus & Hamedani, 2019) to systematically reveal differences in historically derived ideas, policies, norms, cultural practices and products, and beliefs around creative problem-solving. In Sect. 4, we focus on examining some cultural differences using pilot studies. In Sect. 5, we reflect on why there is a lack of attention to East Asian creative processes and the consequences of this, address the relevance of our work for design education and practice, discuss some of the limitations in our studies, and describe future directions. Our paper concludes with Sect. 6. Our work furthers the current understanding about the different, but equally valid and meaningful motivations and behaviors that underpin creativity across different cultures. In doing so, we hope to inspire educators and practitioners to adopt a more culturally resonant approach to design thinking, teaching, and learning. 2 Critical Reflection on Design Thinking Based on Designers’ Cultural Needs We would first like to revisit design thinking development and research by putting on a pair of cultural perspective goggles. The underpinning role of culture was made visible in design thinking from the very beginning. John Arnold (1913–1963), the founding father of design thinking at Stanford, conceived designers as other-directed
294 X. Ge et al. Fig. 1 Life Magazine captures John Arnold’s unconventional teaching (Hunt, 1955) rather than inner-directed, and recognized that culture has a direct influence on how designers view the world (Clancey, 2016). Yet Arnold’s view on culture is also a product of his own cultural experience—he criticizes other-directedness and calls for a recognition of one’s unique individual mind—the so-called Uncommon Man (Clancey, 2016). This is consistent with the mainstream belief in the USA that motivation and action spring primarily from desires, beliefs, and attributes of the independent self (Markus & Kitayama, 1991), which can be activated by freeing individuals from constraints of the environment and of tradition. Arnold’s iconic, unconventional course at MIT in the 1960s also set an example for this (Fig. 1). He presented students with design problems for clumsy birdlike inhabitants on an imaginary planet called Arcturus IV, which was based on the belief that creative problem-solving could be trained by temporarily freeing students from their accustomed environment and placing them in a new imaginary one (Clancey, 2016). In his study of Arnold’s philosophy, William Clancey concludes that, in Arnold’s view, “the creative individual is a positive non-conformist.” Such a view of creativity is reflected in the recent development of design methods and tools, such as the rules of brainstorming (Sutton & Hargadon, 1996) and methods for breaking free from “blocks” in problem-solving (Adams, 2019). In the early years, visitors of Stanford Mechanical Engineering Design Group were sometimes taken to drag racing competitions to understand the “American design” and engineering (masculine) creativity. These tools and practices were designed to allow designers to systematically free themselves from institutional and cultural constraints. However, they do not address designers’ cultural and emotional needs and motives, especially outside of the context of America. Could it be that creativity is fostered by selfsacrifice rather than individual freedom in certain cultural contexts? Rolf Faste’s work offers some insights on this question. In the development of design thinking, Rolf Faste played an important role in bridging Zen and Japanese esthetics with Western thinking of design (Kelley, 2003; Irani, 2019). The design thinking adage of bearing a beginner mindset is partly influenced by Zen (Irani, 2019). In his unconventionally visual essay (Faste, 1995),
The Cultural Construction of Creative Problem-Solving: A. . . 295 where “poached egg” is used as a metaphor for understanding innovation and culture, Faste inquires into the nature of Japanese creativity. He reflects that “Professor Koxvai, widely regarded as Japan’s first Jungian psychologist, suggested that I look at Japanese myths and fairy tales if I wished to understand attitudes about creativity, whether in Japan or elsewhere.” What is perceived as good, creative, desirable, and meaningful in Japan? Faste observes that Japanese stories communicate a very different set of cultural values than Western stories do— Western myths, be they older tales like Andersen’s Ugly Duckling or newer ones like Segal’s [sic] Jonathan Livingston Seagull, all involve heroic and macho images of individual separation and triumphant return. In comparison, Japanese stories are striking for their images of feminine and nurturing self-sacrifice. Faste’s analysis suggests that Western creativity is strongly associated with masculine individuality and expressing self-direction, whereas Japanese creativity seems to suggest the exact opposite. For the last 10 years of his life, Faste worked on an unfinished book titled Zengineering, which incorporates Japanese ideas of “engaging life in real-time” and “non-judgmental mindfulness” into American engineering design practices (Rolf A. Faste Foundation for Design Creativity, n.d.). Consistent with Faste’s hunch, some design creativity scholars in Japan propose that the desire for creating design concepts is essentially led by an inner sense, a sense of resonance in the mind with the product one is working on (Taura & Nagai, 2013; Nagai & Taura, 2017). They argue that design artifacts, although different from what is found in the natural word, “nevertheless, ‘naturally’ resonate with the human mind” (Taura & Nagai, 2013). Notably, what is often regarded as an important criteria of design in the USA—novelty, is conceived to be “implemented as a by-product of concept generation, but not as a causal factor of creativity” (emphasis added). Moreover, the scholars argue that “if a new concept is pursued merely on account of its uniqueness, we say that this pursuit never approaches an ideal” (Taura & Nagai, 2013). As an example from other cultural contexts, Panagiotis Louridas has addressed the cultural needs of designers, using “bricolage” as a metaphor to illustrate how traditions and norms which are accumulated over thousands of years define, forge, and guide the French ways of design and tinkering (Louridas, 1999). A systematic approach for researching design culture is brought in through the ethnographic work of Pamela Hinds and colleagues (Hinds & Lyon, 2011; Kim et al., 2012; Liu & Hinds, 2012). Notably, the analysis of the cultural construction of design behavior acknowledges forces from different cultural layers (Hinds & Lyon, 2011). Their exploratory work suggests that Asian designers tend to blend in, whereas European and North American designers prefer to stand out, and that design qualities, such as creativity, are conceived differently across cultures. However, how modern conceptions, or implicit theories, of creativity are different across cultures has not been adequately tested empirically. In the field of engineering, creativity is an increasingly popular topic in engineering education research and is considered a core component of globally engineering competencies (Lucena et al., 2008), yet little cross-cultural research has been done (Ge et al., 2021). Overall, research evidence is still too scarce
296 X. Ge et al. to draw any conclusive remarks about the cultural needs of designers in creative problem-solving. 3 Creative Problem-Solving: A Culture Cycle Analysis The desire to create ideas seems to be universal, yet beliefs and experiences about the what (new and different, or similar and connected), how (independent with passion, or interdependent with hard work), who (male or female), and why (instigating transformation or preserving connection) may differ across cultural contexts. We take the perspective that culture shapes ideas, practices, interactions, and beliefs around creative problem-solving. In delineating how creative problem-solving is culturally constructed, we use a culturally responsive analytical framework, called “culture cycle” (Markus & Hamedani, 2019; Plaut et al., 2012), to frame and analyze prior research on creativity and problem-solving. Where the word “culture” is used, we intend to align with Adams and Markus (2004) in understanding culture as consisting of: explicit and implicit patterns of historically-derived and selected ideas and their embodiment in institutions, practices, and artifacts; cultural patterns may, on one hand, be considered as products of action, and on the other as conditioning elements of further actions (Adams & Markus, 2004, p. 341). In this conceptualization, culture can be found both in the psychological tendencies of people and in the material and symbolic representations that people create (Plaut et al., 2012). Culture cycle is a framework that delineates and simplifies the many vectors of culture as “dynamically interacting and interdependent layers... made up of ideas, institutions, and interactions that guide and reflect individuals’ thoughts, feelings, and actions” (Markus & Hamedani, 2019). In this paper, we organize the culture cycles into three interacting layers: historically derived ideas and philosophies, institutional policies, norms, practices, and interactions, as well as psychological tendencies—all of which are important in understanding the cultures of creative problem-solving. Figure 2 gives a visual overview of our culture cycle analysis. It delineates two different possible realities of creativity; one is American linearity and the other is East Asian circularity (Biao, 2001). The line in the USA independent model of creativity not only represents the divide between human and nature, subject and object, and mind and matter, but also stands for the linearity of creating—it is about progressing forward. The circle in the East Asian interdependent model of creativity represents oneness and an integration of these elements, as well as the circularity of creativity—it is about returning to the origin. While our goal here is to characterize differences, we acknowledge that these differences are relative and that cultures are dynamic, complex, interacting, and changing.
The Cultural Construction of Creative Problem-Solving: A. . . 297 Fig. 2 Line and circle, as a metaphor (Biao, 2001) for two different possible cultures of creativity and a summary of culture cycle analysis. This model is built upon an earlier version of the figure in Misaki and Ge (2019) 3.1 3.1.1 Historically Derived Ideas of Creative Problem-Solving Historically Derived Ideas in the USA The modern American concept of creativity has a philosophical tradition of inquiry into “the origin of new entities and new ideas” (Weiner, 2000). In the West, people have the long tradition of valuing precise, conceptual knowledge and systematic sciences, which can be traced back to Descartes. Western epistemology tends to value—place truth in—abstract ideas and theories, rather than concrete personal experiences or the embodiment of knowledge. This tradition of inquiry reflects the Western epistemology that humans are separate from others and objects, and that humans as rational thinkers obtain knowledge deductively by reasoning. Historical events and movements such as the Enlightenment have influenced the modern American conception of creativity—a process through which people can
298 X. Ge et al. direct their own destiny. Freedom of choice is therefore often regarded as a prerequisite to enable solving problems creatively. According to Wight, the Western idea of “individual creativity”—whereby new ideas originate in the human mind and in the ability of the individual (Wight, 1998)—became widely acknowledged during the Enlightenment. At this time, people started to emphasize the importance of individual rights and elevate individual rights in order to understand the universe and to direct their own destiny (Szczepański & Petrowicz, 1978; Albert & Runco, 1999, cited in Niu & Sternberg, 2006). As a result, Westerners tend to consider creativity as an ability that one unleashes from within and expresses outwards. Because the modern concept diverges greatly from the ancient divine beliefs of creativity in the West, the latter is not reviewed here (for more, see the philosophical roots reviewed in Niu & Sternberg, 2006). 3.1.2 Historically Derived Ideas in East Asia In contrast, creativity in East Asia emphasizes a reliance on situated experiences (unseparation of mind and body) and a deep connection with other people and things (unseparation of self and others; unseparation of humans and things). East Asians have a tradition to believe that knowledge can be attained inductively from sensory experience (Nonaka & Takeuchi, 1995). The clear distinctions between humans and objects and between embodied experiences and conceptual knowledge can be found rather exotic by East Asians. Instead, what is historically valued is their indistinction, or the so-called oneness—oneness of humanity and nature (“Tian Ren He Yi”), of body and mind (Biao, 2001), and of self and other (Markus & Kitayama, 1991). Oneness is a central idea in Taoist philosophy. Fundamentally, each thing contains within it the entire universe; each thing contains the universe by “feeling with” (having sympathy with) the universe (Chang, 1970). This philosophical view was particularly reinforced during the neo-Confucianism movement. From the neo-Confucianism perspective, things and people in the world are indistinct from one another in that they share the same nature or substance. The shared substance supplies a deep connection among people, creatures, and things, which has been documented to partly explain East Asians’ interdependent construal of the self (Markus & Kitayama, 1991). These ideas are inevitably evidenced in East Asians’ understanding of creativity. Cultural research of creativity broadly accepts that Taoism has a great impact on Chinese creativity (Niu & Sternberg, 2006; Kuo, 1996). Yan (2015) argues that while Western creativity is associated with conquering nature, East Asian creativity is about seeking harmony with nature (“Tian Ren He Yi”). The Chinese ancient military treatise, The Art of War (Tzu, 1971), provides ample examples to substantiate the view that a deep understanding of particular situations is the foremost important capability of creative military strategists or problem solvers (Yan, 2015). Ancient creative problem solvers in China are often depicted as situationattending “observers” (Langer, 2009; Rudowicz & Yue, 2000) who can draw connections from the past (Niu & Sternberg, 2002) rather than being “bolt from
The Cultural Construction of Creative Problem-Solving: A. . . 299 the blue” unconventional thinkers. According to the Taoist classics, the creative process is a process of “the inner apprehension of dao, when all the distinctions between subject and object vanish” (Niu & Sternberg, 2006). Chu argues that, “[Chinese] creativity is related to meditation, because it helps one to see the true nature of the self, an object, or an event” (Chu, 1970, p. 340). In Japan, the creative process is broadly recognized as entering into a free state of “pure experience” (Yuasa, 1987; Nishida, 1960) that transcends body-mind and subject-object distinction. This has been used to explain, for instance, the critical social process of crystallizing new products (Nonaka, 1994) and the superior stage performance of master actors (Yuasa, 1987). Oneness is also exemplified in what has become known as “dialecticism,” a form of folk wisdom in Chinese and other East Asian countries’ cultures, which is seeing oneness of—and seeking a balance between—contradictory propositions in problem-solving. Peng and Nisbett (1999) argue that Chinese ways of dealing with seeming contradictions often result in “retaining basic elements of opposing perspectives by seeking a ‘middle way’.” Partly because of seeing a shared nature with others and the environment, people consciously experience facilitating and restraining forces (Lewin, 1999), to borrow Lewin’s words, from the external, active environment, which act upon them and induce constant changes. A “middle way” is perceived to best handle constant changes. The Chinese Proverb—“Sai Weng Shi Ma,” for instance, tells a story of an old man who finds good in the bad, yet also foresees misfortune in an apparent fortune. Interestingly, although Chinese people admire the versatility that is embedded in ambivalent attitude or a lack of clear position-taking (e.g., “Bian Yi,” in Yan, 2015), such an attitude and behavior can be considered quite undesirable in the USA. 3.1.3 Cultural Ideas Between the USA and East Asia According to linguist Liu (1995), “创造力(chuang zao li)” or “chuang zao xin” (both words mean creativity in Chinese) comes from a modern Japanese word, “sozosei,” which was translated from the modern English word, “creativity” (Note: Niu and Sternberg cited the Japanese word as “kozosei,” which might be a typo, e.g., in Niu & Sternberg, 2006). In Chinese history, the terms “chuang zao li” and “chuang zao xing” are rarely used (Yan, 2015). Nowadays, although “creativity” is no longer a rare word in China, it is relatively new and carries the meanings and cultural ideas of Western creativity. In Hui and Lau’s investigation of educational policies on creativity education in four Asian societies, they find that mainland China is the only place where creativity is not clearly defined (Hui & Lau, 2010). As Yuanqiang Zhou at Tsinghua University contends, “‘creativity’ is a product of the West, of course it’s a Western thing” (via personal communication). Many efforts have been made to reconcile the cultural differences. For instance, in analyzing the philosophical roots, Niu and Sternberg (2006) argue that Chinese natural creativity and Western divine creativity share many similarities. And although Western conceptions of creativity may go against the notion of oneness,
300 X. Ge et al. they match well with Taoism in terms of the pursuit of mental freedom. Many argue that while Confucianism presides over Chinese social life, Taoism presides over their own mental life (e.g., in Lu Xun’s 1918 Letter to Xu Shou-tang; Zhang & Chen, 1991; both cited in Peng et al., 2006). “Obey publicly and defy privately,” as Hwang (2000) puts it. This may explain why many great minds in history are free from conventions and pragmatic concerns despite their Confucian practice. For example, Wei and Jin Dynasties (CE 220–420) are known as a mental freedom era. Poet Li Bai (CE 701–762), arguably the most famous poet of Chinese history, is also known for his high-level pursuit of mental freedom. The Japanese Physicist Nobel laureate Hideki Yukawa (1907–1981) greatly attributes his creativity to his systematic study of Taoism in his book Creativity and Intuition (Yukawa, 1973). He remarks that he is personally docile but mentally rebellious—“I can never work on a problem that I’ve been told to solve by someone else. My subconscious always rebels against being ordered to do something. Personally, I look on myself as a docile kind of man.” In the USA, the co-existence of social conformity and mental freedom may posit tension and contradiction and induce eventual separation spatially in content and temporally in process. This reflects an Aristotle’s “either/or” frame (Li, 2014). Yet from the Chinese philosophical perspective, contradictions are meant to co-exist in harmony. To some extent, “mental freedom” in Taoist tradition also suggests a meditative practice of losing oneself (therefore, the self is set free mentally) to connect and fuse with every other thing. Csikzentmihalyi has also mentioned that people can experience this “flow” during the utilization of Eastern styles of meditation (Csikzentmihalyi, 1997). Yet the experience of “flow” is not unique in the East and can be found across many cultures. 3.2 3.2.1 Policies, Norms, and Practices Around Creative Problem-Solving Policies, Norms, and Practices in the USA In the USA, creative ability is considered essential in revitalizing the economy (Bilton, 2010) and breaking up established systems (Lubart, 1999). As such, creativity-conducive policies and regulations emphasize the provision of autonomy and freedom. For example, Simonton argues that creativity favors a civilization that is composed of a large number of peacefully coexisting independent states rather than dominated by empire states (Simonton, 2000). Policies and practices about creative ability value the creation of the new, whereas connection with the old is less relevant. Amongst others, Venturelli (2005) argues that, “the challenge for every nation is not how to prescribe an environment of protection for a received body of art and tradition, but how to construct one of creative explosion and innovation in all areas of the arts and sciences.” To the opposite, in many fields, distinction from the
The Cultural Construction of Creative Problem-Solving: A. . . 301 past or tradition is viewed as a sign of creative behavior. In entrepreneurship practice, for instance, entrepreneurs are often regarded as a force of “creative destruction” to destablize the established business and disrupt the control of the mainstream industry to enable the formation of new ones (Webster, 1977; Levitt, 2002). We are in the “creative economy,” as Richard Florida puts. According to Florida, about 30% of the workforce are in the creative sector today, in comparison to only 10% in 1990 (Florida, 2007). “The creative sector accounts for nearly half of all wage and salary income in the United States. That’s nearly $2 trillion, almost as much as manufacturing and services combined” (Florida, 2002, 2007). In recognition of that, modern work environments have started to encourage autonomy, freedom, and management empowerment. For example, Google’s 80/20 rule grants its employees free time at work. “This empowers them to be more creative and innovative,” write Larry Page and Sergey Brin in their Founders’ IPO letter in 2004. Today at Google and Moonshot Factory, creativity is represented as “10X thinking.” Frederik G. Pferdt, Chief Innovation Evangelist at Google, encourages Google employees to think big, and to go for monumental change, not incremental improvement (Lafargue, 2016). No question about it—creativity is increasingly recognized as an essential goal of K-12 and college education in the USA. As Obama remarked at the ESSA signing ceremony, “we’re going to have to have our young people master not just the basics but also become critical thinkers and creative problem solvers.” Educational practices that push for tests and standardization, especially through repeated training, face growing criticism of killing creativity. Sir Ken Robinson’s talk Do Schools Kill Creativity? (Robinson, 2006) has remained the top viewed TED talk since 2006. Since the No Child Left Behind Act was initiated in early 2000, it has received numerous criticisms about how it kills creativity (Goldstein, 2017). In response to these problems, the Every Student Succeeds Act was proposed, which emphasizes more creativity-conducive measures, such as creating more access and choice for students (Goldstein, 2017). As an example of college education, Stanford University’s Viewbook (2009) begins with the statement—“The wind of freedom blows.” It continues that the university gives students “the freedom to be themselves: innovative, creative, and unconstrained by any predetermined look or affect” (p. 22, cited in Plaut et al., 2012). In school and at work, “creativity” has become a desirable way of interacting and a new norm of being—it is confidence, self-expression, scientific pursuit, and leadership. Popular books—The Adjusted American, a classic from the 60s and more recently Orbiting the Giant Hairball, to name a few— promote nonconformism and address how to seek one’s freedom to attain a sense of self, to remain creative in bureaucratic work environments and ossified society. Who are creative geniuses in the USA? In the technology fields, Steve Jobs is often recognized as the most creative person of all time. At age 20, the non-conformist cofounded Apple and at just 29, he introduced Apple Macintosh, which soon radically transformed the personal computer industry. In science, often referred to as the most creative genius of the last century, Einstein is one of the few who perfectly bridged, what is perceived in the USA as the intuitive side of
302 X. Ge et al. creativity—art and the rational side of creativity—science. Creative individuals such as Einstein are believed to be creative not because of their hard work (Lin-Siegler et al., 2016). Images of creative people in the USA (e.g., young Steve Jobs) highlight their vision, ability, and passion—“obsessive interest,” as Richard MacCormac puts it (Lawson & Dorst, 2013), not efforts or experience per se. The American pursuit of reason, such as in creative science, gives rise to theories and structured processes of creative idea generation, based on the belief that “creative potential” can be systematically acquired. At Stanford d.school and IDEO, the educational practice of sticking to a structured process (Kelley, 2012) and some brainstorming rules reflects such a notion in the USA. By following these general creativity-nurturing principles, creative ideas can be unleashed from the independent and confident human minds. Creative problem-solving is understood as confident self-expression and quantity. Self-expression is widely believed to signal independent thinking (Kim & Markus, 2002), whereas quantity suggests quality. The Father of Brainstorming—Osborn made popular the idea that “quantity helps breed quality” (Osborn, 1953). Quantity of ideas (fluency) has been a standard measure of creativity in empirical studies in the USA (see more in Sect. 3.3). “Go for quantity,” as one of the brainstorming rules says. According to David Kelley, founder of d.school and IDEO, the emphasis on quantity of ideas is to “generate more ideas so that they can choose” (Kelley, 2012). 3.2.2 Policies, Norms, and Practices in East Asia As discussed above, the East Asian words of creativity come from the West and inevitably carries with it the Western cultural ideas. Despite its relative short history in Chinese language, the word has been quickly incorporated into formal documents and daily use, especially among the younger generations. China’s government has played a big role in the promotion of creativity. However, little research is done and therefore little is known about how much of its meaning and cultural practices get carried over to the East and internalized by the East in its translingual practices (Liu, 1995). Government statements and news reports seem to suggest an acculturation. Specifically, driven by advancing technological and economic development, scientific and technological creativity is the major concern in mainland China’s policy (Hui & Lau, 2010). Creativity as an individual’s ability is often described as “innovative spirit.” As the Higher Education Law (Ministry of Education, China, 1998, cited in Hui & Lau, 2010) states, “cultivating an innovative spirit in the personality development of young talents is an important strategy.” In the last 15 years, China’s government has been greatly advocating the cultivation of creativity and innovation, as reflected in its five-year plans. According to Wang (2015), the state documents of five-year plans from 1949 to 1996 primarily described creativity as the potential of individuals or a way in problem-solving (we should “creatively” solve the problem), and innovation-related creativity only appeared in science and technology sections, serving as the synonym of scientific research. The term “self-independent innovation” first appeared in the ninth five-year
The Cultural Construction of Creative Problem-Solving: A. . . 303 plan (2001–2005), which, according to Wang (2015), was an official signal of China joining the “creativity warfare” in competition with the West. Yet, the contextual meaning of “self-independence” in “self-independent innovation” emphasizes a collective effort to be independent from the West, rather than suggesting individualism. This notion of being less reliant internationally is reinforced in China’s 14th five-year plan as well (Mallapaty, 2021). Since 2009, numerous innovation and entrepreneurship demonstration zones have sprung up across cities and provinces, under the direction of China’s State Council. These strategies are driven by the needs of economic development and global competitiveness. Although these official policy documents borrow concepts of creativity from the West, the emphasis of creativity and innovation almost always comes together with preserving tradition, as well as sustaining and strengthening the classics and the cultural roots. For instance, in the recent series of five-year plans (e.g., Xinhua, 2021), innovation has been regarded as a savior to revive the bankrupt traditional industries and as a promising way to sustain Chinese traditional cultural products and practices, such as historical villages, Chinese medicine, and traditional handcrafts, highlighting the collectivist goal of social contribution and utilitarianism. The Chinese political system and social structure of today have a direct impact on what messages are promoted in social media. “Innovation” and “tradition” are often paired in news reporting, such as: 继承传统、创新经典 (Sustain the tradition; Innovate the classics) 正确传承比盲目创新更重要 (Correctly passing ideas to next generations is more important than blindly innovating) 传承是基础、创新是生命(Inheriting ideas from the past makes the foundation, based on which innovation offers [new] life) 传承不泥古、创新不离宗(Inheriting tradition flexibly; Innovating without going far from the root) In East Asia, modern designs often emphasize preserving the past, connecting with traditional cultural values, and finding consistent meanings in modern practices of traditional ideas. New architectural designs would be endowed with traditional values—Kengo Kuma’s design of the JP Tower is such an example (Kengo Kuma and Associates, 2012). JP Tower was a project to preserve and renovate the historic Tokyo Central Post Office Building by adding a new skyscraper structure. The architect behind JP Tower is Kuma, a renowned Japanese architect who most recently designed the Japan National Stadium for the Tokyo 2020 Olympic Games. A New York Times interview with Kuma (Saval, 2018) describes Kengo Kuma’s design vision as “a story of returning to the values of traditional Japanese architecture.” In Kuma’s mind, architectural design should “through acquaintance with local materials and methods, relate itself harmoniously to its surroundings.” Who is the Steve Jobs in Japan? The name that comes into many Japanese people’s minds is Gunpei Yokoi, known as the “father of handheld games.” Yokoi founded the product philosophy of “lateral thinking of withered technology (枯れた 技術の水平思考)” at Nintendo (Yokoi, 2021). The idea behind this philosophy is to refrain from cutting-edge technologies, and instead focus on past technologies and develop ideas by viewing these ancient technologies through the lens of lateral
304 X. Ge et al. thinking. This philosophy has not only shaped Nintendo’s product development policy, but also influenced generations of designers and technologists in Japan. The Chinese TV show National Treasure featuring stories of past creations is yet another example that emphasizes the importance of bridging the past and the present. Started in 2017, now in its third season, it has remained one of the highest rated shows in mainland China. It received the best TV show award in the 24th White Yulan Prize of Shanghai TV Festival in 2018. The show allows the audience to admire the hardworking creators’ superb skills and high-level experiential state that the creators were able to achieve. The depiction of superb craftsmanship often highlights the creators’ ability to merge themselves with their creation and become one with it. Indeed, although the word “creativity” is relatively new in Chinese history, there are many Chinese characters, terms, and phrases that entail the idea of solving problems in creative ways, such as “Xin Ying” (新颖), “Jiang Xin Du Yun” (匠心 独运), “Qi Si Miao Xiang” (奇思妙想), and so on. Yet, the contextual meanings of these words, terms, and phrases diverge from American conceptions of creativity. A thorough review of anthropological, philosophical, and psychological literature suggests that while American conceptions of creativity focus on novel solution/ product outcomes and individual autonomy and uniqueness, East Asian conceptions of creativity, as consistent with the message of National Treasure, emphasize the creator’s inner processes and fulfillment (Lubart, 1999; Paletz et al., 2011; Shao et al., 2019). East Asians tend to value the embodiment of direct, personal experiences during the process of creation, where ambiguity is preserved yet logic is unquestioned. The emphasis of “on-the-spot” personal experience, rather than reliance on abstract theories in Japanese management, is a manifestation of such an epistemological tendency (Nonaka & Takeuchi, 1995). The Chinese character wu (悟) depicts such a creative mental process that uses metaphorical, intuitive imagination to jump from the known to the unknown (Li, 2012). According to Peter Ping Li, “almost all Chinese leaders prefer wu in their thinking process to rational analysis. In particular, Yun Ma, the CEO of Alibaba, is an excellent example of a wu leader. He practices Zen as well” (cited in Sundararajan & Raina, 2015). In China, every child grows up learning legends about Zhuge Liang, and classic stories about Effendi, Cao Chong, and Sima Guang, to name a few. For instance, the old tales about Sima Guang, who saves a drowning child by quick-wittedly breaking the water tank, and Cao Chong, who creatively solves the problem of weighing an elephant with a boat and rocks, are part of the required reading in the first and second grade of elementary school education. These people are depicted as capable of creatively and calmly solving impossible problems in urgent situations. The Chinese saying “急中生智 (Ji Zhong Sheng Zhi)” depicts a calm thinker who comes up with ingenious solutions amid crisis. Ingenious problem-solving acts are associated with calm and keen observations with few words, as opposed to passionate, eloquent expression of outside-the-box ideas. The East Asian cultural value of silence rather than speaking is more thoroughly examined in Kim and Markus (2002).
The Cultural Construction of Creative Problem-Solving: A. . . 305 In addition, creative people in East Asia, from business magnate Yun Ma, to famous songwriter and singer Jay Chou, to the great chefs Tetsuya Saotome and Jiro Ono, consistently place far greater emphasis on their effort and experience as opposed to ability or passion as the cause of achievement. For example, great effort is needed in the disciplined, embodied creative practice of Japanese ink painting, called sumi-e (or Suiboku-ga), which has a philosophical origin in the Taoist notion of “uncarved block.” Artists in Japan would spend years applying sumi-e ink brush painting to attain higher states of creative experience—the so-called creativity of no mind (Mushin) (Steinbock, 2013). As Yan (2015) argues, “‘aha’ moments in East Asians’ creativity come from hard work, great effort, and long-term accumulation of knowledge and experiences.” 3.3 Psychological Tendencies of Creative Problem-Solving Culture can also be found in people’s psychological tendencies. Partly because of holding different ideas, norms, practices, and interpersonal interactions across cultural contexts, those who are believed to be creative people, and what are perceived as creative activities and traits, also vary from place to place as do what motivates people to create and solve problems. Unfortunately, most cross-cultural empirical research employs methods and theories of the American models that are derived from independence-embracing ideas, norms, and practices (Lubart, 1990). Consistent with such perspectives, creative people are considered those who have independence of judgment (Barron & Harrington, 1981), freedom and choice (Robinson, 2006), risk-taking boldness, and conspicuous behaviors (Simonton, 2000), choose to be in the creative mode— divergent thinking (Guilford, 1967; McCrae, 1987) and exhibit out-of-the-box thinking (Weisberg & Markman, 2009; Sternberg & Lubart, 1995). These people are considered nonconformists (Sternberg & Lubart, 1995; Grant, 2017), good at expressing self-direction and agency (Amabile et al., 1996; Hennessey & Amabile, 2010; Glăveanu, 2014)—“belief in yourself” (Kusserow, 2012), represented as heroic and masculine, especially in business domains (Bilton, 2010). They are perceived to actively seek loose environments that endow freedom and autonomy (Amabile et al., 1996) and hold positive and activating emotions (Baas et al., 2008). Figure 3 provides an overview of the empirical research paradigm of creativity in America. Researchers often operationalize creativity using standardized measures of fluency, originality, and flexibility (Guilford, 1967; Torrance, 1966), such as the Torrance Test of Creative Thinking. Modern creativity studies in the USA also typically define creativity as the generation of ideas that are both novel and appropriate or useful (Amabile et al., 1996; Oldham & Cummings, 1996). Here, creativity is measured through Consensus Assessment Technique (CAT) or external judges’ evaluation of idea outcomes. The famous Duncker’s candle problem (Duncker, 1945) is based on the idea that to be creative, people must think flexibly (i.e.,
306 X. Ge et al. Fig. 3 Empirical research paradigm of creativity in the USA cognitive flexibility) and be able to break free from their setting (De Dreu et al., 2008). In the independent self-construal, the environment is largely perceived as inert, or as the background against which the self stands out (Nisbett et al., 2001). From this perspective, the role of the self is to influence the environment and to enable change (Markus, 2016; Nisbett et al., 2001; Markus & Hamedani, 2019). The motivation to promote change—radical transformation—is widely held to underpin the generation of new ideas and value creation. As the motto of Stanford Graduate School of Business goes, “change lives, change organizations, change the world.” Implicit theories about other creative processes, such as focusing on inner processes, oneness, or connection with others, are less examined. Where dialectic one-ness (Peng & Nisbett, 1999), holistic thinking (Nisbett et al., 2001), or other associated tendencies are examined, research on the relation between these tendencies and creativity is extremely limited. Conflicting findings and opinions, for instance, in the research about the relation between dialectical thinking and creativity, build up more roadblocks for pushing forward new theories of creativity (Paletz et al., 2018). Implicit theories that may be more relevant in East Asia still, unfortunately, mostly stay at the theoretical and philosophical level. For instance, creativity research often takes for granted the supposedly positive relation between self-directed autonomy and creativity and, as a result, attributes
The Cultural Construction of Creative Problem-Solving: A. . . 307 other-directedness as a negative signal of creativity. Consider the following quote: “I believe creativity is born by pushing people against the wall and pressuring them almost to the extreme.” This remark, which can be frowned upon in the USA, is from an executive at Honda, famously quoted by Hirotaka Takeuchi and Ikujiro Nonaka (Takeuchi & Nonaka, 1986). Underlying this message is a belief that making people work harder would produce more creative outcomes (Forrester, 2000). The admonishment-based approach (Kitayama et al., 1997) is embraced in Japan where other people and group relation is a big source of agency (Markus & Kitayama, 1991) and motivation and action are grounded in a sense of self as interdependent with others and with the environments (Markus, 2016; Nisbett et al., 2001; Markus & Hamedani, 2019). However, people in the USA would think such a practice would kill the enjoyment, interest, and satisfaction that are considered necessary for unleashing creativity from within individuals. Similarly, people in the USA that champion “change” to solve social problems may be puzzled at East Asians’ resistance to change. East Asians take the demanding job of preserving the past and sustaining the connections seriously, where “change” can be seen as an unconstrained, irresponsible mission that requires less effort. For East Asians, the context is more likely to be perceived to constantly produce changes. As a result, the role of the self is to contextualize, observe, connect to, and adapt to changes that come from others and the environment. Empirical studies that solely employ an American perspective can lead to mixed findings about East Asian creativity with highly questionable validity (see a review in Morris & Leung, 2010). In their research to identify people’s concepts of creativity among Mainland, Hong Kong and Taiwanese Chinese, Rudowicz and Yue (2000) surveyed associative adjectives of the word “creativity,” which admittedly carries American ideas and practices, rather than addressing it from an East Asian perspective. Niu et al. (2007) assessed both Hong Kong and U.S. participants’ creativity in terms of appropriateness, humor, and originality based on the Consensus Assessment Technique (CAT) (Amabile et al., 1996). Interestingly, while “humor” and “originality” are possibly valued dimensions of creativity in some American populations, they may be largely irrelevant based on Rudowicz and Yue’s (2000) observation of mainland Chinese participants and Taura and Nagai’s prediction (2013). In addition, some studies find that Chinese personality is incompatible with creativity (Hui & Rudowicz, 1997; Rudowicz & Yue, 2002), yet in some other studies, Chinese students performed better (Saad et al., 2015; Wang et al., 2018) or similarly (Riquelme, 2002) as their American counterparts in carrying out creative idea generation tasks. Other problems include cross-cultural survey in the English language (Zha et al., 2006) and biased sampling (Sundararajan & Raina, 2015). Problems have also emerged in cross-cultural studies that involve other countries and contexts. For instance, quantity was found to be an irrelevant factor of creativity in a study of Moroccan students (Peng et al., 2021). The overreliance on these standardized measures of creativity is not only inappropriate in cross-national studies, but also problematic in studies within the multicultural nation of America (e.g., Brannon et al., 2015; Jackson et al., 2019). For example, in Brannon and colleagues’ research (2015) of the double consciousness of
308 X. Ge et al. African Americans (i.e., independent and interdependent self-schemas), they examine whether engagement in African American culture improves students’ academic fit and performance for African Americans. However, the evaluation and measurement of academic performance based on which the studies were run solely reflect an independent schema (e.g., flexible thinking, excitement). Creativity researchers have started to criticize the application of Western tests to different populations because rather than uncovering culturally valued and culturally varied traits and characteristics, such traits may be overlooked (Mistry & Rogoff, 1985; Runco & Bahleda, 1986; Runco & Johnson, 2002; Glăveanu, 2010). The lack of culturally responsive conceptions and measures of creativity is partly responsible for our confusion and continuous misconceptions about East Asian attitude, behavior, cognition, and emotion around creative problem-solving. 4 Overview of Exploratory Studies Instead of adopting standard creativity measures developed in WEIRD contexts (Henrich et al., 2010), we have deviated from the standard but biased measures. We want to examine paradigms of creativity from culturally relevant perspectives. In this section, we share initial findings from a few pilot studies, which are part of our ongoing efforts to explore the cultural differences of creative problem-solving. Specifically, we use survey studies to examine across cultures the desirability and applicability of the American views, which encourage being agentic change-makers in creative problem-solving. In the first study (Sect. 4.1), we posit that instead of seeing the self as the source of agency, people tend to gain agency from context (such as the past, other people, and the physical environment) in East Asia. Our initial findings suggest that there is a cultural difference in people’s perceptions about the context’s agency in producing good ideas, creating changes, and performing human-like tendencies. In addition (Sect. 4.2), people’s evaluation of ideas is affected when their perceptions about the role of the context are manipulated. In the next section (Sect. 4.3), we hypothesize that instead of “change,” “preservation” (e.g., connection with the context) is likely to fuel idea generation in East Asia. The finding suggests that indeed, culturally resonant narratives (e.g., change versus preservation) affect people’s perceptions and motivations in creative problem-solving. Most recently (Sect. 4.4), we are building a new composite measure to examine in different countries people’s perceptions around which make an idea better, specifically—“breaking” or “connecting.” We are still in the iterative design process of the cultural creativity surveys, including balancing contextualization and generalizability, refining the scales, and improving reliability and validity for both American and East Asian participants. The findings shared here are meant to stir conversation and reflection, rather than making assertions about cultural differences.
The Cultural Construction of Creative Problem-Solving: A. . . 4.1 309 Cultural Variations in Perceptions of the Agency of Context In the USA, creative problem-solving champions individual self-direction, agency, and autonomy. In comparison, East Asian creativity highlights the agentic roles of the context (e.g., other people, past ideas, and the physical environment). Therefore, we hypothesize that East Asians tend to perceive the context (i.e., factors external to individuals) has more agency than individuals and that the opposite is true for Americans. In a series of exploratory surveys, we incorporate measures to examine beliefs about the source of change, the source of good ideas, or the source of a broad range of tendencies (e.g., kindness, authority, wisdom) on Likert scales from 1 ¼ completely from individuals to 7 ¼ completely from context. The source of change and the source of good ideas are both examined through a one-item measure, and the source of animated tendencies is based on a composite measure that has high internal consistency. Through iterative survey design, in the surveys about the source of good ideas and the source of animated tendencies, we have provided examples, such as cultural practices, history, and natural environment, to clarify what “context” or factors external to individuals consists of. Since 2019, we have distributed these surveys among USA (adult samples recruited online from Prolific, Mturk, and college student samples), Chinese (adult samples from online survey platform Wenjuanxing), and Japanese participants (adult samples recruited from the online survey platform Lancers and college student samples). Across the board, American participants are less likely to see context as a source of change, good ideas, or various animated tendencies than their Chinese and Japanese counterparts. These differences are all statistically significant. For instance, in one study (Fig. 4), we use an adult sample recruited from Prolific in the USA (N ¼ 187, mean age ¼ 32.3, 86 women and 6 others) and Wenjuanxing in China (N ¼ 176, mean age ¼ 32.2, 74 women and 2 others). We find that compared with U.S. participants, Chinese participants indicate a stronger belief that “change” comes from context, t(361) ¼ 2.974, p < 0.01. In another study (Fig. 5), we recruited participants from Mturk in the USA (N ¼ 150), Wenjuanxing in China (N ¼ 87), Lancers in Japan (N ¼ 165), as well as Japanese engineering college students (N ¼ 124). Similarly, online participants in China (t(236) ¼ 2.522, p ¼ 0.012) and in Japan (t(314) ¼ 2.448, p ¼ 0.015), as well as Japanese engineering students attending a university in Japan (t(273) ¼ 3.736, p < 0.01) are more likely to believe that “good ideas for solving problems” come from context than their American counterparts. Another study on the source of change can be found in our previous conference presentation in Ge et al. (2021). To sum it up, compared to Americans, East Asians are more likely to see a connection with the context and impart agency to factors external to the self. This difference carries important implications for creative problem-solving. We suggest that in East Asian societies, practices to elicit a more generative mindset would be more effective if they placed a greater emphasis on the role of the context and
310 X. Ge et al. Fig. 4 Compared with U.S. participants, Chinese participants indicate a stronger belief that “change” comes from context, t(361) ¼ 2.974, p < 0.01 Fig. 5 Compared with U.S. participants, online participants in China (t(236) ¼ 2.522, p ¼ 0.012) and in Japan (t(314) ¼ 2.448, p ¼ 0.015), as well as Japanese engineering students (t(273) ¼ 3.736, p < 0.01), indicate a stronger belief that “good ideas for solving problems” come from context actively involved factors external to the self (e.g., tradition, other people, situations) in motivating people to come up with sound ideas for solving problems.
The Cultural Construction of Creative Problem-Solving: A. . . 4.2 311 Relation Between Perceived Agency of Context and Other Factors and Its Manipulation In addition to engaging in a comparison between American and East Asian samples, we have also conducted analyses within the U.S. population. Using an online sample of American adult participants, we find that locating the source of change within the context correlated negatively with identifying as being an American, r(277) ¼ 0.20, p < 0.001, as well as trust in American institutions, r(277) ¼ 0.17, p < 0.01. Perceived sources of change, however, do not correlate with participants’ demographic or political orientation, thus suggesting that this is a more general psychological phenomenon. In addition to measuring people’s perceptions of the context, we also experimented with providing materials to directly affect people’s perceptions, which allows us to study the downstream consequences of shifting perception and to understand the direction of causality involved. We recruited 325 U.S. participants from Prolific (mean age ¼ 35.69, SD ¼ 13.38, 204 women, 115 men, and 6 other, 6 did not pass attention checks). Participants were randomly assigned to one of the two conditions. In one condition, they were informed that change comes from individuals. In the other condition, participants read that change comes from context or factors external to individuals. The source of change manipulation was successful, t(318) ¼ 9.26, p < 0.001. After the manipulation, participants were asked to evaluate a series of products or ideas (see examples in Fig. 6) and indicate their perceptions of these ideas. Our empirical findings suggest that people’s perceptions about the relationship between the self and the context (e.g., others and the environment), such as the context’s agency, can partly explain how they understand product ideas. Specifically, participants who were informed that the individual (rather than context) produces change perceived ideas to be more unique, t(319) ¼ 2.21, p ¼ 0.03, and were also likely to believe that these products serve the purpose of change rather than preservation, t(319) ¼ 1,72, p ¼ 0.087. Fig. 6 Examples of ideas and products used in the manipulation survey to examine how people’s perceptions of an idea’s purpose and uniqueness are affected by their beliefs about the agency of the context. (a) Hippo Water Roller (2022). (b) Multi-purpose Tent in Desert (Seikaly, 2015)
312 4.3 X. Ge et al. Engineering Students’ Motivation for Problem-Solving Technological companies in Silicon Valley promote the notion to change, transform, and disrupt the status quo for the better. We examine whether this notion is culturally specific to the context of the USA. Based on our theorization, what drives engineers to create and solve problems could vary across cultures. We hypothesize that engineering students in Japan are more motivated to generate ideas when a task is framed to preserve rather than change a certain situation. In contrast, American engineering students are more motivated by a task frame characterized by change as opposed to preservation. To test this hypothesis, we recruited two different engineering student samples—one from Prolific in the USA (N ¼ 209, mean age ¼ 24.3, 80 women, 128 men, and 1 other) and the other from an engineering university in Tokyo, Japan (N ¼ 158, mean age ¼ 19.96, 7 women, 149 men). Participants were randomly assigned to come up with a new idea to solve a problem, randomly assigned from a corpus of problems, the goal of which was either to change or to preserve a certain target. After writing down their ideas, participants reported their levels of motivation during the idea generation. We found that Japanese engineering students are more motivated to generate ideas when the goal is framed in terms of preservation (e.g., come up with ideas to preserve local transportation) rather than change (e.g., come up with ideas to change local transportation), t(151) ¼ 1.88, p ¼ 0.062. However, no statistically significant difference between conditions is found for American engineering students, t(202) ¼ 0.6, p ¼ 0.5. However, there is a trending moderation effect. This means that the effect of problem framing on participants’ motivation level is moderated by their perceptions about where change comes from, t(357) ¼ 1.63, p ¼ 0.1. Within the American sample, some participants also hold similar views as Japanese participants and perceive context as a source of change. These subsets of American participants also tend to be more motivated by preservation than by change. These initial findings thus suggest that people’s perception of the context affects their motivation to solve problems. This psychological link holds whether we compare people from two different cultural groups or analyze the diversity of people’s perceptions and motivations within the American group. More details about the methods, analysis, and results of this study can be found in our recent conference paper in Ge et al. (2021). 4.4 Which Makes an Idea Better: “Breaking” or “Connecting?” We are currently focusing on the temporal dimension of connecting with or breaking from context. What characteristics make an idea better? What relationships between current ideas and past ideas do people in different cultures desire? We hypothesize
The Cultural Construction of Creative Problem-Solving: A. . . 313 that Americans tend to evaluate ideas more positively when these ideas are perceived as breaking away from past ideas, whereas East Asians may prefer ideas that are connected with practices and ideas people had in the past. Based on our culture cycle analysis, the process of creation that emphasizes continuity with the past and the environment in East Asia is idealized in a dramatically different way than in the USA where such connection is often missing. Through iterative design and pilot testing based on U.S. participants, we have designed a 12-item composite measure that looks at the characteristics of ideas for problem-solving: six items characterize “continuity” and describe current ideas as connecting with, grounded in, and revitalizing past ideas; another six items capture “discontinuity” and picture current ideas as departing from ideas in the past. We believe that the current empirical approach is promising and can pave the way for discovering systematic cultural differences. We will continue to investigate the perceived desirability of ideas that stand away from or build upon past ideas in different cultural contexts. 5 Discussion Based on our comprehensive analyses of how creative problem-solving is shaped in American and East Asian cultural contexts, we discuss the problems and societal consequences of solely relying on a singular view of creativity. We also offer some implications of our work for creative (design) thinking, teaching, and learning. Finally, we reflect on ongoing empirical efforts and summarize several of the other directions that we are pursuing. 5.1 Embracing Diversity Inherent in the Human Processes of Creative Problem-Solving The dominant ways of creativity assessment (e.g., in Fig. 3) not only dominate scientific research, but also decide who excels in school. This essentially rejects diversities that are inherent in the human processes of creativity and creative problem-solving. Why does research on implicit theories that may be more relevant in East Asia still mostly stay at the theoretical and philosophical levels? Is it because we lack exposure to other perspectives and ways of being? In the increasingly globalized society, culture clashes are supposedly abundant. Yet it is difficult to overcome the confusion and rejection when one is confronted by a different reality of creativity. “It is the conventional way of defining creativity that prevents us from measuring it beyond the rigid frame we use in research,” argues Lutz Eckensberger (cited in Sundararajan & Raina, 2015). As Gustav Ichheiser (1970) eloquently writes:
314 X. Ge et al. [W]e fail to understand that people whose personalities are shaped by another culture are psychologically different—that they see the (social) world in a different way and react to it as they see it. Instead, we tend to resolve our perplexity arising out of the experience that other people see the world differently than we see it ourselves by declaring that those others, in consequences of some basic intellectual and moral defect, are unable to see things “as they really are” and to react to them “in a normal way.” Without resolving the confusion and rejection of other ways of creative being, however, empirical studies that employ an American perspective will continue to reinforce misleading conclusions, such as the lack of creativity in Chinese and Japanese people (Riquelme, 2002; Rudowicz & Hui, 1997), and that individualistic culture outperforms collectivistic culture in cultivating creative talents (e.g., Goncalo & Staw, 2006), and so on. On the positive side, there is an emerging effort to resolve the tension between the East and the West. Amongst others, Averill et al. (2001) propose the notion of emotional creativity to incorporate the East Asian perspective of situated experience into the Western model of creativity. There is an emerging consensus in management research that researchers should adopt an interdisciplinary and multiperspective approach in general (see Suddaby et al., 2011 for a review). We urge creativity research across cultures to employ a beginner mindset on what creativity really means and entails for different cultural contexts. On the practical end, the booming “creative economy” (Florida, 2002) continues to evolve without critical reflection on the current evaluation of people’s creative processes and performance. Creativity should be one of the inclusive educational and managerial targets. Schools such as Harvey Mudd College (Cheryan & Markus, 2020) have started launching programs to increase the belongingness and cultural fit for underrepresented individuals and groups. This is not enough, as long as our evaluation of students’ or employees’ creative performance is still narrowly defined by the WEIRD (Henrich et al., 2010). We should not leave underrepresented members with the default option to struggle and adapt to the dominant cultural values (Choi, 2010) or drop out (Felder & Brent, 2005). Embracing diverse cultural ideas and practices is a grand challenge. The independence-based educational and organizational settings, as well as the ideas, policies, norms, practices, and products within such settings, all need to be re-imagined, in such a way that people of all backgrounds are truly equally welcomed. 5.2 Critical Next Steps in Design Thinking, Teaching, and Learning The current paper’s title is named as an allusion to Dym and colleagues’ iconic design education paper (Dym et al., 2005), and the discussion here indeed is to extend their efforts to broaden design thinking (from rational to inclusive), design language (from math to multimodal), design behavior (from individual genius to group effort), and design participation (from male dominance to diversity and
The Cultural Construction of Creative Problem-Solving: A. . . 315 inclusion, e.g., Agogino’s work, cited in Dym et al., 2005). Importantly, Dym et al. (2005) criticize the teaching practice of equating divergent thinking to creativity and urge a critical reflection on “what defines creativity.” We build upon their work by providing a critical cultural perspective. Specifically, we argue that what defines creativity is culture. Culture and global context are part of the fundamentals of design. This is reflected in the multicultural student composition of globalized design classrooms (Fruchter & Townsend, 2003; Daniels et al., 2010; Carleton & Leifer, 2009). We also see an increasing exchange of best practices, especially one-directionally from the USA to the rest of the world. For instance, creative learning process and methods stemming from the best practices of the Stanford d.school and Design Group, IDEO, SAP, and MIT D-lab, to name a few, have influenced educational practices and organizational management in many places around the world (e.g., Misaki et al., 2020; Ge & Maisch, 2016; Drain et al., 2017). For decades, MIT Creative Capacity Building program at D-Lab has provided creativity training for rural communities around the world (Drain et al., 2017). However, for educators and creativity training ambassadors, the consequences of holding false assumptions that certain types of people lack creativity based on certain selected beliefs in the USA, are dire. International students may get culturally biased grades and undergo psychologically difficult times. For instance, in the popular Stanford class ME310—Global Design Innovation, Japanese and Chinese students coming for a co-final presentation with their U.S. student partners may be poorly evaluated for insufficiently explaining how their ideas break the status quo, which is considered desirable in the USA but not so in East Asia. The situation is problematic given that student evaluation is based less on traditional exams of fundamental science knowledge, and increasingly more subject to culturally shaped subjective opinions. A critical next step is to fight against the long-term stigma about the creative ability of certain student groups. A good design teacher today should have a nuanced understanding of the various cultural values and norms that shape designers’ creative behaviors. The current paper has offered a comprehensive analysis with promising study outcomes to potentially expand the understanding of creativity among design educators. An open mind to understanding creative diversity is critical to addressing the remaining question of how to truly fulfill the cultural needs of students of all backgrounds in creative problem-solving. By conceptualizing designers as culturally shaped shapers, we call for design educators and practitioners to explicitly incorporate cultural values into their design processes. We hope to stimulate reflections on principles and practices of design thinking that are widely applicable, as well as to uncover assumptions about design that are culturally specific.
316 5.3 5.3.1 X. Ge et al. Reflection on Studies and Future Work Study Limitations We have shared initial findings from several directions of our ongoing empirical efforts. Potential issues of reliability and validity may exist in the current four exploratory directions. For instance, in the study of culturally varying perceptions of the context’s agency, although clear definitions and instructions are given, we mostly use a one-item, 7-point Likert-scale question—where do you think change (or good ideas) come from? In a more recent version, we start examining human-like tendencies with multiple-item questions, which have reached good internal consistency. In the study of motivations underpinning creative problem-solving, we have framed the problem-solving question as “Come up with a new idea for. . .”. In reflection, we recognize the use of the word “new” resonates with and possibly elicits independent ideas of creativity for both American and East Asian participants. We have adopted “good” instead of “new” or “novel” in later versions of our survey exploration. Additionally, we have learned to adopt neutral-to-positive words. For instance, “sustain” and “connect” may be better words than “preserve,” the latter of which could have a negative connotation among some U.S. participants. 5.3.2 Future Work We are currently extending this line of work to develop a more comprehensive framework to examine people’s perception of the location of a host of different psychological states and tendencies. Further investigation of cultural differences in this aspect would shed light on the psychological mechanisms by which people are motivated to either pursue continuity or discontinuity when generating ideas. We are hopeful that this body of knowledge would inform the design of interventions or educational materials to best tap into people’s motivation for solving important personal or social problems. For instance, while educational systems in different societies are similarly confronted with the challenges posed by the COVID-19 pandemic, people may respond quite differently to different definitions of the social problem. “Come up with ideas to transform the educational system” and “find ways to sustain learning” are two distinctive calls for solutions to the educational challenges posed by COVID-19. The former resonates with cultural values of instigating agentic change-making, while the latter appeals to people valuing continuity. We maintain that such differences in the framing of problems matter for crafting culturally resonant materials to motivate people to come up with ideas to tackle pressing societal problems. Our theorization will continue to guide future designs of new surveys, and the findings will be examined against belief systems—historically derived ideas, norms, practices, and cultural products across different cultural contexts.
The Cultural Construction of Creative Problem-Solving: A. . . 5.3.3 317 Other Potential Empirical Approaches In parallel to the survey study, we have explored other empirical approaches. For example, how to take advantage of naturally-occurring materials designed to motivate people to solve problems? In this regard, we are considering leveraging the IDEO Open Innovation—a platform that encourages people worldwide to collaborate and build on each other’s capabilities and ideas. Our plan is to extract texts of various problem descriptions from the online archives of IDEO Open Innovation and a comparable web platform in East Asia to discover the extent to which descriptions of problems reflect culturally relevant values of creativity. For example, one current problem in IDEO Open Innovation is framed as “Agents of change: Atopic Dermatitis challenge.” The purpose of the task is described in the following way: “Let’s work together to increase the understanding of atopic dermatitis (AD), help break social stigma, and put a stop to the bullying faced by those with AD.” We see this example as representing a problem that is framed mainly in terms of changing and breaking the status quo. We suppose that if the same task were to be framed to reflect an East Asian value, it would read: “Preserving the dignity: Atopic Dermatitis challenge.” Accordingly, the purpose of the task would be described as “Let’s work together to help people with atopic dermatitis (AD) to continue leading their normal lives, support public understanding of AD, and keep the sympathy towards those with AD.” The difficulty with this method is to find a counterpart of IDEO Open Innovation Platform in East Asian Contexts, such as in Japan or China. We have collected some design prompt course materials in Japan. However, the materials are limited and not ideal for text analysis. In parallel, we have been looking for appropriate social media data, website archives, and newspaper articles that are comparable to one another in East Asian and American societies. Another source of data are the archives of popular TV shows in which entrepreneurs pitch new ideas to a panel of potential investors. Based on our initial observation about the winners of funding from the two shows, it would be fruitful to compare the popular Japanese TV show “Dragon’s Den” with the American TV show “Shark Tank.” We are still seeking proper analytical tools to examine this archival data. We also plan to examine cultural variations in theories of creativity through field experiments. Online competitions, for instance, would be a good avenue to crowdsource ideas for solving real-world problems. The outbreak of the global COVID-19 pandemic has made health and well-being a central issue around the world. In this case, both participants in the USA and in East Asia would be recruited to take part in our competition to solve real-world problems related to health and well-being. Multimodal data of body movement, speech, and text could be collected and utilized to distill different cultural signals in participant responses.
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Design Thinking as a Catalyst and Support for Sustainability Solutions Nicole M. Ardoin, Alison W. Bowers, Veronica Lin, and Indira Phukan Abstract Despite often-heroic efforts from dedicated scientists, researchers, educators, policymakers, activists, and everyday citizens, sustainability issues impact communities across the globe, with achievable solutions remaining daunting. Solving these wicked challenges requires creative ways of thinking, including an oftenradical reimagining of the problem space itself. There is a growing recognition that individual action is insufficient as sustainability issues involve a complex web of actors, scales, and systems. Supported by an exploratory literature review, we argue that design thinking is a useful, appropriate, and necessary approach to support collective action in addressing sustainability issues. We outline five design thinking characteristics that support this argument: design thinking inspires creativity; is participatory and people-focused; encourages and inspires diversity in thought and action; adopts a holistic, systems-thinking mindset; and offers a streamlined, actionoriented approach. We conclude with reflections on this union between design thinking and sustainability action and suggest future directions for aligned research and practice. N. M. Ardoin (*) Graduate School of Education, Stanford University, Stanford, CA, USA Stanford Woods Institute for the Environment, Stanford University, Stanford, CA, USA Emmett Interdisciplinary Program in Environment and Resources, Stanford University, Stanford, CA, USA e-mail: nmardoin@stanford.edu A. W. Bowers · V. Lin · I. Phukan Graduate School of Education, Stanford University, Stanford, CA, USA e-mail: awbowers@stanford.edu; vronlin@stanford.edu; iphukan@stanford.edu © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 C. Meinel, L. Leifer (eds.), Design Thinking Research, Understanding Innovation, https://doi.org/10.1007/978-3-031-09297-8_16 325
326 N. M. Ardoin et al. 1 Introduction The complexity, as well as temporal and spatial scales, of today’s sustainability challenges—such as climate change, biodiversity conservation, zoonotic disease, and water management, among others—makes these problems some of the most wicked the world has faced. To address such enormous challenges, various nonprofit organizations and multilateral agencies strive to collaborate in innovative ways, offering a vision and path forward that motivates individual and collective action. The United Nations Sustainable Development Goals (SDGs), for example, offer a blueprint for the future stretching to 2030 and beyond. Developed collaboratively and implemented by UN member states, this ambitious set of 17 goals describes a shared vision for working toward global peace and prosperity for people and the planet (United Nations, 2015). The UN SDGs and similar approaches recognize that sustainability problems are, at their core, people problems (Clayton & Brook, 2005; Schultz, 2011). Thus, addressing and solving today’s intertwined sustainability challenges requires shifting human patterns of behavior toward those that are more sustainable (Balmford et al., 2021). Each of the SDGs, and the interplay among them, highlights the deeply intersectional nature of such issues. Poverty, health, racial and social justice, economic disparity, and environment are intertwined, and deliberate collective action across society, coupled with a systems-thinking perspective, is necessary for transformative change (Lubell, 2002; Matson et al., 2016; Reid et al., 2021; United Nations Environment Programme, 2021). Finding the “recipe” to move individuals and communities to action in a manner that benefits the collective, but relies on the goodwill and agency of the individual, remains a challenge. Over the past two decades, researchers in our Social Ecology Lab at Stanford have collaborated with a range of partners, from nonprofit conservation organizations to government agencies, from local-to-national scales. We conduct descriptive and interventional mixed-methods studies using a range of interdisciplinary theories and approaches derived from social ecology, social psychology, rural sociology, cultural anthropology, learning sciences, and design. Applied to a range of collective-action issues—including, but not limited to, climate change, ocean health, land conservation, and invasive species control—we have frequently employed structures and mindsets of design thinking during the intervention-development process. We consistently find design thinking useful in generating, iterating, reflecting, and adapting at various stages and, in particular, find that such perspectives and tools assist when working across a diverse group of individuals. For example, we have conducted design thinking workshops with the goal of developing interpretive programs and materials to encourage pro-environmental behaviors in visitors to natural areas. Using a design thinking approach allowed us to involve a range of stakeholders, including tourism professional, artists, scientists, researchers, and park staff. Design thinking methods and mindsets employed in the workshops resulted in the generation of richer prototypes and ideas that were grounded in both research and practitioner needs.
Design Thinking as a Catalyst and Support for Sustainability Solutions 327 This use of and interest in design thinking—and our belief, in particular, in its efficacy for sustainability challenges—spurred our desire to explore the ways in which the research literature describes the union of design thinking and sustainability. In this chapter, we present a synthesis of and reflection on that exploratory review of research. We examine the processes and insight-driven aspects of design thinking, considering how such an approach enhances the translational impact of sustainability work. 2 Background Since the mid-twentieth century, researchers and practitioners have championed design as playing a role in achieving sustainable outcomes (Ceschin & Gaziulusoy, 2016). Some researchers have suggested that, due to the historic focus on consumption and profit, designers shoulder one of the greatest imperatives to change the cultural narrative by designing in a way that engenders a more sustainable society (Ceschin & Gaziulusoy, 2016; Dewberry & Sherwin, 2002; Fry, 2020; Manzini, 2007). Many designers have expressed an interest in using their skills and knowledge to improve the world in which we live, including addressing sustainability challenges (Blizzard et al., 2015). Subsequently, designers have developed several sustainability-focused approaches, such as eco-design, green design, and design for sustainable behavior, to ensure that the design process considers sustainability (Ceschin & Gaziulusoy, 2016; Dusch et al., 2011; Young, 2010). Such approaches emerge from and reside most comfortably in the business world, promising tools, methods, and mindsets to help designers develop more sustainable products. Sustainable product development indeed is likely to play a central role in creating pathways toward a more sustainable future. Andrews (2015), for example, outlines the critical role designers play in a theorized circular economy, a sustainabilityfocused economical model of production and consumption. As the scale and complexity of sustainability issues continues to emerge, however, it is clear solutions will not be achieved by creating sustainable products alone—an idea that is, at times, critiqued as a framing of environmental issues within the technocratic, neoliberal perspective that such issues can be “fixed” by increased production and consumption rather than moving toward minimizing our lifestyles and footprints (Prothero et al., 2010; Dilnot, 2017). This tension encourages a consideration of factors beyond the environmental and economic factors associated with product development (Ceschin & Gaziulusoy, 2016; Spangenberg et al., 2010), pushing into sociocultural, political, and ethical decision-making, to name just a few areas of emphasis. Bjögvinsson et al. (2012, p. 102) describe this necessary shift as a “move from designing ‘things’ (objects) to designing Things (socio-material assemblies).” These propositions align with our description, above, of sustainability problems as people problems, requiring a reframing of sustainability issues as broader societal considerations. As Rittel and Webber (1973) describe, intricate and tangled problems cannot be definitively bound by the same rules and guidelines that have been developed to
328 N. M. Ardoin et al. solve “tame” scientific problems. Indeed, by the very social and collective nature of sustainability issues, we enter into a discussion of a “public good,” thus departing from definitive solution sets governed by clear-cut guidelines. Instead, socioenvironmental issues, such as sustainability, call for solutions that must be qualified, are often partial, might only work for certain scenarios and populations, and are subjective by nature. In short, sustainability problems are the ultimate definition of the now-common descriptor “wicked” (Balint et al., 2011), or those that are “social system problems which are ill-formulated, where the information is confusing, where there are many clients and decision makers with conflicting values, and where the ramifications in the whole system are thoroughly confusing” (Churchman, 1967, p. B-141). Wahl (2006, p. 294) postulates that “sustainability is the wicked problem of design in the twenty-first century.” To address the wicked nature of sustainability problems, design researchers and practitioners have acknowledged the need for alternative problem-solving approaches (Gould et al., 2019; Maher et al., 2018; Shapira et al., 2017). Dewberry and Sherwin (2002, p. 135) write about a new way of thinking in design to “capture the imagination, hearts and minds of people” and suggest a shift “away from issues of sustainable production and technology that characterize the supply side alone towards a ‘softer’ more human-focused approach that addresses notions of desire and consumption.” Earle and Leyva-de la Hiz (2021, p. 583) call for “holistic and creative problem-solving that integrates a variety of stakeholders, traverses traditional disciplinary boundaries, considers extended time horizons, and adopts a system-level perspective (Bansal & DesJardine, 2014; Romme, 2003; Shapira et al., 2017; Shrivastava, 2010).” Design thinking has thus emerged as a promising approach to imagining and implementing sustainability solutions (Bermejo-Martín & Rodríguez-Monroy, 2020; Brown & Wyatt, 2010; Erzurumlu & Erzurumlu, 2015; Greenberg & Karak, 2020), with researchers making an explicit link between design thinking and the United Nations Sustainable Development Goals (Clark et al., 2020; Maher et al., 2018). Many researchers and practitioners alike call out the natural fit between design thinking and addressing wicked problems (Buchanan, 1992; Buhl et al., 2019; Clune & Lockrey, 2014; Earle & Leyva-de la Hiz, 2021; Jobst & Meinel, 2014; Kagan et al., 2020; von Thienen et al., 2014). In the subsequent section, we explore the design thinking characteristics that have emerged from our literature review, and which underlie this premise. 3 Design Thinking Characteristics That Support Sustainability Solutions Our exploratory literature review confirms alignment between the sustainability solution space and design thinking approaches. We acknowledge the diversity in design thinking approaches and methods (Lindberg et al., 2010; Schmiedgen et al., 2016) and view this versatility and flexibility as a strength when addressing
Design Thinking as a Catalyst and Support for Sustainability Solutions 329 sustainability challenges. In our review of the research, rather than adopting one definition of design thinking, we view design thinking broadly as a creative approach to solving real-world problems (Royalty et al., 2021). In its many instantiations, design thinking centers on several core principles, including that it inspires creativity; is participatory and people-focused; encourages and inspires diversity in thought and action; adopts a holistic, systems-thinking mindset; and offers a streamlined, action-oriented process (Brown & Wyatt, 2010; Buhl et al., 2019; Clark et al., 2020; Earle & Leyva-de la Hiz, 2021; Fischer, 2015). These primary characteristics support a coming together of individual knowledge, dispositions, and skills that combine to create an ecosystem of shared experiences built on a foundation of collaboration, trust, and synergistic action. We discuss each of the characteristics below and then focus on how they support collective action. 3.1 Inspires Creativity Sustainability issues require innovative solutions that are most likely to emerge from new ways of thinking about and looking at data, context, and problems (Clark et al., 2020; Greenberg & Karak, 2020; Sandri, 2013; Stables, 2009; Westley et al., 2011). Additionally, when working with sustainability issues, people need to be able to envision alternative futures (Kagan et al., 2020; Miller et al., 2014), a task complicated by feelings of hopelessness that can accompany the realization of the seriousness and urgency of sustainability challenges (Ojala, 2012; Wals, 2020; Hes & du Plessis, 2014). To achieve workable sustainability solutions therefore requires creativity, alongside rational hope that derives from imagining and taking action toward a better—more just, equitable, peaceful, and biodiverse—world. Design thinking concurrently inspires and requires creativity, strengthening creative confidence while engaging in problem-solving by drawing on both convergent and divergent thinking (Clark et al., 2020; Geissdoerfer et al., 2016; Jobst et al., 2012; Kagan et al., 2020; Shapira et al., 2017). Core design thinking processes, such as visioning, brainstorming, ideating, experimenting, testing, and learning from initial failures, contribute to generating novel solutions by facilitating a mindset focused on creativity, innovation, and growth. These processes encourage imagination around a range of possibilities and support a hopeful and positive attitude about the future, all of which are critical to sustainability problem-solving (Buhl et al., 2019; Wahl & Baxter, 2008). Design thinking’s creative confidence mindset and iterative nature encourage participants to engage in solution options without fear of failure or judgment. When discussing design thinking and wicked problems, Buchanan suggests that “what many people call ‘impossible’ may actually only be a limitation of imagination that can be overcome by better design thinking” (1992, p. 21). He goes on to say that design thinking does more than result in quick fixes; rather, it leads to “new integrations of signs, things, actions, and environment that address the concrete needs and values of human beings in diverse circumstances” (Buchanan, 1992, p. 21).
330 3.2 N. M. Ardoin et al. Participatory and People-Focused Although the phrase “Save the Earth” may be a popular T-shirt and bumper-sticker slogan, scholars and activists alike increasingly question its wisdom. Many emphasize that the planet itself does not need saving; rather, human society is in trouble and, along with harming ourselves, we are causing irreparable damage to the lifesupport systems of other living creatures and their ecosystems. Within this frame, we can discuss people as both the cause of and solution to sustainability issues—and, moreover, as those who must take action (Clayton & Brook, 2005; Schultz, 2011). Addressing sustainability challenges requires participation from all, including but not limited to policymakers, corporate entities, nonprofits, academics, and everyday citizens (Fischer, 2000; Schmidt et al., 2020; Lang et al., 2012; Ardoin & Heimlich, 2021). Sustainability science researchers have called for knowledge co-production that includes collaboration among scientists and the range of impacted stakeholders (Blackstock et al., 2007; Miller et al., 2014; Moallemi et al., 2021). Relatedly, from the outset, design thinking’s participatory approach involves end users in the solution generation process and encourages active participation by all those involved, regardless of levels of prior formal knowledge or academic/policy expertise, recognizing that firsthand experience is key to solution generation and success (Kagan et al., 2020). Another key aspect of design thinking is the low barrier to entry: all abilities and knowledge sets are valued (Brown & Wyatt, 2010; Fischer, 2015). A range of stakeholders are sought out and invited to contribute, with the process’s ethos emphasizing the message: “we are all in this together” (Clune & Lockrey, 2014; Shapira et al., 2017). Frequently described as user-centric, design thinking honors its business roots in product development. When employed to address sustainability issues—where the desired end-product is often a solution to a sustainability challenge—the “users” may include a broad swath of stakeholders (individuals, as well as organizations) given the inescapable impact of sustainability-related concerns. For example, as everyone breathes air, drinks water, experiences the climate, and requires nutritious food, a healthy and well-functioning, sustainable ecosystem benefits everyone (Buhl et al., 2019). Designers describe design thinking as human-centered, in both process and product, as it draws on human emotions, ingenuity, intuition, and imagination (Bermejo-Martín & Rodríguez-Monroy, 2020; Brown & Wyatt, 2010; Meinel & Leifer, 2015). Thus, design thinking processes have been demonstrated to be effective in engaging communities in sustainability issues (Erzurumlu & Erzurumlu, 2015) and generating high levels of user involvement (Alexandrakis, 2021).
Design Thinking as a Catalyst and Support for Sustainability Solutions 3.3 331 Encourages and Inspires Diversity in Thought and Action In sustainability research there is a frequent call for transdisciplinary work involving collaboration among different fields of study as well as between academic and non-academic stakeholders (Brandt et al., 2013; Lang et al., 2012). Additionally, taking action to address sustainability issues is improved when those involved come from diverse backgrounds and experiences (Ardoin et al., 2022). While invoking a participatory approach to sustainability problem-solving can lead to greater levels of participation, intentional efforts must be taken to ensure that these increased numbers represent diverse groups. Design thinking offers an approach to do just that given its inherent focus on diversity (Buhl et al., 2019; Zheng, 2018). Design thinking goes beyond encouraging diversity and inclusion, it requires diverse teams (Fischer, 2015). A benefit of the people-focused aspect of design thinking described above is that design thinking welcomes all people. Reaching broad audiences results in the inclusion of groups that may have been previously neglected (Geissdoerfer et al., 2016). Research documents that design thinking tools and structures are most likely to produce innovative, effective outcomes when participants bring different perspectives, skills, and knowledge (Buhl et al., 2019; Fischer, 2015; Maher et al., 2018). Including people from a range of backgrounds and with different experiences, guided through a structured design process, can result in improved solution generation as it facilitates open-mindedness and creates a larger pool of varied resources (Bermejo-Martín & Rodríguez-Monroy, 2020; Buhl et al., 2019; Fischer, 2015; Shapira et al., 2017). Rather than merely bringing a diverse group of people together, design thinking processes also provide the tools and mindsets to anticipate and facilitate potential conflicts that can occur when worldviews collide; thus, such structures can facilitate development and support a wider acceptance of proposed solutions (Geissdoerfer et al., 2016; Maher et al., 2018). 3.4 Adopts a Holistic, Systems-Thinking Mindset In acknowledgement of the complex web of multi-scalar economic, social, and environmental factors and interacting systems that underlie sustainability, researchers and practitioners often advocate for applying a systems-thinking lens to developing sustainability solutions (Higgins, 2014; Nguyen et al., 2012; Williams et al., 2017). Systems thinking requires consideration of the whole picture that is essential to first understanding sustainability issues and then move into solving complex challenges (Buhl et al., 2019; Palmberg et al., 2017; Saviano et al., 2019). In design, concurrently, this broader systems perspective emerges when designers recognize that product design and development need to consider social and structural factors—such as lifestyle choices and consumption patterns—in
332 N. M. Ardoin et al. addition to environmental and economic factors (Ceschin & Gaziulusoy, 2016; Spangenberg et al., 2010). Design thinking proponents praise its holistic, integrative nature (Earle & Leyvade la Hiz, 2021; Wahl & Baxter, 2008), emphasizing that it encourages holistic problem-solving (Brown & Wyatt, 2010; Buhl et al., 2019; Dewberry & Sherwin, 2002) and allows participants to see relationships among the “natural” or nature-rich world, the human-built environment, and the sociocultural dimensions that occupy these spaces (Maher et al., 2018). This systems-thinking approach is supported by design thinking’s transdisciplinary nature, moving beyond interweaving fields and disciplines to applying the novel insights derived from those combinations to new areas of practice (Brandt et al., 2013; Lang et al., 2012), as discussed above in relation to diversity. Intentionally bringing together experts from varied fields, as well as non-expert stakeholders, aids participants in seeing the bigger picture and moving beyond each individual’s limited perspective and area of expertise (Fischer, 2015; Wahl & Baxter, 2008). 3.5 Offers a Streamlined, Action-Oriented Process Addressing sustainability issues often presents a tension: their critical and pressing nature, combined with their vast temporal and spatial scales, means time is of the essence. Thus, any problem-solving approach that results in rapid, effective, largescale action is advantageous (Abou Chakra et al., 2018; IPCC, 2021; Lang & Wiek, 2021). At the same time, the scientifically grounded, culturally responsive, and particularistic nature of sustainability issues means that they require care, deep understanding, and intensive study to address. Design thinking can help address this tension by tapping into people’s creativity and supporting systems thinking as part of exploring alternative futures (Dewberry & Sherwin, 2002; Kagan et al., 2020; Pruneau et al., 2014). With its inherent solutions focus, inclusive nature, and ability to rapidly onboard stakeholders with a range of backgrounds, design thinking can spur people into action, quickly and effectively (Greenberg, 2021). The streamlined nature of design thinking processes, combined with the inherent implementability of the design approach, makes it particularly effective in connecting research with actionable solutions. Moreover, design outcomes are built on a foundation of breadth as well as a depth of expertise, providing avenues for people from different disciplinary and professional backgrounds to work together with empathy, understanding, and curiosity. In this way, much of design thinking’s efficiency comes from its accessibility. One does not have to be a designer or a content expert to contribute valuable insights (Brown & Wyatt, 2010; Erzurumlu & Erzurumlu, 2015; Shapira et al., 2017), although given the complexity of sustainability issues, experts with specialized knowledge need to be part of the process (Fischer, 2015; Kagan et al., 2020). Supported by ideas of co-design and participatory design (Bjögvinsson et al., 2012; Steen, 2013; Sutoris, 2021), dedication to including those affected by the
Design Thinking as a Catalyst and Support for Sustainability Solutions 333 design work facilitates gathering a group of people not only to envision solutions but also to take action on them. In such a scenario, everyday citizens are just as legitimately part of the visioning and implementation process, as are scholars, nonprofit professionals, and government officials (Fischer, 2015; Ardoin & Heimlich, 2021). Additionally, design thinking is structured in a way that facilitates and values efficient design, streamlining the process of innovation through to real-world impact. As such, participants are encouraged to fail early and often through processes such as rapid prototyping and multiple iterations pilot-tested in on-the-ground settings (Thakur et al., 2020; Wölbling et al., 2012). Such aspects of design thinking can empower people to take action and improve their self-efficacy and sense of agency (Greenberg & Karak, 2020; Kramsky, 2017; Maher et al., 2018; Young, 2010). Finally, researchers note design thinking’s power to shift cultural practices and ways of thinking through inclusive engagement and iterative action (Buchanan, 1992; Earle & Leyva-de la Hiz, 2021). Such societal transformations are essential if we are to achieve sustainability goals in a timely manner. 3.6 Connection Between Design Thinking and Collective Action Across the literature, evidence surfaces that design thinking characteristics that lend themselves to developing sustainability solutions also center on principles of collective action (Ardoin et al., 2022). A topic of scholarly interest for decades, collective action has grown in interest more recently as socio-environmental issues—such as climate change, invasive species, and COVID-19, among others— have emphasized the need for societal movements to attend to challenges in ways that surpass individual efforts (Harring et al., 2021; Jagers et al., 2020. Perhaps most famously described by Hardin in his (1968) Tragedy of the Commons and in Ostrom’s (2000) rebuttal, describing the potential for sustainability among common-pool resources, many scholars in the environment, natural resource, and sustainability arenas have worked to reframe collective action as more than the compilation of individual actions (Agrawal, 2003; Ferraro & Agrawal, 2021). They emphasize that collective action—a distinct phenomenon arising out of, and sustained by, social processes and structures—is a phenomenon that may be cultivated and nurtured, and that numerous potential pathways exist for motivating individual and collective behaviors to address complex sustainability problems (Agrawal, 2003; Cleaver, 2007; Lukacs & Ardoin, 2014; Niemiec et al., 2016). Table 1 provides a summary of the five design thinking characteristics addressed in this review and their connection to sustainability solutions. Design thinking has been defined, explained, and theorized in multiple ways and the five characteristics detailed here are those that emerge as most salient from our literature review of research and discussion on the relationship between design thinking and
334 N. M. Ardoin et al. Table 1 Design thinking characteristics and their connection to sustainability solutions Design thinking characteristic Inspires creativity Participatory and people-focused Encourages and inspires diversity in thought and action Adopts a holistic, systemsthinking mindset Offers a streamlined, actionoriented approach Connection to sustainability solutions Design thinking emphasizes process over product, meaning that the journey of working toward a solution can be just as important as whether one actually gets to a single “right” answer in the end. The creative confidence mindset and iterative nature of design thinking encourage participants to try taking action and work toward a solution or many possible solutions. Without fear of failure or judgment, individual and collective creativity can lead to accelerated innovation and new ways of thinking, which are critically needed in the sustainability space. Rather than telling people what to do in a top-down manner, a design thinking approach supports curiosity, collaboration, and active, participatory learning. Design thinking lowers, and at times removes, the barrier to entry during the initial solution generation process as a broader group of participants are encouraged to contribute in unencumbered ways. Focusing on people’s needs and experiences builds ownership and enthusiasm, concurrently encouraging participants to construct their own knowledge about sustainability, grounded in personal and socio-culturally relevant experience, including the causes, impacts, and potential solutions. The design thinking process signals that we are all in this together, similar to the collective mindset recognizing that sustainability challenges require perspectives from all experiences, walks of life, and points of view. Design thinking engages, and indeed requires, diverse teams; this diversity enhances the richness of content, innovation, and action that arises from the group process. Diverse groups provide a greater number and variety of resources upon which the sustainability solution process can draw and help ensure that the solution set produced is equitable, inclusive, and appropriate. Combining design thinking and sustainability means emphasizing the multiple spatial and temporal scales of sustainability throughout the design process. By encouraging and employing a systems lens, design thinking helps participants envision the interconnectedness of complex sustainability challenges and, in the process, imagine more creative, collaborative, context-appropriate solutions. Design thinking’s supportive environment can empower people or teams to take action, which can be especially important in situations where they might otherwise be daunted by a large-scale problem or one without an immediate solution. Design thinking provides structures and support to organize the problem-solving process in an efficient, streamlined way, encouraging communication and collaboration among a diverse range of stakeholders, engaged across complex sustainability challenges. The action orientation focuses everyone on a solution space in a collective, community-oriented way.
Design Thinking as a Catalyst and Support for Sustainability Solutions 335 sustainability solutions. In future work, we plan to further explore these connections and examine other perspectives on design thinking that have not yet been explored in the context of sustainability. 4 Reflection and Outlook Through our exploratory literature review and the resulting synthesis, we emphasize the ways in which the design thinking process supports and encourages sustainability solutions to move from ideation to action. Making these literature-based connections helps ground what we see in design thinking practice to theoretical underpinnings and also illuminates areas in the academic literature where we can expand our thinking around design thinking and sustainability. Theory building and testing and generating ideas within the specific content area of sustainability and climate design, for example, offer an opportunity to further push this work ahead in terms of scholarship and practice. In this short chapter, we have barely scratched the surface of the robust bodies of inter- and transdisciplinary literatures that might inform the work going forward in this and related domains. 5 Conclusion Although there is some initial discussion on the suitability of design thinking for addressing sustainability challenges (e.g., Clark et al., 2020; Young, 2010), we push further to suggest that design thinking approaches are vital and necessary to include in the toolbox for addressing today’s urgent sustainability issues. We have undertaken this exploratory review to identify research in support of this idea. Our hope is that by illuminating research-based connections between design thinking and sustainability, more researchers and practitioners will become aware of the powerful intersections between these two fields. Thanks to the flexibility, innovation, and inclusive nature inherent in design thinking approaches, they prove particularly wellsuited to address the wicked nature of sustainability problems as well as the need for collective action to produce, iterate on, and apply innovative solutions. We thus invite colleagues to join us in this ongoing conversational space as we refine our work in design/sustainability theory and practice and continue to pursue sustainability solutions with the requisite care, urgency, and creativity that such issues necessitate.
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