Research Handbook on Design Thinking 1802203125, 9781802203127

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Research Handbook on Design Thinking

Research Handbook on Design Thinking Edited by

Karla Straker Senior Lecturer of Design, School of Architecture, The University of Queensland, Australia

Cara Wrigley Professor of Design, Faculty of Engineering, Architecture and IT, The University of Queensland, Australia

Cheltenham, UK • Northampton, MA, USA

© Karla Straker and Cara Wrigley 2023

Cover image: Kimmi Ko All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA

A catalogue record for this book is available from the British Library Library of Congress Control Number: 2023930299

This book is available electronically in the Business subject collection http://dx.doi.org/10.4337/9781802203134

ISBN 978 1 80220 312 7 (cased) ISBN 978 1 80220 313 4 (eBook)

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Contents List of contributorsvii Introduction to the Research Handbook on Design Thinking1 Karla Straker and Cara Wrigley PART I

PERSPECTIVES ON DESIGNERS

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A design thinker’s mind: insights on the neurocognitive processes of ideation John Gero and Julie Milovanovic

2

Design facilitation practice: an integrated framework Genevieve Mosely and Lina Markauskaite

3

Who gets to wear the black turtleneck? Questioning the profession of design thinking Sally Cloke, Mark Roxburgh and Benjamin Matthews

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Method case study – Making design thinking tactile: unlocking meaning and experiences with tactile tools and generative prototypes70 Rowan Page and Leah Heiss

PART II

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PERSPECTIVES ON DESIGN THINKING AS A PROCESS

5

The agile landscape of design thinking Katja Thoring and Roland M. Mueller

6

Bridging the academia–industry gap through design thinking: research innovation sprints Ivano Bongiovanni, Peter Townson and Marek Kowalkiewicz

7

Design4Health: developing design thinking bootcamps in the Middle East Carlos Montana and Thomas Boillat

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8

Design thinking to improve student mental well-being Jane E. Machin

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From gas to green: designing a social contagion strategy for the energy transition in Rotterdam, the Netherlands Jesal Shah, Rebecca Anne Price and Jotte de Koning v

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Research handbook on design thinking

Method case study – A design thinking toolkit for framing market conditions Ilya Fridman, Robbie Napper, Amrik S. Sohal and Sairah Hussain

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PART III PERSPECTIVES ON DESIGN THINKING AS A PRACTICE 11

The fragility of design thinking: applying symbolic interactionism to promote shared meaning Jan Jervis and Jeffrey E. Brand

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Dealing with the difficulties of policy formulation in policy design: the merits and demerits of the application of design thinking to the policy realm Michael Howlett

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The weakest link: the importance of problem framing in design thinking Martin Meinel, Tobias T. Eismann, Sebastian K. Fixson and Kai-Ingo Voigt

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Factor structure, validity, and reliability of an instrument for assessing design thinking Elena Novak and Ilker Soyturk

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Using action research to facilitate and teach design thinking in graduate management education Judy Matthews

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Method case study – The transmedia journalism design thinking toolkit Dilek Gürsoy

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Conclusion—Beyond normal design thinking: reflections on the evolution of a paradigm and ideas for the new incommensurable Philip Ely

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Index315

Contributors EDITORS Karla Straker (PhD) is a Senior Lecturer of Design in the School of Architecture at The University of Queensland, Australia. Her research is in the field of Design Innovation, with a specific focus on understanding how emotions can inform design practice and methods, contributing to positive behavioural changes in individuals. She has developed research methods that support designers in their attempts to understand the relationship between cognition, affect and behaviours, most notably through her book Affected: Emotionally engaging with customers in the digital age (2018), co-authored with Professor Wrigley. In her most recent book, Design Innovation and Integration (2021), co-authored with Dr Nusem and Professor Wrigley, design processes and methods for product innovation are outlined and the mindset required to integration design within organisations. Cara Wrigley (PhD) is a Professor of Design Innovation at The University of Queensland, Australia. In 2018, she established and directed the Design Innovation Research Group, leading a research team that focused on design-led exploratory research, conducting applied and theoretical research into people, emotions, strategy and business. Cara has published extensively on the application and adoption of design, which has been disseminated through a number of books, including Design innovation and integration (2021), Design innovation for health and medicine (2020), co-authored with Dr Straker and Dr Nusem; Affected: Emotionally engaging customers in the digital age (2018), co-authored with Dr Straker; and Design thinking pedagogy: Facilitating innovation and impact in tertiary education (2022) with Genevieve Mosely. Cara has more than 80 refereed papers in outlets such as Design Issues, Journal of Cleaner Production, Energy Policy, California Management Review, ASAIO Journal, and Journal of Cardiovascular Nursing. She is currently the editor-in chief of the Journal of Design, Business & Society.

AUTHORS Thomas Boillat (PhD) is an Assistant Professor in health technologies at the Mohammed Bin Rashid University of Medicine and Health Sciences. He designs and evaluates digital health solutions in view of increasing patient satisfaction, experience, and safety. He is also a Human-centred advocate and teaches Design Thinking to undergraduates, postgraduates and medical professionals. In addition, he facilitates Design Thinking workshops and projects with medical professionals, designers, and engineers to equip them with the skills that make them actors of change. He earned an MSc and a PhD from the University of Lausanne, Switzerland. vii

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Ivano Bongiovanni (PhD) is a Lecturer in Information Security, Governance, and Leadership & Design Thinking with the UQ Business School and the UQ Cyber team. He has developed his research and practice in the fields of cybersecurity management and design thinking. Since his time with the PwC Chair in Digital Economy at QUT, he has been running design-led projects for public and private sector organisations. His current focus of work is on human-centred physical and cyber security. Jeffrey E. Brand is a professor of communication and media at Bond University, where he develops and teaches undergraduate and postgraduate programmes on digital media, digital transformation, and research methodology. Jeff’s research generates policy-changing evidence of media audience demographics, attitudes, and behaviours, such as his 18-year-long panel studies entitled Digital Australia and Digital New Zealand on video game audiences. He has served as a consultant to organisations such as the Australian Communications and Media Authority and the Australian Classification Board. Sally Cloke is a design researcher at PDR International Centre for Design & Research, Cardiff Metropolitan University, Cardiff, UK. Sally has a PhD in design from the University of Newcastle (Australia). She has been a teacher and practitioner of design thinking and has published on design-related topics including critical/speculative design, design for sustainable behaviour and aesthetic theory. Her current research interests include how design can address the social and economic factors that impact on sleep quality. Jotte de Koning (PhD) is an Assistant Professor of Design for Sustainability Transitions at the Faculty of Industrial Design Engineering, Delft University of Technology. Jotte conducts research at the intersection of design and systems thinking; looking at how design can contribute to societal challenges or how to change complex systems through design. Her expertise lies in strategic design, service design, co-creation, behavioural change, transition management and sustainable innovation. Her work spans different contexts: the agri-food system, energy systems and cities-as-a-system. Tobias T. Eismann (PhD) is a researcher and practitioner in creativity and innovation management. In his work he supports people, teams, and organisations to make the most out of their creative potential. His research focuses on the role of creativity for organisational success, how approaches such as design thinking can help people and teams become more creative, and how physical and digital spaces impact the creative process of people. Philip Ely (PhD, FRSA) is a design practitioner and researcher currently at Curtin University. Having led design-led innovation initiatives at IBM, Telstar Entertainment Group, Granada Media and Design Bridge developing new interactive products and services, his work in academia has focussed on the theoretical and practical role of design to affect positive change. Operating at the nexus of practice and theory, his most recent book, The Climate Domesday Book, was a publicly exhibited hybrid digital-print book that brought together over twenty contributors tackling the climate crisis. Sebastian K. Fixson (Dipl.-Ing., PhD) is Professor of Innovation and Design and Associate Dean of Graduate Programs & Innovation at Babson College. In his work he focuses on helping organisations and individuals to build innovation capabilities. His recent research studies how innovation performance is impacted through choices in process governance (open vs. closed

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innovation), through the use of digital design tools, and through the use of innovation practices such as design thinking. His work stretches across large enterprises and entrepreneurial firms. Ilya Fridman (PhD) is a designer, researcher and educator. As part of the Emerging Technologies Research Lab, his work explores how design thinking and making can contribute to interdisciplinary research contexts and what role designers might play in collaboratively reimagining future possibilities for sustainable development. As a lecturer within the Department of Design, Ilya teaches Design Thinking to aspiring entrepreneurs within the Monash Business School and Faculty of Art, Design and Architecture at Monash University. John Gero (PhD) is a Research Professor in Computer Science and Architecture at the University of North Carolina at Charlotte. He is a member of the Charlotte Neuro-Cognitive Interaction Lab. He is the author or editor of 54 books and over 750 papers and book chapters in the fields of design science, design computing, artificial intelligence, computer-aided design, design cognition and design neurocognition (citations = 26,000, h-index = 73, i10-index = 370). Dilek Gürsoy is Assistant Professor in visual communication design at Istanbul Bilgi University, Turkey. Her research interests include experience design, design thinking and transmedia studies. Her research background merges design, media, and journalism studies. Her last book, Transmediality in independent journalism: The Turkish case (Routledge, 2020) reflects this intertangled perspective. She is also an advisory committee member of the Interdisciplinary PhD Communication Conference (IPCC), which takes place every year at Istanbul Bilgi University. Leah Heiss is the Eva and Marc Besen International Research Chair in Design at Monash University. Her design work has been recognised with six Australian Good Design Awards, the CSIRO Design Innovation Award and the 2022 Women in Design Award. Her co-design methods have been used to evolve new models of care for cancer, eating disorders, aged care, voluntary assisted dying and acquired brain injury and she is using design strategies with the World Health Organisation to improve the uptake and implementation of WHO guidelines. Michael Howlett, FRSC is Burnaby Mountain Professor and Canada Research Chair (Tier 1) in the Department of Political Science at Simon Fraser University in Vancouver BC, Canada. He specializes in public policy analysis, political economy, and resource and environmental policy. His most recent books are The dictionary of public policy (2022), Policy consultancy in comparative perspective (2020), Designing public policies (2019), and the Policy design primer (2019). Sairah Hussain holds a PhD in Business & Management from the University of South Australia and is a consultant for the Australian government’s AusIndustry’s Entrepreneurs’ Programme. Prior to this, she was a research fellow at Monash Business School, focusing on Industry 4.0, sustainable supply chains and innovation ecosystems. Sairah has gained both industry and government experience, having worked in the telecom and not-for-profit sectors in London, and also for the Singapore Ministry of Trade and Industry based in Saudi Arabia. Jan Jervis has a PhD in design and communication and researches strategies for optimising design in business. Her interdisciplinary research has been informed by careers in multimedia and fashion design connected with teaching cross-disciplinary communications. A recipient

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of three teaching awards, Jan is a lecturer at Bond University in the Faculty of Society and Design. She is the author of papers on project-based learning in capstones, and the polysemy and primacy of design. Marek Kowalkiewicz (PhD) is a Professor and Chair in Digital Economy at QUT Business School. He joined QUT after a substantial commercial career, culminating in Silicon Valley as head of global innovation teams at the multinational software corporation, SAP. His career at SAP spanned other senior roles, including research manager of SAP’s largest Asian research lab and the lead of one of SAP’s main global research programmes. Prior to this, Professor Kowalkiewicz was a research fellow at Microsoft Research, Asia. Jane E. Machin (MFA, PhD) is an Associate Professor of Marketing at Radford University. Her research, which examines the intersection of mental health, stigma, and consumer wellbeing, has been published in top journals in her field, such as the Journal of Public Policy and Marketing, Journal of Business Research, and Appetite. Jane is the recipient of numerous awards for her research and teaching, including the Thomas C. Kinnear Best Paper Award and the Donald N. Dedmon Distinguished Professor Award. Lina Markauskaite is a Professor of Learning Sciences at the University of Sydney, Australia. Her research spans three related areas: students’ and teachers’ digital capabilities; professional learning for complex knowledge work and innovation; and technology-enhanced research methods. Her recent research projects have mainly focused on understanding the nature of complex professional knowledge work and learning, in inter-professional and inter-disciplinary contexts, and how to facilitate knowledge co-creation on emerging knowledge frontiers. Benjamin Matthews is a Lecturer in Design at the University of Newcastle, Australia with over 15 years’ experience in creative industries with multidisciplinary teams, guiding Human Centred Design as a strategist, facilitator and producer. His research is transdisciplinary and focuses on emerging technology and patterns of innovation in network cultures. He is a member of the Virtual & Augmented Reality Research Network (VARRN) based at Sunway University, Malaysia and the ARC Centre of Excellence in Synthetic Biology (CoeSB). Judy Matthews (M Social Work, PhD) is a senior academic in the QUT Business School, researching innovation management and design-led innovation. Using action research, she facilitates and teaches problem framing for creative action, design thinking and design-led innovation to experienced professionals and managers in MBA and corporate education programs, and assists MBA alumni in design thinking practices. Her recent research focuses on design and circular economy initiatives in construction, identifying good processes and practices in modern methods of construction. Martin Meinel (PhD) is a researcher and practitioner in creativity and innovation management. In his work he supports people, teams, and organizations to make the most out of their creative potential. His research focuses on the role of creativity for organizational success, how approaches such as design thinking can help people and teams become more creative, and how physical and digital spaces impact the creative process of people. Julie Milovanovic (M. Arch., PhD) is a post-doctoral Fellow in Computer Science at the University of North Carolina at Charlotte. Her research focuses on design cognition, design

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neurocognition and design learning. Through her research, she aims at bridging design science and neuroscience to explore novel ways to design and teach design. Carlos Montana (PhD) is the founding faculty, former assistant dean, and current chair of research in the Dubai Institute of Design and Innovation DIDI, the first specialised design university in the UAE, in collaboration with MIT and Parsons. A multi award-winning design and academic leader, he has 30+ years’ experience in Colombia, Italy, Japan, Singapore, Australia and the UAE. His transdisciplinary research in design, innovation and entrepreneurship focuses on sustainable development, with expertise in biomimicry and design for health. Genevieve Mosely is a PhD candidate at The University of Queensland, Australia. Genevieve’s research focuses on design education and the application of design thinking to help capture new value through better understanding users and their needs. She has practical experience working in partnership with industry, including the Royal Australian Air Force, the Australian Defence College and TAFE NSW. Her PhD research utilises a qualitative approach to investigate current design discourse on design practice, through specifically drawing attention to design facilitation. Roland M. Mueller (PhD) is Professor of Information Systems, especially Business Intelligence at the Berlin School of Economics and Law, Germany. There he is co-director of the Institute of Data-Driven Digital Transformation (d-cube). He also has a visiting professorship appointment at the University of Twente, the Netherlands. His research areas are in the fields of data thinking, user-driven innovation methods, meta-theoretical analysis as a research method, as well as the use of ontologies and knowledge graphs for information extraction. Robbie Napper (PhD) is an Industrial Designer, Researcher and Senior Lecturer with the Department of Design at Monash University. Robbie is an expert in the design and manufacture of vehicles, services, systems and objects for mobility, especially examining themes of modularity, mass customisation, user experience and user-centred design in zero and low-carbon transport. Robbie is a standing member of the US National Academy of Science’s Transportation Research Board in Bus Transit Systems and Deputy Director of the Mobility Design Lab. Elena Novak is an Associate Professor of Educational Technology at Kent State University. Her research centres on innovative uses of technology to support learning, teaching, and design. Elena’s current research projects focus on the integration of learning technologies such as 3D printing, robotics, video games, and simulations in education to provide educators with research-informed guidelines on how to integrate technology in various educational settings. She designs curricula, assessments, and tools to support and evaluate students’ learning and creativity. Rowan Page (PhD) is the Program Director of Industrial Design at Monash University. His PhD research collaborated with Cochlear to embed user experience design practices to improve human factors and usability in medical device design. His current research explores how design and product development can support the translation of fundamental research into commercial devices. He works closely with interdisciplinary teams (Monash Institute of Medical Engineering, SensiLab), and industry product development projects with organisations such as Cabrini, Blundstone, Circadian Health Innovations.

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Rebecca Anne Price (PhD) is an Assistant Professor of Transition Design at the Faculty of Industrial Design Engineering, Delft University of Technology, the Netherlands. Dr Price works with public and private organisations to support the application of design upon complex innovation challenges in various domains ranging from mobility, public health to energy transitions. As a Senior Comenius Fellow, Dr Price is further tasked by the Netherlands Initiative for Education Research to advance design education through development of the concept of ‘designer resilience’. Mark Roxburgh is Honorary Associate Professor in Design at the University of Newcastle, Australia. He retired from full-time academia in 2021 and now works as an open-source free range academic and fading failed indy rock star. His research interests cover design research, visual communication, and photography. He has grown suspicious of the instrumentalisation of the anthrochauvinist bandwagon known as human-centred design and has recently headed to the hills to dig the bunker and prep for the zombie apocalypse. Jesal Shah has a background in Industrial Design and a master’s degree in Strategic Design. She works as a lead venture builder and business designer at oneUp, Amsterdam, the Netherlands. Jesal applies her expertise in strategic design theory and methodology, service design, design for behavioural change and systemic design to help private and public sector organisations with sustainable innovation challenges. She has been involved in academic research and consultancy projects within the domains of healthcare, energy transition, city-making, aviation, agri-food systems and fashion. Amrik S. Sohal is a Professor at Monash University. He holds a PhD in Operations Management from the University of Bradford (UK). His research focuses on process improvement and innovation, supply chains and the circular economy. He has authored/co-authored over 250 papers, as well as three books and a number of chapters contributed to books. He has received numerous grants and awards for his research. In 2011 he was awarded Life Fellow of the Australian and New Zealand Academy of Management. Ilker Soyturk received his master’s and doctoral degree in the Research, Measurement and Statistics program at Kent State University, USA. He is currently a National Education Expert in the Ministry of National Education in Turkey. Since 2008, he has collaborated on many research projects with field experts across disciplines. His research interests include math education, factors affecting students’ success, (short) test development and validation studies. His technical areas of interest include Structural Equation Modelling, Rasch, and Item Response Theory. Katja Thoring (PhD) is Professor for Integrated Product Design at the Technical University of Munich, Germany and Visiting Professor at Delft University of Technology, the Netherlands. She has a background in Industrial Design and researches on topics such as creative workspaces, technology-driven design innovation, design methodologies, and design education. Her main quest is to understand how the work environment can influence creativity, and to turn these insights into tools and techniques for improving the innovation process. Peter Townson (MRes) is a design innovation catalyst for the Centre for the Digital Economy and the Centre for the Future Enterprise at Queensland University of Technology. Since 2015, Peter has led research innovation sprints with businesses, institutions, and government depart-

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ments. Peter’s specialty is in managing and facilitating high-functioning teams of researchers and clients through the use of design methods. His work focuses on the relationship between design and organisational change, disruption, and digital transformation in complex and highly regulated industries. Kai-Ingo Voigt has a PhD in Business Administration and is Full Professor and holder of the Chair of Industrial Management at Friedrich-Alexander-Universität Erlangen-Nürnberg. Since 2010, he also has a Guest Professorship at the University of Business and Economics (UIBE) in Beijing. Coming from intensive research about innovation and especially idea management, his research scope encompasses empirical studies about creativity-enhancing methods such as design thinking and activity-based workspaces.

Introduction to the Research Handbook on Design Thinking Karla Straker and Cara Wrigley Good design is invisible, hidden from conscious observation by the untrained eye. Good design fits seamlessly and integrates completely, delivered through an understanding of the end user. Bad design is overt, being perceptible to the end user, and often making a lasting impression. Beyond the lasting negative impressions bad design can introduce faults that can lead to fatal outcomes. Design thinking is even more difficult to distinguish within the landscape of ‘good’ and ‘bad’ design. The qualitative nature of design thinking leads to a broader spectrum of the definition of ‘good’. The global field of design thinking is outcome focused, rarely using comparative analysis to draw conclusions between ‘good’ and ‘bad’. This book serves to move beyond the outcome-focused orientation to examine the diversity of methodologies and processes employed, to deepen knowledge of the ‘how’ to extend beyond the present ‘what’. The design thinking process is an iterative (back and forth) traversing loop of question and answer. Drawing concepts, building models, experimenting with elements, reframing problems – underpinned by the requirement for continual learning. This abductive approach to thinking is fuelled by curiosity. Enterprises and ecosystems without a background or education in design thinking often miss this critical point, looking to distil the field into one ‘design recipe’ that can be applied generically to all problems. If only this was that simple. The extraction and dilution of this ‘design recipe’ is attractive in the short term but will ultimately lead to poor long-term results. This conundrum led us to the compilation of this book. Through a global search for best practice, we aimed to extend the understanding of design thinking through the inclusion of uncommon debates, bespoke methods and the continual questioning of the field. A key part of any design process is critical feedback and this Research Handbook explores this feedback loop and its criticisms further. It does so by examining arguments and critiques throughout the spectrum of assertive and contradictory definitions. The Research Handbook seeks to unpick the creative process by isolating process frames and examining them in detail. Debate is raised through these examinations, tracing success through empirical evidence back to design origins. It also raises the observation of the key tenet of design thinking – iteration – and the lack of this in poorly applied design thinking ‘recipes’. The underlying premise for this Research Handbook is to present and debate these different perspectives traversing theory through to practice globally. The outcome of this book should be to stimulate and drive further discussion on the field of design thinking. Preparing a book on design thinking, which has multiple definitions, processes, methods, applications and experiences, was not an easy task. When we thought we had a grasp on what it was a new chapter would arrive and shift our positions, raising more questions than answers. 1

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This Research Handbook aims to illustrate the constant reimagining of what design thinking is and can be. This collection of work continues to challenge current perspectives and practices now and into the future. Each chapter in isolation provides a novel perspective of design thinking; however, when read as a collective work, it should challenge the reader to reposition their own perspectives continuously. This Research Handbook provides an overview of the field’s design thinking history, theoretical approaches, key concepts, perspectives, and methods across a board range of applications. It offers a comprehensive international exploration of design thinking from interdisciplinary perspectives. Researchers from design, education, policy and business provide an overview of the diversity in application of design thinking through theoretical and practical case studies. This book has specifically selected work from established and emerging scholars and practitioners to pursue a lively debate throughout the chapters, encouraging aspirational developments for future research studies.

HOW THIS RESEARCH HANDBOOK IS STRUCTURED The Research Handbook comprises a total of 36 authors globally, representing a diverse ecosystem of distinct disciplines. Demonstrating the multidisciplinary, multisector, and diverse group of contributors that the field of design thinking now comprises. It is structured into three main sections, organised into 17 chapters, each of which focuses on a common theme or topic. Each main section concludes with a novel case study which outlines the development of bespoke design thinking methods in unique project contexts. The section introduction is not meant as a summary or critique of the chapters in that section; rather, it incorporates a reflection of the editor on the topic and, as such, it is meant to be thought-provoking. Part I is focused on the Perspectives on Designers. In this part we highlight the fundamental challenges that have emerged regarding the professionalisation of the field of design thinking. Such expertise is required to facilitate this emerging practice and the types of cognitive loads required to successfully deliver in the transdisciplinary arena. This section begins with John Gero and Julie Milovanovic’s chapter, ‘A design thinker’s mind: insights on the neurocognitive processes of ideation’ (Chapter 1), which highlights an emerging field bridging design thinking and neuroscience – design neurocognition. Using scientific medical research methods, they analyse the cognitive load of the design thinking process to highlight the challenges and implications of studying ideation, a foundational component of design thinking, through the lens of neuroscience. As the title suggests, this provides a unique insight into the design thinking mind and what makes it tick. Genevieve Mosely and Lina Markauskaite (Chapter 2, ‘Design facilitation practice: an integrated framework’) present a novel framework on design facilitation. This construct borrows from the seminal theories of practice architecture to move the needle on design thinking practice and who, more importantly, practices it successfully. This chapter contributes to the debate surrounding the facilitation practice of design thinking and future capabilities of such a thinker. Chapter 3 presents ‘Who gets to wear the black turtleneck? Questioning the profession of design thinking’ by Sally Cloke, Mark Roxburgh and Benjamin Matthews. It focuses on the profession of a designer and questions the role of accreditation on this newly established role. They ask three fundamental questions; whether problems of the Anthropocene could be

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better addressed through a less anthropocentric, less ‘problem-solving’ oriented approach to thinking and designing than permitted by current orthodoxies? And what if it matters less who gets to ‘wear the turtleneck’ than who dares to pick up the mantle of some of design history’s less-heeded prophets? They explore these questions via the history of thinking that informs common criticisms of Design Thinking and conclude with calls-to-action for designers. Part I concludes with the case study by Rowan Page and Leah Heiss, ‘Making design thinking tactile: unlocking meaning and experiences with tactile tools and generative prototypes’ (Chapter 4). In this case study the practice of design thinking is explored through tactile activities embodied in physical manifestations used to integrate end-user participation hosted by designers in a medical design project context. Part II is focused on Perspectives on Design Thinking as a Process. A quick online search under the terms ‘design thinking process’ will yield a deep dataset of different process models and approaches. These different perspectives on the field have generated a worthy platform for debate. The section includes five chapters that raise fundamental issues related to the nature of different design thinking processes and their implications on broader design thinking principles. Katja Thoring and Roland M. Mueller present ‘The agile landscape of design thinking’ (Chapter 5), detailing the crossover between design thinking and agile design processes. In this chapter they explore the conceptual influence and impact these two related process models have on each other as well as the debated differences in approach. This amounts to the culmination of a newly titled innovation approach – the agile design thinking process. This contributes to a better theoretical understanding of the design thinking process and allows for more agile innovation processes. In Chapter 6, ‘Bridging the academic–industry gap through design thinking: research innovation sprints’, Ivano Bongiovanni, Peter Townson and Marek Kowalkiewicz examine this difference in detail. Focusing on a demand-driven model for design thinking research they dissect the different approaches, methods, and even funding arrangements for delivering real-world impact. Research innovation sprints are discussed in more detail as a way to bridge the gap between both parties. Carlos Montana and Thomas Boillat (Chapter 7), ‘Design4Health: developing design thinking bootcamps in the Middle East’ present an intensive, multi-disciplinary, multi-collaborator design thinking programme conducted in Dubai, UAE, in 2019 and 2021. This medical-design focused bootcamp brings universities and hospitals together in the Middle East, seeking to implement design thinking approaches and improve outcomes in health systems. This chapter discusses design thinking in relation to experiential learning through the evolution of the Design4Health Bootcamp. This contextually novel chapter contributes to a global conversation on design thinking processes and applications. Jane E. Machin (Chapter 8), author of ‘Design thinking to improve student mental well-being’, considers not only a design thinking approach within the underexplored context of mental health issues but also the difference in perspective from a marketing disciplinary home. Meriting inclusion in this book, this chapter presents lessons from design thinking processes and practices utilised in a multidisciplinary student cohort in order to better understand design thinking’s value in the mental health problem context. ‘From gas to green: designing a social contagion strategy for the energy transition in Rotterdam, the Netherlands’ (Chapter 9), by Jesal Shah, Rebecca Anne Price and Jotte de

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Koning, finds inspiration at the nexus of design, psychology and sociology to influence greener behaviour in Dutch energy consumers. The results presented in this chapter derived from the project demonstrate how design can play a critical role in shaping sustainable systemic transitions needed for the future and how this novel scaled-up version of the design thinking process can be utilised for such success. The case study ‘A design thinking toolkit for framing market conditions’ by Ilya Fridman, Robbie Napper, Amrik S. Sohal and Sairah Hussain (Chapter 10), concludes Part II by presenting a research partnership with Volgren – Australia’s largest bus manufacturer. This unique project disseminated new design thinking methods by way of a toolkit to provide managers with the ability to frame market conditions and collectively explore different opportunities presented by a technological transition within their industry. This is another example of a differing research perspective of design thinking methods and approaches in the automotive sector. Part III is focused on Perspectives on Design Thinking as a Practice. In this part of the book we explore the individual components of this field and how challenging these constructs can lead to further developments. It is only through drawing on these global multidisciplinary perspectives we begin to understand the collective shared practice of design thinking. Jan Jervis and Jeffrey E. Brand in ‘The fragility of design thinking: applying symbolic interactionism to promote shared meaning’ (Chapter 11) build upon the previous work that showcased disparity between the vocabulary used by design professionals and business and management professionals and thus the need for such a shared meaning. In this chapter they present the Acceptance, Vocabulary and Acknowledgement (AVA) model for design thinking as it provides a united lexicon in order to cross disciplinary boundaries. Chapter 12 by Michael Howlett, ‘Dealing with the difficulties of policy formulation in policy design: the merits and demerits of the application of design thinking to the policy realm’, examines the role and contributions design thinking practice has towards policy invention and innovation. It is concluded that such a unique approach can indeed contribute in a way that more traditional policy generation techniques fall short. This methodology serves to produce policies more likely to resolve important social problems and concerns in a concise and efficient way. Martin Meinel, Tobias T. Eismann, Sebastian K. Fixon and Kai-Ingo Voight (Chapter 13, ‘The weakest link: the importance of problem framing in design thinking’) discuss a critical point in the design thinking process, transitioning from inspiration to ideation. Highlighted in this chapter is the lack of literature on this process which has led them to question how this is done. From their analysis on problem framing, they explain the effects of different framing to provide advice for users of design thinking in business and academia. They conclude their chapter by speculating on how highly topical trends may impact problem framing in design thinking in the future. Elena Novak and Ilker Soyturk (Chapter 14, ‘Factor structure, validity, and reliability of an instrument for assessing design thinking’) examine instruments that assess design thinking skills, an under-studied aspect of design thinking, stating there is a limited array of tools for measuring quantitative variables. Their study provides empirical evidence regarding the Design Thinking Scale factor structure and its internal consistency and validity. Overall, the findings suggest that the Design Thinking Scale has a promising validity and reliability and

Introduction

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can be used for assessing the outcomes of design thinking education and investigating the impacts of various interventions that aim to enhance design thinking skills. Judy Matthews in Chapter 15, ‘Using action research to facilitate and teach design thinking in graduate management education’ adopts an action research model to explore the benefits and challenges of adding design thinking and design methods to a Masters of Business Administration, also known as an MBA, programme. This chapter contributes to design thinking practice and more importantly reflects on experienced professionals and managers in a graduate higher education context, presenting their reaction, learning, changes in behaviour and outcomes to a new perspective to corporate innovation approaches. The method case study, ‘The transmedia journalism design thinking toolkit’ (Chapter 16) by Dilek Gürsoy, concludes Part III, providing insights into the challenges of teaching transmedia journalism and details the development of the Transmedia Journalism Design Thinking Toolkit to overcome some of these difficulties. Inspired by IDEO’s design thinking process, the toolkit is designed to act as a guide for students to use during each stage of a transmedia news story production. The Conclusion is written by Philip Ely, ‘Beyond normal design thinking: reflections on the evolution of a paradigm and ideas for the new incommensurable’ (Chapter 17), and draws upon the work of Thomas Kuhn and the notion of paradigms and normal science. The design thinking paradigm is conceptualised as a disciplinary matrix of symbolic representations, metaphysics and models, values and exemplars, identifying the controversies around its origins and its continued development. Ely states that we are in a state of great expansion in the field of design making, so it is increasingly hard to make sense of the paradigms we align ourselves with, questioning what will become the new design thinking. Designers look at the world in a different and unique way. They are creative experts, balancing artistic flare with rigour. After decades straddling multiple faculties, disciplines, and research institutes we have always felt like we didn’t fit. Our value and contributions were evident but the discipline silos in which universities structure themselves and apply themselves is cumbersome to the field of Design Thinking. This Research Handbook came about as we felt a greater understanding was required in a field that has often been overlooked by the hard sciences and more structured academic disciplines. The field of design thinking requires more rigorous debate, more transparency, ultimately building to a more respected position similar to other transdisciplinary fields. In summary, we as editors wish to thank the authors for sharing their unique perspectives on the ever-growing field of design thinking, and hope that you, the reader, will learn as much as we have in editing it.

PART I

Perspectives on Designers

1. A design thinker’s mind: insights on the neurocognitive processes of ideation John Gero and Julie Milovanovic INTRODUCTION Design thinking stands as a common approach adopted by companies to develop innovative products (Brown, 2008; Carlgren, Rauth et al., 2016; Norman, 2013). In the design process, designers execute several steps where they empathize with users to clarify their needs concerning the artefact to design – the process of framing. This is a prerequisite to defining design goals before ideation begins. The development of prototypes to be tested by users provides feedback to the design team to refine their designs. Designing occurs in iterative cycles through convergent and divergent thinking processes involving a range of stakeholders. This chapter will focus primarily on the ideation phase of the design process, when designers generate concepts to address a design task. During ideation, designers will seek to generate a possible novel solution to a design task, while responding to constraints (e.g., functional, technical, ethical). Here, we approach ideation from the lens of neurocognition. Tools from neuroscience offer a potential to better understand designers’ brains while ideating that could lead to a new family of design tools to assist designers in their ideation phase. Design science research on ideation has grown in the past decades, and findings have helped build a clearer picture of the underlying cognitive processes of ideation (Hay et al., 2017a, 2017b). Designing was first understood through an information processing model (Simon, 1969; Simon, 1973) which led to the definition of the design activity as consisting of specific phases or steps (Alexander, 1964; Asimow, 1962; Pahl et al., 2007). A complementary approach to designing positioned it as a social process (Bucciarelli, 1988) situated within a specific context (Clancey, 1997; Schön, 1983). Design cognition is only one aspect of designers’ behaviour while ideating. Designers’ brain behaviour and physiological reaction also provide information about the ideation process (Gero & Milovanovic, 2020). Researchers draw upon different research fields such as cognitive psychology and behavioural sciences to develop methodologies to study design thinking (Coley et al., 2007). For example, the protocol analysis methodology relies on designers’ verbal utterances to infer cognitive processes (Ericsson & Simon, 1984; Gero & McNeill, 1998; Van Someren et al., 1994). Other methods include the observation of sketching behaviour (Suwa & Tversky, 1997; Yang & Lee, 2020), ethnography and diary methods (Baird et al., 2000), retrospective interviews (Dorta et al., 2018) and surveys (Blizzard et al., 2015; Coleman et al., 2019). Recent research started approaching the study of design thinking and ideation through studying the neurophysiology of designers as they designed (Borgianni & Maccioni, 2020; Gero & Milovanovic, 7

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Research handbook on design thinking

2020; Hu & Shealy, 2019; Seitamaa-Hakkarainen et al., 2016). The seminal work of Alexiou et al. (2009) provided a first example of this novel dimension to define underlying neurocognitive processes of ideation. Design neurocognition has emerged as a promising direction in design research that could lead to the development of new models of design thinking and new tools to assist designers (Gero & Milovanovic, 2020). Research in neurocognition aims at exploring relationships between cognitive behaviours and their neural correlates. The interest in approaching research on ideation through neurocognition is to gain a better understanding of designers’ mind and brain behaviours while designing. The development of relatively low-cost brain scanning devices has made neuroscience methods to study the cognition of design thinking more accessible (Borgianni & Maccioni, 2020). Initial findings supported our cognitive understanding of design ideation. For example, neurological patterns of designing tend to differ from problem-solving ones (Alexiou et al., 2009; Vieira et al., 2020). Still, little is known about the underlying neural process of design thinking and more research is needed. Creativity and divergent thinking have been widely studied by neuroscientists (Beaty et al., 2016; Dietrich, 2019; Fink et al., 2009; Pidgeon et al., 2016), but their findings only provide partial information about the neurocognition of design thinking. We argue that creativity is one aspect of design thinking, out of many (Gero, 1990; Visser, 2009). From a design research perspective, creative thoughts are embedded within the design activity. The neuroscience approach to studying divergent thinking is usually limited to discrete constrained tasks and tests, such as the Alternate Uses Task (Guilford, 1967). The situated dimension of design thinking does not fit within the discrete approach taken by most creativity research in neuroscience. Experiments exploring the neurocognition of ideation need a more naturalistic environment to gain better insights about the underlying neural correlates of design thinking. The aim of this chapter is to describe a new area of research at the intersection of design thinking and neuroscience, by illustrating how studies from design neurocognition have common research questions in design research, and expanding our understanding of the cognition of ideation. Some of the challenges in implementing design thinking are acquiring design skills and communicating with other team members (Carlgren, Elmquist et al., 2016). Measuring designers’ brain behaviours, allows us to gain access to information about ideation that has not yet been widely explored in design research that could address those challenges. In this chapter, we will explore differences in brain patterns based on designers’ domain knowledge and lay foundations for the development of tools to assist designers in developing design skills. A better understanding of designers’ brain patterns will provide inputs to develop novel types of design aids that have the long-term potential to revolutionize how we design and lead to new forms of design thinking.

NEUROCOGNITION OF IDEATION PROCESSES IN DESIGN THINKING In this section, we tackle common questions explored by researchers to better understand underlying processes of ideation. The first one deals with examining whether thinking processes in designing differ from thinking processes in solving structured problems. Design thinking provides a method to address problems through an iteration of intuitive and analytical processes that differentiate it from a goal-oriented approach. The second research question

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9

tackled deals with domain knowledge effects on designing processes. Design teams are composed of members from different backgrounds which can be a challenge for the team. A better understanding of how domain knowledge impacts ideation processes could provide insights on ensuring team cohesion. The third topic explores the effect of using different idea generation techniques on the design activity. Finally, we will reflect on using design tools to assist designers and the implication of design neurocognition in the development of new types of design tools. Each example uses different tools to measure brain activity. The three most commonly used brain scanning devices used in design research are fMRI (functional Magnetic Resonance Imaging), fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). Each is non-invasive and uses different approaches to measure activations in the brain. Aside from its cost, a significant limitation of using fMRI machines to study designers’ brains is that designers are enclosed in a tube and their heads need to remain fixed. However, today a designer in an fMRI machine can sketch, verbalize and interact with a computer. fMRI machines provide measurements of the whole brain compared with fNIRS and EEG devices that only captures brain surface or near-surface activations. Some fNIRS and EEG headsets are portable, thus allowing experiments in more realistic settings (Brockington et al., 2018; Mayseless et al., 2019; Nguyen & Zeng, 2010; Vieira et al., 2020).

COGNITION AND NEUROCOGNITION OF PROBLEM-SOLVING AND DESIGNING One core research question in design studies on ideation is whether designing and problem-solving involve different cognitive processes or if design is a specific case of problem-solving (Cross, 2006; Dorst, 2011; Gero, 1990; Visser, 2006). Designing can be considered as a form of problem-solving where problems are ill-structured (Simon, 1973). In problem-solving, the problem is defined, meaning that the boundaries of the problem and solution spaces are known. In this case, the variables within the problem and solution space are constant, and the outcome can be predicted (Simon, 1969). Design problems are a specific type of problem as they are situated within a context and the context can vary as the design progresses (Gero, 1990; Schön, 1992). Since the design problem space and design solution space are not defined, designers first have to frame and structure the problem to propose a design solution within that frame. Empirical studies comparing basic design with defined requirements and open-ended design tasks highlighted differences in approaches to designing (Jiang et al., 2014; Kan & Gero, 2017). Open-ended design tends to require more focus on reframing the design problem during the design task, while this process does not occur when design requirements are well defined (Jiang et al., 2014). Alexiou et al. (2009) studied the relation between designing and problem-solving using a neurocognition approach. They compared designers’ brain activations while problem-solving with those while designing for an open design task. Findings support that designing and problem-solving involve distinct cognitive functions that are associated with distinct brain networks (Alexiou et al., 2009). Moreover, analysing the design task compared with the problem-solving task recruited a more extensive network of brain areas (Alexiou et al., 2009).

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This finding aligns with the necessity to start by framing the design problem when engaging in a design task (Dorst, 2015). Similar results were found from another study comparing problem-solving and designing while exploring behaviour differences of designers from diverse backgrounds – architects, mechanical engineers and industrial designers (Vieira et al., 2019a, 2019b). In this study, participants engaged in four tasks: (1) problem-solving, participants were asked to arrange furniture in a room following a set of precise requirements; (2) basic design, participants were asked to place the furniture inside the room according to their notions of functionality and comfort; (3) open design, participants were told to create a space and arrange the furniture to provide a functional and comfortable space; and (4) open sketching design, a freehand sketching design task (Vieira et al., 2019a, 2019b). Participants were equipped with a portable 14-channel EEG headset to collect continuous signals of electrical activity on the surface of the brain. A common approach to analysing EEG data is to break the signal into its constituent frequency bands, which are associated with a particular range of cognitive activities in a similar manner to the way white light can be broken into its constituent colours based on their wavelength frequency. Higher power in specific bands is indicative of different activities in the designing brain (Nguyen & Zeng, 2014). For more details on these measures and data processing, see Vieira et al. (2020, 2022). Mechanical engineers had a higher brain activation during the open sketching task compared with the other tasks (Vieira et al., 2019b). They engaged more cognitive effort for the open sketching design task than for the problem-solving task, specifically in the occipital and temporal cortex. The mechanical engineers and the industrial designers both showed significant differences of activation in the secondary visual cortex, on the left hemisphere of the brain (Vieira et al., 2020). This region is significantly more activated during the open design task than for the problem-solving task. The secondary visual cortex is usually recruited for visual mental imagery (Pearson, 2019). For architects, they also displayed higher activation in their brain for the open design task than for the other tasks, especially in the first half of the open design task (Vieira et al., 2019b). Here again, structuring and framing the design problem while engaging in the development of a design solution tend to require more cognitive effort. The analysis per EEG band also highlights differences between layout elements in the problem-solving task and sketching in the open sketching design task (Vieira et al., 2022). Significant differences in activation were found for multiple channels for alpha and beta waves, as illustrated in Figure 1.1. The results from this study support that the neurocognition of designing is different from the neurocognition of problem-solving (Vieira et al., 2019a, 2020). Yet, it is not clear whether designing involves a similar brain pattern activation to problem-solving with a different intensity or if it relies on a unique set of brain pattern activations.

DIFFERENCES BETWEEN MECHANICAL ENGINEERS AND ARCHITECTS DURING IDEATION Another focus of design research is the study of factors that influence ideation patterns based on discipline-specific knowledge (Gericke & Blessing, 2012; Kan & Gero, 2011; Purcell & Gero, 1996). For instance, in some domains requirements and performance can be assessed through more objective measurements (structural load) than other domains where the artefact

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Source: Vieria et al. (2022); base of brain image © Society for Neuroscience (2017).

Figure 1.1

Top view of the brain showing the position of channels with significant differences in activation between layout (problem solving task) and sketching (open sketching design task), in alpha 2, beta 1, beta 2 and beta 3 frequency bands that were observed for industrial designers and mechanical engineers

is evaluated through aesthetic considerations (Visser, 2009). The evaluation method for the design artefact shapes how the designer engages in design tasks. Architects, compared with engineers, rely on a greater number of alternatives in their exploration of design solutions (Akin, 2001). Differences in design patterns based on disciplines found in cognitive studies suggest that different brain activation patterns could relate to specific discipline knowledge. Exploratory results confirm this, as significant differences in the design neurocognition between mechanical engineers and architects have been found (Vieira et al., 2019b). When comparing the four tasks presented above, namely problem-solving, basic design, open design and open sketching design, activation differences appeared between mechanical engineers and architects. More specifically, architects show higher brain activation than mechanical engineers, especially in the first half of the tasks as illustrated in Figure 1.2 on the left-hand graphs. Architects also show more differences in brain activation between the problem-solving and designing tasks

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compared with mechanical engineers (Vieira et al., 2019b). Overall, engaging in design tasks had different effects on designers’ brain activations depending on domains. Mechanical engineers tended to have similar activations for the pretask and Tasks 1, 2 and 3, with Task 4 being different, while the architects showed a higher activation for all tasks than the mechanical engineers. Further, for the architects the pretask was different to the other tasks. Between mechanical engineers and industrial designers, differences in activation amplitudes were found in the open sketching design (Vieira et al., 2020). The differences found between domains in this study could represent differences in the confidence in sketching between those disciplines, as sketching skills might differ between mechanical engineers, architects and industrial designers.

Note: The spider graph is oriented so that it illustrates the approximate position of the channels (i.e., AF4, F4) on the brain viewed from the top, with the frontal part on the top. Source: Vieira et al. (2019b); © Society for Neuroscience (2017).

Figure 1.2

Graph comparing the brain activation of (a) mechanical engineers and (b) architects across time for all the problem-solving and design tasks

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Cognitive studies have highlighted that even though commonalities are found in how designers from different domains engage in ideation (Gero & Kannengiesser, 2014), differences in cognitive focus appear (Jiang et al., 2014; Kan & Gero, 2011). For example, architects tend to engage more in synthesis design processes compared with mechanical engineers where design activity involves more analysis (Kan & Gero, 2011). Findings from neurocognitive studies are comparable to the ones from cognitive studies as different patterns of design cognition and neurocognition unfold but offer considerably more details and are in the form of objective measurements that do not require any human judgement in their production. Continuing research on the effect of task and the effect of design domain is needed to determine their significance. Findings from such research could help address cognitive dissonance in design teams with members from different discipline backgrounds.

EXPLORING THE NEUROCOGNITION OF IDEATION WITH THREE CONCEPT GENERATION TECHNIQUES Many techniques and strategies can support designers in generating concepts (Camburn et al., 2017; Kannengiesser et al., 2013; Smith, 1998; Tang et al., 2011). Brainstorming is widely used in design thinking and involves suspending judgement and criticism during the fluid ideation of concepts (Osborn, 1993). Other techniques are well-structured such as TRIZ (the Theory of Inventive Problem Solving), which provides a set of procedures to generate inventive solutions by defining the problem and looking at existing solution principles at a conceptual level, before developing a solution (Altshuller, 1997; Ilevbare et al., 2013). Characteristics of concept generation techniques have an impact on the design processes engaged by designers (Chulvi et al., 2012; Gero et al., 2013; Kannengiesser et al., 2013). In the following, we examine the effect of three concept generation techniques (brainstorming, morphological analysis and TRIZ) on ideation processes. These techniques provide different levels of structuredness in their approach (Table 1.1). Brainstorming is unstructured and more intuitive than the other two techniques. When using morphological analysis and TRIZ, designers follow a set of steps to generate creative ideas (Allen, 1962; Altshuller, 1997). TRIZ intentionally elicits a cognitive behaviour more focused on the problem compared with brainstorming. More structuredness in the concept generation technique, as in morphological analysis and TRIZ, leads to more reasoning about and on the problem space than the solution space (Gero et al., 2013; Kannengiesser et al., 2013). Using these techniques affects the evolution of design cognition when generating concepts. Over time, the first half of a concept generation session using morphological analysis looks similar to a TRIZ session, and its second half looks like a brainstorming session (Gero et al., 2013; Kannengiesser et al., 2013). Using these concept generation techniques also impacts design neurocognition (Milovanovic et al., 2021a; Shealy & Gero, 2019; Shealy, Gero, Hu et al., 2020). In this study, 30 graduate engineering students engaged in three different concept generation tasks using one of the concept generation techniques presented in Table 1.1. Their brain activities were monitored while participants engaged in (1) designing a device to assist the elderly with raising and lowering windows; (2) designing an alarm clock for the hearing impaired; and (3) designing a kitchen measuring tool for the blind. In this experiment, engineering students were equipped with an fNIRS cap (Figure 1.3(a)). This tool does not measure electrical activity but changes in blood flow. Changes of blood

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Table 1.1 Techniques

Characteristics of concept generation techniques Brainstorming

Morphological analysis

TRIZ

Intuitiveness

Intuitive

Intuitive

Logical

Motivation

Inner sense driven

Problem-driven

Problem-driven

Structure

Unstructured

Partially structured

Structured

1. Generate as many solutions

1. Define and decompose the

1. Define the problem

as possible and suspend Steps

evaluation

problem 2. Generate multiple sub-solutions to each sub-problem 3. Generate final solutions

2. Search for standard engineering parameters 3. Search for standard catalogued solutions 4. Generate final solutions

flow are accounted by tracking changes of oxygen in the blood, and changes in oxygen in the blood of the cells in the brain are directly related to cognitive activity. Using the fNIRS cap, designers could engage in the task in a naturalistic position, sitting in front of a computer screen. Here, the frontal part of the brain is monitored (Figure 1.3). The findings from this experiment highlighted that using TRIZ requires less cognitive effort in the prefrontal cortex (PFC) compared with the other two concept generation techniques (Shealy, Gero, Hu et al., 2020). Brainstorming and morphological analysis tend to require similar cognitive effort in the right part of the PFC while using TRIZ reduces the cognitive load in that part of the PFC. Generating concepts with morphological analysis significantly increases cognitive effort in the right part of the PFC. The structuredness of TRIZ might help reduce cognitive load. With TRIZ, participants engaged in one activity at a time, breaking down the problem, then focusing on one possi-

Source: Base of brain image © Society for Neuroscience (2017).

Figure 1.3

(a) Placement of fNIRS cap on a participant and (b) frontal view of channel placement of the fNIRS cap

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ble solution. Shifting attention between problem and solution through TRIZ can reduce the cognitive complexity in design, compared with using brainstorming. During brainstorming, participants consider the problem and solution concurrently. This has a direct effect on design thinking. Designing evolves over time. Time dynamics appear as an essential aspect of ideation. The temporal dimension of design relates to the situatedness of design, anchored in a particular context. Depending on the concept generation techniques used, the focus of the activity differs over time (Gero et al., 2013). Looking at the temporal neurocognition of designing with brainstorming, morphological analysis or TRIZ, we observed different patterns depending on the concept generation technique being employed (Milovanovic et al., 2021a). For example, over time, the highest activated part of the PFC changed in a different pattern when participants used brainstorming (Figure 1.4(a)) compared with using TRIZ (Figure 1.4(b)). During brainstorming, the activation tends to switch between the left and the right part of the PFC whereas for TRIZ, higher activation changes between the left part of the PFC and the medial part of the PFC (see arrows in Figure 1.4). The temporal dynamics of brain activation during brainstorming and TRIZ differs and the cognitive functions associated with regions of activation agree with previous findings from cognitive studies (Shealy & Gero, 2019). Knowledge of the neurocognitive behaviour associated with design techniques can form the basis of strategic guidance as to what techniques to employ during ideation. This phase is crucial in the design process. Innovation can be supported by using the right tool at the right time. Design thinking can benefit from the insights provided by neurocognitive studies on ideation techniques.

Note: The black numbers refer to the time periods; the circles refer to the measurement channels. Source: Base of brain image © Society for Neuroscience (2017).

Figure 1.4

Transition paths of the highest activated channels across time for (a) brainstorming and (b) TRIZ

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USING NEUROFEEDBACK TO BOOST DESIGN CREATIVITY In the previous section, we expanded on the effect of using techniques to help designers generate concepts on brain activation. With more knowledge about designers’ brain behaviour while generating concepts, tools based on neurofeedback could be developed. Such tools would act as thinking caps and assist designers in generating creative ideas. In an exploratory study, we explored the use of neurophysiology as an input to assist designers in brainstorming ideas. The motivation of this study was to investigate the prospective use of neurocognitive feedback to enhance creativity. Designers’ generation of new ideas tends to decrease over time and providing a cue on their process can positively impact their creativity (Hu et al., 2021; Shealy, Gero, Milovanovic et al., 2020). To explore the effects of neurocognitive feedback on idea generation, we studied graduate engineering students while brainstorming to generate solutions to design problems. The design ideation task focused on the first/last mile mobility problem about transporting people between mass transit stops and their residential dwellings. Before the task began, students were outfitted with the fNIRS instrument presented in Figure 1.3. Changes in activation in the prefrontal cortex (PFC) were measured during the brainstorming task. Half of the students received real-time feedback about their brain activation. The purpose of providing this feedback was to raise self-awareness about their cognitive activation patterns and help them sustain activation through self-regulation. Participants were instructed to look at their feedback and to sustain activation in their brain. The participants who received the neurocognitive feedback produced twice as many ideas in the same time period as the group who received no feedback (Shealy, Gero, Milovanovic et al., 2020). One focus of this experiment was to investigate the effects of neurocognitive feedback on hemispheric lateralization (which half of the brain was used the most) during brainstorming (Hu et al., 2021). Hemispheric lateralization plays a role in creative design, and relies on both halves of the brain to solve real-world problems (Goel, 2014). For the participants who did not receive neurofeedback, more than half of the participants had a left-hemispheric dominance. In contrast, all the participants that received neurocognitive feedback displayed right-hemispheric dominance. These findings are illustrated by the representation of the average activation of the participants’ PFC during the brainstorming tasks (Figure 1.5). In Figure 1.5(a), the higher activation appears on the left and the medial right part of the PFC for the participants who did not receive neurofeedback. On the other hand, there is a higher activation on the right part of the PFC for the participants who received the neurofeedback during the brainstorming task (Figure 1.5(b)). The right part of the PFC tended to be recruited for divergent thinking (Aziz-Zadeh et al., 2013; Goel & Grafman, 2000) and results in a higher originality in solution generation (Fink et al., 2009). The left part of the PFC is associated with rule-based design and goal-directed planning as well as making analytical judgement (Aziz-Zadeh et al., 2013; Gabora, 2010). These findings suggest neurocognitive feedback alters hemispheric lateralization and leads to right-hemispheric dominance during brainstorming. It changes brain patterns related to designing and potentially enhances creativity. A positive relationship was found between the brain behaviour and cognitive behaviour while generating ideas. A higher percentage of dominance of the right hemisphere positively correlates with better idea fluency and the production of more ideas. This exploratory experi-

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Note: The dark colour represents a higher activation while the light colour represents a lower activation. Source: © Society for Neuroscience (2017).

Figure 1.5

The average brain activation in the PFC for a brainstorming task shown as a heatmap: (a) participants who did not receive feedback and (b) participants who received neurofeedback

ment opens avenues for research to develop new types of ideation tools that embed neurophysiological information as inputs and potentially brain–computer interfaces.

IMPLICATIONS FOR DESIGN THINKING The studies presented in this chapter provide examples of experiments that tackle common research questions in design thinking studies focusing on the ideation phase: designing compared with problem-solving, discipline-based difference in ideation, effect of concept generation techniques and effect of design tools on ideation. Such studies provide new knowledge about design thinking, explored through the lens of neurocognition, with research methods and tools from neuroscience. The new knowledge stemming from design neurocognition studies has the potential to support the development of new models to describe design thinking, specifically design cognitive processes through the integration of cognition and neurophysiology. A holistic approach to design thinking through designers’ mind and brain behaviour can open an avenue for new tools for designers and design researchers, and lead to the development of new research questions (Gero & Milovanovic, 2020).

IMPLICATIONS FOR DESIGNERS Designers face challenges in their practice as demands for creativity, innovation, collaboration, efficiency and management increase. One challenge on implementing design thinking in industry relates to acquiring design skills (Carlgren, Elmquist et al., 2016). A better understanding of how designers’ brains function while designing could provide a means to

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increase designers’ performance, skill development and enhance creativity. Using neurofeedback appears as a promising avenue for the development of tools to enhance designers’ creativity. We can draw a parallel between neurofeedback to increase sports performance and neurofeedback to increase a designer’s performance and skills. For example, neurofeedback is proposed as a way to improve soccer goalkeepers’ visual spatial attention (Jeunet et al., 2020). These types of innovative sports training methods can be mapped onto a designer’s situation. Neurofeedback to designers could be given in real time concerning their design processes to enhance creativity (Hu et al., 2021), divergent or convergent thinking, or to provide the inspirational stimuli for idea generation (Goucher-Lambert et al., 2019). Moreover, ideation is partly driven by emotions that can affect decision-making processes while designing. Using models to monitor designers’ emotions based on neurophysiological signals (Balters & Steinert, 2017; Liu et al., 2014) could provide direct feedback to designers on their affective state. Such feedback can help designers self-regulate to enhance their ideation process. Another challenge of implementing design thinking deals with supporting communication and collaboration between team members (Carlgren, Elmquist et al., 2016). Domain knowledge influences one’s mode of thinking and engaging in project development. Neurocognitive studies highlight that domain knowledge leads to different brain patterns. A better understanding of those patterns can provide insights on cognitive misalignments between team members and be put in use to provide better support for collaboration in design teams. For instance, neurofeedback provides input to a design team in collaborative settings. Whether a designer collaborates with an AI design tool (Davis et al., 2017) or other designers (Meinel & Leifer, 2020), the measure of affective and cognitive states can offer more information to better adapt to the design situation in real-time. Measuring inter-brain synchronization in design teams assesses brain dynamics between team members (Mayseless et al., 2019; Reinero et al., 2021). Gaining knowledge of designers’ brain behaviour in teams opens directions for the development of tools integrating BCI (Brain Computer Interface), which could render team design more seamless. The current GUI (Graphical User Interface) used for designing tools is limited. BCI can provide an interface that would suit designers’ tasks better (Esfahani & Sundararajan, 2012; Huang & Chen, 2017). For instance, manipulating 3D objects (zoom in and out, rotate and scale) by thought alone and using gestures to draw and model in virtual spaces offer a more natural way to design than current GUI-based tools. Tools used by designers such as design software (CAD, BIM) and their interfaces (Graphical User Interface, Tangible User Interface) affect the design activity. With tools and methodologies from neuroscience, we can question what would be the effect of using design tools with BCI and neurofeedback on ideation processes.

IMPLICATIONS FOR DESIGN RESEARCHERS Bridging neuroscience and design science is a way to expand knowledge on design thinking, to develop new methods to analyse design thinking and to develop new research questions. For example, researchers in design science are confronted with problems related to the feasibility of large-scale studies on design thinking. Using protocol analysis is time and resources consumptive that are hindrances to the development of wider experiments. Using neurocognitive data to study design thinking can be a more efficient way to analyse design situations, espe-

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cially as the cost of the brain measurements tools keeps dropping. We exemplified possibilities to study the ideation phase of design thinking in this chapter. Current models of design thinking processes are grounded in the design cognition paradigm (Hay et al., 2017a, 2017b; Meinel & Leifer, 2020). Cognitive processes of design thinking include problem framing, problem structuring, concept generation, visual reasoning to name a few. One aim of studies in design neurocognition is to move toward a mapping between cognitive processes and neurological measurements to generate objective knowledge about the designing mind (Chrysikou & Gero, 2020). The discrete approach to neurocognition is limited when analysing higher-order cognitive processes such as designing. Recent advances in neurocognition suggest considering brain networks to analyse neurocognitive processes (Fornito et al., 2016). Ideation uses multiple regions of the brain that coactivate during the design process (Lazar, 2018; Milovanovic et al., 2021b). Studies suggest that two types of networks are engaged during concept generation, the executive network and the default mode network (Beaty et al., 2016, 2020). The executive network is usually solicited during a cognitive task while the default mode network accounts for brain activity while resting or mind wandering (Raichle, 2009). We can question whether designing implies a unique association of common cognitive brain activation and networks, or if designing equates to the solicitation of a specific network of brain region activation. Exploring such design questions can help us refine current ideation models to integrate cognition and neurophysiology within a unique framework.

CONCLUSION In this chapter, we highlighted the challenges and implications of studying ideation, a foundational component of design thinking, through the lens of neuroscience. Approaching this topic using tools, method and knowledge from another field of study enriches our understanding of it. Research on design cognition in the past 50 years generated in-depth knowledge of the underlying cognitive processes engaged in design thinking. Creativity has been a prolific research topic in neurocognitive studies but creative cognition represents only a fraction of the neurocognition of design thinking processes. The emergence of affordable and portable tools to monitor brain behaviours opened a novel field of research for design researchers. The emergence of design neurocognition as a field of research within the design science field is promising as new knowledge emerges from it. Several studies were presented to provide examples of studies in design neurocognition that support and augment current knowledge of ideation processes. The illustration of the findings from these exploratory studies gave an overview of the type of results one can obtain from this kind of research. These examples represent only the tip of the iceberg of what can be found and more research is needed to provide a holistic view of design thinking that integrates designers’ mind and brain behaviours. Continuing research in design neurocognition will likely lead to updates of current models of design thinking. More importantly, it could lead to the development of new tools to support designers in their ideation process, and mediate team collaboration and synchronization. This chapter focused on the ideation phase of design thinking but could be extended to other phases of the design process. Designing is a social process and multiple stakeholders collaborate on a project bringing challenges along the way. Synchronization between team members fosters a suitable environment for performance and innovation. Recent studies used

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brain imaging methods to monitor brain synchronization between team members in a creative task. Inter-brain synchrony studies offer an interesting approach to studying teams across all phases of design thinking.

REFERENCES Akin, O. (2001). Variants in design cognition. In C. Eastman, M. McCracken, & W. Newstetter (Eds.), Design knowing and learning: Cognition in design education. Elsevier. Alexander, C. (1964). Notes on the synthesis of form. Harvard University Press. Alexiou, K., Zamenopoulos, T., Johnson, J. H., & Gilbert, S. J. (2009). Exploring the neurological basis of design cognition using brain imaging: Some preliminary results. Design Studies, 30(6), 623–647. https://​doi​.org/​10​.1016/​j​.destud​.2009​.05​.002 Allen, M. S. (1962). Morphological creativity. Englewood Cliffs, NJ: Prentice-Hall. Altshuller, G. (1997). 40 Principles: TRIZ Keys to Technical Innovation (Technical Innovation Center, INC.). Asimow, M. (1962). Introduction to design. Englewood Cliffs, NJ: Prentice-Hall. Aziz-Zadeh, L., Liew, S.-L., & Dandekar, F. (2013). Exploring the neural correlates of visual creativity. Social Cognitive and Affective Neuroscience, 8(4), 475–480. https://​doi​.org/​10​.1093/​scan/​nss021 Baird, F., Moore, C. J., & Jagodzinski, A. P. (2000). An ethnographic study of engineering design teams at Rolls-Royce Aerospace. Design Studies, 21(4), 333–355. https://​doi​.org/​10​.1016/​S0142​ -694X(00)00006​-5 Balters, S., & Steinert, M. (2017). Capturing emotion reactivity through physiology measurement as a foundation for affective engineering in engineering design science and engineering practices. Journal of Intelligent Manufacturing, 28(7), 1585–1607. https://​doi​.org/​10​.1007/​s10845​-015​-1145​-2 Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative cognition and brain network dynamics. Trends in Cognitive Sciences, 20(2), 87–95. https://​doi​.org/​10​.1016/​j​.tics​.2015​.10​.004 Beaty, R. E., Chen, Q., Christensen, A. P., Kenett, Y. N., Silvia, P. J., Benedek, M., & Schacter, D. L. (2020). Default network contributions to episodic and semantic processing during divergent creative thinking: A representational similarity analysis. NeuroImage, 209, 116499. https://​doi​.org/​10​.1016/​j​ .neuroimage​.2019​.116499 Blizzard, J., Klotz, L., Potvin, G., Hazari, Z., Cribbs, J., & Godwin, A. (2015). Using survey questions to identify and learn more about those who exhibit design thinking traits. Design Studies, 38, 92–110. https://​doi​.org/​10​.1016/​j​.destud​.2015​.02​.002 Borgianni, Y., & Maccioni, L. (2020). Review of the use of neurophysiological and biometric measures in experimental design research. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1–38. https://​doi​.org/​10​.1017/​S0890060420000062 Brockington, G., Balardin, J. B., Zimeo Morais, G. A., Malheiros, A., Lent, R., Moura, L. M., & Sato, J. R. (2018). From the laboratory to the classroom: The potential of functional near-infrared spectroscopy in educational neuroscience. Frontiers in Psychology, 9, 1840. https://​doi​.org/​10​.3389/​fpsyg​ .2018​.01840 Brown, T. (2008). Design thinking. Harvard Business Review, June. Bucciarelli, L. L. (1988). An ethnographic perspective on engineering design. Design Studies, 9(3), 159–168. Camburn, B., Viswanathan, V., Linsey, J., Anderson, D., Jensen, D., Crawford, R., Otto, K., & Wood, K. (2017). Design prototyping methods: State of the art in strategies, techniques, and guidelines. Design Science, 3, e13. https://​doi​.org/​10​.1017/​dsj​.2017​.10 Carlgren, L., Elmquist, M., & Rauth, I. (2016). The challenges of using design thinking in industry— experiences from five large firms. Creativity and Innovation Management, 25(3), 344–362. https://​doi​ .org/​10​.1111/​caim​.12176 Carlgren, L., Rauth, I., & Elmquist, M. (2016). Framing design thinking: The concept in idea and enactment. Creativity and Innovation Management, 25(1), 38–57. https://​doi​.org/​10​.1111/​caim​.12153 Chrysikou, E. G., & Gero, J. S. (2020). Using neuroscience techniques to understand and improve design cognition. AIMS Neuroscience, 7(3), 319–326. https://​doi​.org/​10​.3934/​Neuroscience​.2020018

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Chulvi, V., Sonseca, Á., Mulet, E., & Chakrabarti, A. (2012). Assessment of the relationships among design methods, design activities, and creativity. Journal of Mechanical Design, 134(11), 111004. https://​doi​.org/​10​.1115/​1​.4007362 Clancey, W. J. (1997). The conceptual nature of knowledge, situations, and activity. Human and Machine Expertise in Context, 247–291. Coleman, E., Shealy, T., Grohs, J., & Godwin, A. (2019). Design thinking among first-year and senior engineering students: A cross-sectional, national study measuring perceived ability. Journal of Engineering Education, jee.20298. https://​doi​.org/​10​.1002/​jee​.20298 Coley, F., Houseman, O., & Roy, R. (2007). An introduction to capturing and understanding the cognitive behaviour of design engineers. Journal of Engineering Design, 18(4), 311–325. https://​doi​.org/​10​ .1080/​09544820600963412 Cross, N. (2006). Designerly ways of knowing. Springer. Davis, N., Hsiao, C.-P., Singh, K. Y., Lin, B., & Magerko, B. (2017). Creative sense-making: Quantifying interaction dynamics in co-creation. Proceedings of ACM SIGCHI Conference on Creativity and Cognition, 356–366. https://​doi​.org/​10​.1145/​3059454​.3059478 Dietrich, A. (2019). Where in the brain is creativity: A brief account of a wild-goose chase. Current Opinion in Behavioral Sciences, 27, 36–39. https://​doi​.org/​10​.1016/​j​.cobeha​.2018​.09​.001 Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. https://​doi​.org/​10​.1016/​j​.destud​.2011​.07​.006 Dorst, K. (2015). Framing innovation, Cambridge, MA: The MIT Press. https://​ doi​ .org/​ 10​ .7551/​ mitpress/​10096​.001​.0001 Dorta, T., Beaudry-Marchand, Emmanuel, & Pierini, D. (2018). Externalizing co-design cognition through immersive retrospection. In J. S. Gero (Ed.), Design Computing and Cognition DCC’18 (Springer, pp. 101–119). Ericsson, K. A., & Simon, A. H. (1984). Protocol analysis: Verbal reports as data. MIT Press. Esfahani, E. T., & Sundararajan, V. (2012). Classification of primitive shapes using brain–computer interfaces. Computer-Aided Design, 44(10), 1011–1019. https://​doi​.org/​10​.1016/​j​.cad​.2011​.04​.008 Fink, A., Grabner, R. H., Benedek, M., Reishofer, G., Hauswirth, V., Fally, M., Neuper, C., Ebner, F., & Neubauer, A. C. (2009). The creative brain: Investigation of brain activity during creative problem-solving by means of EEG and FMRI. Human Brain Mapping, 30(3), 734–748. https://​doi​ .org/​10​.1002/​hbm​.20538 Fornito, A., Zalesky, A., & Bullmore, E. (2016). Fundamentals of brain network analysis. Elsevier. https://​doi​.org/​10​.1016/​B978​-0​-12​-407908​-3​.00001​-7 Gabora, L. (2010). Revenge of the ‘Neurds’: Characterizing creative thought in terms of the structure and dynamics of memory. Creativity Research Journal, 22(1), 1–13. https://​doi​.org/​10​.1080/​ 10400410903579494 Gericke, K., & Blessing, L. T. M. (2012). An analysis of design process models across disciplines. DESIGN2012, Dubrovnik, Croatia. Gero, J. S. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine, 11(4), 26–36. https://​doi​.org/​10​.1609/​aimag​.v11i4​.854 Gero, J. S., & Kannengiesser, U. (2014). Commonalities across designing: Evidence from models of designing and experiments. In J. S. Gero (Ed.), Design Computing and Cognition’12, 285–302. Gero, J. S., & McNeill, T. (1998). An approach to the analysis of design protocols. Design Studies, 19(1), 21–61. https://​doi​.org/​10​.1016/​S0142​-694X(97)00015​-X Gero, J. S., & Milovanovic, J. (2020). A framework for studying design thinking through measuring designers’ minds, bodies and brains. Design Science, 6(e19). https://​doi​.org/​10​.1017/​dsj​.2020​.15 Gero, J. S., Jiang, H., & Williams, C. B. (2013). Design cognition differences when using unstructured, partially structured, and structured concept generation creativity techniques. International Journal of Design Creativity and Innovation, 1(4), 196–214. https://​doi​.org/​10​.1080/​21650349​.2013​.801760 Goel, V. (2014). Creative brains: Designing in the real world. Frontiers in Human Neuroscience, 8. https://​doi​.org/​10​.3389/​fnhum​.2014​.00241 Goel, V., & Grafman, J. (2000). Role of the right prefrontal cortex in ill-structured planning. Cognitive Neuropsychology, 17(5), 415–436. https://​doi​.org/​10​.1080/​026432900410775

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Goucher-Lambert, K., Moss, J., & Cagan, J. (2019). A neuroimaging investigation of design ideation with and without inspirational stimuli—understanding the meaning of near and far stimuli. Design Studies, 60, 1–38. https://​doi​.org/​10​.1016/​j​.destud​.2018​.07​.001 Guilford, J. P. (1967). The nature of human intelligence. McGraw-Hill. Hay, L., Duffy, A. H. B., McTeague, C., Pidgeon, L. M., Vuletic, T., & Grealy, M. (2017a). A systematic review of protocol studies on conceptual design cognition: Design as search and exploration. Design Science, 3. https://​doi​.org/​10​.1017/​dsj​.2017​.11 Hay, L., Duffy, A. H. B., McTeague, C., Pidgeon, L. M., Vuletic, T., & Grealy, M. (2017b). Towards a shared ontology: A generic classification of cognitive processes in conceptual design. Design Science, 3. https://​doi​.org/​10​.1017/​dsj​.2017​.6 Hu, M., & Shealy, T. (2019). Application of functional near-infrared spectroscopy to measure engineering decision-making and design cognition: Literature review and synthesis of methods. Journal of Computing in Civil Engineering, 33(6), 04019034. https://​doi​.org/​10​.1061/​(ASCE)CP​.1943​-5487​ .0000848 Hu, M., Shealy, T., Milovanovic, J., & Gero, J. (2021). Neurocognitive feedback: A prospective approach to sustain idea generation during design brainstorming. International Journal of Design Creativity and Innovation, 1–20. https://​doi​.org/​10​.1080/​21650349​.2021​.1976678 Huang, Y.-C., & Chen, K.-L. (2017). Brain-computer interfaces (BCI) based 3D computer-aided design (CAD): To improve the efficiency of 3D modeling for new users. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Augmented Cognition. Enhancing Cognition and Behavior in Complex Human Environments (Vol. 10285, pp. 333–344). Springer International Publishing. https://​doi​.org/​10​.1007/​ 978​-3​-319​-58625​-0​_24 Ilevbare, I. M., Probert, D., & Phaal, R. (2013). A review of TRIZ, and its benefits and challenges in practice. Technovation, 33(2–3), 30–37. https://​doi​.org/​10​.1016/​j​.technovation​.2012​.11​.003 Jeunet, C., Tonin, L., Albert, L., Chavarriaga, R., Bideau, B., Argelaguet, F., Millán, J. del R., Lécuyer, A., & Kulpa, R. (2020). Uncovering EEG correlates of covert attention in soccer goalkeepers: Towards innovative sport training procedures. Scientific Reports, 10(1), 1705. https://​doi​.org/​10​.1038/​ s41598​-020​-58533​-2 Jiang, H., Gero, J. S., & Yen, C.-C. (2014). Exploring designing styles using a problem–solution division. In Design Computing and Cognition’12 (pp. 79–94). Springer. Kan, J. W., & Gero, J. S. (2011). Comparing designing across different domains: An exploratory case study. 18th International Conference on Engineering Design (ICED’11). Kan, J. W., & Gero, J. S. (2017). Quantitative methods for studying design protocols. Springer Netherlands. https://​doi​.org/​10​.1007/​978​-94​-024​-0984​-0 Kannengiesser, U., Williams, C., & Gero, J. S. (2013). What do the concept generation of TRIZ, morphological analysis and brainstorming have in common? Proceedings of the 19th International Conference on Engineering Design (ICED13), Design for Harmonies, Vol.7: Human Behaviour in Design, Design Society, pp. 297–306. Lazar, L. (2018). The cognitive neuroscience of design creativity. Journal of Experimental Neuroscience, 12, 117906951880966. https://​doi​.org/​10​.1177/​1179069518809664 Liu, Y., Ritchie, J. M., Lim, T., Kosmadoudi, Z., Sivanathan, A., & Sung, R. C. W. (2014). A fuzzy psycho-physiological approach to enable the understanding of an engineer’s affect status during CAD activities. Computer-Aided Design, 54, 19–38. https://​doi​.org/​10​.1016/​j​.cad​.2013​.10​.007 Mayseless, N., Hawthorne, G., & Reiss, A. L. (2019). Real-life creative problem-solving in teams: FNIRS based hyperscanning study. NeuroImage, 203, 116161. https://​doi​.org/​10​.1016/​j​.neuroimage​ .2019​.116161 Meinel, C., & Leifer, L. (Eds). (2020). Design thinking research: Investigating design team performance. Springer International Publishing. https://​doi​.org/​10​.1007/​978​-3​-030​-28960​-7 Milovanovic, J., Hu, M., Shealy, T., & Gero, J. (2021a). Temporal dynamics of brain activation during three concept generation techniques. Proceedings of the Design Society, 1, 2961–2970. https://​doi​.org/​ 10​.1017/​pds​.2021​.557 Milovanovic, J., Hu, M., Shealy, T., & Gero, J. (2021b). Characterization of concept generation for engineering design through temporal brain network analysis. Design Studies, 76, 101044. https://​doi​ .org/​10​.1016/​j​.destud​.2021​.101044

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Nguyen, T. A., & Zeng, Y. (2010). Analysis of design activities using EEG signals. Volume 5: 22nd International Conference on Design Theory and Methodology; Special Conference on Mechanical Vibration and Noise, 277–286. https://​doi​.org/​10​.1115/​DETC2010​-28477 Nguyen, T. A., & Zeng, Y. (2014). A physiological study of relationship between designer’s mental effort and mental stress during conceptual design. Computer-Aided Design, 54, 3–18. https://​doi​.org/​ 10​.1016/​j​.cad​.2013​.10​.002 Norman, D. A. (2013). The design of everyday things (Revised and expanded edition). Basic Books. Osborn, A. F. (1993). Applied imagination: Principles and procedures of creative problem-solving, 3rd edition (3rd Rev edn). Creative Education Foundation. Pahl, G., Beitz, W., Feldhusen, J., & Grote, K. (2007). Engineering design: A systematic approach (3rd edn). Springer. Pearson, J. (2019). The human imagination: The cognitive neuroscience of visual mental imagery. Nature Reviews Neuroscience, 20(10), 624–634. https://​doi​.org/​10​.1038/​s41583​-019​-0202​-9 Pidgeon, L. M., Grealy, M., Duffy, A. H. B., Hay, L., McTeague, C., Vuletic, T., Coyle, D., & Gilbert, S. J. (2016). Functional neuroimaging of visual creativity: A systematic review and meta-analysis. Brain and Behavior, 6(10), e00540. https://​doi​.org/​10​.1002/​brb3​.540 Purcell, A. T., & Gero, J. S. (1996). Design and other types of fixation. Design Studies, 17(4), 363–383. https://​doi​.org/​10​.1016/​S0142​-694X(96)00023​-3 Raichle, M. E. (2009). A paradigm shift in functional brain imaging. Journal of Neuroscience, 29(41), 12729–12734. https://​doi​.org/​10​.1523/​JNEUROSCI​.4366​-09​.2009 Reinero, D. A., Dikker, S., & Van Bavel, J. J. (2021). Inter-brain synchrony in teams predicts collective performance. Social Cognitive and Affective Neuroscience, 16(1–2), 43–57. https://​doi​.org/​10​.1093/​ scan/​nsaa135 Schön, D. (1983). The reflective practitioner: How professionals think in action. Temple Smith. Schön, D. (1992). Designing as reflective conversation with the materials of a design situation. Research in Engineering Design, 3(3), 131–147. https://​doi​.org/​10​.1007/​BF01580516 Seitamaa-Hakkarainen, P., Huotilainen, M., Mäkelä, M., Groth, C., & Hakkarainen, K. (2016). How can neuroscience help understand design and craft activity? The promise of cognitive neuroscience in design studies. FORMakademisk – Forskningstidsskrift for Design Og Designdidaktikk, 9(1). https://​ doi​.org/​10​.7577/​formakademisk​.1478 Shealy, T., & Gero, J. S. (2019). The neurocognition of three engineering concept generation techniques. Proceedings of the Design Society: International Conference on Engineering Design, 1, 1833–1842. https://​doi​.org/​10​.1017/​dsi​.2019​.189 Shealy, T., Gero, J., Hu, M., & Milovanovic, J. (2020). Concept generation techniques change patterns of brain activation during engineering design. Design Science, 6, e31. https://​doi​.org/​10​.1017/​dsj​.2020​ .30 Shealy, T., Gero, J., Milovanovic, J., & Hu, M. (2020). Sustaining creativity with neuro-cognitive feedback: A preliminary study. In Boujut, J-F., Gaetano, C., Ahmed-Kristensen, S., Georgiev, G. & Iivari, N. (Eds), Proceedings of the Sixth International Conference on Design Creativity (ICDC 2020), Design Society, pp.  84.91. https://​doi​.org/​10​.35199/​ICDC​.2020​.11 Simon, H. A. (1969). The sciences of the artificial. Dunod. Simon, H. A. (1973). The structure of ill structured problems. Artificial Intelligence, 4(3–4), 181–201. Smith, G. F. (1998). Idea-generation techniques: A formulary of active ingredients. Journal of Creative Behavior, 32(2), 107–134. https://​doi​.org/​10​.1002/​j​.2162​-6057​.1998​.tb00810​.x Suwa, M., & Tversky, B. (1997). What do architects and students perceive in their design sketches? A protocol analysis. Design Studies, 18. Tang, H.-H., Chen, Y.-L., & Gero, J. S. (2011). The influence of design methods on the design process: Effect of use of scenario, brainstorming, and synectics on designing. In P. Israsena, J. Tangsantikul and D. Durling (eds), Proceedings Design Research Society 2012, Chulalongkorn University, Bangkok, pp. 1824–1838. Van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994). The think aloud method: A practical guide to modelling cognitive processes. Academic Press.

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Vieira, S, Benedek, M, Gero, JS, Cascini G and Li, S., (2022) Design spaces and EEG frequency band power in constrained and open design. International Journal of Design Creativity and Innovation, doi: 10.1080/21650349.2022.2048697 Vieira, S., Gero, J. S., Delmoral, J., Fernandes, C., Gattol, V., & Fernandes, A. (2019a). Insights from an EEG study of mechanical engineers problem-solving and designing. In Y. Eriksson and K. Paetzold (eds), Human Behavior in Design, UniBw M, Germany, pp. 23–34. Vieira, S., Gero, J. S., Delmoral, J., Gattol, V., Fernandes, C., & Fernandes, A. (2019b). Comparing the design neurocognition of mechanical engineers and architects: A study of the effect of designers’ domain. Proceedings of the 22nd International Conference on Engineering Design (ICED19), 10. https://​doi​.org/​10​.1017/​dsi​.2019​.191 Vieira, S., Gero, J. S., Delmoral, J., Gattol, V., Fernandes, C., Parente, M., & Fernandes, A. A. (2020). The neurophysiological activations of mechanical engineers and industrial designers while designing and problem-solving. Design Science, 6, e26. https://​doi​.org/​10​.1017/​dsj​.2020​.26 Visser, W. (2006). The cognitive artifacts of designing. Lawrence Erlbaum Associates. Visser, W. (2009). Design: One, but in different forms. Design Studies, 30(3), 187–223. https://​doi​.org/​ 10​.1016/​j​.destud​.2008​.11​.004 Yang, E. K., & Lee, J. H. (2020). Cognitive impact of virtual reality sketching on designers’ concept generation. Digital Creativity, 1–16. https://​doi​.org/​10​.1080/​14626268​.2020​.1726964

2. Design facilitation practice: an integrated framework Genevieve Mosely and Lina Markauskaite INTRODUCTION Throughout history, the design discipline and profession has constantly reformed in response to numerous disruptive changes and innovations, such as technological advances and pandemics of new scales. Globalisation, rapid technological developments, and unsustainable economic growth have resulted in complex, wicked problems that require new collaborative problem-solving approaches. This evolution has changed the role of the designer and their skills, knowledge and values (Heskett, 2002; Sanders & Stappers, 2008). Since the early 2000s, design practices, and more specifically design thinking, have become increasingly prevalent outside of the professional design field as a way to help solve complex problems and take advantage of the innovative potential of both people and technologies (Brown, 2008). Design thinking is commonly known as a human-centred approach to problem-solving with the goal of creating innovative products, services, environments, processes, and organisations (Brown, 2008; Wrigley & Mosely, 2022). While design thinking has attracted considerable attention from practitioners and academics, many questions remain unsolved. As the application of design thinking is so broad, there are many definitions of design thinking within the literature. The views about what is entailed in design thinking are diverse and fragmented, which often hinders its effective application in research and practice (Micheli et al., 2019). Co-design and participatory design that draw on a design thinking approach often involve workshops with key stakeholders and users to develop solutions to complex problems. Design thinking methods focus on being human-, user- or customer-centred. Therefore, exploring and understanding concerns and motives of people are at the centre of problem solving (Bjögvinsson et al., 2012; Brown, 2008; Micheli et al., 2019; van der Bijl-Brouwer & Dorst, 2017). These methods encourage the user to be a part of the design process, changing the role of the designer (Sanders & Stappers, 2008). Design facilitation plays an essential role in the effective implementation of design thinking within organisations, particularly in the context of co-design and participatory design. With the expanding practice of design, the ability to effectively facilitate design thinking processes and use professional facilitation methods is now becoming essential for professional designers. However, currently, designers are not specifically taught facilitation (Napier & Wada, 2016), and it is a significantly underlooked area within design research (Granholt & Martensen, 2021). Design practice, including design thinking practice, is both relational and situated. A designer may have the capabilities to suc25

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cessfully facilitate design thinking in one context with a particular group of people, but not in another, and may need different tools and skills in different contexts. Therefore, this chapter explores the evolving practice of design facilitation aiming to construct an integrated framework for conceptualising design facilitation practice and the capabilities that professional designers need in different contexts. Specifically, it focuses on co-design and participatory design practices where designers take the roles of facilitators of collaborative design thinking processes. First, the chapter briefly introduces design thinking and the emergence of design facilitation. Then, it explores the meanings of facilitation across three other fields in which facilitation practices play a critical role: management, deliberation, and education. By expanding the focus outside the field of design, it offers a fresh perspective on facilitation, teasing out what the practice of design facilitation can draw on from these perspectives to inform future research directions in the field. From here the chapter utilises the theoretical ideas of practice architectures to propose a novel framework through which design facilitation can be conceptualised as a situated and relational practice and empirically explored. By doing this, the chapter makes a unique contribution to the theory and methods in the field of design.

DESIGN THINKING AND THE EMERGENCE OF DESIGN FACILITATION The present popularity of design thinking is most often attributed to IDEO, a design and innovation consultancy firm founded by Tim Brown and David Kelley, and the Stanford d.school. They offered a process-oriented representation of design thinking, where one moves through a sequential series of steps and methods to solve the problem at hand. This approach has led to its popularity, across a diverse range of fields outside of design, including business, management, education, law, food, policy and the military (for example, Hagan, 2020; Howlett, 2014; Koh et al., 2015; Martin, 2009; Olsen, 2015; Wrigley et al., 2021). As Manzini (2015) points out, design has become a ubiquitous practice in present times, where the terms “design” and “the designer” are found in contexts beyond the field of design (p. 29). Although this escalation has had some positive effects on the design field, it has also brought ambiguities and challenges. An increased number of non-design professionals talking about and applying design thinking across a range of activities has contributed to a wide range of meanings and misunderstandings of design thinking processes and methods (Manzini, 2015). In order to assist non-designers to gain a “threshold understanding” of design thinking needed for them to apply it in their own context, it is often simplified and reduced down to its “bare bones”. For example, Foster (2021) presents an experiential design thinking learning activity specifically designed for both instructors and students who have limited design thinking experience. The activity adopts IDEO’s terminology and graphics as they are easy to comprehend and visually appealing. Scholarly literature that addresses these challenges is also absent, particularly in the field of design. For example, Wrigley and Mosely (2022) observe that there is currently no historical literature review of design thinking. While systematic reviews exist (for example, Micheli et al., 2019; Razzouk & Shute, 2012), beyond two PhD dissertations (Camacho, 2020; Di Russo, 2016) that have reviewed the antecedents of design thinking since the 1950s, a review examining the origins and evolution of design thinking in the design discipline is absent. This

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absence contributes to the ambiguities around design thinking, particularly for those from non-professional design backgrounds who refer to design thinking as a suite of tools, skills and mindsets for a step-by-step problem-solving process. In order to overcome these ambiguities, some design scholars distinguish between two discourses: design thinking and designerly thinking. Design thinking is used to refer to “discourse where design practice and competence are used beyond the design context (including art and architecture), for and with people without a scholarly background in design, particularly in management” (Johansson-Sköldberg et al., 2013, p. 123). Comparatively, designerly thinking is used to refer to the professional designer’s practice, “founded on the design research community’s longstanding desire to understand design practice and to establishing itself as a discipline in its own right” (Laursen & Haase, 2019, p. 815). Succinctly, design thinking is described as “a simplified version of designerly thinking or a way of describing a designer’s methods that is integrated into an academic or practical management discourse” (Johansson-Sköldberg et al., 2013, p. 123). However, this terminology has not been used consistently within the literature, and “designerly thinking” is often referred to as “design thinking”. Ultimately, to non-designers, design thinking can be seen as expert designers’ ways of thinking that are rooted in designerly thinking (Laursen & Haase, 2019). Additionally, design thinking is practised by a spectrum of people ranging from novices and non-designers, who often rely on pre-existing tools and heavily scaffolded methods to work through a design process, through to expert designers who have developed their own unique style to approach complex problems. Further, with the uptake of design thinking across diverse fields, it is increasingly used in more complex environments for large-scale system-level design including policy design, social infrastructure and public service. Therefore, the role of the design professional, their level of design expertise, has become even more important and is now expanded to include facilitation, specifically the role of “design facilitator” or “designer as facilitator”, where designers are expected to guide collaborative design processes within heterogeneous interdisciplinary contexts (Lee, 2008; Manzini, 2015; Wahl & Baxter, 2008). Mosely et al. (2021) explain design facilitation as “the act of drawing on and applying design processes and approaches to enable dialogue and ideas to emerge within participatory design contexts, in order to develop novel solutions to complex problems” (p. 11). This role requires a distinct skill set and capabilities, such as relational qualities, impartial mediation and the ability to create collaborative conditions (Mosely et al., 2021). However, this role has only recently emerged and what constitutes design facilitation or how designers are taught and learn facilitation is not well established. Some literature claims that there is a difference between generalist facilitators and design facilitators, as design facilitators are guided by a clear aim to produce a specific design outcome and take “a group through a collaborative process of design thinking to create a picture of a future state that doesn’t yet exist” (Body et al., 2010, pp. 63–64). While this literature acknowledges that there are some shared characteristics between general teamwork facilitation and design facilitation, there is limited research in the field that explores this link and the facilitation literature deeper. This inward focus and insulation could hinder the development of the field. We believe design facilitation could benefit from drawing on the broader literature on facilitation. Therefore, in the next section, we take a broader perspective and explore the meanings, conceptualisations and practices of facilitation by drawing on the literature from diverse disciplinary and professional fields.

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FACILITATION AS A CROSS-DISCIPLINARY PRACTICE Meanings of Facilitation Facilitation has a long history which, as Hogan (2002) observes, spans many fields, including education, experiential learning, health, business management, organisational learning, public engagement, and community development. There are many different kinds of facilitation and, similarly with many “evolving phenomena, there is no single agreed definition” (Hogan, 2002, p. 10). Facilitation comes from the French verb faciliter “to render easy”, which stems from Latin facilis “easy to do” (Online Etymology Dictionary, 2022). Most definitions agree that facilitation is concerned with encouraging open dialogue among individuals with different perspectives so that diverse assumptions and options may be explored (Hogan, 2002, p. 10). Ultimately, facilitators are experts at leading a process (Havergal & Edmonstone, 2003). However, as Hogan (2002) states, facilitation sounds “simple” and is at times “straightforward” but “at other times it may be very complex and may involve some of the highest levels of human interaction and communication skills” (p. 10). Jenkins (2005) extends this by focusing on the power of the facilitator to make a difference in how people work and think, “facilitation is about enabling change – change in organisations, teams and individuals” (p. 474). Schwarz (2005) focuses on the distinct role of the facilitator and defines group facilitation as, a process in which a person whose selection is acceptable to all members of the group, is substantively neutral, and has no substantive decision-making authority diagnoses and intervenes to help a group improve how it identifies and solves problems and makes decisions, to increase the group’s effectiveness. (Schwarz, 2005, p. 21)

Facilitators carry out a variety of roles and responsibilities within facilitative contexts; as well as this, there are a variety of different principles that influence facilitation practice. Kaner (2014) states a facilitator has four functions: (1) to encourage full participation, (2) to promote mutual understanding, (3) to foster inclusive solutions, and (4) to cultivate shared responsibility. Wayne (2005) presents the facilitator’s role differently by highlighting the importance of the connection between the facilitator’s theoretical understanding and situated action: “the effective facilitator integrates methods (the philosophy of the facilitator and specific tools in achieving a set objective) with a process (the important dynamics of how we all function as individuals and as groups)” (p. 36). Havergal and Edmonstone (2003) identify a variety of principles that govern facilitation. These include equality, empathy and mutual trust among participants, group focus, sharing decision-making and ensuring participants can make free and informed choices. What is less clear across the literature is if facilitation is a discrete field of expertise, or if it is a skill set that complements expertise in other disciplinary fields. Facilitation is becoming a recognised practice across many industries. Hogan (2002) attributes the rise of facilitation to an increase in participatory approaches and acknowledging the value users and stakeholders can bring to solutions. Escobar (2019) states facilitation is “learned by doing, imitating and adapting, in a developmental process in which personality traits and contextual demands are entangled” (p. 179). No facilitative experience is ever the same, different facilitators and different participants bring their set of experiences, skills, values and other personal resources to facilitation practice resulting in a unique outcome that emerges from the design facilitation process (Havergal & Edmonstone, 2003). As such, every

Design facilitation practice: an integrated framework

Table 2.1 Level

29

Typology of design facilitation

Typology

Description

1

Tool

Material components used in participatory design activities

2

Activity

Individual and collective exercises that support an event phase

3

Event phase

Overarching theme of a series of activities

4

Event

An entire participatory session

5

Series of events

The sequence of multiple events over time

Source: Aguirre et al., 2017, p. 208.

facilitative practice varies depending on the content, context, and participants as well as the facilitator themselves. Facilitation in Design Contexts Facilitation is an emerging aspect of design practice, and the majority of literature on design facilitation focuses on the emergence of the new role of the “designer as facilitator” and the skills required for design facilitation practice (Body et al., 2010; Howard & Melles, 2011; Napier & Wada, 2016). Wahl and Baxter (2008) identify designers as “having the potential to act as transdisciplinary integrators and facilitators” (p. 72). Building on this, Howard and Melles (2011) argue that designer roles are diversifying, which requires new skills in order to fulfil these new roles, including design for learning, active listening, mindfulness and coaching. However, often design facilitation is referred to as many things across the literature, making it hard to locate. Mosely et al. (2021) in their critical review on design facilitation, identify ten key constructs of design facilitation practice, which demonstrate design facilitation as an interdependent and dynamic practice between the expertise and behaviours of the design facilitator and the shared characteristics of joint activity and the environment. The facilitator’s characteristics and behaviours include core design competencies (design knowledge and skills, visualisation), design expertise (reflective practice, problem setting and complexity), creative process (improvisation, adaptability), design tasks (workshop preparation and planning) and relational qualities (the ability to tolerate ambiguity, empathy and leadership). The shared characteristics encompass design dialogue (conversation and discussion), design tools and objects (deign materials and artefacts), design infrastructure and spaces (physical space and environment), impartial mediation (neutrality, bias and power) and collaborative conditions (group dynamics, trust and building relationships). The implementation and application of design thinking in different contexts requires engagement with users and stakeholders. Often this takes the form of workshops; however, it can also extend to broader design interventions across organisations. Design facilitation, therefore, spans different scales. Aguirre et al. (2017) propose a five-level typology that spans from contextually designed facilitation tools at the micro-level to a series of design facilitation events at the macro-level (Table 2.1). The typology reveals the different levels of interactions required for design facilitators to work across demonstrating facilitation ranging from small interactions over group activities to larger project implementation within organisations. The emergence of the role of designer as facilitator and the requirement of a new skillset is well established in the literature. However, what is entailed in design facilitation is far less

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Table 2.2

Six dimensions of design facilitation tools

Category

Dimension

Description

Core facilitation tools

Functional

Considers usability, and logistics, ergonomics, and implementation feasibility

Intentional

Purpose-seeking; differentiates between intermediary and final outcomes

Participatory

Enables collaboration, conversation, and action among diverse stakeholders

Designerly facilitation tools

Creative

Promotes abductive and lateral thinking; produces novel design material

Experiential

Uses immersive, extraordinary, multi-sensorial, and aesthetic interactions

Human-perspective

Prompts empathetic insights; embodies relational perspectives

Source: Aguirre et al., 2017, p. 203.

established. In terms of facilitation methods and processes, Aguirre et al. (2017) identify six dimensions of design facilitation tools, across two categories: core facilitation tools and designerly facilitation tools (Table 2.2). Core facilitation tools include operational and function logistics of organising an event, such as scheduling and project management. Designerly facilitation tools create contextual experiences, make use of the diverse human perspectives the participants bring along with them and elicit participants’ creative potential. Designerly facilitation tools cannot occur without core facilitation tools being in place first. This taxonomy makes some aspects of the design facilitation practice explicit. Building on the understanding of design facilitation from Aguirre et al. (2017), Starostka et al. (2021) present a taxonomy of two approaches to design facilitation based on four dimensions: (1) understanding of design thinking, (2) focus, (3) process and (4) leadership (Figure 2.1). These four parameters guide a design facilitator’s approach to design thinking workshops and interventions. Starostka et al. (2021) found design facilitation is practised differently due to diverse interpretations of design thinking and the facilitator’s approach to the workshop. The taxonomy presents two extremes for each dimension – a method approach, or a co-facilitation approach – but it acknowledges that each dimension is a continuum, which can change throughout the workshop (Figure 2.1). For example, a design facilitator might go into a workshop with a clear plan for how the workshop will progress; however, due to the unpredictability of group dynamics, the process may change and become more emergent. As Mosleh and Larsen (2020) describe, participation is uncontrollable, meaning design workshops “can act as powerful invitations for new conversations to emerge, yet the outcome remains unpredictable” (p. 15). Emergent design facilitation processes require design facilitators to be flexible and possess the ability to improvise and think on the spot (Granholt & Martensen, 2021). There is a limited amount of research investigating the level of design expertise, the relevance of facilitator design skills and the success of the facilitation activity in co-design settings. Luck (2007) explores how design expertise impacts the performance of facilitation and conversational behaviour, arguing facilitation expertise is performed and displayed during design dialogue and conversation. Mosely et al. (2018) expand on this, comparing how the

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Source: Starostka et al., 2021.

Figure 2.1

Taxonomy of two design facilitation approaches

design facilitator’s expertise and the complexity of the problem being solved in the workshop, impact the outcome of the co-design workshop. Overall, research on design facilitation is limited, leaving much to be learnt from facilitation practice in different contexts, including management, deliberation practice and education, which will be explored in the next three sections. Facilitation in Management Contexts Facilitation within organisational management contexts started to rise and become a central process for group problem solving during the 1970s and 1980s (McFadzean & Nelson, 1998). Facilitation is now conceived as a key competency for managers, which “enables increasing integration of managers and knowledge workers in decision making and implementation” (Hogan, 2002, p. 12). Within management literature, Wardale (2013) observes that “facilitation” is often used interchangeably with mediation, moderation, negotiation, training, coaching, intervention, group work, chairing and process consultation. Schwarz (2017) similarly argues that organisations now use this term to refer to a whole spectrum of activities. The term “facilitator” has become particularly multifaceted over recent years. The key defining characteristics of a group facilitator in an organisation include not being a member of the group, being content-neutral, having no decision-making authority and the ability to improve how the group works together (Schwarz, 2017, p. 14). Thomas (2008) identifies facilitators as requiring self-awareness and self-management to understand their relationships with groups and self-reflect on their practice. On a more practical side, Mann (2013) identifies facilitators

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as having “a comprehensive catalogue of: approaches, models, tools and techniques” (p. 61). Facilitators within management contexts seek to enable people to work in collaborative and participatory ways, to understand what makes groups productive, challenge key issues and make fundamental decisions (Mann, 2013; McFadzean & Nelson, 1998). Within management contexts, McFadzean and Nelson (1998) assert four phases in the facilitation process: (1) pre-session planning, (2) running the group session, (3) producing a post-session report, and (4) holding a post-session review. However, the ability of the facilitator to construct and develop these phases requires an understanding of learning design and instruction. Schwarz (2005) breaks down general facilitation activity by discussing process and structure. Process refers to interactions within a group and how they work together, including “how members talk to each other, how they identify and solve problems, how they make decisions and how they handle conflict” (p. 22). Structure refers to stable and recurring group features, such as group membership or group roles. Overall, facilitation in management contexts seeks to develop and maintain a meaningful dialogue with team members for effective group problem-solving. However, Kirk and Broussine (2000) in their reflections of working as facilitators within organisations recognise facilitators and facilitation as political, requiring awareness, where the position of the facilitator is never neutral. Facilitation in Deliberation Contexts Deliberation studies explore the discursive dimension of participation in decision-making processes aiming to ensure that they are just and free from the distortions of unequal power and other biases. Within these participatory contexts, facilitation practitioners are required to facilitate deliberation as a mode of decision-making. While facilitation is an “indispensable” aspect of deliberation practice, Moore (2012) highlights that it is “largely absent from deliberative theory” (p. 147). Molinengo et al. (2021) identify three distinct strands of facilitation in deliberation practice literature: the first strand focuses on the skills that support collaborative work, the second strand focuses on uncovering the rationale, which guides the facilitator’s actions, and the third strand focuses on facilitators’ activities and approaches to shape the communicative process with norms and rules (p. 3). True to general facilitation, in the context of public participation, the “facilitator must be perceived as an honest broker by all participants, the facilitator’s focus is on shaping the deliberative process, rather than the substantive arguments of deliberation” (Escobar, 2019, p. 184). The facilitator is an expert in decision-making processes. Molinengo et al. (2021) refer to the expertise underpinning bespoke strategies for collaboration as process expertise, where “researchers engage with other kinds of knowledge in the room and use their expertise to create an arena of productive interaction” (p. 9). Facilitators seek to ensure the discussion and process are kept on track, by maintaining inclusivity through responding to situations that might close down dialogue, reframing questions, drawing threads together or enforcing ground rules. Within participatory contexts, facilitators are impartial, “facilitators are charged with maintaining a studiously impartial stance on the topic, but they are not expected to be neutral about the process” (Escobar, 2019, p. 184). As facilitators are not neutral about the process, they use their process expertise to establish and foster collaborative, participatory environments (Molinengo et al., 2021). Within deliberation contexts, facilitation is about ensuring all voices are heard, by turning “diverse communication patterns into aligned and public-spirited dialogue and deliberation” (Escobar, 2014, p. 248).

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Facilitation in Educational Contexts Within the learning sciences and educational practices, the role of teacher as facilitator is well acknowledged. There is a large body of literature on orchestration, moderation, and facilitating productive dialogues which represent different approaches to facilitation (King, 1993; Roschelle et al., 2013; Schwarz & Asterhan, 2011). Here, we focus on two prominent pedagogical approaches in which the role of a facilitator is seen as central: problem-based learning (PBL) and knowledge creation. PBL focuses on a student’s learning – where the learning and teaching are structured through engaging with and solving problems “centring learning around domains, themes, and issues rather than disciplinary silos” (Bridges et al., 2020, p. 285). Within PBL, the teacher acts as a facilitator modelling “good strategies for learning and thinking, rather than providing expertise in specific content” (Hmelo-Silver & Barrows, 2006, p. 24). The role of the facilitator “is crucial, as the facilitator must continually monitor the discussion, selecting and implementing appropriate strategies as needed” (Hmelo-Silver & Barrows, 2006, p. 24). The main argument is that facilitators of PBL do not need to know content; rather, the facilitator’s role is to “model good strategies for learning and thinking, rather than providing expertise in specific content” (Hmelo-Silver & Barrows, 2006, p. 24). However, more recent research suggests facilitators need to have, at minimum, a “threshold level of content understanding” (Hmelo-Silver et al., 2019, p. 301). Their role is to ensure that PBL is effective, and to monitor learners’ interactions and discussions, intervening and probing as necessary. Specifically, as Hmelo-Silver et al. (2019) synthesise, within PBL contexts the facilitator, • guides the development of higher-order thinking skills, • externalises self-reflection by directing appropriate questions to individuals, • models the problem-solving and self-directed learning skills needed for self-assessing one’s reasoning and understanding, • helps students to learn to collaborate effectively, • helps the group to identify the limits of their understanding by pushing students to explain their thinking and define terms that might be used without understanding (Hmelo-Silver et al., 2019, pp. 299–301). As students begin to take responsibility and agency over their learning, the scaffolding of the facilitator gradually decrease (Hmelo-Silver et al., 2019). Examples of facilitation strategies within PBL learning contexts include: open-ended questioning, pushing students for an explanation, revoicing, summarising, generating and evaluating hypotheses, documenting the discussion and focusing (divergent and convergent), identifying learner’s gaps in understanding and encouraging visual representation of ideas (Hmelo-Silver et al., 2019). Facilitation is also used broadly in pedagogical strategies of knowledge creation (Paavola et al., 2004). Knowledge creation is a “collaborative, systematic development of common objects of activity” (Paavola & Hakkarainen, 2005, p. 536). This perspective emphasises the authentic collaborative co-construction of knowledge as a way of learning. Here, teachers primarily have the roles of both designers and facilitators. First, they design tasks and pre-configure the environment in ways that support learning through collaborative construction of shared knowledge objects. Second, they facilitate and mediate joint knowledge-building processes and help learners master collaborative knowledge creation practices (Markauskaite & Goodyear, 2017).

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Different from PBL, teachers’ role as a “knowledge expert” is highly valued, but the focus is less on teachers’ content knowledge and more on their epistemic expertise in knowledge construction. Further, knowledge objects play a critical role in this process. Objects support shared focus between different people, enabling them to externalise ideas, establish connections and learn through collaboratively creating a tangible artefact. Objects include a range of material/ digital things that mediate joint meaning making, such as notes, databases, models and other embodiments of knowledge. Similarly, as in PBL, the teacher’s role is not simply to be the “sage on the stage” who has the knowledge and transmits it to learners but to be the “guide on the side” who facilitates collaborative knowledge co-creation (King, 1993). Generally, the purpose of facilitation in teaching and learning contexts is to ultimately design a learning environment that mediates students’ learning processes and promotes deep engagement of students with knowledge. Similar to facilitation in management and deliberation, teachers as facilitators guide collaborative processes and group work. Synthesising Different Perspectives on Facilitation Facilitation as a domain of practice has some common functions, features, and values, such as promoting participation, mutual understanding, and inclusive solutions. Simultaneously, the facilitation approaches explored within the different domains above have distinct goals, purposes, and other characteristics. While the approaches are all different, they are highly complementary and often relevant to the designer’s work in participatory design contexts. Table 2.3 shows a comparison of the main aspects. Facilitation in management contexts is directed towards group problem-solving through the integration of knowledge that different people within the organisation bring. Within deliberation practice, facilitators are process experts, enabling different voices to be heard through ensuring inclusivity, preventing distortion from unequal power, and enabling democratic decision-making. Facilitation in educational contexts focuses on how people learn individually and as a group, aiming to design for and ensure productive social interaction and engagement with the construction of shared knowledge objects. Facilitation in design contexts brings a unique focus on design of innovative user-centred products. Such work requires design expertise to produce a professional design solution, however it also requires the capabilities that are central for facilitation in other domains, such as facilitation of group problem-solving in organisational management (e.g., integration of knowledge brought by different people), decision-making in deliberation (e.g., enabling voices of all users to be heard) and learning in education (e.g., developing users’ capabilities to participate in the co-construction of a solution). From this review it is evident that while design facilitation has distinct features, it has also strong “family resemblance” (Wittgenstein, 2009 [1953]) with facilitation practices in other domains and could benefit from a broader cross-disciplinary view. This broader view of facilitation needs further conceptualisation and integration with design practice, to which we turn next.

CONCEPTUALISING DESIGN FACILITATION PRACTICE While design thinking emphasises thinking or a cognitive dimension, the literature demonstrates that co-design and participatory design practices are not only about thinking but also social interaction, individual values, and relational qualities (Oak, 2011). As such, design

Design facilitation practice: an integrated framework

Table 2.3

35

Comparison of different perspectives on facilitation

Aspects of facilitation

Management

practice Goal of facilitative

Group problem-solving

Deliberation

Education

Design

Democratic decision

Individual and group

Design of innovative

making

learning

user-centred products,

Integrating workers’

Enabling different

Designing

Enabling participatory

knowledge

voices to be heard,

arrangements for and

and co-design practices

preventing distortion

orchestrating learning

activity

services, environments Purpose of facilitation

from unequal power Who does it?

Manager, team leader

Expert in decision

Teacher, educator

Designer

making process Required expertise

Facilitation is

An expert and authority

An expert and role

An expert in

a component of

in the democratic

model in thinking

collaborative design

a manager’s domain

process, but impartial

and learning who has

process, including

expertise to include,

about the topic/domain

threshold domain

visualisation,

how groups work

knowledge and

improvisation, design

together, and

expertise in knowledge

facilitation tools and

understanding of group

construction

design dialogue

roles Role of facilitator

Not a member of the

Has decision power

A guide on the side.

Guides the design

group, neutral, has

about the process, but

Designs learning tasks

process to understand

no decision-making

not the topic/issue.

and environments,

the problem at hand

power, self-reflection

Keeps discussion

orchestrates and

and to develop

and self-management

and process on track,

monitors learners’

a solution. Takes

ensures inclusivity,

interactions and

responsibility for the

prevents close of

discussions and

final product

dialogue, reframes

intervenes with probing

questions, draws

or guidance when

threads, enforces

necessary

ground rules Characteristics of

Interactions within

A communicative

Indirect involvement

Design dialogue that

practice

groups focusing on

process with shared

in learning through,

requires toleration of

different roles of

norms and rules

scaffolding, guidance

ambiguity

and feedback

participants in these groups What is distinct to

Focus on social

Focus on the process

Focus on how people

Focus on the design

facilitation

relationships within an

through dialogue,

learn and creation

process and guiding

organisation

ground rules

of space-time

towards a specific

arrangements

design outcome

facilitation is a multifaceted practice that encompasses a number of different aspects (Mosely et al., 2021). The existing literature describes design facilitation in a somewhat piecemeal way and rarely looks deeper into how this practice could be conceptualised and empirically investigated. Therefore, here, we draw on practice theories and use the practice architecture framework as a way to identify and map out the critical aspects of design facilitation practice and bring

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them together. Practice theory is not often drawn on in the field of design and is even less prevalent in design thinking research, Rylander Eklund et al. (2021) even go so far as to state, “practice theory is virtually absent in the design thinking nomenclature” (p. 13). Therefore, here, we first introduce the main concepts of practice theories and practice architectures. Then, we briefly review how design practices have been conceptualised and we then expand this to design facilitation. Practice Theory and Practice Architectures Contemporary practice theories encompass a broad family of theoretical approaches which are connected through historical and conceptual similarities (Nicolini, 2013, p. 1). Practice theory as described by Trowler (2020) encompasses “the context-specific nature of knowing, saying, doing and relating” (p. 36). Mahon et al. (2017) define practice architectures as “an account of what practices are composed of and how practices shape and are shaped by the arrangements with which they are enmeshed in a site of practice” (p. 7). Practice architectures place the focus on what practices are and how they are enacted as opposed to one’s expertise and what one needs to know in order to practise them (Kemmis & Edwards-Groves, 2018, p. 134). Kemmis et al. (2014) explain practice architectures as encompassing three interrelated characteristics of practice, sayings, doings and relatings. A practice is a form of socially established cooperative human activity in which characteristic arrangements of actions and activities (doings) are comprehensible in terms of arrangements of relevant ideas in characteristic discourses (sayings), and when the people and objects involved are distributed in characteristic arrangements of relationships (relatings), and when this complex of sayings, doings and relatings “hangs together” in a distinctive project. (Kemmis et al., 2014, p. 31)

As illustrated in Figure 2.2, characteristics of practice are shaped and preconfigured by intersubjective spaces (semantic, physical space–time and social) which are enabled and constrained in three arrangements – cultural-discursive, material-economic and social-political (Kemmis, 2022). Sayings are the shared languages and discourses that shape how we interpret the world, these include discursive and cognitive aspects or arrangements of practice (ideas, thinking, understanding, etc.). They occupy the semantic space and are realised through language (Kemmis et al., 2014). Doings are the modes of action or work and activity realised through material things which include material and economic aspects of practice (objects, tools, special arrangements, etc.). They are realised through space–time arrangements in activity. Relatings are the social aspects and the ways of relating to one another and the world through social groups and relationships that point to power and solidarity. These include social and political aspects of practice (norms, values, personal relationships, power, etc.) and are realised through social arrangements and ways in which people interact and relate with each other and the world. These interconnected actions of saying, doing and relating are “bundled together in the projects” of practices and the “dispositions (habitus) of practitioners” (Kemmis et al., 2014, p. 38). The theory of practice architectures recognises the complexity of practice through acknowledging that “people learn the dispositions appropriate to the practices into which they are being initiated” but people also learn through “the process of being initiated into a practice” by engaging with and drawing “upon the practice architectures in a particular

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37

Source: Adapted from Kemmis et al., 2014, p. 38.

Figure 2.2

Practice architectures

site” (Kemmis et al., 2014 p. 38). Wilkinson and Kemmis (2015) explain practices as being ecologically arranged in two ways, they arise in relation to one another in a particular site and they are interdependent and interrelated. Through considering these interdependent aspects of practice, a holistic conceptual framework of design facilitation can be developed. We first review current conceptualisations of design practice through the lens of practice theories and then build on this work further to develop a conceptual framework of design facilitation. The Practice of Facilitation and Design It is well acknowledged that design practice is a social process that involves interaction and negotiation with a range of participants, including communication with each other (designers and members of the design team) as well as clients and stakeholders. Kimbell (2012), in a seminal work, Rethinking design thinking, explores design practice and activity through two key aspects of practice theory; first, through the way knowledge is constructed (Schatzki, 2001) and second, through paying attention to the role of objects in constituting practices (Knorr Cetina, 2001). Kimbell (2012) uses these two perspectives on practice to develop the concepts, “design-as-practice” and “designs-as-practice”. Kimbell (2012) describes design-as-practice as mobilising “a way of thinking about the work of designing that acknowledges that design practices are habitual, possibly rule-governed, often shared, routinised, conscious or unconscious, and that they are embodied and situated” (p. 135; emphasis added). The first concept highlights the verb of design, demonstrating it as a situated and distributed process beyond the individual designer. Comparatively, designs-as-practice “acknowledges the emergent nature of design outcomes as they are enacted in practice”, specifically through drawing “attention

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to the impossibility of there being a singular design (Kimbell, 2012, p. 135; emphasis added). The second concept of design activity acknowledges, designs (the noun) are constituted in relation to professional designers, customers, and identifiable known end-users as well as other people who are not known, but also to other elements of practice such as knowledge, feelings and symbolic structures. (Kimbell, 2012, p. 136)

These two concepts are not mutually exclusive, they are interconnected, moving our understanding of design thinking from the individual designer or the participatory group, towards a relational, embodied understanding showing how they work together (Kimbell, 2012). As design and facilitation can be considered social practices (see, for example, Schön, 1988), design facilitation sits at the intersection of facilitation practice and design practice (Figure 2.3). Social construction focuses on how social and cultural forces construct knowledge and how meanings are formed through the kinds of knowledge they construct (Burr, 2015; Creswell, 2013). By considering design facilitation through this lens, a link can be made between what design facilitators say, do and relate to the materials and objects found in the environment of design facilitation practice (Kimbell, 2012).

Figure 2.3

Design facilitation at the intersection of facilitation practice and design practice

Design Facilitation Practice: A Conceptual Framework Design facilitation practice can be conceptualised by drawing on the theory of practice architectures and Kimbell’s (2012) conceptualisation of design-as-practice and designs-as-practice. The framework focuses on: (1) the design facilitator, and (2) the environment and arrange-

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39

ments of design facilitation practice (Figure 2.4). A brief overview of the two sides of the framework is discussed below before a discussion of the framework as a whole.

Source: Synthesised from Kemmis et al., 2014 and Kimbell, 2012.

Figure 2.4

Design facilitation practice conceptual framework

The design facilitator and the practice of design facilitation The left-hand side of Figure 2.4 depicts the design facilitator and design-as-practice as the individual enacting design facilitation practice. The knowledge and identity of the individual, their understandings and self-understandings (mediated through communication, sayings), their skills, capabilities and capacities (mediated through production, doings) and their values, behaviour and emotions (mediated through social connection, relatings) form their design facilitation practice (Kemmis & Mahon, 2017, pp. 230, 235). Arguably, the design expertise of the design facilitator, affects their forms of understanding, modes of action and ways of relating. As Kimbell (2012) suggests, design facilitators’ sayings, doings and relatings are contingent and they change and evolve; additionally, one design facilitator’s practice will be different to another’s, raising the question: what role does design expertise play in design facilitation practice? The environment of design facilitation practice The right-hand side of Figure 2.4 demonstrates that design facilitation practice extends beyond the individual design facilitators and what they bring to the environment (e.g., design expertise, personal attributes, beliefs) to also encompass the arrangements and structures, discursive-cognitive, material-embodied and relational-affective found in and brought to the environment by others (Mahon et al., 2017). Design facilitation practice is “enmeshed” within

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the arrangements of the environment, which recognised the “fluidity and volatility with which practice engages with the particularities of arrangements in sites, and also recognise the variation, improvisation and innovation with which practices are enacted” (Mahon et al., 2017, p. 11). As the role of a facilitator is to guide group processes, including interactions that occur within an environment, the design facilitator and their practice are interrelated with the site where design facilitation is occurring as well as with the individuals, artefacts and symbols of that site. As Mahon et al. (2017) explain, the dialectal relationship between the practitioner and practice architectures is demonstrated through the infinity symbol which is intended to be read a kind of flow, holding together bundled-together sayings, doings, and relatings, on the one side, with, on the other, the cultural-discursive [discursive-cognitive], material-economic [material-embodied] and social-political [relational-affective] which make them possible. (Mahon et al., 2017, p. 13)

The two sides of the framework are mutually constituted through practice (Kemmis & Mahon, 2017). The conceptual framework demonstrates the relationship between the design facilitator and the environment of design facilitation practice by connecting the roles that the facilitator and material objects and artefacts play in constituting design facilitation practice (Kimbell, 2012). Mosely et al. (2021) categorise constructs of design facilitation across three practice dimensions: discursive-cognitive (sayings), material-embodied (doings), and relational-affective (relatings). These dimensions sit across the personal characteristics and behaviours of the facilitator and the shared characteristics of joint behaviour and the environment. Drawing on the cross-disciplinary perspectives of facilitation presented, design facilitation in participatory design contexts does not only require an understanding of design practices and processes but also establishing an environment that promotes deep engagement (similar to in education), consensus-seeking (similar to in deliberation) and effective teamwork (similar to in management). While not exclusively, facilitation practices in education historically had a stronger focus on creating space–time arrangements for supporting students’ activities for autonomous sense making, facilitation in deliberation primarily focused on language and creating a semantic space for democratic dialogue, and facilitation in management had a stronger focus on social arrangements that enable the realisation of organisational potential. Therefore, much could be learnt from facilitation in these fields about how to assemble space–time arrangements and create semantic and social spaces during design facilitation.

IMPLICATIONS FOR THEORY, PRACTICE AND FUTURE RESEARCH This chapter aimed to present an integrated conceptual framework of design facilitation that helps address the conceptual challenges within this emerging field and to explore design facilitation practices holistically. In order to do this, it explored diverse perspectives of facilitation and showed how they could inform and broaden current understandings of design facilitation. It is clear across the design literature that design facilitation is often seen as a distinct domain, while in fact it shares features with facilitation practices in other professional domains. Taking a broader cross-disciplinary perspective on design facilitation helps see similarities and

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differences, understand unique features of design facilitation and learn from other domains. This analysis also helps understand why not every facilitator who has developed facilitation expertise in other domains necessarily has capabilities to facilitate participatory, co-design processes and design thinking. It also helps understand why not every designer has such capabilities as well. In short, while the rise in popularity of design thinking can be attributed to non-design fields and professionals, applying a basic knowledge of design thinking does not make someone a professional designer. The effective facilitation of design requires a professional designer who has an understanding of design practice, theory, and methods as well as facilitation skills and techniques. The explored perspectives lead to further questions such as, • What are the characteristics (sayings, doings and relatings) that distinguish design facilitation practice? • What are the arrangements (discursive-cognitive, material-embodied and relational-affective) that distinguish the environment of design facilitation practice? • How does the environment of design facilitation practice and design facilitation characteristics influence and impact each other? • How can different disciplines contribute to the future development of design facilitation practices, including skills, methods and techniques? • What and how can facilitation processes from management, deliberation practice and education be used to inform effective design facilitation? • What role does design expertise play in the effective design facilitation? • How can design facilitation practice enable cross-disciplinary collaboration? Design facilitation practice also lacks a cohesive theoretical foundation. We, therefore, proposed to embrace practice theories and suggested how the framework of practice architectures can be used to conceptualise design facilitation and integrate key aspects of this practice in a coherent whole. We aimed to provide a foundation for further application of this framework to better understand design facilitation practice and open new directions for development of design facilitation theory, practice, and research. Through drawing on diverse perspectives from the management, deliberation and educational literature, much remains to be discovered about design facilitation practice.

ACKNOWLEDGEMENTS This research was supported by an Australian Government Research Training Program (RTP) Scholarship. The work of Lina Markauskaite was part-funded by the Australian Research Council through Discovery Project grant DP200100376 (Developing interdisciplinary expertise in universities).

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3. Who gets to wear the black turtleneck? Questioning the profession of design thinking Sally Cloke, Mark Roxburgh and Benjamin Matthews INTRODUCTION One of the authors recently attended a Design Thinking (DT) workshop held at an Australian university for academics and industry practitioners of design. During the refreshment break, a User Experience (UX) designer employed by a large bank expressed a great passion for the formulaic nature of their approach, insisting on its consistent reliability and efficiency. To drive home the point, they lifted a shirt sleeve and sure enough, there inscribed on the delicate flesh of the inner left wrist was a tattoo in red ink: the hallowed ‘double diamond’. This focus on suggestions for action, tools and techniques has been well documented elsewhere, notably in Laursen and Haase’s (2019) excellent literature review, the ‘Shortcomings of design thinking’, where the authors conclude that unlike the more adaptive paradigm of ‘designerly thinking’, DT adopts a singular methodological approach, namely, ‘explorative learning’ (p. 9). The authors argue for an expanded engagement with the methodological frame for design thinking, since it is ‘too easy to simply say that design thinking is nonsense’ (p. 17). Not exactly a stirring call to action, but certainly an acknowledgement of the fact that practitioners of designerly thinking are compelled by the rise and rise of design thinking into a conversation they would perhaps rather avoid. This is an ambivalent dialogue with history that seeks to define the boundaries to certain categories on which scholars, teachers and practitioners of design frequently call, and on behalf of which they ultimately decide a definitional position – however conditional – and a defence thereof. In this chapter we will provide a very brief history of key ideas about design, and how in turn this history has informed critiques of DT. Here, the kind of thinking ‘artificial’ designers use is often set against what ‘real’ designers rely upon in a manner that frames strong criticism of the ubiquity of DT. Kimbell (2011) characterises this distinction broadly as being between designerly thinking and DT, with ‘real’ designers using the former and ‘artificial’ designers the latter. When framed from the designerly thinking perspective, DT is understood to be the unfortunate by-product of businesses that would package and sell design thinking beyond the usual circuits of design. From this position, DT and its proselytes are cast as representative of the post-industrial circumstances that have accompanied the rise and rise of neoliberalism, and the modes of capitalism it promotes. Here, design is considered instrumental to processes of industry, and completed in the reductive modes of speed and efficacy. This cultural narrative would eventually become branded and succeed in the market as DT, its adherents often elevating the processes of design to the status of ritual, and simultaneously reducing them to 45

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the function of an algorithm that sets out to address ‘wicked problems’. In other words, DT is understood to have become part of the systems that now present us with an overwhelming array of challenges, rather than the means by which to address them. There is a certain hypocrisy in this designation, because designerly thinking has played its role in the creation of the circumstances we currently experience. Indeed, there is something revealing in the way design as a professional institution has responded to the rise and rise of DT, and to better understand this reaction it is useful to take a brief journey through the history of design theory. Ours is an to attempt to determine what is most useful in this history as a defence against what may be genuinely dangerous approaches to design in the time of major existential threats thrown up as the Anthropocene progresses. What are these learnings, and what are some ways of thinking and designing that could broaden and enrich how DT is taught and practised? It is our contention these deserve more attention from designers and thinkers of any persuasion, and our purpose is to encourage diversity and discussion. The context we emphasise is the welter of accelerating crises endangering the future of life on earth, and our provocations can be framed as follows: what if the problems of the Anthropocene could be better addressed through a less anthropocentric, less ‘problem-solving’ oriented approach to thinking and designing than permitted by current orthodoxies? And what if it matters less who gets to ‘wear the turtleneck’ than who dares to pick up the mantle of some of design history’s less-heeded prophets? We explore these questions via the history of thinking that informs common criticisms of DT, and conclude with three calls-to-action, or turns, for designers en masse. Yes, it’s our very own listicle: • A move away from a narrow paradigm of problem solving that fails to recognise design’s contribution to the multiple social and ecological crises confronting the planet – toward ‘discursive’ design that opens new possibilities and bolder futures. • A move away from human-centred design that uncritically multiplies human wants, and toward designing for and with our more-than-human world. • A move away from seeing empathy as a panacea, and toward understanding design as a mode of caring.

‘REAL’ DESIGN VERSUS DT But why the turtleneck, or more specifically the black turtleneck, and why the question over who gets to wear it? Whether sartorial, metaphorical, or the basis to outright caricature, the turtleneck has long been the mark of the ‘real’ designer. Think Steve Jobs, perhaps its most famous exponent. Today we see this sacred garment on the backs of bankers and civil engineers, supermarket executives and public health consultants. It’s not always easy these days to tell the ‘real’ designer from the copy (aka artificial designer). But does that matter? Only in the sense that it is emblematic of the co-option of a poorly understood aspect of creative professional activity (the designerly thinking used by ‘real’ designers) by a managerial class (our ‘artificial’ designers) in ‘accounts of design thinking’ that ‘do not draw extensively on research in either design studies or management and organization studies’ (Kimball, 2011, p. 294). But just in case the ‘real’ designers out there feel smug about the alleged thinness of the intellectual foundations of DT, it’s worth noting that Kimball (2011, p. 294) also argued that, a decade ago, research into such designerly thinking had ‘not yet generated a definitive or

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historically-informed account of design thinking.’ Over its shortish lifespan, ‘real’ design has given shelter to a host of diverse and sometimes contradictory ways of designing and thinking. This situation is in part a consequence of the diversity of practices that emerged in the early days of the ‘professionalising’ of ‘real’ design practices – such as furniture and product design, graphic design, fashion and textile design, architectural design, etc. – and the attendant ideological contexts and material and historical legacies of their ‘pre-industrial’ equivalents. Despite this diversity, the nature of both design and thinking are often treated reductively – and our history is a modest attempt to respond to this absence. We will consider such closely related and contested fundamental questions as what design is, what kind of practices it might encompass, and its significance and purpose in society and human experience. For the moment, though, it is worth noting the problem-solving model for design practice emerged from and is much more closely related to product design and urban planning, which are much more deeply connected to processes of manufacturing and large ensembles of technologies, than say fashion design and visual communication that have closer connection to the craft skills of making by hand (Roxburgh, 2021). In sum, the kind of thinking used by people we call designers to design things has a history, and likewise, that history has been informed by its unfolding context. That history has recently led us to a moment when theories about the kind of thinking that people we will call ‘real’ designers use to design things (historically objects or communications) has been purportedly appropriated by people we will call ‘artificial’ designers – that is, people who don’t design objects or communications but instead design things such as services, business strategies or processes.1 Thus, the rise of the modern-day phenomena, and attendant theories, of DT. The apparent ubiquity of DT outside of the world of ‘real’ design has inspired critique from those that create and live within the ‘design world’. Whilst that critique, and the growing criticism that defines it is interesting, one can’t help but feel it is yet another in the long list of grievances that ‘real’ designers have expressed in the period that ‘real’ design has underpinned a distinct area of professional practice. Most of those grievances relate to ‘real’ design’s inextricable links to market capitalism, and while this is not the focus of the current chapter, it will be touched upon for those links are a part of ‘real’ design’s history. We assume design to be defined broadly as the activities through which people purposefully create things that have not previously existed in their experience. For a more detailed discussion on that front, we refer you to the oft-cited and seminal text on the topic, Herbert Simon’s (1996 [1969]) The Sciences of the Artificial. We also argue that for its history as a profession, design has been inextricably tied to consumer culture and market capitalism, governed by technocratic and anthropocentric logic. The ubiquitous practice of design is such that as a species we have decreasingly adapted to our planet in order to survive, and increasingly reshaped it to provide us with convenient, virtual and more survivable habitats – if only in the very short term. In this manner we have moved away from what one might describe as the natural world and created an artificial (human made – not fake) world. Whether or not this is a good thing remains to be seen. The ‘Problem’ of Professional Design The response of ‘real’ designers to the rise of DT mirrors that of any professional group whose boundaries feel threatened. And professions are all about boundaries. The role of a typical

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professional body focuses on setting and upholding the educational, technical and ethical standards of its members (Chellew et al., nd). This protects the public from unsound work, untested claims and unscrupulous practitioners: those who fail to meet the profession’s standards are barred from (re)entry. Just as importantly, these boundaries protect the professionals themselves by safeguarding the specialist mana – body of knowledge, tacit know-how, cultural norms, etc. – and securing livelihoods and reputations. Two situations place professional boundaries under particular stress. In the first, the specialist mana has become devalued. For whatever reason, going to a professional for a particular service is no longer seen as worth the cost or effort, while the risks of going elsewhere are regarded as less significant. The digitisation of ‘real’ design and the accessibility of affordable computer software and hardware and the rise of the DIY designer in the late 1990s and early 2000s is emblematic of the first situation.2 This trend has more recently been exacerbated with the emergence of machine learning and AI and its application in design. Entirely new businesses have emerged that offer design services done by machines not ‘real’ designers (Matthews et al., 2020). In the second situation, the specialist mana has become over-valued, so that more people want a share of it, and the approved routes of acquiring it are seen as too arduous or artificially prohibitive. Kelly (2018), for example, argues that for communication design there is a huge volume of ‘workshops and lessons on YouTube, Skillshare, Lynda, and Inlearning’ providing ‘a certain amount of training’ with industry-leading tools ‘for a fraction of the cost’ of a formal degree. Moreover, the rise of non-standard patterns of work examined in countless reports on the future of work note that the economic contribution of workers is becoming increasingly detached from the formal qualification system (Pennington & Stanford, 2019). A thorough exploration of why design currently has cachet – why so many people want to wear the turtleneck – is beyond the scope of this chapter. Nevertheless, we would like to posit two suggestions. Some blame could be placed at the feet of uber turtlenecker, Steve Jobs and Apple. More than any other company, Apple has raised consumer expectations that products are ‘well designed’ (Boradkar, 2010). But this is ‘design’ in the narrow sense of aesthetics, and a particular modernist/minimalist aesthetic at that (de Moncheau, 2007). We will return to the matter of aesthetics shortly, but aesthetics in this form is principally concerned with how the designed artefacts (be they objects or forms of communication) look. This pre-occupation with formal aesthetics promotes the view that design is a superficial matter rather than a foundational principle – and hence is something anyone with a bit of flair and a working knowledge of Adobe products can achieve. ‘Real’ design shares responsibility for this situation, having founded its professional ideology and pedagogy on art and craft skills, and the romantic ideal of the ‘real’ designer as a species of creative genius (Forty, 1986). Roxburgh (2005) calls this delusion the ‘myth of creativity’. Apple’s success also fuels the belief that the addition of ‘design’ to an artefact can command a significant financial premium: X + design = $. Hence, design thinking. But we cannot blame Jobs and Apple alone for this effect. ‘Real’ designers have long been promoting the virtue of the idea that good design is good business. Indeed, the formalisation of design education programmes in the UK in the mid-19th century, and a range of accompanying design reforms, were ‘initially bound to British colonial trade’ (Huppatz, 2015). It is telling that the UK’s first design school was established in 1837 ‘under the superintendence of the Board of Trade’ with the board establishing the Department of Practical Art in 1852 (UK National Archives) to oversee art, design and technical education. A government agency, principally concerned

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with catalysing the commodity market, was tasked with overseeing design education in Great Britain. This can be seen as the result of the conviction ‘in government circles since the 1830s’ that good design means good business, and that ‘Sir Robert Peel had attributed the decline in her [sic] exports to poor standards of design in British manufacture’ (Woodham, 1997, p. 12). If we examine the emergence of the prototypical ‘real’ design profession, as Adrian Forty (1986) has done, we can observe it was an entrepreneur – Josiah Wedgewood – who in the mid-1700s tasked artists with creating drawings and models for crafts people to base their work on during the manufacture of pottery ware. Bremner (2010) argues this signalled the separation of ‘idea from manufacture and turned the imagination of change into an image’ (p. 48). Into the bargain, the image became the hallmark of ‘real’ design in a manner that clearly tied its birth to an entrepreneurial innovation and to consumer-driven market capitalism. It is also noteworthy that long before Fredrick Winslow Taylor commenced his time and motion studies, and Ford developed the assembly line in his Detroit factories, Wedgewood tasked different crafts people to make different parts of a product – for example, one was tasked with making spouts, another making lids, and so on – in a pre-industrial form of the production line, thus ‘making machines of men’ (Forty, 1986, p. 44). Given that the artist/designer is a part of the production line it is evident that an instrumental logic is also at play. Setting aside the commercial imperative for design, its desirability may also be related to something anthropologist David Graeber pointed out about the nature of modern work. He argued that jobs in which people actually produce or make things are increasingly rare in late capitalist society. Instead, large proportions of the middle class spend their lives in jobs they recognise as being little more than bureaucratic busy work. This leads, argues Graeber, to a feeling of resentment toward anyone who makes or does something with evident, tangible results (Graeber, 2013, 2020). Perhaps the popularity of DT is in part based on a desire to experience the satisfaction of making without having to acquire the requisite technical skills. Such appropriation of the terminology of design serves to emphasise just how apt the thinking in the phrase DT is. Meanwhile, the related growth of service economies has started to supplant manufacturer economies and the need to design those services has become more pressing (Roxburgh & Cox, 2016). This demand has catalysed the success of DT through its application in this rapidly growing area of professional endeavour. Whatever the reasons for its rise, it is evident from a quick survey of trade and professional media that ‘real’ designers feel the need to assert themselves against the encroachment of DT. Here we read such headlines as: ‘Design Thinking is bullshit’ (99U, 2017) and ‘Design Thinking is kind of like syphilis: it’s contagious and rots your brains’ (Vinsel, 2017). The common themes are that DT is over-simplified and reductionist, lacks evidence for its effectiveness beyond the anecdotal, it overpromises, overcharges and fosters a naïve over-optimism that fails to consider political, social or and economic complexities beyond the narrowly defined ‘problem’. Scholarly publications use more measured language but offer similarly stinging criticism. Hernández-Ramírez (2018) argues that DT curriculum and discourse is often full of obfuscating jargon that equates superficial technical improvement with innovation and excludes any acknowledgement of design methodologies, past or present, beyond the ‘tasteless echo chamber of [its] five-step design process’ (p. 54). Laursen and Haase (2019) criticise DT’s ‘bias towards action’ and emphasis on ‘tools and techniques’ for its failure to ground its impatient drive to ‘do something’ in any explicit theoretical foundation (p. 15). Carlgren and

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colleagues (2016) chastise DT proponents for their slippery use of terminology – ‘discipline vs. approach vs. way of thinking’ – arguing this suggests such acolytes are not sure precisely what DT is (p. 40). Iskander (2018) questions DT’s reputation for creativity, arguing instead that it is ‘fundamentally conservative and preserves the status quo’. Mosely et al. (2018) argue that DT ‘has been over-simplified in many industry realms, leaving behind a trail of design thinking experts and a frustrated design research community’ (p. 177). Gregory (2018) accuses DT of appropriating tools and terminology not just from ‘real’ design but from anthropology, making a mockery of the skills and scholarship of professional social scientists. Ironically enough, others have argued ‘real’ design itself has plundered ethnography for its own purposes, with a similar lack of regard for its theoretical framework (Roxburgh & Bremner, 2015). We could go on, but it suffices for now to conclude that such criticism asserts the superiority of ‘real’ design from a vantage point positioned outside the market system. However, as Whitely (1993) has argued, ‘real’ design’s propensity for ‘repacking and re-designing’ (which is tantamount to superficial technical improvement and a bias towards action) is ‘part of a socio-economic system that assumes limitless growth’ that ‘goes far beyond the idea of meeting human needs’ and ‘seeks to create and constantly stimulate human desires’ (p. 3; emphasis in original). This is the same socio-economic system that DT has emerged in and helps to perpetuate. The squabbling about what is and isn’t design misses the point as ‘real’ design and ‘artificial’ design (aka DT) are two sides of a coin that circulates in a shared socio-economic system. They are both a product of that system, and both work to perpetuate it. One can visualise the ultimate futility of such squabbling by using the diagram (Figure 3.1) developed by Victor Papanek (1972, p. 68), a pioneering practitioner and theorist of sustainable design. Papanek sought to visualise his concern that ‘real’ designers were too often restricted to ‘solving’ tiny, often visually and aesthetically-defined ‘problems’, when their talents could be more usefully applied to addressing larger underlying issues. Too many of the criticisms of DT raised by ‘real’ designers – or defences mounted by DT’s proponents – boil down to

Figure 3.1

Papanek’s Triangle

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quarrelling over who has the right to the tip of the triangle. Questions of what design could be – increasing the designer’s share – are not addressed, as we will discuss below. At this point, we note that some ‘real’ designers take the opposite path, withdrawing from the turf wars and claiming different ground from DT’s advocates. This may involve repositioning or rebranding themselves as offering niche or boutique creative services. We might characterise this response as William Morris-lite. Morris (1834–1896) was a designer and political theorist who sought to counter the tawdrifying effects of burgeoning industrialisation and market-capitalism on both producer and consumer alike by refusing to accept that either the making or the enjoyment of beauty and quality were only for the rich (Morris, 1885). Morris took this position, not because he had found some market niche to exploit, but as the (to him) inevitable out-workings of his deep-held political and economic – socialist – principles. He sought to overturn capitalism, not find a safe bolthole within it. Of course, the market won in the end: Morris’s political legacy is far overshadowed by his formal aesthetic one, and the Arts and Crafts movement and its heirs have ironically become yet one more way of signalling ‘middle class supremacy’ (Morris, 1884) in taste and budget. The differences between Morris and Morris-lite are clear. Boutique design does nothing to counter the general perception that ‘real’ design is all about/restricted to the formal aesthetic. Additionally, and more concerning, it buys into ‘capitalist realism’, Mark Fisher’s term describing the prevailing mindset in which ‘it is easier to imagine the end of the world than it is to imagine the end of capitalism’ (Fisher, 2009, p. 2). This latter point matters, not for some purely ideological reason, but because limiting the scope of potential political/economic arrangements for a society necessarily limits the range of solutions to the ‘real problems’ Papanek believed designers should be addressing. Then again, maybe confining design’s purview to solving problems, whether consumer or existential, is itself a problem – a question we will discuss in some detail below. Neither fighting over turf nor abandoning the field are helpful approaches. So instead of focusing on what DT has allegedly co-opted from ‘real’ design, let’s consider what it’s left out, as well as what they have in common.

THE HISTORY OF PROFESSIONAL DESIGN It is impossible to analyse the various kinds of thinking deployed by ‘real’ designers in the work of designing without giving a very brief history of design. Before the industrial revolution, the designer as a separate profession simply didn’t exist. But that doesn’t mean there was no designing – everyday objects that couldn’t be made at home were created in small workshops by artisans and craftspeople. They learned their skills as apprentices by copying the practices of their masters. Although tacit knowledge, practical know-how and workplace culture were passed on as well as craft skills, design was primarily about doing.3 In the 19th century the birth of the machine age and growth of consumer capitalism called for more efficient modes of production. As we outlined earlier, the work of the single skilled craftsperson was broken down into stages and parcelled out to more specialised and lower paid workers – the origins of the production line. ‘Modellers’ – artisans who designed the model for the item that would go into mass production – emerged as the first recognisable ancestors of the modern professional designer (Forty, 1986). Recognising the significance of this, the 1830s Cole reforms saw the British government establish the first dedicated ‘schools of design devoted to the education of artists designing specifically for manufactured goods’ (Raizman, 2003)

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with drawing, painting and sculpture regarded as the appropriate basis for their education. As industrialisation escalated, various schools of thought on design pedagogy began to formalise to support and develop this nascent profession. Some, such as those employed by Morris’s Arts and Crafts movement, emphasised the importance of artistic ability, creative thinking, aesthetic taste and manual craft skills. Sketching and drawing were seen as key competencies for both developing design concepts and communicating them to others. It also had a broader agenda of educating the public at large about the value of aesthetic taste and judgement and the dehumanising aspects of industrialisation – for example Wedgewood’s desire to make machines of men. However, apart from garnering ‘interest amongst progressive designers and critics of the industrialization process’ its ‘actual impact upon the majority of manufacturers involved in the mass-production of consumer goods was extremely limited’ (Woodham, 1997, p. 11). As the 20th century progressed, the dominant approach, however, was the one promulgated by such august names as the Bauhaus, the ULM School and eventually the Design Methods movement. Where the Bauhaus could be seen, to some extent, as continuing aspects of the philosophy of the Arts and Craft movement,4 in terms of its focus on workshop and craft skills, its students were also trained in ‘science and theory’ (Findeli, 2001, p. 6), marking something of a departure. However, it is worth noting that the Bauhaus in particular continues to have an impact on ‘real’ design pedagogy to this day, which plays into the continued preoccupation with the formal aesthetic aspects of design artefacts. The ULM School of design was established as a kind of new Bauhaus in Germany in the 1950s. An ideological split developed, though, between adherents of the Bauhaus model of design and those that rejected its emphasis on the acquisition of workshop skills. Led by Thomas Maldonado, the rebels pushed for a design curriculum that included ‘mathematical, statistical and analytical methods, together with sociology, anthropology, physical and behavioural psychology and 20th century social history’ (Woodham, 1997, p. 178). This ‘new educational philosophy’ had as its foundation ‘scientific operationalism’ (Maldonado 1958, p. 40). The ULM School was in turn hugely influential in the development in the UK of what became known as Design Methods whose adherents argued that old fashioned pen and pencil thinking was not up to the demands of modern industry and systems. Like the ULM school, Design Methods drew on ideas from social science, management and engineering to promulgate an approach to design that privileged logic, rationalism and method. Educating designers was about training the brain to think systematically, to follow step-by-step procedures and to mimic the scientific method of hypotheses and test. It is ULM and Design Methods where the systematic evaluation of the design process and the notion of problem solving took a route that led to the inevitable rise of the notions of design and/or designerly thinking. We could call this model thinking as design because of the clear priority it places on thinking over doing. In contrast, the Morris approach, and indeed that of the Bauhaus, could be labelled designing to think because it regards the embodied practices of design as tools with which to think. Viewed like this, these two models may seem almost opposites, but in fact they are closely related: they are both anchored in a problem-solving paradigm. They both centre on using design to solve a problem. The difference lies in whether intellectual or embodied processes are seen as having priority (we’ll talk more below about the problem of problems). DT appears to be firmly in the camp of thinking as design. Method, system, step-by-step procedures – it all happens in the head. As noted above, Laursen and Haase (2019)

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call this singular methodological approach, ‘explorative learning’ (p. 9). In this way the DT curriculum focuses on mental techniques such as brainstorming, lateral thinking and empathetic imagination. Diagramming and mind mapping are encouraged – but these are very different activities to drawing and sketching. While there can be an element of physical making in the prototyping stage, the focus is on quick and dirty, low-resolution prototypes intended to test functionality and usability. No craft or studio skills are taught, or required, and questions of aesthetics are rarely if ever considered.5 Does this matter? It certainly leaves out a very large chunk of what historically both lay people and designers have considered ‘real’ design to be about: that the activity of designing leads to the creation of some material product. More than that: it is not unreasonable to expect that a product be judged on its formal aesthetic qualities or on the sensory satisfaction it provides (which relates to the broader conception of aesthetics being embodied), not just its functionality. This is especially applicable to the design of consumer goods, visual communication and the built environment. Design is not art, yet as Prasad Boradkar argues, ‘design considers aesthetics one of its core duties and values’ (Boradkar, 2010, p. 128). DT can of course participate in the design of such material products, and even work to countermand the prevailing forces the market asserts over design processes, but can it do this without reference to a history explorative learning cannot encompass? This is a question further complicated by the products of design becoming less tangible with outcomes such as interactions and systems growing in prevalence (Buchanan, 2001). Experiences and interactions can also be aesthetically rich in terms of broader sensory experience, and there is evidence that paying attention to the aesthetic quality of interactive design can create experiences that are deeply rewarding, significant and valuable (Wright et al., 2008). Yes, emphasising the formal aesthetic side of design risks collapsing into that designer’s bugbear: a reductionist identification between design and (surface) decoration (Folkmann, 2010). But the opposite is equally problematic and more of a pressing reality, as Mario de Liguori (2017) argues, for in ‘design research and training we are facing a sort of a neo-social-functionalism devoid of aesthetics, risking erasing the goals painstakingly achieved over design history [and] relegating the aesthetics of products to a minor executive phase’. The result: ‘an ugly world’ (p. 313).

THINKING ABOUT DT Roxburgh (2010) argues a broader conception of aesthetics, beyond a preoccupation with the visual or formal property of things, that encompasses embodied sensory experience is a pressing issue all forms of design have to come to terms with. Given the rise of the Anthropocene and the impact our actions as a species have on the planet, and given that these actions are all designed, this issue extends beyond whether or not the world is ugly and goes to the heart of the question: ‘what kind of world do we want to create and live in/experience?’ This is fundamentally a question of aesthetics more broadly conceived, and in turn is concerned with ethics. Roxburgh (2010) discusses this in terms of the design imperative which urges designers to take responsibility for how they see and imagine the world, and to think through the consequences of this, for each design action, no matter how small, transforms the world in some way. Fundamental questions such as whether one can claim to be a designer without making anything, or how important formal aesthetics are, are complex matters that force a return to an examination of the boundaries to design. Another combination of the terms design and

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thinking are required to respond to this challenge: thinking about design, or design theory. In a nutshell, design theory is concerned with mapping and ‘expand[ing] the ‘mental space’… of design/ing’ (Rodgers & Bremner, 2021, p. 1). Design theory attempts to answer such questions as: what design is and what it does (ontology of design), what design has been (history of design), what design could be (speculative design), what design means and how it creates knowledge and meaning (epistemology and semiotics), what design could or should do (ethics of design) and how to teach people to become designers (design pedagogy). As outlined above, authors from scholarly and grey literature note the lack of any significant discussion of these important topics in DT (99U 2017; Carlgren et al., 2016; Hernández-Ramírez, 2018; Iskander, 2018; Laursen & Haase, 2019; Vinsel, 2017). Most egregiously, DT discourse fails to acknowledge the existence of the questions of what design is, could be. There is talk of social responsibility or being human-centred (something we will return to below) but this is unanchored in any articulated ethical or philosophical framework. This means DT offers the practitioner little resistance to going with the flow, and in consumer capitalism, the tide favours a market-driven, pragmatic, instrumentalist and short-term outcomes-focused approach to design. In other words, design without thinking. Does this sound too harsh? Perhaps the one exception to this is Mosely et al.’s (2021) systematic review of literature concerning the skills required by those working in the participatory/co-design/design thinking space as design facilitators. Their research has identified three key characteristics the literature argues are required to work in this space, these being: • Discursive-cognitive – an understanding of design processes. • Material-embodied – an understanding of the material dimensions of designing. • Relational-affective – an ability to manage relationships. However, whilst they acknowledge the discursive and contested nature of design facilitation – and two of the authors have critiqued shortcomings of DT (Mosely et al., 2018) – their review of the literature reveals that the fields of participatory and co-design are wedded to a problem-solving paradigm. To sum up our thoughts so far, as far as the thinking part of design thinking goes, we observe a lack of what we have labelled designing to think, a time-honoured method of designing by doing based on embodied practices and studio skills. On the other hand, we see an exclusive focus on what we have called thinking to design, in which design is seen as a matter of performing out the correct intellectual processes. DT’s dearth of theoretical apparatus gives its proponents no standpoint from which to reflect on or evaluate this imbalance, or to critique DT’s wholesale adoption of the mantra that design is or should be a (commercial) problem-solving activity. So, what are we suggesting? It’s our belief that the way to give more substance to the thinking part of design thinking is for all designers – ‘real’ or ‘artificial’ – to widen the kinds of designing beyond those currently associated with the DT brand. As we’ve touched upon, when it comes to practice, ‘real’ design has long been a broad church. In contrast, DT has a narrower canon of design orthopraxy. In the following discussion, we will single out three doctrines of DT which we see as foundational in shaping and constraining how DT designs: problem solving, human-centredness and empathy. None of these seem particularly pernicious in theory and would certainly be less so in practice if they were deployed on an a posteriori needs basis rather than as a priori assumptions. However,

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there is a circular relationship between the DT’s approach to practice and the kinds of thinking used (or not used). In particular, the widespread lack of thinking about design, or design theory, means that questions about what design could be or do are left unasked. This enables hegemonic assumptions about design practice to become rapidly entrenched and reified (see Figure 3.2). As we signposted above, we are optimistic that formulating some arguments for new ways of practice might stimulate increased activity and acuity in theoretical debate.

Figure 3.2

The circular relationship between ways of thinking and designing in DT

In this section of the chapter, we will explore some suggestions which we believe will broaden the designing aspect of DT: a move beyond problem solving to a more discursive understanding of design; less focused on human-centred or anthropocentric design to designing for and with the more-than human world, and a shift from empathy as a foundation of the DT process to exploring the potential of care.

FROM PROBLEM SOLVING TO DISCURSIVE DESIGN One of the key tenets – and selling points – of DT is its purported worth as a problem-solving tool. Google ‘What is design thinking?’ and phrases such as ‘a problem-solving approach’, ‘a process for creative problem solving’, or a method that ‘approaches problems from a human perspective’ appear repeatedly. The appeal of such claims is obvious, particularly in the management and business environments which have been so enthusiastic in their adoption of DT. No-one likes a problem, especially shareholders, and anything that promises an edge in solving them is worth a try. To be fair, defining itself around its ability to create solutions is not just a quirk of DT but a characteristic of much ‘real’ design – at least since the early 20th century when, as we discussed above, leading design schools and theorists increasingly adopted ideas from science, management and engineering to create what we have called the thinking to design approach. Design became less about artistic ability, formal aesthetic taste or craft skills than learning to systematically follow step-by-step procedures and mimic the scientific method of hypothesise and test.

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So what’s our issue with DT (or design more broadly) and problem solving? After all, it could be argued that anything is preferable to the dilemma that Papanek (1972) visualised with his triangle diagram (see above), that of design’s purview being constrained to putting the aesthetic icing on the cake, leaving untapped the discipline’s potential to make more substantive contributions to human social life. It comes down to how problems are defined and who defines them. Since the mid-20th century, the dominant problem-solving paradigm in business and organisational theory has been the so-called ‘deficit model’ (Cooperrider, 2011). A system or process is analysed to find the weakest link in the chain or bottleneck in the flow and that part is ‘fixed’ to bring it up to the same level of operational efficiency as the rest. This model deserves critique on both pragmatic and epistemological levels. It makes the focus of problem solving ‘what is’ and ‘what’s wrong’ rather than ‘what’s possible’. It leaves little room for imagination and creativity – arguably some of the best qualities ‘real’ design has to offer. It also requires that the systems or processes that make up the problem space to be analysed are pre-and-narrowly defined, with all variables as controlled and measured as in a laboratory experiment. There’s little room for complexity, uncertainty and interconnectedness – the hallmarks of human societies and natural ecosystems. This paradigm also presupposes a positivist understanding of knowledge that has long fallen from favour even within ‘hard science’ (Lee, 1987). Positivism is based on the assumption that knowledge exists objectively ‘out there’: human researchers just have to discover it. Maybe, when it comes to chemistry or physics – though quantum mechanics has thrown doubt on the idea that it’s possible to observe anything without influencing the outcome – but very unlikely when it comes to understanding human social behaviour. The idea of the designer (or anyone) as an impassive, unbiased observer able to get ‘outside’ a situation has been unmasked as a fallacy (Dudovskiy, n.d.), especially in reaction to the ‘wicked’ or systemic problems which are threatening the future of life on this planet. Just as designers aren’t impassive observers, nor are they innocent actors. The emphasis on problem solving fails to acknowledge the extent to which design is culpable for the very crises it purports to be able to address (Whitely, 1993). We – humans more broadly, and designers in particular – are problem creators as much as solvers. As Papanek put it: thanks to this instrumental, dominionist mindset which positions human rationality over against nature, the designer ‘shares responsibility for… nearly all of [the west’s] environmental mistakes… either through bad design or by default’ (Papanek, 1972, p. 56). As designer David Rudnick, noted, it’s ‘[h]ard to love a design industry that monopolises the privilege of [finding] a solution whilst structurally rejecting responsibility for the problem’ (quoted in Peart, 2017, n.p.). Framing design as problem solving fosters a baseless optimism that clouds our ability to grasp the seriousness of our current global crises. It perpetuates the myth that ‘tech will save us’: with enough large-scale, sophisticated and massively expensive interventions humans can resolve all the planet’s problems – or leave them behind by moving to Mars. Such thinking acts as a barrier to demonstrably more effective small-scale actions and fails to question the underlying ideology of unlimited economic growth (Alexander & Floyd, 2015). Rather than identifying closely with such a problematic proposition we suggest a reconceptualising of design as ‘transform[ing] the world in our image through acts of human imagination’ (Roxburgh, 2021). This is far from a modest proposal – transformation can cover any sized change from the microscopic to the seismic. But by avoiding the value-laden term problem solving, it permits a more clear-eyed consideration of what design has done

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or could be. This redefinition offers multiple advantages. First, the term ‘transformation’ is broad enough to admit that, far from the neat, cause-and-effect model presupposed by the problem-solving mindset, some if not most of a design intervention’s effects may be unexpected, counter-productive or destructive in ways which may not be evident for decades. Second, the rather Old Testament phrase ‘in our image’ recognises that design has an inbuilt bias to the anthropocentric and anthropomorphic, as we will discuss below. Third, the juxtaposition of ‘acts’ and ‘imagination’ neatly gestures at a rapprochement between two models of conceptualising design: the traditional craft/aesthetic driven understandings we’ve labelled design as thinking and the all-in-the-mind approach favoured by DT which we’ve called thinking as design. It is here the design imperative brings these together in a more all-encompassing conceptualisation of embodied perceptual aesthetic experience and action. This brings us back to DT. What ways of designing would we like introduce to the DT curriculum which would help move from a problem-solving to a transformational mindset? The last few decades have seen the emergence of various sub-disciplines of design that seek to raise possibilities, spark conversations and imagine alternative realities rather than solve or sell anything. They include speculative and critical design, or SCD (Dunne & Raby, 2013), design fiction (Bleecker, 2009) and design futuring (Fry, 2009). Tharp and Tharp (2019) use ‘discursive design’ as an umbrella to bring together the commonalities of these approaches and we will make use of this expression in what follows. Discursive design employs methods of explorative making that contest ‘“official reality” in order to… give form to the multiverse of worlds our world could be’ (Dunne & Raby, 2013, p. 159). This makes it a potentially powerful tool for positing social, political and ethical alternatives rather than mere consumer choice: the illusory agency of choosing between brands of soap powder. Discursive design can encourage people to explore questions such as ‘what do I really want’? This enables us to interrogate the ‘capitalist realist’ ideology that the good things we desire can only be satisfied through consumer capitalism (Fisher, 2009). Futurists working in design and other fields, such as Charles Taylor (1990), Joseph Voros (2001), Stuart Candy (2010) and Anthony Dunne and Fiona Raby (2013) have employed concentric cone diagrams to visualise the relationship between present and futures. (For our own simplified two-dimensional version, see Figure 3.3.) As the diagram shows, the further we move forward in time, the number of potential futures available to us narrows. Unless we deliberately keep widening out vision of the possible, we will take the shortest line between two points and end up with the probable. As Dunne and Raby (2013) note, ‘most design methods, processes, tools, acknowledged good practice, and even design education are oriented toward [the probable]’ (p. 3). Discursive design seeks to counteract this, by populating the realm of the possible with tangible artefacts and scenarios that are neither utterly fantastical nor logically unfeasible out-workings of the present (we could get there from here). These concretised imaginings are not about predicting the future but opening space for ‘all sorts of possibilities that can be discussed, debated, and used to collectively define a preferable future’ (Dunne & Raby, 2013, p. 6). What does it look like in practice? Discursive design can take a multitude of forms. Here we’ll focus on the work of Paulo Cardini from Rhode Island Design School. His Global Futures Lab has run a series of workshops called ‘Souvenirs from the Future’. These four-day intensives invite design students from the two-thirds world to create artefacts and narratives from imagined futures as if they have emerged from a reverse time capsule. The project

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Figure 3.3

Concentric cone diagram of probable, plausible and possible futures

enables students to explore non-Western and post-colonial viewpoints to visualise scenarios which, whether dystopic or utopic, are certainly far from the mainstream dreams manufactured by Hollywood or big tech. Participants are encouraged to remix local craft practices with existing or imagined digital innovations to use design in ways that challenge viewers, start conversations and disrupt expectations (Tharp & Tharp, 2019, pp. 502–516). One project that speaks directly to ecological challenges is ‘Listening to the Trees’, created by students from Iran. It imagines a future in which trees are honoured as wise, sentient creatures, individuals with their own long memories and unique songs. Humans can listen to a tree’s music using a transmitter that connects to a home speaker that based on the organic shape of a traditional Iranian wooden instrument (Global Futures Lab, n.d.). This idyllic vision, in which communication, respect and understanding between species replaces dominion, chauvinism and exploitation, may not ‘solve’ any of pressing problems such as deforestation, habitat destruction and climate change. What it does do is push those who are willing to engage out of the model of deficit thinking – ‘What’s wrong?’ – and into the realm of the creative imagination – ‘What could be? What could we?’. Beyond Human-centred Design Along with ‘problem solving’, the other great DT catchphrase is ‘human-centred’. In fact, DT and human-centred design (HCD) are frequently described as if they were one and the same. According to IDEO’s website: ‘Human-centered design is about cultivating deep empathy with the people you’re designing with; generating ideas; building a bunch of prototypes; sharing what you’ve made together; and eventually, putting your innovative new solution out in the world’ (IDEO, n.d.). HCD has its origins in the work of Don Norman in the 1980s in the fields of human–computer interaction and usability studies/ergonomics (Cruickshank & Trivedi, 2017, p. 562). Norman’s aim was to design new products ‘physically, perceptually, cognitively and emotionally intuitive’ (Giacomin, 2014, p. 610). All this, whilst prioritising

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‘the needs and capabilities of the people for whom they are intended’ (Norman, 2013, p. 9). HCD represented a huge step forward from the previous technology-driven design paradigm (Giacomin, 2014, p. 607). In the latter, design innovation was a matter of giving a cosmetic facelift to an existing product or bolting on whatever new ‘feature’ the R&D department produced with scant consideration of the potential ‘benefit’ to the end user (Boradkar, 2010, pp. 167–168). HCD’s foundational principle – the ‘belie[f] that the people who face… problems every day are the ones who hold the key to their answer’ (IDEO, 2015) – is entirely laudable and lies at the foundation of other participatory approaches such as co-design. At its best, HCD can genuinely be empowering for ‘the people involved [by] obtaining an understanding of their needs, desires and experiences which often transcends that which the people themselves actually realised’ (Giacomin, 2014, p. 610). We agree that ‘[t]here are excellent reasons why HCD or some version of it has become the de facto approach to professional design’ and DT alike (Cruickshank & Trivedi, 2017, p. 564). But we do have some criticisms to offer. Our first is that HCD employs ‘a somewhat reductive representation of ‘the human’’ (Coulton & Lindley, 2019, p. 465). Although its former name of ‘user-centred design’ has largely become outmoded, in HCD humans are at root always users. Designers don’t care about their taste in music or what they had for breakfast – unless they’re designing a new streaming service or cereal. Conversely, the users of the product or service are the most significant humans. The potential consumers of a new gadget are observed, surveyed, turned into personas and stuck on the designer’s pinboard, but what about the workers who will make the gadget or the communities affected by the sourcing of the resources or the disposal of the waste at the end (Sherwin, 2018)? Our second criticism concerns HCD’s focus on prioritising human needs. Papanek attempted to differentiate between ‘the genuine needs of man [sic]’ and ‘evanescent wants and desires’ (Papanek, 1972, p. 15). Guess which one he felt design was more interested in satisfying? But the difference between the two is not that obvious. A basic need such as thirst could easily be met by a glass of water – so why do we pay through the nose for a can of sugar-laden fizzy drink with a famous red logo? Maslow’s famous hierarchy may over oversimplify things, but it makes the point that psychological needs such as belonging, self-esteem and status are just as real as physiological ones. Whether drinking a particular brand of soda pop will satisfy any of them is another question. Our understanding of human needs and wants has been so ‘manipulated’ by consumer capitalism that ‘it has come to the nonsense of believing in consumption and consumption theories as the obvious and logic[al] way of solving our wellbeing as a specie[s]’ (Acosta & Romeva, 2010, p. 30). In all this, design has too frequently played the role of ‘handmaid to commerce, not merely meeting our “evanescent wants and desires” but fuelling them’ (Stairs, 2020, p. 95). ‘Do designers create products to satisfy people’s needs, or do they actually design new needs that can only be satisfied with by the acquisition of new products’ (Boradkar, 2010, p. 162)? We suspect the answer is both. Amongst all the research carried out by HCD practitioners, the question as to whether anyone really needs ‘Product X’ is rarely investigated (Boradkar, 2010, p. 168). This takes us to the issue of HCD and sustainability. Apart from offering no intrinsic resistance to the production of wasteful, unnecessary, and even blatantly destructive goods, HCD has other drawbacks. It encourages designers to focus on making the purchase and use of

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a product ‘frictionless’, ‘intuitive’ or ‘delightful’, but is less concerned with making recycling or other sustainable end-of-life behaviours more user-friendly (Sherwin, 2018). And where does a HCD practitioner turn if their research finds their users are quite happy to keep using up the Earth? As designer Jusi Pasanen bluntly expresses it, human-centred design is literally anthropocentric design. By focusing only on humans, we frame out the rest of the living planet. Mountains, rivers, oceans, rivers, wildlife and other animals, insects, bacteria and the rest of the bio- and geosphere become irrelevant. If their destruction is required to improve the human experience, so be it. (Pasanen, 2019, n.p.)

We believe that both DT practitioners and ‘real’ designers need more than human-centred design. A number of different design approaches are beginning to take shape in this area, using terms such as multi-species design (Metcalfe, 2016; Gatto & McCardle, 2019), design for multispecies cohabitation (Roudavski, 2020), non-anthropocentric design (Rosińska & Szydłowska, 2019) and interspecies design (Hook, 2019). They exhibit a diversity of standpoints and objectives. Some aim to facilitate mutually non-destructive ways for humans, plants and animals to live together in the face of habitat loss and increased urbanisation (Roudavski, 2020). Others take a more political perspective, seeking to create ways to give non-human actors a voice in their own self-determination in an attempt not just to design for other living entities, but with them (Rosińska & Szydłowska, 2019). So far, most instances of design for and with the more-than-human world are experimental and provocative rather than mainstream or commercial: the project we discussed above under discursive design, ‘Listening to the Trees’, could arguably do double duty as an illustration here. A more fully realised example is a prize-winning student project from the University of Oregon. In ‘Co-Creation with Animals’, Toni Talbott sought to ecologically rehabilitate a section of vacant land by co-designing with local wildlife. She built a variety of roosting and feeding structures that would attract native creatures – primarily birds but also rodents and other small animals – and provided them with a range of habitat-appropriate food seeds. Not only did the animals aid in seed dispersal and plant propagation, they contributed to pollination, soil aeration, stormwater management and a host of other pro-environmental actions (ASLA, 2019). ‘Co-Creation with Animals’ is cost effective, sustainable and low-tech. It recognises non-human actors as agents of systems transformation and allows them to exercise agency and autonomy in shaping the outcome of the project. This represents a significant shift in power relations from the instrumental, dominionist – western – mindset which has driven much of mainstream ‘real’ design and contributed to the world’s environmental crises. Instead of the hubristic positioning of human rationality over/against nature, we see here a stepping back, space-making or withdrawal to allow room for non-humans to act as co-producers of design and knowledge in ways that don’t assume simplistic binary distinction between the so-called ‘natural’ world and the ‘artificial’ world that humans have created through their propensity to design in the endless pursuit of adapting the planet to our needs.

FROM EMPATHY TO CARE Our critique of the human-centredness of DT brings us to a discussion of empathy. Empathy, which gives its name to the first stage in the IDEO DT model, has been described as ‘an

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essential element in today’s views of design thinking’ (Liedtka, 2015, p. 927) and ‘[o]ne of the most powerful tools designers offer’ (Cooper et al., 2014 quoted in Heylighen & Dong, 2019, p. 108). It is foundational, not just to DT’s emphasis on HCD, but to its approach to problem solving (Carlgren et al., 2016). To quote a d.school handbook: ‘as a design thinker, the problems you are trying to solve are rarely your own – they are those of a particular group of people; in order to design for them, you must gain empathy for who they are and what is important to them’ (Stanford d.school, n.d.). In DT, empathy is the key to bridging this gap between designer and the user of the product or service concerned, offering ‘a means for gaining deep insight and understanding… [by unlocking] knowledge that is rich and qualitative’ (Hamington, 2019, p. 95). Despite its apparent power as a concept, empathy is a relative newcomer to design discourse (Liedtka, 2015, p. 927). Although Nigel Cross includes empathy on a list of values associated with ‘design culture’ in 1982 (Cross, 1982), it is not until 1997 that Leonard and Rayport coin the term ‘empathic design’ and what was once seen as a character trait or soft skill is deployed as part of a step-by-step technique to ‘spark innovation’ (Leonard & Rayport, 1997). Also interesting is how hard the concept of empathy is to pin down. Common-sense definitions include ‘understanding [another person’s] emotions as our own’ (Goleman, 2008, n.p.) and ‘coming to experience the world as you think someone else does’ (Bloom, 2016, p. 16). A review of more scholarly literature, such as that carried out by Heylighen and Dong, endorses the view that ‘there are probably nearly as many definitions of empathy as people working on this topic’ (Heylighen & Dong, 2019, p. 110). Some proponents of empathy, such as author Brené Brown, painstakingly differentiate it from sympathy, which they characterise as an attempt to distance ourselves from another’s pain or problems (RSA 2013). But discriminating between the two in practice is not so clear cut, as we tend to experience them together (Heylighen & Dong, 2019). We have several criticisms to offer when it comes to the privileged role empathy is given in DT. First, there are questions as to whether we can ever exercise it. As Weiner and Auster argue, ‘to think one is experiencing or feeling what another is experiencing or feeling… is an ungrounded assumption’ especially when based on a ‘brief sojourn’ in another’s world (Weiner & Auster, 2007, p. 125). How do we know we are not projecting our own emotional state or extrapolating from what we would feel in a similar situation? And if we struggle to achieve empathy with another human, where does that leave the more-than-human world? How can we possibly know what a bird or a tree is feeling, let alone understand what it might be like to experience that state? Second, empathy may not be the ‘bridge builder’ with other humans that DT claims. Far from it enabling us to ‘walk in the shoes of’ those we view as different or may experience antipathy towards, it appears we naturally empathise with people we already like or see as similar to us (Bloom, 2016). This innate empathy with ‘our kind of people’ can encourage conformity to group norms, reinforce existing prejudices and actually shore up boundaries between ‘us and them’ (Szanto & Krueger, 2019, n.p.). Third, the concept of empathy in no way implies or impels the taking of action, let alone one that is better informed or likely to produce more effective results (Weiner & Auster, 2007). It may even lead to worse outcomes: as Heylighen and Dong (2019, p. 117) argue, ‘[e]mpathy is a spotlight focusing on certain people in the here and now; this makes you care more about them than about the long-term consequences of your acts or the suffering of those you do

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not or cannot empathise with’. This is a particular concern in DT: stressing the importance of empathising with ‘the (human) user’ downplays the necessity of incorporating the needs or perspectives of other human or non-human actors into the design process (Mesut, 2018). DT’s reliance on empathy ultimately calls into question its utility in addressing systems-wide challenges with multitudes of human and non-human actors, such as such as climate change (Bloom, 2016) – the ‘wicked’ problems it purports to be most suited to solving. Instead of a focus on empathy, we suggest DT practitioners (and ‘real’ designers) explore the possibilities of care. Care as an ethical approach developed from the work of pioneering feminist scholars Carol Gilligan and Nel Noddings, among others, who investigated whether there were differences in how men and women undertook ethical decision making (Gilligan, 1982; Noddings, 1985). As with empathy, care has numerous, and not always easy to harmonise, definitions – something which theorists have argued is productive as much as problematic: ‘to try to summarise the plurality of the contradictory viewpoints would be a reductive act at odds with the liveliness of care’ (Pennington, 2018, p. 583). Nevertheless, we will follow political theorist Joan Tronto, who identifies two key dynamics: ‘First, care implies a reaching out to something other than the self: it is neither self-referring nor self-absorbing. Second, care implicitly suggests that it will lead to some type of action’ (Tronto, 1993, p. 103). These two aspects encapsulate why we believe that care is so relevant. In reverse order, first, unlike empathy, the concept of care implies action, and particular types of action at that. As Tronto puts it: ‘We can recognise care when a practice is aimed at maintaining, continuing, or repairing the world’ (Tronto, 1993, p. 103). This is of particular interest when we consider our discussion of designing for and with the more-than-human world. Eco-feminists such as Carol J. Adams have worked on establishing an ecological ethics build on care, recognising ‘that [the] ‘other’ isn’t just another so-called ‘human being,’ but is potentially any part of this planet’ (Adams, 2018, n.p.). In contrast to empathy, caring does not require putting ourselves through any mental gymnastics to try to get inside others’ heads – they don’t even need heads. We can care for a person, a flower or a bee. We (arguably) may not be able to care for a rock, but we can certainly care for a landscape. Second, care entails a commitment to the other and the other’s wellbeing that may be entirely absent in empathy. We can genuinely feel (or think we feel) empathy for a person we have never met or never will: that’s what human interest news stories are for. But care involves paying ‘attention’ to the other (Adams 2018) and is ‘based on knowledge and responsiveness to the one cared for’ (Hamington, 2019, p. 92). Care is situated: in caring we cannot defer to overarching principles but deal with specifics (Collins 2020). Such attentiveness and insights require an investment of time, of getting to know the other in a range of contexts, and a degree of trust and vulnerability. As post-human theorist Donna Haraway notes: ‘caring means becoming subject to the unsettling obligation of curiosity, which requires knowing more at the end of the day than at the beginning’ (Haraway, 2007, p. 36). Contrast this mindset with the ‘design sprint’ mentality pervasive in DT, during which completing an empathy map takes less than half an hour. The ‘engrossment’ of caring, as Noddings (1985) puts it, leads to a recognition of mutuality and a degree of what we might call ‘altruistic selfishness’. In a caring relationship, the care giver and receiver are ‘in it together’. The entangled nature of caring in turn shapes the nature of caring acts. I am less likely to act in ways that are paternalistic, exploitative or instrumentalising if I understand that ‘your liberation is bound up with mine’, to quote Aboriginal activist

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Lilla Watson. Care works to curb human exceptionalism and dominionism in other ways. Theorists assert that the fundamental driving force in care is our recognition of ourselves as cared-for beings (Noddings, 1985). We do not have to descend into anthropomorphism to recognise the ways in which the more-than-human world cares for us. Care has much to offer our critique of the problem-solving mindset dominant in DT and ‘real’ design. If care is about ‘maintaining, continuing, or repairing the world’ (Tronto, 1993, p. 103), a designing grounded in care would be a much more humble and less interventionist activity than has generally been practiced. The designer’s work might be more about literal maintenance or repair, or the creation of products designed to be mended, repurposed or simply cared for by their owners. This would have significant ecological impact: ‘If consumers would… nurture what they have, rather than looking for something new, then that already owned would be used and not be found in landfills’ (Lastovicka & Sirianni, 2011, p. 339). Care may foster a more partnership-like relationship between designers and consumers such as that envisioned by models of the circular economy. The attentiveness and investment of time required by care may encourage the growth of ‘slow design’ in a cycle of longer research and development phases leading to products with longer lifespans that are both physically and ‘emotionally durable’ (Chapman, 2005). Design Might Be … At this point we turn to Clive Dilnot’s ‘The Science of Uncertainty’ (1999) where he suggests an openness in how design might be conceived and practised, characterised design as being concerned with what might be, and posited that it operated in the realm of uncertainty and contingency. It is a given that this chapter, and the volume in which it sits, is concerned with both current and future states of design thinking – what it might be. We note that in any design exploration many things might be, but choices are made as to which of the many things that might be come into being and we suggest that the increasingly formulaic nature of the human-centred DT agenda has limited what DT might be, its choices being circumscribed by the application of the formula. In keeping with Dilnot’s embrace of openness, uncertainty and contingency, we will resist a conclusion that presents a declarative set of statements or call to action, for that too risks circumscribing choice. Instead we will summarise what we consider to be optimistic shifts for readers to consider (to take up or ignore) in an effort to expand the DT agenda beyond a reductive and (inescapable) anthrochauvinism. To summarise they are shifting DT from: • seeing empathy as a panacea… to understanding design as a mode of caring; • human-centred design that uncritically multiplies human wants… to designing for and with our more-than-human world; • a narrow paradigm of problem solving that fails to recognise design’s contribution to the mess we’re in… to discursive design that opens new possibilities and bolder futures. … Caring We emphasise that while empathy is synonymous with the first stage of the DT process, we can envisage integrating care into every step of the model, from defining to ideating to prototyping and finally testing. Furthermore, focusing our attention on care may prompt us to look

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beyond the five steps, to what DT leaves out. As we’ve discussed, DT operates on the principle of thinking as design: the head takes priority over the hand. So while the singular methodology of DT may be ‘explorative learning’, the focus is on logic, rationalism and method, not making and doing. Yes, prototypes are produced, but the emphasis is on usability and testing, not materials or aesthetics be they formal or sensorial. A care-based DT may help counteract this imbalance, leading to a rebirth of design as thinking in which making and doing are means of thinking not merely the results. … More than Human A refocus on design-as-doing may also prompt a return of craft to ‘real’ design and a discovery of it in DT. Craft, after all, is ‘not only… a way of making things by hand, but… a way of thinking through the hand manipulating a material’ (Nimkulrat, 2012). A refocus on craft could also counter the prevailing fetishising of (a particular modernist/minimalist) visual aesthetic which encourages the conflation of design with decoration. It may also contribute to a rediscovery of the delights of designing for the tangible/analogue as a form of respite from the intangible/ digital. The rise of interest in craft during the period that saw the digitisation of ‘real’ design is indicative of the pull of the embodied experience of making. Care and craft have much in common: both are practiced-based, context-specific and time-intensive. Both have been stigmatised as ‘women’s work’ then reclaimed and revalued by feminist thinkers. And both care and craft have the potential to be rewarding to the practitioner as well as demanding. Craft brings us back to sustainability. In considering ‘how we might redirect our societies out of the unsustainable situations design has created’, Cameron Tonkinwise argues that ‘the kinds of societies that have craft at their core will be sustainably slower and local while still being creative and diverse’ (Tonkinwise, 2021). Craft, argues Tonkinwise, ‘is an expertise that comes from experience, from having encountered a broad range of contexts from which a store of patterns has been drawn… This would suggest that, in an era of pervasive complexity, the leaders we need are craftspeople’. So what does a careful (and crafts-based) design look like in practice? William Morris, whom we discussed earlier in this chapter, was a pioneer in this area. Care underpinned his philosophy of design. In contrast to the exploitative production lines of the industrial revolution, Morris established a workshop in which artisans were valued, paid decent wages and were given autonomy in using their skills. Instead of chasing profits with cheap and crude mass production, Morris’s objects were designed with care, combining beauty and utility as a way of expressing care for the dignity and worth of those who bought and used them. … Discursive In case this seems too backward looking, care also has a role to play in the experimental practices of discursive design. We can see care in operation in both the projects we have used as exemplars in this chapter, ‘Listening to trees’ and ‘Co-creating with animals’. Feminist scholars and practitioners such as Maria Puig de la Bellacasa (2011; 2017), Luiza Prado (2014) and Sarah Pennington (2018) have all argued for using discursive design to explore care, and for using design to widen the scope of those we care beyond other living things to include objects and artefacts (Puig de la Bellacasa, 2017).

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Care and discursive design make sense together because care is fundamentally future-oriented. Implicit in caring for something is imagining a future for it that is worth persevering for and investing in. As Imrie and Kullman (2016, p. 15) argue, ‘within every moment of caring there is a possible future in the making’. Creating and holding space for these futures and nurturing them as they come into being is part of the transformative work of design.

A FEW LAST WORDS We finish with Vilém Flusser (1995) who in one of his philosophical provocations steps back 5,000 years to offer a quick sketch of those who ‘stood on the hills of Mesopotamia… predicting floods and droughts’ sketching out plans for levees and canals: ‘At that time, these people were seen as prophets, but today we would call them designers’ (p. 53). Our discussion has explored ambivalence that arises when we attempt to set down boundaries to the various professional and scholarly contexts of design, the history of which has given rise to the dialectical opposition of designerly thinking and DT. Regardless of your position on this matter, we contend that the turns prescribed in our listicle are enabling for all designers. Is it possible to make these few alterations to the turtleneck… and even turn it into a new garment all together? Perhaps. This would be no bad thing, in our opinion. Arguments over who can wear it only get us so far: a more pressing question is whether we can equip people to take up the prophetic mantle. We hope the answer is yes.

NOTES 1. We are aware that this stark distinction between people that design things that have some material form (real designers) and those that design things that have no obvious material form (artificial designers) is an over simplistic binary distinction that is not borne out by the complexities of practices that work across this apparent divide. However, we highlight this binary to draw attention to the simplistic rhetoric that has prevailed to date. Our choice of the terms real and artificial are also by no means happenstance. They are a nod to the work of Herbert Simon and thus an acknowledgement that all things that designers create, be they material or immaterial are artificial in the sense that they are human made but simultaneously real because the design of these things transforms both our material and social realities. 2. To be fair, the impact of that was not felt evenly across ‘real’ design professions with those accredited practices such as architecture far less vulnerable than say visual communication. Nonetheless, the history of DIY design predates the digital and cuts across many practices (Atkinson, 2006). 3. See Forty (1986); Jones (1970). 4. This link can be traced back to Herman Muthesius, German cultural attaché from 1896–1903, who brought back many of its ideas when he returned to Germany, ‘was appointed to the Prussian Ministry of Trade and Commerce, and was charged with particular responsibility for art and design education’ (Woodham, 1997, p. 18). 5. See Roxburgh and Irvin (2018) for a critique of the paucity of interest in visual aesthetics of design practices such as service design, which we might characterise as a form of ‘artificial’ design.

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Acosta, G. G. & Romeva, C. R. (2010, May 17–20). From anthropocentric design to ecospheric design: Questioning design epicentre [Paper presentation]. Design Theory and Research Methodology International Design Conference: Design 2010, Dubrovnik, Croatia. Adams, C. J. (2018). About ecofeminism. Carol J. Adams. https://​caroljadams​.com/​about​-ecofeminism Alexander, S., & Floyd, J. (2015, August 28). The ‘green-tech’ future is a flawed vision of sustainability. The Conversation. https://​theconversation​.com/​the​-green​-tech​-future​-is​-a​-flawed​-vision​-of​ -sustainability​-46681 ASLA. (2019). Co-creation with animals. ASLA. https://​www​.asla​.org/​2019studentawards/​ 679945_ Cocreation_With_Animals.html Atkinson, P. (2006). Do it yourself: Democracy and design. Journal of Design History, 19(1), doi:​10​ .1093/​jdh/​epk001 Bleecker, J. (2019, March 17). Design fiction: A short essay on design, science, fact and fiction. Near Future Laboratory. http://​blog​.​nearfuture​laboratory​.com/​2009/​03/​17/​design​-fiction​-a​-short​-essay​-on​ -design​-science​-factand​-fiction Bloom, P. (2016). Against empathy: The case for rational compassion. Harper Collins. Boradkar, P. (2010). Designing things: A critical introduction to the culture of objects. Bloomsbury. Bremner, C. (2010). ‘Image residue.’ In M. Roxburgh (ed.), Light Relief (Part II), 48–61. Sydney: DABDOCS. Buchanan, R. (2001). Design research and the new learning. Design Issues, 17(4), 3–23. Candy, S. (2010). The futures of everyday life: Politics and the design of experiential scenarios [Doctoral dissertation, University of Hawai’i]. ResearchGate. https://​ www​ .researchgate​ .net/​ publication/​ 305280378 Carlgren, L., Rauth, I., & Elmquist, M. (2016). Framing design thinking: The concept in idea and enactment. Creativity and Innovation Management, 25(1), 38–57. Chapman, J. (2005). Emotionally durable design: Objects, experiences and empathy. Earthscan. Chellew, J., Rogers, J., & Kingsford Smith, D. (n.d.). Professionalism. Professional Standards Councils. https://​www​.psc​.gov​.au/​sites/​default/​files/​1b​.​%20Professionalism​.pdf Collins, S. (2020, March 25). Why we should care about ‘care ethics’. ABC Religion and Ethics. https://​ www​.abc​.net​.au/​religion/​why​-we​-should​-care​-about​-care​-ethics/​12087656 Cooperrider, D. (2011). Beyond problem solving to AI. David Cooperrider and Associates. https://​www​ .davidcooperrider​.com/​wp​-content/​uploads/​2011/​10/​BeyondProblemSolving​-x​.pdf Coulton, P., & Lindley, J. G. (2019). More-than human centred design: Considering other things. The Design Journal, 22(4), 463–481. Cross, N. (1982). Designerly ways of knowing. Design Issues, 3(4), 221–227. Cruickshank, L., & Trivedi, N. (2017). Beyond human-centred design: Supporting a new materiality in the internet of things, or how to design when a toaster is one of your users. The Design Journal, 20(5), 561–576. de Liguori, M. (2017, April 12–14). Returning the aesthetics to the heart of the design process: On the conflict between social design and product beauty [Paper presentation]. Design for Next, 12th EAD Conference, Sapienza University of Rome. de Moncheau, T. (2007, April 25). What if Apple is bad for design? Design Observer. https://​ designobserver​.com/​feature/​what​-if​-apple​-is​-bad​-for​-design/​5437 Dilnot, C. (1999). The science of uncertainty: The potential contribution of design to knowledge. In R Buchanan et al. (eds), Doctoral Education in Design: Proceedings of the Ohio Conference, October 8–11, 1998, pp. 65–97. Pittsburgh: Carnegie Mellon University. Dudovskiy, J. (n.d.). Positivism research philosophy. Business Research Methodology. https://​research​ -methodology​.net/​research​-philosophy/​positivism Dunne, A., & Raby, F. (2013). Speculative everything: Design, fiction, and social dreaming. MIT Press. Findeli, A. (2001). Rethinking design education for the 21st century: Theoretical, methodological, and ethical discussion. Design Issues, 17(1), Winter. Fisher, M. (2009). Capitalist realism: Is there no alternative? Zero. Flusser, V. (1995). Three essays and an introduction. Design Issues, 11(3), 50–61. Folkmann, M. N. (2010). Evaluating aesthetics in design: A phenomenological approach. Design Issues, 26(1), 40–53.

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Forty, A. (1986). Objects of desire: Design and society 1750–1980. Thames and Hudson. Fry, T. (2009). Design futuring. Berg. Gatto, G., & McCardle, J. R. (2019). Multispecies design and ethnographic practice: Following other-than-humans as a mode of exploring environmental issues. Sustainability, 11(5032), 1–18. Giacomin, J. (2014). What is human centred design? The Design Journal, 17(4), 606–623. Gilligan, C. (1982). In a different voice: Psychological theory and women’s development. Harvard University Press. Global Futures Lab. (n.d.). Listening to the trees. Global Futures Lab. https://​www​.globalfutureslab​.com/​ the​-tree​-of​-life Goleman, D. (2008, March 1). Hot to help: When can empathy move us to action? Greater Good Magazine. https://​greatergood​.berkeley​.edu/​article/​item/​hot​_to​_help Graeber, D. (2013, August). On the phenomenon of bullshit jobs: A work rant. Strike! Magazine. https://​ www​.strike​.coop/​bullshit​-jobs Graeber, D. (2020, May 27). Lessons from Coronavirus: Not all jobs are bullshit (but yours might be). Politico. https://​www​.politico​.eu/​article/​lessons​-from​-coronavirus​-covid19​-confinement​-crisis​-not​ -all​-jobs​-are​-bullshit Gregory, S. (2018). Design anthropology as social design process. Journal of Business Anthropology, 7(2), 210–234. Hamington, M. (2019). Integrating care ethics and design thinking. Journal of Business Ethics, 155, 91–103. Haraway, D. (2007). When species meet. University of Minnesota Press. Hernández-Ramírez, R. (2018). On design thinking, bullshit, and innovation. Journal of Science and Technology of the Arts, 10(3), 45–57. Heylighen, A., & Dong, A. (2019). To empathise or not to empathise? Empathy and its limits in design. Design Studies, 65, 107–124. Hook, A. (2019). Exploring speculative methods: Building artifacts to investigate interspecies intersubjective subjectivity. Alphaville: Journal of Film and Screen Media, 17, 146–164. Huppatz, D. J. (2015). Globalizing design history and global design history. Journal of Design History, 28(2), 182–202,  https://​doi​-org​.ezproxy​.newcastle​.edu​.au/​10​.1093/​jdh/​epv002 IDEO. (n.d.). What’s the difference between human-centred design and design thinking? IDEO. https://​ designthinking​.IDEO​.com/​faq/​whats​-the​-difference​-between​-human​-centered​-design​-and​-design​ -thinking IDEO. (2015). The field guide to human-centred design. Design Kit. https://​www​.designkit​.org/​ resources/​1 Imrie, R., & Kullman, K. (2016). Designing with care and caring with design. In C. Bates, R. Imrie & K. Kullman (eds.), Care and design: Bodies, buildings, cities. John Wiley & Sons. Iskander, N. (2018). Design thinking is fundamentally conservative and preserves the status quo. Harvard Business Review, Digital Articles 9/5/2018, pp.1–9. Jones, J.C. (1970) Design Methods: Seeds of human futures. John Wiley and Sons. Kelly, R. (2018) Design in decline: Breathing new life into an industry through education. Design Management Journal, 13(1), 41–52. https://​doi​.org/​10​.1111/​dmj​.12041 Kimbell, L. (2011). Rethinking design thinking: Part I. Design and Culture, November. DOI:​10​.2752/​ 1754​70811X1307​1166525216 Lastovicka, J. L., & Sirianni, N. J. (2011, August). Truly, madly, deeply: Consumers in the throes of material possession love. Journal of Consumer Research, 38, 323–342. Laursen, L. N., & Haase, M. (2019). The shortcomings of design thinking when compared to designerly thinking. The Design Journal, 22(6), 813–832. Lee, A. S. (1987). Positivism: A discredited model of science still in use in the study and practice of management. ResearchGate. https://​www​.researchgate​.net/​publication/​279176586 Leonard, D., & Rayport, J. F. (1997). Spark innovation through empathic design. Harvard Business Review, November–December, 102–113. Liedtka, J. (2015). Perspective: Linking design thinking with innovation outcomes through cognitive bias reduction. Journal of Product Innovation Management, (32)6, 925–938.

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Maldonado, T. (1958). New developments in industry and the training of the designer. Ulm 2, October, 25–40. Matthews, B., Shannon, B. and Roxburgh, M. (2020). The robot ate my homework: A primer. Presented at Design Research Society 2020, available at www​ .academia​ .edu/​ 90263833/​ UON​ _DRS2020​ _ROBOT​_PRIMER​_V3 Mesut, J. (2018, December 10). The dilemma of designers’ empathy delusions. Shaping designers and design teams. https://​medium​.com/​shapingdesign/​the​-dilemma​-of​-designers​-empathy​-delusions​ -a61f0663deaf Metcalfe, D. J. (2016). Principles of multispecies design [Doctoral dissertation, University of the Arts London/Falmouth University]. Falmouth University research repository. http://​repository​.falmouth​.ac​ .uk/​3223/​1/​D​_Metcalfe​%20Multispecies​%20Design​%20PhD​%20Thesis​%20​-​%20FINAL​.pdf Morris, W. (1884, March 15). Art or no art? Who shall settle it? Justice, 1(9), 2. https://​www​.marxists​ .org/​archive/​morris/​works/​1884/​justice/​06artno​.htm Morris, W. (1885, April). The worker’s share of art. Commonweal, 1(3), 18–19. https://​www​.marxists​ .org/​archive/​morris/​works/​1885/​commonweal/​04​-workers​-art​.htm Mosely, G., Wright, N., & Wrigley, C. (2018). Facilitating design thinking: A comparison of design expertise. Thinking Skills and Creativity, 27, 177–189. Mosely, G., Markauskaite, L., & Wrigley, C. (2021). Design facilitation: A critical review of conceptualisations and constructs. Thinking Skills and Creativity, 42, 100962. Nimkulrat, N. (2012). Hands-on intellect: Integrating craft practice into design research. International Journal of Design, 6(3). http://​www​.ijdesign​.org/​index​.php/​IJDesign/​article/​view/​1228/​521 Noddings, N. (1985). Caring, a feminine approach to ethics and moral education. University of California Press. Norman, D. (2013). The design of everyday things (revised & expanded edition). Basic Books. Papanek, V. (1972). Design for the real world. Bantam Books. Pasanen, J. (2019, January 28). Human centred design considered harmful. Jussi Pasanen. https://​www​ .jussipasanen​.com/​human​-centred​-design​-considered​-harmful Peart, R. (2017, January 19). Why design is not problem solving & design thinking isn’t always the answer. AGDA Eye on Design. https://​eyeondesign​.aiga​.org/​why​-design​-is​-not​-problem​-solving​ -design​-thinking​-isnt​-always​-the​-answer Pennington, S. (2018). Taking care of issues of concern: Feminist possibilities and the curation of speculative and critical design. In C. Storni, K. Leahy, M. McMahon, P. Lloyd & E. Bohemia (eds), Proceedings of Design Research Society (DRS) 2018 International Conference, Limerick, Ireland. Pennington, A. & Stanford, J. (2019). The Future of Work for Australian Graduates: The Changing Landscape of University-Employment Transitions in Australia. Canberra: Centre for Future Work, The Australia Institute. Prado, L. (2014). Privilege and oppression: Towards a feminist speculative design. In Y.-K. Lim, K. Niedderer, J. Redstrom, E. Stolterman & A. Valtonen (Eds.), Proceedings of Design Research Society (DRS) 2014 International Conference, Umeå, Sweden. Puig de la Bellacasa, M. (2011). Matters of care in technoscience: Assembling neglected things. Social Studies of Science, 41(1), 85–106.. Puig de la Bellacasa, M. (2017). Matters of care: Speculative ethics in more than human worlds. University of Minnesota Press. Raizman, D. (2003). History of modern design: Graphics and products since the Industrial Revolution. Laurence King Publishing. Rodgers, P., & Bremner, C. (eds) (2021). Introduction. In P. Rodgers & C. Bremner (eds), Theories of designing. Vernon Press. Rosińska, M., & Szydłowska, A. (2019). Zoepolis: Non-anthropocentric design as an experiment in multi-species care. Proceedings of Nordes 2019: Who Cares? Espoo, Finland. Roudavski, S. (2020). Multispecies cohabitation and future design. In S. Boess, M. Cheung & R. Cain (eds), Proceedings of Design Research Society (DRS) 2020 International Conference, Brisbane. Roxburgh, M. (2005). Seeing and seeing through the crisis of the artificial. DESIGNsystemEVOLUTION European Academy of Design Conference Proceedings, Bremen.

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Roxburgh, M. (2010) Photography and the design imperative. Light Relief (Part II), DAB DOCS, Sydney, Australia, 7–16. Roxburgh, M. (2021). Design does/does not solve problems. In E. Igoe (ed.), Textile design theory in the making. Bloomsbury. Roxburgh, M. & Bremner, C. (2015). A photograph is evidence of nothing but itself. The Routledge Companion to Design Research, 203214. Oxon: Routledge. Roxburgh, M. & Cox, S. (2016). Visualisation and the service sector: Why visual communication design is central to designing the immaterial. Studies in Material Thinking, 15, 1–19. Roxburgh, M. & Irvin, J. (2018). The future of visual communication design is almost invisible or why skills in visual aesthetics are important to service design. In ServDes2018 - Service Design Proof of Concept. Politecnico di Milano, Italy. RSA. (2013, December 10). Brené Brown on empathy [Video]. YouTube. https://​www​.youtube​.com/​ watch​?v​=​1Evwgu369Jw Sherwin, C. (2018, October 11). Sustainability means shifting from human-centred to ‘humanity-centred’ design. Design Business Association. https://​www​.dba​.org​.uk/​human​-centred​-humanity​-centred​ -design Simon, H. (1996 [1969]) The sciences of the artificial. Cambridge, MA: MIT Press. Stairs, D. (2020). Designing ourselves to death: The politics of progress versus an ethics of survival in a diminishing world. In L. Scherling & A. DeRossa (eds), Ethics in design and communication: Critical perspectives. Bloomsbury. Stanford d.School. (n.d.). An introduction to design thinking: Process guide. Stanford d.School. https://​ web​.stanford​.edu/​~mshanks/​MichaelShanks/​files/​509554​.pdf Szanto, T., & Krueger, J. (2019). Introduction: Empathy, shared emotions, and social identity. Topoi, (38), 153–162. Taylor, C. (1990). Creating strategic visions. Strategic Studies Institute. Tharp, B. M., & Tharp, S. M. (2019). Discursive design: Critical, speculative, and alternative things. MIT Press. Tonkinwise, C. (2021, October 5). Making futures VI – not complex [Video]. Vimeo. https://​vimeo​.com/​ 623942400 Tronto, J. (1993). Moral boundaries: A political argument for an ethic of care. Routledge, Chapman and Hall. UK National Archives. https://​discovery​.nationalarchives​.gov​.uk/​details/​r/​C810 accessed 4 October 2021. Vinsel, L. (2017, December 7). Design thinking is kind of like syphilis: It’s contagious and rots your brains. Noteworthy. https://​blog​.heyday​.xyz/​design​-thinking​-is​-kind​-of​-like​-syphilis​-its​-contagious​ -and​-rots​-your​-brains​-842ed078af29 Voros, J. (2001). A primer on futures studies, foresight and the use of scenarios. The Voroscope. https://​ thevoroscope​.com/​publications/​foresight​-primer Weiner, J. J., & Auster, S. (2007). From empathy to caring: Defining the ideal approach to a healing relationship. Yale Journal of Biology and Medicine, 80, 123–130. Whitely, N. (1993). Design for society, London: Reaktion Books. Woodham, J. M. (1997). Twentieth century design, Oxford; New York: Oxford University Press. Wright, P., Wallace, J., & McCarthy, J. (2008). Aesthetics and experience-centred design. ACM Transactions on Computer-Human Interaction, 15(4), Article 18.

4. Method case study – Making design thinking tactile: unlocking meaning and experiences with tactile tools and generative prototypes Rowan Page and Leah Heiss INTRODUCTION Design thinking can be seen as a series of tools and practices for separating the processes of design from the activity of (traditional) design and making. Often conceived as having five stages – empathise, ideate, define, prototype, and test – design thinking is defined by the Interaction Design Foundation as “a non-linear, iterative process that teams use to understand users, challenge assumptions, redefine problems and create innovative solutions to prototype and test” (Interaction Design Foundation, 2021). Countering this separation, Lucy Kimbell proposes a way forward for an approach to design thinking that focuses on “situated and embodied routines of designers” and does not fall into a dualism between thinking/knowing and acting in the world (Kimbell, 2011). “Design thinking” and “design doing” are inextricably linked as part of the design practitioner’s creative process (Neubauer et al., 2020). Through building or modifying a prototype, articulating an idea, or role-playing a scenario, participants can embody and externalise abstract ideas allowing them to be shared, evaluated and improved (Stappers, 2013). The tactile experience of creating, evaluating, and interrogating an idea provides a different level of understanding than simply thinking about it, and the materiality of co-design tools has a marked impact on social and cognitive processes (Clatworthy, 2011). In our experience in designing healthcare services and technologies, we have found value in engaging diverse groups of participants through design thinking activities. Yet, as traditionally trained object designers we couldn’t resist the urge to augment these collaborative thinking sessions with situated, embodied, and object-oriented activities centred on tactile artefacts. Representing design concepts in tangible form allows people to interact with ideas during the development process in ways that approximate how they use and think about finished design products, systems and services. Across all scales of work, we focus extensively on the design and development of physical artefacts to support and engage users. Our projects aim to bring participants and end-users into the richness of the material, haptic, and tactile elements of the design process, enriching design thinking with elements of design doing. Throughout the stages of design thinking, particularly in the five-stage models described above, it can be difficult for people to have the language to express ideas around complex systems and product design with words alone. Without participants having the language to express ideas, user experience insights are often not unlocked until very late in the 70

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design process, when change is difficult. ‘Traditional’ fast-paced design thinking workshop approaches tend to move from ideation to prototyping very quickly and are not always appropriate in complex healthcare projects, where iteration often takes place over a longer duration. We find that this tacit and experiential knowledge can be foregrounded through conversations and activities grounded in physical engagement with tactile artefacts. By integrating this tactility into design thinking activities we open people up to processes of making, enacting, and exploring through embodied practices. We see this as bringing design doing into the design thinking process. By centring the active role of tactile artefacts within design thinking we explore the potential of artefacts to facilitate engagement and support collaboration. In this case study we propose two ways of integrating ‘tactile thinking’ into complex healthcare design projects. The first is a tactile co-design method used in the define stage of design thinking in contexts where the understanding of a problem is not yet developed and shared – and there is a need to balance up big picture ‘systems’ thinking with lived experience in the design of new models of care. In the second context, where the design problem is more defined – such as in the design and development of wearable medical devices – we propose the use of ‘generative prototyping’ (Figure 4.1) as a way to further integrate end-user participation and design thinking within the later stages of the product development processes.

Source: Adam R. Thomas.

Figure 4.1

Physical prototypes and tactile artefacts embedded in design thinking engagements

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CONTEXT 1: DEFINING COMPLEX PROBLEMS The development of technologies and services in the healthcare industry is a complex undertaking requiring sophisticated interdisciplinary expertise. Additionally, there is a strong imperative for end-user consultation and collaboration, in order to engage with and represent the lived experiences of often marginalised groups across all stages of the design process. While a lot of interest has been paid to the potential of design thinking approaches in the healthcare industry over the last decade, these design thinking interventions often focus on engagement at the ‘fuzzy front end’ of idea generation, representing user insights in service blueprints, journey maps and other communications. Yet the complexity of healthcare challenges necessitates a longer-term engagement, scaffolded by tactile co-design methods to engage participants throughout the entirety of the design process. Furthermore, in the context of medical device development, there are necessary trade-offs between what people want and what is technologically, scientifically, and medically possible. For example, when designing hearing aids, people ‘just want to hear’. They want a cure for their hearing loss, not hearing aids. This conflict raises a challenge around how to scope end-user engagement, and how to include articulations of what is possible and probable while still allowing creativity, engagement, and idea generation. To enable interdisciplinary collaboration in the co-design of new models of care, Heiss evolved the Tactile Tools to help coalesce interdisciplinary teams and break down complex healthcare journeys into their constituent parts (Heiss & Kokshagina, 2021). The method enables groups to collaboratively prototype the lived experience of healthcare journeys. Interdisciplinary conversations are facilitated by tactile engagement with a toolkit of physical tiles (Figures 4.2 and 4.3) that represent elements of the healthcare journey, including goals (large rectangles), roadblocks (hexagons), workarounds (large circles), stakeholders (small circles), moments of empathy (petals) and pathways (small rectangles). The tiles are accompanied by personas that are co-created with health experts to capture the story and medical history of the person seeking care. The tiles have weight and substance, facilitating different modes of engagement than paper-based tools; participants slide the tiles, hold them, and build care pathways with them. The tactile mapping of the problem space supports participants to view challenges from a variety of perspectives. Participants report viewing roadblocks and workarounds as shiftable and changeable, rather than immovable, that the method “allowed us to iteratively mindmap the issues and their interrelationships”. The tactile nature of the tools provides participants, particularly those with lived experience, with a medium through which to discuss healthcare challenges. In a workshop with cancer staff and patients, a cancer survivor, her oncologist and a nurse were able to record their thoughts on tiles and find new ways for them to weave together and create new narratives. In this way, the method facilitates each member of a group, irrespective of seniority, to record roadblocks from their unique perspective, prior to spatially negotiating the location of these on the work surface. A participant in a maternal health workshop suggested this was “a positive way to give the whole group a voice”. Such ‘playful’ methods enable interdisciplinary teams and lived experience advocates to engage with emotionally charged topics in a way that is neither grave nor irreverent and this promotes empathy with health seekers. The power of tactile thinking was highlighted by a participant in an end–end of life workshop with an aged care industry partner. While holding

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Source: Adam R. Thomas.

Figure 4.2

Tactile Tools used in co-design settings (left) and generative prototypes of hearing aids being handled by participants in co-design sessions (right)

Source: Adam R. Thomas.

Figure 4.3

The Tactile Tools toolkit with persona, and tiles that represent goals, roadblocks, workarounds, stakeholders, empathy and pathways

an ‘empathy tile’ in his hand the participant suggested, “in this activity, Vera’s life is in our hands”.

CONTEXT 2: GENERATIVE PROTOTYPING There is an established acceptance of the importance of using artefacts and tools to enable deep collaboration with users in the front end of design projects (Sanders & Stappers 2008, 2014;

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Sanders et al., 2010). These generative engagements, such as card-sorting activities, can act to foster collaboration between diverse groups by focusing on tools that assist people in generating ideas and discussions. These discussions give people a voice and agency in the design process. Yet, the ways in which we can engage and direct the generative involvement of users as design thinkers in the middle to later stages of design processes are less well understood. Involving users in these later stages is critical when designing products in the healthcare space, where issues of usability, human factors, user experience, and empowerment and expression are critical. Such design, user experience, and usability issues are nuanced and manifest in physical details and micro-interactions. Through our work, we have found that using ‘generative prototyping’ promotes engagement with participants through physically engaging them in the artefacts of the design process. The process we term ‘generating prototyping’ allows participants to explore, generate, and debate a wide range of possibilities and visualise potential futures scaffolded by speculative technologies. This physical engagement through prototypes enables us to encourage the participation of diverse groups, embrace experimentation across fidelity, speculate on possible futures, enact user experiences along with end-users, and scaffold and scope collaboration. These ideas were tested through the development of two devices to aid with hearing loss undertaken with medical device manufacturers: a cochlear implant technology, undertaken by Page with a large multinational company (Figure 4.4); and the design of a novel modular hearing aid, undertaken by Heiss, in an Australian-based SME (Figure 4.5). We approached these projects by holding regular collaborative sessions with end-users and diverse stakeholder groups throughout all stages of the product development process. Within these sessions, we foreground the role of both low- and high-fidelity prototypes as active participants in co-design processes, not just embodiments of final design ideas. We mix these artefacts with card sorting, collage, group discussion, and mapping to centre artefacts

Source: Narelle Portanier.

Figure 4.4

Speculative cochlear implant devices

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Source: Narelle Portanier.

Figure 4.5

The modular hearing aid

and tactility within co-design and design thinking processes. We focus on not just tools of early-stage idea generation, and abstract idea expression, but bring participants along further into the design process by co-investigating physical artefacts, enacting use and encouraging discussion about the nuanced decision making designers make in the later stages of executing a design. Through this phase we seek to shift the design thinking phases of ideating, defining and prototyping to move towards a more generative and iterative model. The sessions bring together five to ten end-users in conversation for two to four hours. Within these conversations, generative prototyping plays a central role in mediating and directing the conversation. Early in the process, generative prototypes consist of existing products, found objects (such as stones, minerals, and wooden blocks), image collages, and card sorting activities that invite open-ended speculation. As the development progresses, abstract 3D printed forms are designed and introduced to be explored together with the users. As users enact use and try things on, these speculations highlight probable and possible directions for the technology. Ultimately, high-resolution prototypes are presented to allow users to explore early versions of design proposals for nuanced feedback. The location of generative prototypes in the design thinking process is not determined by fidelity or resolution but rather by their ability to engage the participation and imagination of stakeholders (see the outer ring of Figure 4.6). For instance, high-fidelity speculative probes are used in the early stages of the design process while low-fidelity found objects are used in the mid-stage of designing (Figure 4.6).

KEY FINDINGS AND INSIGHTS As a creative process, design thinking relies on synthesis, iteration, and interdisciplinary collaboration, playing a central role in mediating complex interdisciplinary projects (Neubauer et al., 2020). In healthcare projects, designers must engage with and synthesise a broad range

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Figure 4.6

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The use of generative prototyping across all phases of the design process

of inputs from diverse stakeholders including technical inputs from engineering, clinical information, and subjective insights developed with co-design participants. This collage of objective, subjective, tacit, and abstract inputs and creative insights forms an acquired design knowledge iteratively built upon within the design process. Through our projects, we have discovered the importance of translating these diverse collages of information into coherently designed artefacts, to share with others. As Akama and Prendiville (2013, p. 31) suggest, “Knowledge is active, created in the ‘living’ moment and affective, bodily encounters in our world”. Within our projects, tactile thinking artefacts can be grasped, held, explored in context and use can be acted out with participants. In the Tactile Tools example (Context 1), the tiles support sensitive conversations between health providers and patients and provide a tangible way for teams to break down complex healthcare journeys. Through these engagements we find that active engagement with tactile thinking artefacts helped to stage a common ground for the participants, enabling them to engage more easily with one another and facilitate the co-creation of a shared possible future (Brodersen et al., 2008). Participants bring a variety of lived experiences to co-design sessions, layering external experiences on top of the prototypes and artefacts (Halskov & Dalsgaard, 2007). A participant

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in the hearing aid project (Context 2), while holding a regular flesh tone hearing aid responded, “It’s still such an icky skin colour … it looks like a bedpan”. This provoked nuanced reflections on colour from other participants, highlighting the association of hearing aids with ageism and the complexities of designing for disability. While trying on other wrist-mounted wearable technologies in the cochlear project one participant remarked ‘why does it [hearing device] have to be on the ear, I find it insulting!’ This challenged fundamental assumptions about the design of these devices in ways that more general discussions, without the artefacts, had not.

Source: Adam R. Thomas.

Figure 4.7

Engaging with prototypes and other tactile thinking artefacts to tell stories and communicate ideas about hearing loss, personal identity and hearing aid use (left); participants in a Tactile Tools workshop using the toolkit to map the end-of-life journey (right)

KEY LEARNINGS These two contexts illustrate how tactile artefacts aided in stimulating engagement and empathy with healthcare technology and service users. The use of tactile artefacts and prototypes in co-design processes provides participants with a prompt to communicate ideas and feelings in response to the artefacts. The tactility of design thinking artefacts enabled participants to communicate complex ideas in a playful way (Figure 4.7). In the device project (Context 2) participants interacted with generative prototypes and tested them on the body and in handbags and pockets, exploring the possibility of these speculations integrating with their lived experience. This engagement with tactile thinking artefacts supported the telling of stories but also the deepening of bonds between participants. Participants could see beyond the tools and generative prototypes, using the artefacts to see and create meaning. We have learnt that using tactile thinking artefacts in co-design contexts enables a non-verbal mode of interaction and expression to promote diverse and equitable engagement. The artefacts operated as mnemonic devices to surface memories that were then shared with other participants and with the designers, in turn creating trust. They also provided a common ground for all participants

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to interact. Across all contexts the representational artefacts ‘scaffolded’ participation, “providing support for interaction and performance” (Morrison & Dearden, 2013, p. 184). The examples presented in these case studies highlight design thinking approaches that go beyond the translation of design methods and processes of thinking to other disciplines. Rather, we emphasise the importance and centrality of craft and object-centred design practices, artefacts, and ‘design doing’ as key components of designerly thinking in collaborative contexts. Physical artefacts and designed prototypes were essential to engaging users in the beginning, middle, and end of projects. These tactile artefacts and prototypes – at various levels of fidelity – invited participants into the world of design, encouraging generative conversations. We found novelty, and value, in injecting these intentionally designed prototypes into conversations and co-design sessions. The artefacts elicited unexpected feedback that meaningfully informed the development of our projects. Through a focus on artefacts and materiality, we direct participants to the rich world of possibility and expression that exists within materiality, tactility, weight, colour, form, affordances and interaction. By way of this tactile focus, we engage with a diverse range of expertise and lived experience and enable collaboration across disciplinary and experiential divides.

REFERENCES Akama, Y., & Prendiville, A. (2013). Embodying, enacting and entangling design: A phenomenological view to co-designing services. Swedish Design Research Journal, 1(1), 29–41. Brodersen, C., Dindler, C., & Iversen, O. S. (2008). Staging imaginative places for participatory prototyping. CoDesign, 4(1), 19–30. Clatworthy, S. (2011). Service innovation through touch-points: Development of an innovation toolkit for the first stages of new service development. International Journal of Design, 5(2), 15–28. Halskov, K., & Dalsgaard, P. (2007). The emergence of ideas: The interplay between sources of inspiration and emerging design concepts. CoDesign, 3(4), 185–211. Heiss, L., and Kokshagina, O. (2021). Tactile co-design tools for complex interdisciplinary problem exploration in healthcare settings. Design Studies, 75, 1–42. Interaction Design Foundation. (2021). What is design thinking? Accessed July 2021. https://​www​ .interaction​-design​.org/​literature/​topics/​design​-thinking. Kimbell, L. (2011). Rethinking design thinking: Part I. Design and Culture, 3(3), 285–306. DOI: 10.27 52/175470811X13071166525216 Morrison, C., and Dearden, A. (2013). Beyond tokenistic participation: using representational artefacts to enable meaningful public participation in health service design. Health Policy, 112(3), 179–186. Neubauer, R., Bohemia, E., & Harman, K. (2020). Rethinking design: From the methodology of innovation to the object of design. Design Issues, 36(2), 18–27. Sanders, E. B. N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. Codesign, 4(1), 5–18. Sanders, E. B.-N., & Stappers, P. J. (2014). Probes, toolkits and prototypes: Three approaches to making in codesigning. CoDesign, 10(1), 5–14. https://​doi​.org/​10​.1080/​15710882​.2014​.888183. Sanders, E. B. N., Brandt, E., & Binder, T. (2010, November). A framework for organizing the tools and techniques of participatory design. In Proceedings of the 11th Biennial Participatory Design Conference (pp. 195–198). Stappers, P, J. (2013). Prototypes as a central vein for knowledge development. In L. Valentine (Ed.). Prototype: Design and craft in the 21st century. New York: Bloomsbury.

PART II

Perspectives on Design Thinking as a Process

5. The agile landscape of design thinking Katja Thoring and Roland M. Mueller INTRODUCTION The world is becoming volatile, uncertain, complex, and ambiguous. This phrase, known under the acronym VUCA, was originally coined by the US military (Stiehm, 2002) but nowadays describes the need for organizations to adapt to changing requirements (Lawrence, 2013; Mack, 2016). Such changes could be caused either by external circumstances, for example changing markets caused by disruptive technologies (Bower & Christensen, 1995), or simply because a client has changed their mind and added new requirements to the briefing. Companies need to be able to react to such changes in a fast and flexible way, especially when they want to be at the forefront of innovation. This capability for operational flexibility is widely known under the term “agile”. It comes as no surprise that companies are desperately relying on agile methods to keep up with these requirements. Today, they are offered a potpourri of tools and methods that are believed to make them faster, more flexible, and more innovative; for example, lean startup, design thinking, and agile principles, such as scrum. These methods may appear as buzzwords to some, but as the holy grail of innovation to others. But how do they work? What are their differences and which method is the right one for what situation? Does it make sense to use them in combination, and if yes, how? This chapter1 aims to shed light on these emerging concepts that promise to deal with unpredictable and rapidly changing situations in order for a company to be more innovative. Since the mid-1990s, design thinking was considered the central driver of innovation and change for organizations (Brown, 2009; Liedtka, 2015; Martin, 2009). It can be argued that this user-centred design approach also involves agile concepts because it suggests the testing of early prototypes and iterative feedback loops (Mueller & Thoring, 2012). Simultaneously, other agile concepts emerged in the area of software development (e.g., scrum, extreme programming), that for quite some time had little to no overlap to design thinking (Mueller & Thoring, 2012). However, more recently, the different concepts converged and those methods from the software development field were adopted by other disciplines. This situation leads to the following research questions: RQ1: What are the characteristics and historical roots of design thinking, lean startup, and agile principles? RQ2: How can the innovation process be improved by merging design thinking, lean startup, and agile principles? 80

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In order to answer these questions, we need to take a look at innovation processes in general. According to Koen et al. (2002), the innovation process can be distinguished into three stages: the fuzzy front end (FFE), the new product development (NPD), and the commercialization of an idea. Agile approaches are believed to decrease the cycle time of innovation processes from idea to market (Griffin et al., 2019). Traditional innovation processes often follow a linear process; for example, the stage-gate or waterfall process that focuses on the NPD process and is divided into distinct stages (Cooper, 1986). However, these processes typically do not address the FFE or the commercialization stage (Ajamian & Koen, 2002). Design thinking, on the other hand, typically focuses on the FFE and does not address the commercialization stage as well, but often ends with a prototype (Mueller & Thoring, 2012). By contrast, lean startup focuses on the commercialization stage; more specifically on customer development and business integration (Ries, 2011). We argue that managers need a comprehensive overview and a deep understanding of the different methods in order to apply them to their respective context and requirements. This can become a challenge because each of these methods has a different focus, such as identifying user needs, efficiently developing features, or developing a successful business model. Consequently, an integration of the different methods may result in a more comprehensive approach that covers all stages of the innovation process – a view that is also shared by other researchers (Micheli et al., 2019). Literature that discusses the integration of agile methods into the design thinking process or that compares different agile approaches is surprisingly scarce (Lichtenthaler, 2020). There is a substantial amount of research that aims at improving the traditional stage-gate innovation process through agile concepts (Cooper & Sommer, 2016; Sommer et al., 2015). Other research focuses on the possible benefits of agile NPD processes in general product development projects (Fekri et al., 2009; MacCormack et al., 2001), or in virtual product development projects (Enkler & Sporleder, 2019). Furthermore, some authors have suggested that agile concepts can improve cycle times in the NPD process (Griffin et al., 2019). Karlsson and Åhlström (1996) have explored the potential of lean principles to improve NPD processes and suggested a lean product development process. The integration of agile concepts into user-centred innovation processes, such as design thinking, is discussed by a few authors. For example, Tessarolo et al. (2019) have suggested combining user-centred co-design and agile methodology for developing ambient assisting technologies. Combining design thinking with agile approaches has been suggested for software engineering (Corral & Fronza, 2018) and data modelling (O’Driscoll, 2016). Some authors have tried to integrate lean startup and design thinking and suggested a combination of both methods (Koen, 2015; Lichtenthaler, 2020; Mueller & Thoring, 2012). Others call for better integration of agile concepts into the design thinking process to address the entire innovation process (Micheli et al., 2019), which is what we aim for with the work presented in this chapter. In summary, the literature on the topic of agile innovation processes is scattered. A holistic perspective that provides an overview of the available concepts and integrates them into a comprehensive model is lacking. However, such a holistic model would provide practitioners with the required knowledge to select the appropriate method for their innovation project, as well as with actionable advice and prescriptive guidelines to follow.

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The contribution of this chapter is threefold. First, we present an overview of the three core concepts – design thinking, lean startup, and agile principles – and discuss several related influences. We present short descriptions and process models of each introduced concept and align them on a historical timeline. Second, we analyse an existing agile case study according to its use of and interplay with design thinking, lean startup, and agile principles. Finally, based on the case analysis, we develop a novel process theory of “agile design thinking” that integrates relevant aspects of the three core concepts in a prescriptive process model. We argue that following the suggested process model can enhance the innovation processes of organizations. In summary, the insights presented in this chapter constitute a holistic perspective on the agile landscape of design thinking that contributes to a better understanding of agile innovation methods and guides companies who want to employ an agile design thinking process.

METHODOLOGICAL APPROACH Our main goal is to understand the relationships between the three core concepts of (1) design thinking, (2) lean startup, and (3) agile principles, and to identify differences, similarities, and potentials for integrating their benefits into a merged process model. In order to understand the historical roots of the concepts as well as their influences, additional concepts are explored as well, namely scrum, service design, engineering design, design sprints, lean manufacturing, customer development, the agile manifesto, extreme programming, customer development, and business model generation. For re-engineering the various concepts, we analyse two types of data sources: (1) published literature and case studies, and (2) process models for the different concepts, where available. The process models were analysed by method engineering (Brinkkemper, 1996; Welke & Kumar, 1992). Originated in the Information Systems discipline, method engineering is concerned with the description, design, adaption, and evaluation of methods, using engineering principles (Brinkkemper, 1996; Welke & Kumar, 1992). Method engineering allows easier method adaption to project-specific needs, so-called method tailoring (ter Hofstede & Verhoef, 1997). Method fragments can also be combined to create new methods, so-called method composition (Blom et al., 2010). The formal description of a method allows the reproducibility of methods by other researchers and therefore the testability of the method’s utility claims. Also, method engineering is able to support the teaching of methods. For the formal description of methods, different elements are recommended – the purpose and scope, the process model, and the involved constructs of the methods (Blom et al., 2010). Typically, a method has specific testable utility claims based on the purpose of the method (Blom et al., 2010; Brinkkemper et al., 1999). Sometimes these utility claims are based on kernel theories (Walls et al., 1992). The utility of the method should also be evaluated (Moody, 2003). However, in this chapter, we will not discuss any evaluation. We focus on the process model and the involved concepts. For the modelling process we refer to the Business Process Modelling Notation (BPMN) (White, 2004). We are aware that the discussed concepts are not just processes but also consist of tacit elements, such as practices, experiences, specific mindsets, and company cultures. These intangible elements are important and not everything in those methods can be made explicit and reduced to a process description. However, we think that a detailed comparison of the process steps is still useful to better understand the discussed innovation approaches.

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The insights from our two data sources (literature review and process model comparison) are summarized in a conceptual map and a structured table overview that highlights similarities and differences. Following this step, we compare the derived insights with an existing, published case study that involves agile concepts (Hildenbrand & Meyer, 2012). The case was selected because it presents the attempt to combine aspects of agile methods (here scrum) with design thinking in order to improve the innovation process. Through this comparison, we identified opportunities to further improve the innovation process through design thinking and agile concepts. The results from this comparison are then transferred into a novel process theory. Theories in the social sciences can be classified into either variance theories or process theories (Mohr, 1982). A variance theory is a graph of nodes (constructs or variables) and directed links (propositions or hypotheses) that predict the level of the dependent variables based on the level of independent variables (Crowston, 2000). Variance theories are the dominant paradigm in the social and management sciences (Chiles, 2003). Process theories, on the other hand, represent typical sequences of events over time (Mohr, 1982). Therefore, they can show rich details and interactions that are not possible to represent in a variance theory (Chiles, 2003). The following section introduces our theoretical framework of related agile concepts that will guide our development of an agile design thinking process theory.

THEORETICAL FRAMEWORK In order to better understand the interplay of the different concepts, it is helpful to see them from a historical perspective. Therefore, we outline them chronologically (see Figure 5.1) and discuss each concept in more detail in the following subsections.

Figure 5.1

Chronological overview of relevant concepts

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Design Thinking Design thinking is a user-driven innovation strategy that has become popular during the last few decades. Based on designerly methods and principles, a systematic user-centred design approach is employed to solve complex engineering and design problems. Nowadays, the approach has also been adopted by neighbouring disciplines, such as management, medicine, and the public sector (Junginger, 2013). There have been various publications that outline the historical roots of design thinking along with descriptions of its development process (Buchanan, 2009; Johansson-Sköldberg et al., 2013). For this reason, we will not go into detail regarding the history of design thinking in this chapter but outline only some core incidents that define its historical development. It can be argued that the core principles of design thinking have been in place since the late 1960s (for example in the engineering design class ME310 at Stanford University (Carleton & Leifer, 2009)). Later, in the early 1980s, the concept of service design emerged. Service design employs similar principles as design thinking, such as a user-driven approach and iterative testing cycles, but with a focus on the design of intangible services along with their tangible touchpoints (Shostack, 1982, 1984). In 1984, the Stanford Center for Design Research (CDR) was established at Stanford University, which focused on engineering design innovation and design education (Stanford University, n.d.). Simultaneously, a movement originated in Europe that explored designerly methods and processes under the term “design thinking research”. Several Design Thinking Research Symposia (DTRS) were held at various locations; the first one was in 1991 in Delft (Cross, 2018). The starting point of the design thinking movement that focuses on facilitating innovation processes in organizations can be located in the early 1990s. Design firm IDEO coined the term “design thinking” which was widely promoted through their founder David Kelley and later CEO Tim Brown (Brown, 2008, 2009; Kelley & Littman, 2001). Also, in the educational field, design thinking gained a lot of interest. In 2005, the first “School of Design Thinking” (for short: D-School) was founded at the Hasso Plattner Institute (HPI) in Stanford. The founding of the D-School was mainly initiated by Hasso Plattner, CEO of global software company SAP, who was introduced to and intrigued by the user-centred design approach of design firm IDEO. The Stanford D-School was later complemented by a second institution – the D-School in Potsdam, Germany in 2007. It is important to distinguish these two streams of design thinking that both use the same term but address slightly different goals. The DTRS symposia on design thinking focus specifically on a descriptive analysis of designerly activities, whereas design thinking as originated in the US introduced a novel prescriptive process of how to design something, which constitutes more of a practitioner’s perspective (Badke-Schaub et al., 2010). Design thinking aims at solving complex (wicked) problems (Buchanan, 1992; Rittel, 1972) and at generating innovative solutions, based on a user-centred approach with multi-disciplinary teams. Design thinking makes use of extensive user research, feedback loops and iteration cycles. It is becoming more and more popular among business schools (e.g. the Rotman School of Management (Martin, 2009; Martin & Christensen, 2013)), and it is applied in R&D departments of companies to foster innovation. The model of the design thinking process (based on Plattner et al., 2009) describes the six steps of the process and the iteration loops that result from the last step ‘test’. Notably, this

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process does not start with an idea, but with a problem or a question, instead. Usually, the ideas are developed within the process, in the fourth step ‘ideation’. Before that, there is an extensive focus on the research, where ‘understand’ means secondary research and ‘observe’ means user research. Here, design thinking makes use of research methods from other disciplines such as ethnographic methods and other qualitative methodology. The acquired knowledge is then condensed into a sort of micro-theory about the problem or the user needs, the ‘point of view’ (POV) that is afterwards used to develop solution concepts in the ‘ideation’ step. It is here where innovative ideas are developed that aim at solving the previously identified problem or addressing the users’ needs. The selected idea is then visualized or built (‘prototype’) in order to test it and gather feedback from prospective users (‘test’). According to the feedback the concept is iterated, by returning to one of the previous steps. Figure 5.2 presents a more detailed process model of design thinking based on method engineering. The model illustrates the input for and outcome of each process step as a separate layer, as well as the conditions that lead to an iteration loop. See Thoring and Müller (2011) for a more detailed description of the design thinking process model, including all mentioned concepts in the figure. Only recently, a direct relationship between design thinking and service design was established and a collection of similar methods and tools have been presented (Stickdorn & Schneider, 2011). Lean Startup Lean startup (Ries, 2011) is based on the concept of lean manufacturing but transfers this to the creation of a startup company. It is an innovation method that claims that the most efficient innovation is the one for which there is an actual demand by the users. Or, in other words, the biggest waste is creating a product or service that nobody needs. This concept is highly relevant for any strategy or method that aims at creating innovations. Although the term “lean startup” was developed in the IT industry for software startups, it is more and more commonly used also for other sorts of innovation projects in other disciplines (Ries, 2011). A startup is defined as “a human institution designed to create new products and services under conditions of extreme uncertainty” (Ries, 2011, p. 8). Therefore, not all new companies are classified as a startup and also an established department in a big company could be a startup. Unlike the design thinking process, which begins with the “understand” phase, the lean learning cycle has no clear beginning or ending – the circular alignment of the steps suggests that they are supposed to be executed continuously and repetitively. The goal of the build–measure–learn cycle is learning (Ries, 2011). What is built is based on a problem or solution hypothesis. The test of a hypothesis is therefore the intended learning step. For testing the hypothesis, appropriate metrics must be defined (measure). For generating these metrics and then testing the hypothesis, an experiment has to be designed (build). Therefore, the build–measure–learn cycle could also be regarded as a classical scientific hypothesis–metric– experiment cycle that starts with the learning goal (theory or hypothesis) and ends with an experiment (prototype) to test the hypothesis. When comparing the individual steps of design thinking and lean startup, some interesting similarities with the design thinking process become obvious: e.g., “learn” in lean startup could be interpreted as “understand” or as “point of view” in design thinking. “Build” in lean

Figure 5.2

Detailed process model for design thinking

Source: Thoring & Müller, 2011.

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startup might be similar to “prototype” in design thinking. And “measure” in lean startup can either be “observe” or “test” in design thinking. This is in line with the before-mentioned assumption that the lean learning cycle could start at any step of the process model. Finally, the lean learning cycle might be applied to different levels of a project. On a meta-level, it could be applied to the entire process, and on a micro-level, it could be applied to specific details (such as the colour of a signup button). That means, it is possible to zoom into sub-processes and execute the lean learning cycle also for smaller design decisions. The design thinking process model, however, seems to be only applicable to the entire problem; not to specific sub-problems. Lean startup aims to build a continuous feedback loop with customers during product development cycles (Maurya, 2012). It tries to test the core business assumptions early in the product development process, sometimes even before any product is built at all. Lean startup is a trademark by Eric Ries and combines customer development with ideas of agile software development, lean management (Womack, 2003), and open source software (Ries, 2011). Customer Development Lean startup (see the previous subsection) evolved from the “customer development” method (Blank, 2006). The idea behind this method is that, in addition to a process for “product development”, a startup also needs a process for “customer development” to find and understand the customers. This goal mainly addresses the commercialization stage of the innovation process (Koen et al., 2002). Customer development leads to developing solutions based on a user-centred approach and adapting to customer needs. The customer development process consists of four steps: “customer discovery”, “customer validation”, “customer creation”, and “company building” (Blank, 2006). In the customer discovery phase, the founders discover the appropriate customer group and market segment and validate if the product solves a problem for the customer group. This phase tries to find indications of a so-called ‘problem-solution fit’. The goal is to discover a customer problem and to test if the problem is worth solving (Blank, 2006). Central to this is finding the minimal set of features for solving the core problem: the so-called minimal viable product (MVP). An MVP “is that version of the product that enables a full turn of the build-measure-learn loop with minimum amount of effort” (Ries, 2011, p. 77). In the early stages of the process, this can be tested, and feedback from potential customers can be gathered with, for example, minimal landing pages, paper prototypes, or early working prototypes. In the customer validation phase, it will be checked if the market is reachable and large enough for a viable business (Cooper & Vlaskovits, 2010). The goal is to find some validation of a “product-market fit” and to answer the question if the developed product is something that people want (Maurya, 2012). A product-market fit means that (1) a customer is willing to pay for the product, (2) there is an economically viable way to acquire customers, and (3) the market is large enough for the business (Cooper & Vlaskovits, 2010). After this step, the innovation is validated. The company creation phase is concerned with building a scalable business through a repeatable sales and marketing roadmap (Cooper & Vlaskovits, 2010). In the company building phase, departments and business processes are defined to support scale (Blank, 2006).

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The concept of lean startup is based in large parts on “customer development” and, thus, its main goal lies on company building and customer development, which entails testing the user demand and the product-market fit. Therefore, the process model for customer development also applies to the lean startup process. Lean Manufacturing The concept of agile processes can be traced back to the early 1970s. Lean principles were developed by Toyota in Japan, called lean manufacturing, to optimize production processes (Womack, 2003). The idea of lean principles is to make the production process more efficient by reducing any sort of waste in the process – this could mean either the reduction of resources (human or material) or the elimination of needless or redundant activities or expenses, such as the reduction of storage space. This strategy revolutionized production processes in the automotive industry. By now, lean principles have also become important for general management, and other disciplines such as IT development, which makes use of lean concepts but transfers them to non-manufacturing contexts. Business Model Generation Lean startup makes use of Osterwalder’s Business Model methodology (Osterwalder & Pigneur, 2010) that helps to systematically align stakeholders (partners, customers), value propositions, required resources, cost and revenue structure, and channels, etc., for a startup business model. The elements of the introduced business model canvas are considered hypotheses that must be tested as early as possible (Blank & Dorf, 2012). Maurya suggests an adapted business model framework called ‘lean canvas’ (Maurya, 2012). By contrast, design thinking does not suggest such a focus on the business model of an idea. Agile Principles We use the term “agile principles” for the entirety of agile methods, including scrum, the agile manifesto, and extreme programming. In the following subsections, each of these concepts is briefly described and illustrated as a process model. Scrum Scrum addresses the problem that customers sometimes might change their minds (Schwaber, 1997). Therefore, scrum offers an iterative approach that tries to maximize the speed of delivery of working code by incrementally developing and deploying the application (Schwaber, 1997; Schwaber & Beedle, 2002). Within the scrum process, there are three distinct roles: (1) the product owner, who should represent the needs of the end-user, (2) the scrum master who coaches the team in the scrum method, and (3) the team. The list of all features for the project is managed and prioritized by the product owner in the product backlog. The software is developed in so-called sprints that can last one to four weeks. For each sprint, there is a sprint planning phase in which the product owner and the team decide what features from the product backlog should be developed and put into the sprint backlog. During the time-boxed sprint, the sprint backlog will be not changed anymore. In short daily stand-up meetings, the team shares

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their progress and roadblocks. After each sprint, the team reflects on the last sprint and tries to come up with improvements in their work routines according to discovered problems. Similar to extreme programming (see next subsection), scrum focuses on improving the speed of the development and deployment of individual features of the software. However, scrum does not address the question of whether the product to be developed is the right one. This decision is out of the scope of the scrum method but is directed to the product owner’s responsibility, instead. In addition, scrum is not addressing any concerns about finding a viable business model for the product. To better understand the development of the three core concepts and their historical influences, we take a closer look at several related concepts: design sprints, lean manufacturing, the agile manifesto, extreme programming, customer development, and business model generation. Agile Manifesto In 2001, the so-called Agile Manifesto was published (Beck et al., 2001). Among the authors were the inventors of “extreme programming” (Ken Beck) and “scrum” (Ken Schwaber and Jeff Sutherland) and other thought leaders in the emerging field of agile software development. The principles advocated in this manifesto suggest a continuous and iterative delivery of software to the customer. This has multiple benefits. Compared with the waterfall model, the customer gets the benefits of the software earlier, even though maybe not all features are finished. The customer should be able to change the requirements after the start of the project. The focus is not on a long list of specifications and plans but a working software prototype. Extreme Programming Extreme programming was, together with scrum, one of the early methods of agile software development (Beck, 2000). Extreme programming proposes several best practices. Test-driven development suggests writing automatic tests for pieces of software code before implementing the software. Automatic (unit and integration) tests enable fast refactoring (changing) of the code, without introducing unrecognized errors. This allows postponing a large-scale architectural design of the code upfront and starting with the simplest design that is necessary. This reduces waste because often the scope of a project may change later on anyway and the work for the upfront design is wasted. Knowledge sharing is done mainly face-to-face through, for example, pair programming (two programmers work next to each other on the same code) and stand-up meetings (short meetings for synchronizing teams). The customer representative is regularly testing the working prototypes (acceptance tests). Ken Beck developed extreme programming during his work at Chrysler during the development of their internal payment program “C3” (Beck, 2000). Based on this origin, there are a couple of assumptions baked in. One assumption is that there is a customer representative available who fully understands the problem the software should solve and can also describe and prioritize the requirements for the final solution. That means extreme programming does not focus on finding the problem or developing solution ideas. These questions are out of the scope of extreme programming and are delegated to the customer representative.

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Design Sprint The design sprint, developed at Google Ventures by Jake Knapp, combines agile principles with design thinking. It compresses the design thinking steps of user research, ideation, prototyping, and testing into a compact five-day workshop (Knapp et al., 2016). The different steps in the five days are timeboxed. The participants of the workshop should include relevant stakeholders, such as the decision-makers of the company, and represent a diverse set of competencies, such as design or marketing. At the beginning of the workshop, interviews are conducted with experts to learn about the problem context. The team should develop a multitude of different ideas and build upon and combine the ideas of different team members. Similar to design thinking, the ideas are prototyped quickly with low fidelity paper prototypes or high fidelity click-dummies, without programming anything. Then the prototypes and their assumptions are tested. The goal of the design sprint is to learn about the problem and solution before already building and launching the product. That means the design sprint constitutes a shortcut between the idea and the learning, while the build and launch steps are skipped. The different concepts described in this section are summarized in a conceptual map that is presented and discussed in the next section.

CONCEPTUAL MAP OF DESIGN THINKING AND AGILE PRINCIPLES To summarize the previously presented concepts, we present a comparative overview in the form of a conceptual map. Figure 5.3 illustrates the overlaps and interrelationships between the discussed concepts. We can identify the three core concepts: design thinking, lean startup, and agile principles. Agile principles stem from methods for IT agility, especially scrum and extreme programming. Lean startup is influenced by the Japanese concept of lean manufacturing but transferred to a business context. It has a stronger focus on the business side of a company as it is influenced by the concept of business model generation and customer development. By contrast, design thinking has originated from the engineering design and service design disciplines. Agile design thinking is our suggested approach to merging the best of the three worlds. A design sprint is a specific condensed instantiation of design thinking that follows agile principles and therefore is positioned at the intersection. Figure 5.3 illustrates the relationships and influences for the three core concepts (design thinking, lean startup, and agile principles), whereas Table 5.1 juxtaposes the main goals, methods, and process steps in comparison for the three core concepts. In summary, we argue that design thinking aims at “doing the right thing” in terms of identifying the user need and solving a problem for a user. Design thinking is the only method among the discussed concepts that specifically addresses the FFE stage of the innovation process because it presents a structured approach for identifying user needs and framing the problem context. However, design thinking is not specifically focusing on the commercialization stage of the innovation process but often ends with a prototype. By contrast, agile principles aim at “doing things right” by replacing the traditional waterfall model with smaller iterations that allow feedback about individual features of a concept and

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Figure 5.3

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Conceptual map of agile design thinking

its implementation. Scrum does not question the identification of the problem or ideation of solutions, though. These issues are delegated to the customer representative. Finally, lean startup aims at company building and developing the customer. This strategy addresses the goal of “how to make the company or the innovation viable”. Often, the development of a business model and the use of the Business Model Canvas is part of lean startup approaches. All of the three discussed concepts involve agile elements, for example iteration cycles, albeit in different degrees, at different stages of the process, and with different names. However, the table overview also highlights fundamental differences, for example regarding the target group and the employed methods.

COMPARISON WITH AN INDUSTRY CASE In order to show possible interconnections of agile methods, we look into a published practice case and analyse how design thinking and scrum are used together in an agile project. This should validate the theoretical analyses of the last section and show the need for further integration of different agile methods. Hildenbrand and Meyer (2012) describe a case study in SAP that combines design thinking and scrum. We will analyse this published case study through the theoretical lens of our previously developed comparison. The case describes the use of design thinking within SAP for developing an application for the Audi Sailing Team Germany. The project goal was to develop software that improves the knowledge transfer between trainers and sailors. The project had three phases. The first phase combined the “understanding” and “observe” phases of design thinking. The developers researched books and papers, interviewed sailors,

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Table 5.1

Comparison of design thinking, lean startup, and agile principles

What

Design thinking

Lean startup

Agile principles

Goal

Doing the right thing

Making it viable

Doing things right

Scope, Focus

General innovations

High-tech innovations for startups

Software development

Approach

User-centred

Customer-oriented

Time-boxed development

Uncertainty

User need

Viable customer demand

Technical feasibility

Testing

Fail early to succeed

Pivoting is at the heart of the ‘fail

Test driven development

sooner

fast’ concept. The sooner you realize a hypothesis is wrong, the faster you can update and retest it

Iteration

Yes (“Iteration”)

Yes (“Pivoting”)

Yes (“Sprint”)

Ideation

Ideation is part of the

Ideation is not part of the process;

Not part of the process. Product

process; solutions are

product vision is initially provided

vision comes from product

generated in the process

by company founders

manager.

Strong focus: elaborated

Not a focus

No

Strong focus: metric-based

Burn down chart

Qualitative methods

ethnographic methods, user research, observations, etc. Quantitative methods

Not a focus

analysis; provides matrices, and testing Business model

Not a focus

Focus

Not focus

Adaption of

Not a focus

Five whys method

Continuous integration

Shadowing, Qualitative

Qualitative interview, Smoke test,

Sprint planning, Daily standup,

interview, Paper

Paper prototyping, Innovative

Test driven development, User

prototyping, Brainstorming

accounting, Split (A/B) tests,

story mapping

(with specific rules),

Cohort analysis, Funnel metrics,

Synthesis, etc.

Business model canvas, Five

deployments Typical methods

whys, etc. Hypothesis testing

Not a focus

Focus

No

Prototype testing

Yes

Yes

Partially (user should get a working

Rapid iteration

Yes

Yes

Yes

Target group

Users (usually end

Customers (distinguished among

Users/stakeholders

users, sometimes other

users, influencers, recommenders,

stakeholders)

economic buyers, decision makers)

prototype after each sprint)

trainers, and experts, and even tried sailing themselves. The second phase was conducted as a three-day workshop, similar to a design sprint. The workshop included the synthesis of the observations and interviews, ideation, paper prototyping, testing, and the creation of a user story map. The user story map was the main input for the backlog of the scrum-based development in the third phase (Hildenbrand & Meyer, 2012). For specific user stories, the team conducted additional ideation and prototyping. After only three months, the first prototype was deployed to the sailing team and received positive feedback.

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We use this case study to compare it with our juxtaposition of different aspects of the three methods, design thinking, lean startup, and agile principles (Table 5.1), in order to identify the potential for an improved innovation process. This comparison reveals that in the case study some potentials from different methods were already employed, but other aspects were not utilized to their full potential. More specifically, design thinking was used for filling the backlog in scrum (Hildenbrand & Meyer, 2012). Furthermore, there was a clear division of responsibilities between design thinking and scrum. Design thinking focused on solving the right problem with the right idea (“doing the right thing”), scrum helped with the efficient execution of the idea (“doing the thing right”). The process is illustrated in Figure 5.4.

Source: Based on Hildenbrand & Meyer, 2012.

Figure 5.4

Innovation process as employed at SAP sailing boat challenge

However, several aspects of the case study show that further development of the combination of design thinking, lean startup, and agile principles warrants further research. The case demonstrates a typical situation of a design thinking or scrum project: the initial problem definition (in this case, to develop software that improves knowledge transfer between trainers and sailors) was taken for granted and not questioned anymore. Similarly, the main synthesis results and assumptions were not tested or iterated during the later stages of the project, even though ideation and prototyping were also partly used during the third phase. Added to that, aspects of the lean startup method were not considered at all. Developing a business model for the application (“making it viable”) was not mentioned and the question, of whether the developed software would have a market, other than the internal client, was out of the project’s scope. The analysis of the presented case exemplarily demonstrates the drawbacks of current attempts to integrate different agile methods, lean startup, and design thinking. Although the benefits of the combined approach were evident, comprehensive integration of different agile methods was lacking. However, we argue that only through such an integrated approach would a company be able to address the entire innovation process, including the FFE, the NPD, and the commercialization of the idea. Based on these insights, we see a need for an integrated process that combines design thinking, lean startup, and scrum.

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In the following section, we present our suggested “agile design thinking process model” that constitutes a process theory that combines aspects of design thinking, lean startup, and agile principles.

AGILE DESIGN THINKING PROCESS MODEL Based on the insights from our theoretical framework and the analysed case study, we see potential to improve the innovation process by merging design thinking with lean startup, and by integrating agile principles. In order to address the entire innovation process with the three distinct stages (FFE, NPD, and commercialization, as defined by Koen et al. (2002)), we suggest a novel process theory that constitutes a more “agile design thinking process model”. This process model integrates: (1) Design thinking elements to identify user needs and problems at the FFE and to rapidly ideate and prototype solution ideas. Design sprints could be integrated to develop strategic iterations of the idea, for example to react to changing requirements. (2) The innovation process could be further improved by integrating agile principles, such as scrum. Scrum offers a process of iteratively developing and deploying ideas on a technical level. On the one hand, the scrum sprints provide agility because after each sprint the direction can be changed. On the other hand, it offers accountability because the team gets feedback on how fast they can go and they can reflect on any roadblocks during the project. (3) Elements of the lean startup method could be added to the innovation process in order to identify a potential customer and develop a viable business model. We argue that the difference between a creative idea and an actual innovation is the successful implementation of the idea. Just having a lot of Post-It notes is not in itself a sign of successful innovation. Without the deployment in a market, the many ideas from a design thinking process are not creating any value for the organization or society. Design thinking, lean startup, and agile principles address adjacent questions and partially share common mindsets and methods. Together they represent answers to different questions. (1) Design thinking offers answers to the question of “how to do the right thing” in terms of serving an actual need or solving an actual problem. (2) Scrum provides answers to the question of “how to do the things right” in terms of technical features and functionality. The method suggests test and iteration loops that improve a new product idea or features thereof. And (3) lean startup answers the question of “how to make the thing viable” in terms of generating a target audience and a profitable business model. Consequently, we argue that an agile innovation process needs to combine all the three approaches. Figure 5.5 shows our suggested process theory, called the “agile design thinking process model”. The process model suggests a combination of design thinking, lean startup, and agile principles. In addition to the integration of the main process steps of each method, we also include several smaller aspects from each of the three concepts. Pivoting as it is practised in lean startup seems to be a promising opportunity to strengthen the design thinking part of the agile innovation process. This means implementing feedback testing and iteration loops earlier in the process, even before there is a prototype. This could happen for example after the Point of View or after Ideation. The testing of early problem

Figure 5.5

Suggested agile design thinking process model

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hypotheses, which can be falsified or validated, might save time and resources and could result in a better output of successful project results. Design sprints would be an appropriate method for this purpose, because they offer a systematic approach to develop, test and, reframe problem hypotheses. Moreover, we suggest implementing metric-based evaluation techniques as they are commonly used in lean startup. For example, testing in design thinking is mostly performed qualitatively, according to the analysed literature. Therefore, checklists or specific test environments that allow for quantitative measuring of user feedback (such as landing page design, etc.) could be implemented in the design thinking part of the agile innovation process. Moreover, we suggest developing a business model in addition to the prototype, to validate the viability of the concept. Vice versa, the lean startup part of the agile innovation process can also be improved through elements from design thinking. Unlike design thinking, lean startup does not describe specifically how customer input could be collected. Qualitative research methods – e.g. ethnographic methods – could be applied to improve the definition of the targeted customers and to identify their needs and problems. Similarly, we suggest adapting the synthesis methods from design thinking. Structured frameworks or the generation of a qualitative persona might help lean startup to better understand and develop their customers and their respective needs and problems. Both should be scheduled at the beginning of the process. Lean startup could also benefit from the use of ideation techniques, as they are applied in design thinking, to develop concept variations. Although lean startup usually starts with a concrete business idea, it might be helpful to use structured ideation methods to iterate that idea within the process, specifically before the problem–solution fit is achieved. Consequently, pivoting should be applied earlier (already on the initial concept). And finally, qualitative feedback evaluation, such as qualitative user interviews, could be implemented in the pivoting steps, in addition to the metric-based evaluation techniques. Based on the analysis of the literature review and process model comparison, as well as on the before-mentioned ideas to improve the discussed strategies, a more radical merging of the three processes suggests itself. As a consequence, we propose an interlaced process theory that combines the main aspects of the three innovation strategies, called “agile design thinking”. This suggested adaptation combines the most promising aspects of the three strategies and addresses the identified gaps. Figure 5.5 shows this model of agile design thinking and highlights the relevant aspects adapted from the three source processes.

IMPLICATIONS The work presented in this chapter has implications for both theory and practice. Our extensive theoretical framework described at the beginning of this chapter brings clarity into the landscape of agile principles. The different concepts are each described in detail and explained in relation to the bigger picture of the innovation process, which advances the field of innovation management further. Moreover, our comparative analysis of design thinking, lean startup, and agile principles indicates that the three concepts evolved in different communities that have similar mindsets but do not interact much with each other. The insights presented in this chapter may contribute to spillover effects between the three communities and facilitate learning from each other. The introduced process model of agile design thinking can be used

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to facilitate descriptive analyses (as, for example, conducted by the DTRS community) of successful or failed innovation projects and the respective processes employed therein. Practitioners can benefit from the insights presented in this chapter in two ways. First, we argue that companies need to understand the entire innovation process including the FFE, the NPD, and the commercialization of an idea. The conceptual map of agile principles brings clarity to the fuzzy potpourri of different methods and concepts that might facilitate this goal, so that each method can be applied and utilized more deliberately in the appropriate situation. Second, the introduced agile design thinking process model constitutes a prescriptive model that enhances innovation processes through agile concepts. We argue that the introduced model is able to decrease the cycle times of an innovation process from ideation and problem validation to company growth and deployment. The work presented in this chapter may contribute to a better understanding of the three concepts – design thinking, lean startup, and agile principles – and it may help entrepreneurs to utilize any of the three strategies for improving their innovation projects. Practitioners familiar with only one of the three concepts can use the model as a source of inspiration to enrich their innovation strategies by adopting the identified relevant tools and methods of each of the other concepts. For entrepreneurs, innovators, and startup companies who may want to develop high-tech innovations, the model provides a more complete view of innovation strategies in general. For researchers, this chapter provides an analytical deconstruction of both methods through method engineering, including a comparison, a mapping of both methods, and the identification of gaps, differences, and intersections. Educators who may want to teach one of the two methods will also benefit from the detailed analysis. And finally, the chapter highlights the relevance of innovation strategies in general for management, business innovation, and user-centred design.

CONCLUSIONS This chapter aims to bring clarity to the agile landscape of design thinking. According to our research questions stated in the introduction, we presented descriptions of nine different approaches that each involve some kind of agile concepts (design thinking, lean manufacturing, the agile manifesto, lean startup, customer development, business model generation, extreme programming, scrum, and design sprints), as well as process models for most of them. These insights were summarized in a conceptual map that outlines the different agile methods in terms of their relationships, overlaps, proximities, and historical roots, with a particular focus on how they relate to design thinking (RQ1). Finally, we introduced the “agile design thinking process model” that constitutes a novel process theory and a prescriptive model to guide and enhance innovation processes (RQ2). Through the combination of design thinking, lean startup, and agile principles, the introduced process allows companies to follow a more agile and flexible innovation process that addresses all stages, from the FFE to the NPD and to the commercialization of an innovation, as well as to react quickly to any occurring changes. We argue that a successful innovation process needs to address three kinds of questions: (1) “How to do the right thing” in terms of serving an actual need or solving an actual problem, which is addressed through design thinking. (2) “How to do the things right” in terms of frequent test and iteration loops that improve a new product or features thereof, mainly in terms of technical functionality. And (3) “making

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the thing viable” in terms of generating a target audience and a profitable business model. These three aspects can be achieved by integrating design thinking, lean startup, and agile principles into one process model. To the best of our knowledge, until now there has been no innovation process model that addresses all of these aspects. Consequently, we argue that the suggested agile design thinking process provides a novel contribution to improving innovation processes for established companies and startups. A few limitations apply to this study. First, we rely in our analysis and suggestions mainly on the mentioned literature and available published process models. These sources may not adequately reflect the actual practices and applications of the respective processes in practice. It is possible that, for example, qualitative ethnographic methods are already well established in lean startup, or that design sprints are already implemented to reframe design problems. However, since these aspects are not yet explicitly defined in a comprehensive process model, they are not yet available in a prescriptive form for others to adapt. Second, the introduced process model of agile design thinking is developed based on evidence from literature and insights from a published case study. Future studies will have to apply and validate the process model through empirical studies or action research.

NOTE 1. Parts of this chapter are based on two previously presented conference papers (Mueller & Thoring, 2012; Thoring & Müller, 2011).

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Liedtka, J. (2015). Perspective: Linking design thinking with innovation outcomes through cognitive bias reduction. Journal of Product Innovation Management, 32(6), 925–938. https://​doi​.org/​10​.1111/​ jpim​.12163 MacCormack, A., Verganti, R., & Iansiti, M. (2001). Developing products on “Internet time”: The anatomy of a flexible development process. Management Science, 47(1), 133–150. Mack, O. (Ed.). (2016). Managing in a VUCA world. Springer. Martin, R. (2009). The design of business: Why design thinking is the next competitive advantage (3570). Harvard Business Press. Martin, R., & Christensen, K. (Eds.). (2013). Rotman on design: The best on design thinking from Rotman Magazine. University of Toronto Press. Maurya, A. (2012). Running lean: Iterate from plan A to a plan that works (2nd ed). O’Reilly. Micheli, P., Wilner, S. J. S., Bhatti, S. H., Mura, M., & Beverland, M. B. (2019). Doing design thinking: Conceptual review, synthesis, and research agenda. Journal of Product Innovation Management, 36(2), 124–148. https://​doi​.org/​10​.1111/​jpim​.12466 Mohr, L. B. (1982). Explaining organizational behavior. Jossey-Bass. Moody, D. (2003). The method evaluation model: A theoretical model for validating information systems design methods. ECIS 2003 Proceedings. ECIS. Mueller, R. M., & Thoring, K. (2012). Design thinking vs. lean startup: A comparison of two user-driven innovation strategies. Leading innovation through design: Proceedings of the DMI 2012 International Research Conference, 151–161. O’Driscoll, K. (2016). The agile data modelling & design thinking approach to information system requirements analysis. Journal of Decision Systems, 25(sup1), 632–638. https://​doi​.org/​10​.1080/​ 12460125​.2016​.1189643 Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers. Wiley. Plattner, H., Meinel, C., & Weinberg, U. (2009). Design thinking. mi-wirtschaftsbuch. Ries, E. (2011). The lean startup: How today’s entrepreneurs use continuous innovation to create radically successful businesses. Crown Business. Rittel, H. (1972). On the planning crisis: Systems analysis of the first and second generations. Bedriftskonomen, 8, 390–396. Schwaber, K. (1997). Scrum development process. In Business object design and implementation (pp. 117–134). Springer. Schwaber, K., & Beedle, M. (2002). Agile software development with Scrum. Prentice Hall. Shostack, L. (1982). How to design a service. European Journal of Marketing, 16(1), 49–63. Shostack, L. (1984). Designing services that deliver. Harvard Business Review, 62(1), 133–139. Sommer, A. F., Hedegaard, C., Dukovska-Popovska, I., & Steger-Jensen, K. (2015). Improved product development performance through agile/stage-gate hybrids: The next-generation stage-gate process? Research-Technology Management, 58(1), 34–45. https://​doi​.org/​10​.5437/​08956308X5801236 Stanford University. (n.d.). The Center for Design Research | Mechanical Engineering. Retrieved February 8, 2020, from https://​me​.stanford​.edu/​research/​labs​-and​-centers/​center​-design​-research/​ center​-design​-research Stickdorn, M., & Schneider, J. (2011). This is service design thinking. Wiley. Stiehm, J. (2002). The U.S. Army War College: Military education in a democracy. Temple University Press. ter Hofstede, A. H. M., & Verhoef, T. F. (1997). On the feasibility of situational method engineering. Information Systems, 22, 401–422. Tessarolo, F., Nollo, G., Conotter, V., Onorati, G., Konstantinidis, E. I., Petsani, D., & Bamidis, P. D. (2019). User-centered co-design and AGILE methodology for developing ambient assisting technologies: Study plan and methodological framework of the CAPTAIN project. 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), 283–286. https://​doi​.org/​10​.1109/​ISCE​ .2019​.8901003 Thoring, K., & Müller, R. M. (2011). Understanding design thinking: A process model based on method engineering. Proceedings of the 13th International Conference on Engineering and Product Design Education (E&PDE). London, UK.

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6. Bridging the academia–industry gap through design thinking: research innovation sprints Ivano Bongiovanni, Peter Townson and Marek Kowalkiewicz INTRODUCTION Calls for a closer relationship between academic researchers, and industry partners and research end-users have been multiplying in recent years, to strengthen the translation of scientific results into usable and useful applications for organisations and society in general (Perkmann et al., 2021). This is the symptom of an underlying misalignment between how academia conceives the research activity and how industry and society at large do. The former is traditionally associated with concepts such as fundamental or pure knowledge, academic freedom, peer recognition, and free dissemination of research results. The latter responds to commercial dynamics in which research activities are instrumental to the elaboration of solutions for perceived problems (applied research). These differences have been brilliantly synthesised as conflicting logics (Sauermann & Stephan, 2013), whose root cause resides in the orientation asymmetry (Estrada et al., 2016; He et al., 2021) that arises when academics collaborate with industry partners and other external stakeholders: different goals and interests mean different approaches, incentives, timeframes, adopted methods, and, ultimately, arising challenges. Responses to these challenges have been proposed for a long time. In particular, the academic side has elaborated several strategies to escape the ivory tower (Haeussler & Colyvas, 2011) and approach industry partners, government organisations, and society in general to foster fruitful collaboration. Among others, innovative engagement and research methods have been suggested to bridge the gap between academia and industry. In business and organisational studies (and in the information systems discipline in particular), a non-exhaustive list includes, among others, engaged scholarship (Van de Ven, 2007), action research (Baskerville & Myers, 2004), design science research (Gregor & Hevner, 2013), action design research (Sein et al., 2011), and last research mile (Nunamaker et al., 2015). At the same time, the proliferation of innovative methodologies at the intersection between scholarly inquiry, R&D, and business consulting has offered new avenues for academics and industry partners to collaborate. Among these, lean management (Hildenbrand & Meyer, 2012), the agile software movement (Highsmith & Cockburn, 2001; Schwaber & Beedle, 2001), and design-led innovation (Wrigley, 2017) have facilitated the creation of multidisciplinary teams composed of scholars and industry partners. 102

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We propose in this chapter a novel approach to doing research with practice (Rai, 2019) called Research Innovation Sprints (RIS). The conceptual and procedural approaches adopted in the RIS are guided by Design Thinking: the priority is satisfying end-user and stakeholders’ needs, whilst meeting the business requirements of client organisations and, where suitable, leveraging the affordances of digital technologies (Verganti, 2009). After initial problem-framing, user and stakeholder needs are unpacked through data collection, solutions are ideated and co-designed (creative divergence), selected (creative convergence), then prototyped and tested with end-users, before the solution specification is delivered to the client organisation for implementation. Design Thinking also informs the toolset and techniques utilised by the research team. We present a selection of our RIS to illustrate the suitability of Design Thinking and design-led approaches in bridging the academia–industry collaborative gap. In the next section, we outline the main components of our innovative method. Then, we offer an overview of 30 RIS conducted by our team, the QUT Centre for the Digital Economy1 (Queensland University of Technology), in the timeframe December 2015–December 2020. Subsequently, we extract learnings from our experience with running RIS on team composition and roles. We then conclude by illustrating how RIS can help bridge the gap between academia and industry by leveraging the potential of Design Thinking and design-led approaches.

RESEARCH INNOVATION SPRINTS: THE METHOD RIS are a type of commercial or contract research (The University of Queensland, 2021) – they differ from traditional research projects for several reasons, which can be summarised as follows: (1) research is applied; (2) they generate income; (3) the university may make some in-kind contributions; (4) publications are likely, but they are not the main purpose of engagement. In RIS, the client organisation commissions the research team to conduct an intervention on a specific problem or sets of problems, to be addressed within a given time (typically between six to eight weeks) and within a certain budget. At the same time, our RIS method differs from similar initiatives executed, for example, by consulting companies. Conducted in a university environment, RIS employ researchers that are experts in academic research and who strive to maintain the methodological rigour typical of scholarly investigation. This encompasses the need for researchers to obtain ethical clearance before collecting data; carefully consider qualitative research criteria (e.g., credibility, transferability, dependability, and confirmability (Lincoln et al., 2011)); and compare and contrast their findings with existing literature. At the same time, the RIS team can leverage the subject matter expertise of fellow academics on specific topics, increasing the knowledge that leads the problem-solving approach typical of RIS. For further differences between research conducted in RIS and similar methods adopted by consulting firms, see the section entitled “Eight Lessons Learned from Running Research Innovation Sprints” below (in particular, Lesson 7). As the RIS developed over time, so did our attempts to apply the method to different classes of problems. We refer to four types of RIS in this chapter, product, process, strategy, and system, based on the framed problems and focus, as detailed in Table 6.1. RIS are constituted of three chronological phases (Pre-Sprint, Sprint, and Post-Sprint), and seven procedural stages (Incubate, Research, Design, Ideate, Validate, Implement, Integrate). From the perspective of the research team, RIS require alternating between part-time and

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Table 6.1 Type of RIS Product

RIS types Summary Focus on the development and re-design of products, services, and user experiences Focus on the development and re-design of organisational business processes and the management, technologies

Process

and policies that drive them, as they relate to products, services, and user experiences

Strategy

Focus on the development and re-design of organisational, civic, and technology-led strategies

System

Focus on the development and re-design of sectors, ecosystems, and legislative change

Source:

The authors.

Figure 6.1

The Innovation Lab at QUT

full-time engagement. The central weeks of the RIS are those in which the team mostly works in a full-time capacity. RIS are also characterised by the regular presence in the research team of staff members of the client organisations, who are “seconded” to the project to corroborate relevance, maximise impact, and rapidly connect with the client organisation, when needed. Generally, RIS take place in a dedicated Innovation Lab at QUT (Figure 6.1), but various phases and activities can take place elsewhere on, or off the university campus. The Innovation Lab is a physical space free from the distractions of normal business practice (McGann et al., 2021). A recurring feature of the RIS is the provocation and presentation

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Figure 6.2

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Typical RIS timeline

workshops, aimed, respectively, at supporting the framing of the problems (Beckman, 2020) and presenting relevant stakeholders with the solutions elaborated during the RIS. Figure 6.2 illustrates the typical structure and the three chronological phases of a RIS. During the Pre-Sprint, the focus is on unpacking the problem space; familiarising the research team with the investigated topics (e.g., through desktop research); consolidating the engagement format with the client organisation; reviewing and selecting the most appropriate research methods to achieve the intended goals; and preparing for the provocation workshop. The latter usually marks the formal start of the Sprint phase. In the Sprint phase, the team works on further comprehending the problem space; “cleaning”, analysing, and drawing conclusions from, the data gathered during the provocation workshop; conducting qualitative and desktop research; mapping customer journeys; co-designing the solutions; visualising and prototyping them; testing them with client organisations and external stakeholders; and preparing the presentation workshop. Typically, the latter represents the end of the Sprint phase. Finally, the Post-Sprint phase is intended to “clean”, analyse, and draw conclusions from, the data gathered during the presentation workshop; set the next steps of the project, by preparing the integration of the solutions into the client organisation; report on the whole Sprint; and deliver the final outputs. Figure 6.3 illustrates the RIS process and its seven stages (Tate et al., 2018). The RIS is built on the model proposed by Google Ventures (2019; Knapp et al., 2016) in both terms of scope (complexity of the problem space) and duration (from 5 days to 6–8 weeks). Conceptually, they both follow a design-led approach to solving complex organisational problems and largely draw from a broad range of tools typically used in Design Thinking (Straker et al., 2021). During a RIS, the research team is composed of a variety of professionals with different roles. At a minimum, the team is composed of design innovation catalysts (from now, catalysts) (Wrigley, 2016), researchers, problem/solution owners (from the client organisation) and digital designers. A more detailed discussion on RIS roles is found in the “Focus on the Research Team” section.

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Source:

Adapted from Tate et al. (2018).

Figure 6.3

RIS stages

Stages of the RIS During the central weeks of the Sprint phase, the catalysts in the team lead the project stakeholders through a problem divergence process that results in concept convergence (UK Design Council, 2019). A detailed analysis of the stages of the RIS is reported in Table 6.2. As shown in Figure 6.3, the RIS process is not entirely linear. The Incubate and Integrate stages open and close the process which, from Research to Implement, tends to be cyclical. In line with the typical development of design-led projects (Wrigley et al., 2020), the understanding of the problem space, the design of the engagement activities, the ideation of solutions and their validation, and the implementation stage are often iterated, based on collected data, feedback from end-users, and emerging needs. However, time availability, the commitment by the client organisation, the complexity of the addressed problem, and resource constraints can enable only one iteration (e.g., from Implement to Integrate, and not back to Research). Further, the seven stages of the process do

Research

space.

organisation agree. Based on our

organisation.

discussed.

Families at Risk. Citizen First Responders), the starting problem space and focus of the project was on contributing solutions to mitigate child protection authority interventions. Iterations of problem framing allowed the team to land on a specific question to address: How do we digitally enable

at different phases of the RIS, not only in the initial ones. Research is also conducted to test prototypes and solutions and collect feedback from end-users. Along these lines, the provocation and presentation workshops always have a research component, more emphasised for the former than the latter.

are most frequently applied in the Research stage. For example, in empathy interviews(Straker et al., 2021; Tripp, 2013), the team conducts semi-structured interviews with stakeholders, clients, and end-users to immerse themselves in their reality and capture data (Marshall & Rossman, 2011).

Incubate stage, desktop research

(academic as well as grey literature

analysis) is conducted to improve the

team’s understanding of the problem

space, by narrowing down its scope to

more defined areas and theories.

families at risk?

community responders to support

During RIS 3 (Supporting

The Research stage usually occurs

Qualitative data collection methods

As an expansion of the previous

and areas of expertise of the team.

considering the diverse backgrounds

space. This is particularly necessary,

terms of knowledge of the problem

Sprint phase.

a particularly complex problem

stakeholders from the client

usually the first deliverable for the client

methods, and various technicalities are

Incubate stage concludes well into the

of the Sprint phase, due to

team and the most relevant

preparation to the provocation workshop,

in which details of mandate, process,

members to reach an alignment in

stretched into the second week

a version upon which the research

problem scoping, and, in general,

meeting with the client organisation,

exercise and allows research team

businesses), problem framing

when the problem is framed in

desktop research, stakeholder mapping,

milestone in a RIS is an introductory

experience, in several instances, the

In RIS 6 (Attracting digital

Incubate is considered concluded

The first (often informal) engagement

The Incubate stage is an exploratory

Example from RIS

Approach

Method Adopted methods in this stage include

Purpose

Stage

Overview of RIS stages

Incubate

Table 6.2

Bridging the academia–industry gap through design thinking 107

Stage

Ideate

Design

drawing of customer journey maps (Solis, 2015) to gauge end-users pain points (e.g., RIS 11, Re-imagining the future of record-keeping). Similarly, user personas are elaborated, stereotypical customer categories to facilitate the subsequent Ideate stage (e.g., in RIS 15, several examples of gig-economy workers

catalyst, whose main task is to ensure the participants appropriately understand the problem space and actively provide their views on both the problem and the potential solutions. At the end of the workshop, the research team moves the collected data and the produced artefacts to the Innovation Lab and sets the stage for the following steps.

to more immediately conceptualise the potential directions of the project (Liedtka, 2018). As in most qualitative methods, data analysis can include coding around emerging themes (Marshall & Rossman, 2011). Frequently, the provocation workshop also offers data to help the team identify design criteria (Liedtka, 2017) and high-level principles to inform the solutions that will be co-created during

participants contribute their “solutions”

to the problem under investigation.

Provocation workshops are designed

and facilitated by the research team

and attended by internal (employees,

managers) as well as external

stakeholders (customers, regulators,

etc.) of the client organisation.

a prototype, or a Minimum Viable

a café).

assistance or RIS 2, Starting

Product (e.g., RIS 8, Grants and

(e.g., RIS 13, Digital strategy),

al., 2015) for further development to the client organisation.

Rosemann, 2015).

collected around the problem space

ideation and co-design.

a proof-of-concept (Nunamaker et RIS process until they are presented

patterns such as ideation lenses (Recker &

the team has processed the information

and is ready to transition to solution

Solutions can take different forms:

These solutions “mature” along the

Ideate is facilitated through thinking

Within the central Sprint phase, after

were conceived by participants).

provocation workshop is the

activities are performed by the

and visuals are often utilised for the team

workshop, during which the

the project.

Example from RIS A common activity during the

Approach In these workshops, most facilitation

Method Collected data are cleaned and organised

Purpose

This stage begins with the provocation

108 Research handbook on design thinking

Stage

Validate

summit) the team presented the proposed solution (a platform to engage students in an international sports event through a large-scale idea and voting mechanism) to the students of one high school, through a simple paper prototype. The students offered their feedback on the prototype, which was then adjusted accordingly.

an essentially customer-focused approach to a more organisational one. As RIS incorporate the presence of professionals from the client organisation throughout most of the project, validation and relevance of the proposed solutions are also built in the process itself. As a result, our experience with running RIS demonstrates that the Validate

research components, for instance, identifying how similar solutions have been proposed in other contexts and organisations and with what outcomes. In Validate, professionals from the client organisation and external stakeholders (e.g., end-users) are usually invited for an informal session of feedback on the solutions.

with external and internal stakeholders

the validity of the proposed solutions,

mainly from a user experience and user

interface perspective.

solutions.

any) adjustments to the proposed

phase usually results in minor (if

Example from RIS During RIS 9 (Youth leadership

Approach This phase marks a transition from

Method The Validate phase encompasses critical

Purpose

In the Validate stage, the team tests

Bridging the academia–industry gap through design thinking 109

Integrate

this included a high-level presentation to a group of strategic decision-makers of one client organisation (RIS 20: Process Transformation Sprint: Payroll Tax).

overarching report, a written/visual document with a twofold purpose: as an accountability and learning tool, to report on the initiative; and as a communication tool, to provide the client organisation with a comprehensive document that can be shared internally and externally. In the two weeks of the Post-Sprint phase, the research team is often engaged on a part-time basis, and team members from the client organisation are engaged on an ad hoc basis, as they transition back to

initiated by the presentation workshop, which summarises and illustrates the works conducted during the RIS. Designed by the research team in the last days of the Sprint phase, the presentation workshop is attended by internal and external stakeholders, who, following presentations by the research team on the solutions elaborated during the project, offer their final feedback. Similar to the provocation workshop, data collected from the presentation are cleaned and migrated into the Post-Sprint phase for re-design and reporting purposes.

Integrate is, chronologically and

conceptually, the closest phase to the

proposed solutions migrating into the

client organisation.

Besides the larger presentation

workshop, the Integrate phase may

require the team to hold smaller-scale

presentations for the client

organisation.

the client organisation.

version of the report is approved by

considered concluded once the final

full-time work. Formally, a RIS is

In the past, for example,

report, a slide deck, or a visual

proposed solutions could have during

focuses on preparing the final

international sports event).

take different forms (a dedicated

what requirements and limitations the

from the RIS are finalised. This phase is

constraints represented by the

a three-year horizon, etc.) and can

help the client organisation realise

step into the Post-Sprint phase),

leadership summit: the temporal

timeframes (a 100-day scale,

is drawn. Assumptions and constraints

representation).

Limitations: (e.g., RIS 9, Youth

action plan can span over different

identified; second, a roadmap to do so

by the client to do so.

In the Integrate phase the team

proposed solutions).

solutions to become reality. Such an

the client to implement the solution are

but more the establishment of a process

implementation.

directly involve students in the

a staged action plan for the proposed

first, assumptions and constraints for

their launch by the client organisation,

In this phase, outputs and deliverables

Proactive University: the need to

recommends the client organisation

the team carries out two essential tasks:

indicate the roll-out of the solutions or

The final stage of the RIS (and first

Requirements: (e.g., RIS 14,

The implementation roadmap

After approval of the proposed solutions,

Despite the name, this phase does not

Implement

Example from RIS

Approach

Method

Purpose

Stage

110 Research handbook on design thinking

Bridging the academia–industry gap through design thinking

111

not necessarily reflect the chronological phases of Pre-Sprint, Sprint, and Post-Sprint. As an example, elements of the Incubate stage can stretch well into the Sprint phase, or components of the Research stage can still be present in the Post-Sprint phase. The next section offers an overview of the 30 RIS analysed in this chapter.

RIS TEAM MEMBERS Since the first RIS run in December 2015, one recurring element has been the changing nature of the research team involved. RIS teams have also changed in terms of size, from smaller RIS run by three team members to larger ones where the team was composed of up to 14 professionals (see Table 6.3), based on the different phases of the RIS, addressed topics, availability of resources, strategic or technical nature of the project, etc. This section presents an overview of the typical roles (see Figure 6.4) in the research team, bearing in mind that each role can be played by more than one team member.

Figure 6.4

Typical roles of RIS team members

Design innovation catalysts are the experts in RIS. Working in close contact with the client organisation, the catalyst operates at the intersection between the customer, the business, and the design “to translate abstractions of research and the realities of practice into value for the organization” (Price et al., 2018, p. 322). Researchers in the team complement the role of the catalysts in facilitating engagement with the client organisation. Their number per RIS varies from one to four, based on the addressed topic and their areas of expertise. Compared with the catalysts, the researchers’ role is more specific, and instrumental to the research component of the RIS. They lead all data collection phases and, due to their familiarity with research, are often in charge of the reporting component of the RIS (Integrate). Problem/solution owners are selected by the client organisation and have a commercial interest in utilising the outcomes of the projects; they are therefore usually well versed in innovation and co-design projects. They work closely with the research team and become part of it for the duration of the RIS. Usually, the client organisation commits up to three staff members to a RIS, part-time (Pre-Sprint and Post-Sprint) or full-time (Sprint). Executive Leadership acts as the steering committee for the RIS. They are typically leaders within the client organisation and their primary role is to act as a sponsor for the problem/

112

Research handbook on design thinking

solution. Problem/solution owners usually report directly to one or more of the executive leadership members. The executive leadership are active in four distinct stages of the RIS, namely Incubate, Design, Validate, and Integrate. Digital designers, despite not always being experts in design thinking, have a background similar to the catalysts and are quite familiar with the format and the engagement model. Among the research team, digital designers have the widest range of tasks. First, the affiliation of the digital designers is variable in the RIS. Often, they belong to a service provider of the client organisation (usually, a digital agency). Sometimes, however, the QUT Centre for the Digital Economy hires digital designers, tapping into its reservoir of graduates with digital design skills. Second, the engagement of the digital designers varies based on the topic, the strategic or technical nature of the RIS, and within the same project. Digital designers have in fact a typically secondary role in the Pre-Sprint (and, often, they are not involved in this phase at all).

OVERVIEW OF 30 RESEARCH INNOVATION SPRINTS (2015–2020) In the timeframe 2015–2020, the QUT Centre for the Digital Economy conducted 30 RIS based on contractual agreements with the following client organisations: a State government agency in charge of innovating and streamlining public services (with the involvement of numerous other departments from the State government) (14 RIS); a State government agency in charge of economic and financial matters (7); a State government agency in charge of environmental and science matters (2); a local government agency responsible for marketing (1); a government-funded organisation responsible for the uptake of precision healthcare technologies (1); a public university (1); financial service providers (2); a large timber manufacturer (1); and a large processed food manufacturer (1). The first block of three RIS was delivered in the timeframe December 2015–February 2016. Over the years, the growth in the number of RIS and client organisations proved the success of this programme of research and engagement, which is still running to date. Depending on RIS requirements and specific outcomes needed for each project, a variety of organisations have been involved as partners in the projects. For half (15) of the RIS, the collaboration between academia, industry/government, and design agencies made up the foundation of the research teams. Where specific technology insights and outcomes were critical to the outcomes of the project, software vendors were also brought into the team. As an additional demonstration of the quality and success of the RIS, of the 30 analysed in this chapter, 19 projects have progressed or are progressing towards implementation by the client organisation, indicating a 63% success rate. It is worth noting that even when the solutions elaborated during a RIS have not been implemented, key learnings and recommendations have nonetheless had a significant impact on the participants and their organisations. Table 6.3 provides an overview of the 30 RIS analysed in this chapter, including details on topics, client organisations, concepts summary, and outcomes.

EIGHT LESSONS LEARNED FROM RUNNING RIS Each of the 30 RIS described in this chapter adopted a mixed-methods approach to collect and analyse data for the purposes of each project, with the different involved organisations (detailed in Table 6.3). In addition, sprint retrospectives were conducted with each RIS team.

(2016)

9

Summit

Youth Leadership

Dept. H

State Gov. Agency

Digital Design

Agency

Dept. D

assistance across the

(2016)

state

Digital Design

Agency

Digital Design

Agency

State Gov.

Dept. G

State Gov.

Dept. F

Digital Design

Agency

Dept. E

State Gov.

Digital Design

Agency

State Gov.

Dept. D

Digital Design

Agency

State Gov.

Digital Design

Dept. C

Agency

State Gov.

Digital Design

Dept. B

Agency

State Gov.

Digital Design

Dept. A

access to innovation

Improving business

through digital

Measuring a state

businesses

Attracting digital

service delivery

b

team partners

Key research

State Gov.

Clienta

8

(2016)

7

(2016)

6

(2016)

and convergence in

Personalisation

comms. challenges

5

Internal ICT and

4

interventions

protection authority

Mitigating child

restaurant business

running a café and

Establishing and

Youth homelessness

Topic

Overview of the 30 RIS

(2016)

(2016)

3

(2016)

2

(2015)

1

(Year)

RIS No

Table 6.3

Prod.

Strat.

Prod.

Syst.

Prod.

Syst.

Prod.

Prod.

Prod.

RIS c

Type of

26

70

36

30

25

19

18

20

7

9

6

5

9

6

7

8

N

N

N

N

N

N

N

N

N

with RIS

Team

engaged 7

education

RIS

25

Client

No in

Stakeholders

No of

generate actionable insights

Commonwealth Games to

mechanism for the

A scaling youth engagement

innovation

a cluster producer for

Government acting as

services

measurement of government

Digital performance

and work

productive people want to live

Where smart, creative, and

service delivery

Autopilot of government

Recommender, Assistant,

knowledge-sharing manifesto

Government

responders

MyVillage: Community first

Proactively starting a café

services

Interface-less government

Sprint concept summary

–

Yes

–

–

Yes

–

–

Yes

–

Implemented?

Bridging the academia–industry gap through design thinking 113

Software

Dept. D

(2017)

16

(2017)

for export markets

ready meals business

Australian-made

of an integrated

Development

and the gig economy

Superannuation

University

15

Proactive

14

of a city

Digital Strategy

re-design 1

Process

(2017)

(2017)

13

(2017)

digital

food package design agency

manufacturer,

consultancy

Food

–

–

–

–

Food

Company

Superannuation

University

Dept.

Local Gov.

Dept. J

State Gov.

Agency

Dept. I

outcomes through

(2017)

12

Digital Design

State Gov.

Vendor

Agency,

Digital Design

team partnersb

Key research

State Gov.

Clienta

youth employment

Transforming

recordkeeping

the future of

Reimagining

Topic

11

(2017)

10

(Year)

RIS No

Prod.

Prod.

Prod.

Syst.

Prod.

Prod.

Prod.

RISc

Type of with RIS

Team

engaged

24

12

192

25

41

21

41

10

4

14

5

5

8

N

N

N

N

Y

N

Y

education

RIS

10

Client

No in

Stakeholders

No of

from ready-eat meals

A global distribution model

platforms

that integrates into gig-job

A superannuation company

university

Prototype your life at

enabled world

a globally connected, digitally

and businesses to thrive in

Empowering residents

tax

A digital platform for Land

services

create future of employment

Using job skill clusters to

insight, and foresight

using. To generate hindsight,

From recordkeeping to record

Sprint concept summary

–

–

Yes

Yes

Yes

–

Yes

Implemented?

114 Research handbook on design thinking

Reducing scam

victimisation

(2019)

Digital strategy

disposal processes

record appraisal and

Reimagining public

re-design: 6

Process

re-design: 5

Process

b

Digital Design Agency

State Gov.

Dept. K

company

services

–

Agency

Dept. D

Financial

Digital Design

Vendor

Software

–

–

–

–

–

Agency

Digital Design

team partners

Key research

State Gov.

Dept. J

State Gov.

Dept. J

State Gov.

Dept. J

State Gov.

Process

re-design: 4

Dept. J

State Gov.

company

manufacturing

Timber

Dept. J

State Gov.

Dept. D

State Gov.

Clienta

re-design 3

Process

resilience

Timber revenue

Process re-design: 2

information

Managing building

Topic

26

(2019)

25

(2019)

24

(2019)

23

(2019)

22

(2019)

21

(2019)

20

(2019)

19

(2018)

18

(2018)

17

(Year)

RIS No

Prod.

Strat.

Syst.

Syst.

Proc.

Proc.

Proc.

Prod.

Proc.

Syst.

RIS c

Type of with RIS

Team

engaged

33

25

80

19

57

52

55

25

57

62

6

9

8

4

8

5

7

5

5

Y

N

Y

Y

Y

Y

Y

Y

Y

N

education

RIS

8

Client

No in

Stakeholders

No of

and nor do we

Scams do not discriminate,

framework

Living digital strategy

retention and disposal

of digital recordkeeping

The cascading pyramid

digital transformation change

translate and implement

as boundary objects to

Using informational artefacts

providers

a network of duties service

Using protocols to enable

across the royalties lifecycle

Front-loading compliance

business and pay its tax

Making it easy to grow my

slave to demand mastery

Timber economy: demand

economic contribution

Client and land profiling for

information value

management to building

Building information

Sprint concept summary

Yes

Yes

Yes

Yes

Yes

Yes

Yes

-

Yes

–

Implemented?

Bridging the academia–industry gap through design thinking 115

re-design:7

Process

precision medicine

Genomics and

tools

Plastic waste policy

service delivery

Proactive government

Topic

Dept. J

State Gov.

Institution

Research

Dept. K

State Gov.

Dept. D

State Gov.

Clienta

–

–

–

–

team partnersb

Key research

Proc.

Sys.

Syst.

Syst.

RISc

Type of with RIS

Team

engaged

57

144

40

40

5

7

4

Y

N

N

N

education

RIS

6

Client

No in

Stakeholders

No of

process

an end-to-end penalty debit

Often, for the delivery of

Engage Early and Engage

Governance Mechanism

State-wide Genomics

of a state

shifts in the circular economy

processes to initiate and lead

procurement and recovery

Waste-considered government

responsive and secure

convenience while remaining

our customers choice and

transformation to give

customer experience

Whole-of-government

Sprint concept summary

Yes

Yes

Yes

Yes

Implemented?

Note: a The Client column indicates the funding partner associated with each research project; b In addition to the QUT Centre for the Digital Economy, based on RIS requirements; c Type of RIS = Product, Process, Strategy, System.

(2020)

30

(2020)

29

(2019)

28

(2019)

27

(Year)

RIS No

116 Research handbook on design thinking

Bridging the academia–industry gap through design thinking

117

A sprint retrospective is a common process reflection activity embedded in agile methods for software development (Dingsøyr et al., 2018). We conducted sprint retrospectives within one month of the Post-Sprint phase; they lasted up to one hour and were carried out either in a face-to-face group or via video conferencing. Each Sprint retrospective discussion revolved around three main topics, namely: what worked well; what could have been improved; and what the team would want to change for the following RIS (Scrum.org, 2022). This process aligns with the action research framework (Susman & Evered, 1978). Data were collected through documented participant observation in RIS, as well as the Sprint retrospectives themselves and RIS project documentation. Data were then coded by chronological sorting of RIS iterations and a thematic analysis of key themes relating to lessons learnt. Building on this retrospective analysis, the following sections outline eight lessons learnt from running our RIS, to help researchers and other professionals replicate our method. Lesson 1: Appoint the design innovation catalyst as RIS leader and project manager The catalyst in the team has a boundary-spanning role tasked with “building a bridge” among client organisations, partner institutions, and end-user communities. This role requires the catalyst to be cognisant of the different attitudes, cultures, norms, and beliefs within their constituencies (Siegel et al., 2004). Whilst academics may be uniquely positioned to facilitate this knowledge transfer (Kunttu et al., 2018), additional design and innovation skills are required to support the translation between research and practice (Norman, 2010). As project managers, catalysts facilitate, and coach the RIS by leveraging their expertise in the use of design methods and tools (Townson et al., 2015; Wrigley, 2016). Among the catalyst’s main tasks, we include the design and lead of both the provocation and the presentation workshops, as well as other internal, ad hoc workshops. Given their skillsets, catalysts often also contribute to the digital design component of the RIS. The catalysts’ role naturally drives them towards product/project ownership (internal to the research team) and makes them the leaders in the engagement with the client organisation. Lesson 2: Support the solution owner to become the design champion The problem/solution owners are the representatives from the client organisation tasked with managing the RIS and actively contributing to its outputs and outcomes. The problem/solution owners are selected by the client organisation based on their expertise and knowledge of the problem space, their ownership of the proposed solutions (once integrated back to their organisation), and their position in an internal team (for example, a manager can participate in a RIS together with their employees). Through an evaluation of the 30 RIS projects, the most successful projects were those where the solution owner became a design champion, advocating for the outcomes of the RIS. A design champion can leverage their knowledge, position, and power within an organisation to advocate, engage and disseminate RIS insights with staff and executive management alike (Wrigley, 2016). New design champions require mentorship to overcome siloed constraints, develop closer cross-functional links and challenge and grow mindsets within their organisation (Bucolo et al., 2012). Design innovation catalysts need to support solution owners on the RIS journey, helping them challenge institutional beliefs and uncover stakeholder insights to develop new innovations. Researchers can also facilitate the development of the solution owner with the reassur-

118

Research handbook on design thinking

ance of a rigorous research project, comprehensive primary and secondary research, and an authoritative voice on the subject matter. Lesson 3: Be deliberate in questioning systems thinking boundaries Good practice in any design-led method is to question the boundaries assumed in any situation. Wrigley’s Assumption Testing Principle (Wrigley, 2017) encourages practitioners to understand that systems thinking approaches alone only enable understanding of a problem through its parts, and that there is benefit in questioning the existence and location of the systems’ parts. The risk of not questioning these boundaries assumes that interactions, processes, technologies, policies, and regulations are fixed, and the impacts of future signals and trends are not considered, leaving the RIS at a high risk of failing. The issue emerges when this boundary questioning is not formally discussed with the client organisation beforehand. If the value in this activity is not understood, stakeholders may disregard the activity or discussion topic, or even fundamentally question the contractual scope of the RIS. We have found that methods that draw on exploring uncertainties, such as futures thinking (Buehring & Bishop, 2020), scenario planning (Colombi & Zindato, 2019), or provocations (Ozkaramanli et al., 2016) help legitimise the discussion within client and stakeholder groups and advance strategic decision-making beyond short-term market needs (Buehring & Liedtka, 2018). By deliberately assigning these conversations to formal and tested future-focused methods, stakeholders gain confidence in the approach and are provided with authority to carry out the discussions. Lesson 4: Instil design thinking learning styles in executive leadership While the executive leadership of an organisation may not be present for the entire RIS, their role is critical in ensuring the RIS are free from unnecessary complications and are provided with agency to challenge current thinking. Mismanaged project expectations, interpersonal conflicts, and misaligned priorities can quickly erode the relationship between executive leadership and the RIS. The RIS team must utilise project resources, stakeholder insights, and facilitation techniques to build the capabilities required to address leadership gaps that are common in design-led projects (Bucolo et al., 2012): • Capability 1: Provide the necessary conditions to maximize the design thinking learning styles of a cross-functional team; • Capability 2: Align the identified competitive advantage to company strategy and brand values; • Capability 3: Scaffold team with the necessary internal resources and guidance to enhance productivity of the process. Based on our learnings, to build these leadership capabilities we must understand the learning styles needed from the executive leadership at each stage of the RIS that they are actively engaged in (Incubate, Design, Validate, and Integrate). Beckman and Barry’s (2008) four design thinking learning styles of diverging, assimilating, converging and accommodating provide the foundation to frame engagement and education for executive leaders during the RIS. Table 6.4 presents the key lessons to design executive leadership engagement across four stages of the RIS, as they align to the design thinking learning styles. These lessons refine attempts to instil design thinking learning styles in executives as well as gaps in organisational leadership found in design-led projects.

Bridging the academia–industry gap through design thinking

Table 6.4 RIS Stage

119

Lessons for executive leadership roles and learning styles Design thinking learning

RIS:

RIS:

style (Beckman & Barry,

executive leadership roles

engagement lessons to develop executive leadership capabilities

2008) Offer initial perspectives of the Incubate

Diverging:

problem/solution space and provide

Education of design thinking learning

See concrete situations from

broader organisational and external

styles; Multiple points of engagement

many perspectives

context for the research team to

and collaboration early on

consider Assimilating: Design

Understand a broad range of data and information and synthesising it logically Converging:

Validate

Find practical uses for ideas and use theories to address problems Accommodating:

Integrate

Support integration by sharing their hands-on experiences

Leaders may participate in or observe

Facilitate group articulation of

stakeholder workshops where

competitive advantage, brand values,

appropriate

and future uncertainties

In course correction workshops, the

The use of underpinning academic

executive leadership contributes to

theories as justification has a polarising

further understanding the insights

effect on stakeholders; test early on

gathered and providing feedback,

how much academic theory is used in

direction, and inspiration

discussion

The executive team receive the

Road mapping, concepts to solutions,

presentation, implementation plans,

further prototyping needs, key issues to

and reports and make decisions

overcome and success metrics are key

on how best to progress the RIS

internal resources executive teams need

outcomes within the organisation

to engage with RIS outcomes

Lesson 5: Pre-empt and design-in mindsets to learn from failure Disciplines that have learnt to manage failure effectively understand that a culture of learning is important, highlighting that exportation-based and hypothesis-testing “failures” are in many ways successful (Edmondson, 2011). As RIS explore innovative concepts within uncertain futures, failure is quick, and the learning potential is high. However, many RIS face organisational blockers impeding the projects’ ability to properly succeed and fail. For managers and executives alike, a number of cognitive traps cloud the ability to learn from failure, including: the dissonance of new ideas conflicting with the status quo, leading to denial and self-justification; narrative fallacy which creates a story where there isn’t one; confirmation bias, which looks for supporting evidence of existing decisions; and retrospective biases, which affirm past successes (Catalano et al., 2018). Research teams in RIS and their client organisations should learn to pre-empt these traps as they are ubiquitous to all RIS. As these run for only a few weeks, the ability to facilitate the psychological safety of staff involved is limited. In innovation teams, psychological safety influences the ability for individuals, teams or organisational cultures to contribute to novel innovations (Edmondson & Mogelof, 2006). Of the 30 RIS, 11 projects involved the client organisation in a “Disruptive innovation leadership course” (Queensland University of Technology, 2021) prior to the RIS commencement, to develop innovation mindsets and practical skills commonly used in RIS. This approach yielded far greater “implemented” RIS than those that did not receive this training (see Table 6.3). This lesson is to add additional time to educate and develop psychological safety for innovation before commencing RIS activities.

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Table 6.5 RIS type Product RIS

Process RIS

Strategy RIS

System RIS

Common visualisation languages used in RIS types Common visualisation languages

Reference (example)

User journey mapping

Lewrick et al. (2018)

Personas

Solis (2015)

Experience prototyping

Keane & Nisi (2014)

Process mapping

Damelio (2011)

Service blueprints

S. Gibbons (2017)

Golden circles

Sinek (2009)

Business model canvas

Osterwalder & Pigneur (2010)

Scenario planning

Wright & Cairns (2011)

Ecosystem mapping

Vink et al. (2021)

Program logic

Goertzen et al. (2003)

This creates organisational mindsets more willing to explore and learn from project successes and, more importantly, perceived failures. Lesson 6: Visualise and communicate intangible and complex concepts Digital designers mainly create the visuals for a RIS. Their skillset can include service, product, UX, and UI design, software development, communication, etc. The relevance of the digital designers grows as the RIS progresses, peaking in the final days, where they are required to produce the visuals of the final artefacts. Digital designers are also actively involved in the design of the presentation and provocation workshops. For product design, service design and user experience projects, Lesson 6 is well known with many authors highlighting sketching, visualisation, mock-ups, and prototyping as core activities throughout design thinking methods (Knapp et al., 2016; Straker et al., 2021). For these types of RIS, the visualisation and communication of physical and digital concepts is well known, utilising journey maps, wireframes, and canvasses to convey ideas. As RIS explore different innovation types across a technology/strategy spectrum, effective visual commutation of intangible and complex concepts becomes more difficult and, as a result, ideas can get lost or misinterpreted. We extracted the following, associated lessons. First, engaging in the visual communication throughout the entire RIS, including the project plan, data analysis, and verbal discussions, is fundamental. Second, there is a need to find common visual languages that the project audience relates to, and to use them consistently. Table 6.5 exemplifies the most common visualisation languages based on the different RIS types we conducted. Third, the research team needs to learn from problem/solution owners, executive leadership, and other external stakeholders what visual language resonates with them. A good starting point is the exploration of published literature and industry reports in similar industries. Lesson 7: Supercharge collaborative researcher practice Researchers, especially upon joining their first RIS, tend to be the least familiar with this type of project. First, the speed and variety of research carried out is usually much higher than in more traditional research projects. This is seen in varying data collection methods; the ambiguity in the several phases of a RIS; and the emergent nature of the findings (Bongiovanni & Louis, 2021). The second challenge sees researchers as a contributing team member in the

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RIS, where they do not typically manage the overall process themselves. The third frustration for the researchers comes with the scheduling issues of balancing their workload with the full-time requirements of a RIS. Over time, researchers do become more accustomed to this type of engagement as they gain confidence and experience the multi-disciplinarity and teamworking of an RIS, two recurrent imperatives in contemporary research policy (M. Gibbons, 1994; Leydesdorff & Etzkowitz, 2000). Overall, research conducted through the RIS enables a closer relationship between researchers and end-users, which numerous researchers and stakeholders have been recently calling for (Rau et al., 2018; Toe, 2021; Tsey et al., 2019). The role of the research component in RIS deserves a separate discussion, as it represents the most dramatic difference between RIS and other design-led engagement formats. Conducted within an academic environment and by professionals that are either academics themselves or have an affiliation with academic institutions, RIS are deeply intertwined with the practices of scholarly research. From a research team perspective, researchers involved in RIS have their performance measured against typical academic KPIs (e.g., quality and quantity of publications). As a result, RIS are structured in a way to also promote theoretical contributions, besides practical impact. The following features are designed into the RIS, to achieve this goal: 1. Client organisations are made aware of the scholarly research requirements of the RIS and the process of publishing all or part of the data collected during RIS is agreed upon ex ante; 2. Ethical clearance is obtained prior to the RIS for data and findings to be published in academic and non-academic outlets; 3. Data collection, as a result, follows academic standards (e.g., informed consent; etc.); 4. Especially during the problem framing phase (Incubate and Research), abundant literature is explored to gain a better understanding of the issues at stake; this then supports the write-up of the literature review section in ensuing publications; 5. Knowledge is typically built over series of RIS (concatenation; Nunamaker et al., 2017), especially when these revolve around similar topics. Despite these features in-built in the RIS, our experience with running these initiatives has taught us that it is not always possible to extract sufficient knowledge from one or a limited number of RIS to lead to an academic publication. There are several reasons for this: first, the prominent focus on problem-solving and elaboration of solutions through creativity, and the search for a prototype (with just enough features to make it testable by end-users), can lead to cutting the research phase short of achieving saturation typical of scholarly research (Ando et al., 2014). Second, and associated with the previous point, the commercial nature of the engagement underlying the RIS makes client organisations’ goals a priority, in which academic publications can, at times, take a secondary role. Third, time constraints require the research team to investigate a particular field of study just as much to achieve an understanding sufficient to then elaborate solutions and test them with end users. In this sense, RIS are exercises in innovative exploration, where knowledge acquisition is instrumental to incrementally solving existing problems, and not so much answering research questions (Stebbins, 2001). Nonetheless, the aforementioned strategies developed by the authors proved helpful in corroborating the solidity of the research component of the RIS and allowed us to publish some good-quality journal articles (Dootson et al., 2021; Scholta et al., 2019; Tate et al., 2018). On this note, over the years, we have reflected on the attractiveness that the RIS format has for client organisations. We concluded, supported by explicit mentions from client organisa-

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tions’ involved in RIS, that clients highly value the academic rigour fostered by the work of the research team. This is line with recent calls for academic research to expand its services towards an entrepreneurial model (Etzkowitz & Zhou, 2017). In addition, this separates the work conducted by the RIS team from more traditional business consulting tasks and prevents the research team from running into competition from more established players. Lesson 8: Bridge the academia–industry gap As an external engagement and research format, the RIS constitute an attempt to address criticism that the university sector has been receiving in the last decades. The main point of contention is the capability of universities to produce research with an impact, which is to say “the contribution that research makes to the economy, society, environment or culture, beyond the contribution to academic research” (Australian Research Council, 2021). High-impact research guides design and practice and produces improvements in outcomes of interest across its field and in the society; despite the growing number of published research papers, there is, however, a shortage of such high-impact research (Nunamaker et al., 2017). This is in stark contrast with the dramatically rapid business and social changes that are currently driven by digital technologies: in this dynamic and competitive environment, the need for impactful and consumable research to assist businesses and society is more pressing than ever. The friction between the ability of scholars to produce impactful research and the demand of society for the same has several root causes that, for the purpose of brevity, we will only list here: scholars’ performance is mainly measured against criteria that do not necessarily reflect how impactful their research is2 (e.g., publication on academic outlets); academic research often takes too long to be completed in a timeframe acceptable and consumable by industry and society in general; academic outputs are often made available to academic audiences only, contributing to a self-referential system; the nature itself of academic enquiry seems to prefer “answering questions” rather than “producing solutions”; fruitful collaborations between academia and industry face important hurdles, magnified by intrinsic, structural differences in the modi operandi of the two; and so on. As a response to these different challenges, similar to some of the approaches discussed at the beginning of this chapter (Hevner & Chatterjee, 2010; Nunamaker et al., 2015; Sein et al., 2011; Van de Ven, 2007), RIS leverage a practice-focused, design-led approach which is demonstrably effective in bringing industry partners and academics together, to propose solutions to real-world problems through the tools and techniques typical of Design Thinking. One last positive aspect of RIS makes them a great approach to strengthen academia–industry collaboration: RIS are topic-agnostic, as their structure and format can be applied to most business and societal problems, multiplying opportunities for multi-stakeholder collaboration across a variety of disciplines and business domains.

CLOSING REMARKS Conceptually, Research Innovation Sprints “live” at the intersection of traditional business research and Design Thinking. They certainly “belong” to the realm of design-led innovation approaches, but see the active, and crucial, participation of researchers that have a solid background in conducting scholarly enquiry. This makes RIS a suitable approach not only to ideate solutions to solve client organisations’ problems, but also to expand current knowledge

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around complex phenomena. The eight Lessons presented in this chapter offer learnings from extensive application of RIS in the nexus between Design Thinking, academia, and industry. We hope to have laid the foundations for other research teams to join us in continuously experimenting with this approach and more effectively bridging the gap between academia and industry.

ACKNOWLEDGEMENTS The RIS described in this chapter were financially supported by the client organisations outlined in this chapter.

NOTES 1. 2.

https://​research​.qut​.edu​.au/​cde/​; until 2020, the team’s name was PwC Chair in Digital Economy at QUT. Current trends in how research councils and funding partners assess quality of research indicate that this is changing. In the UK, for example, the Research Excellence Framework (https://​www​.ref​.ac​ .uk/​) evaluates the quality of research produced by higher education institutions in the country. In the latest editions (2014 and 2021), the impact criterion has gained progressively more weight. The pace of change, however, stays slow.

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7. Design4Health: developing design thinking bootcamps in the Middle East Carlos Montana and Thomas Boillat INTRODUCTION For years, healthcare has been facing several challenging issues, such as limited resources, increasing demand and increasing expectations, as well as increasing pressure on medical professionals (Kelly & Young, 2017). More recently, in 2020, the COVID-19 pandemic and associated consequences brought another level of complexity to healthcare, requiring fast and agile adaptations to cope with unknown situations. To solve these complex challenges, being able to identify user needs, understand constraints, find ideas, and quickly test and refine them, are the skills required to maximise the chance of successful adaptation and innovation. In this regard, design thinking, often defined as a human-centred design approach to innovation, has received increasing attention from healthcare stakeholders (Ku & Lupton, 2020). In this chapter, we briefly describe the evolution of design thinking and human-centred design, to provide the reader with a background for a better understanding of its development and relevance. We then present the pedagogical approach taken for the development of the Design4Health Bootcamps, leading to a description of the programme developed in Dubai by multiple collaborating higher education institutions. The aim was to include students from different disciplines (health, engineering, and design) to explore the art and science behind health innovation, through multidisciplinary collaborative workshops with multiple stakeholders. We then introduce the Design Thinking Journey Map (DTJM), a methodology and tool designed and developed by one of the authors, to facilitate the implementation of design thinking in this context and explain its use in the Design4Health Bootcamp. We end this chapter with some recommendations on how to implement our findings in health innovation training.

MULTIDISCIPLINARY DESIGN THINKING APPROACHES IN HEALTHCARE Design thinking is a practical approach to innovation that offers an iterative process to solve complex problems, building upon collaborative co-creation activities. It has been widely used to innovate in industry, and academia as a tool for collaborative, problem-based learning towards innovation. Design thinking combines convergent and divergent thinking, visualisation techniques, multidisciplinary teams, and a structured approach to human-centred design. This human-centred approach to overcoming challenges has direct and relatable advantages to future health professions, by encouraging and providing strategies to empathise with different 127

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stakeholders: being a patient, their family, a nurse, or a medical practitioner, among others. Design thinking can be at the centre of healthcare innovation and applied to different aspects of healthcare practice, be it in situ or remotely, as exemplified in the now ubiquitous e-health, digital health, and telemedicine practices. In this section, we summarise the evolution of user-centred and human-centred design practices, as a background to better understand the design thinking process as applied to healthcare. Human-Centred Design (HCD) User-Centred Design (UCD) or Human-Centred Design (HCD) are terms closely associated with design thinking. Related topics such as human factors, ergonomics and anthropometrics were some of the seminal approaches of design and engineering focused on the wellbeing of people, developed mostly in the last decades of the 20th century, and focused on the user. While human factors and ergonomics are often interchangeable, one of the simplest definitions is proposed by Dempsey et al. (2000, p. 6.), as “Ergonomics is the design and engineering of human–machine systems for the purpose of enhancing human performance”. Early pioneers of industrial design such as Norman Bel Geddes and Henry Dreyfuss also pioneered a focus on people, exemplified by the 1955 classic Designing for People, a book by Dreyfuss and Associates, which included many anthropometric charts where different dimensions of men and women were compiled. This focus of design towards people has been evolving from initial physiological approaches to subsequent psychological and emotional approaches. For example, initial physiological approaches included ergonomic studies where the postures of workers and their interaction with furniture and machines were studied with the aim of enhancing comfort and minimising human risk. With the advent of computers and a new HCI field of study, authors as Donald Norman formalised a user-centred approach to design, by placing end-users at the forefront of the design process (Norman, 2013). In this new approach, psychological and emotional aspects became a focus. Related topics, such as “usability” or “user-friendliness” were also developed, especially in the digital realm. While there are many discussions around differences or similarities between UCD and HCD, we will argue that a user-centred approach focuses on individuals as “users” (or even consumers) of products and technologies, while a human-centred approach is more holistic. According to Giacomin (2014, p. 3), “Today’s human centred design is based on the use of techniques which communicate, interact, empathize and stimulate the people involved, obtaining an understanding of their needs, desires and experiences which often transcends that which the people themselves actually realized”. Furthermore, Steen (2011) describes six human-centred design approaches, including participatory design, ethnography, the lead-user approach, contextual design, co-design and empathic design. A main component of Human-Centred Design, as well as design thinking, is empathy. According to The Greater Good Science Centre at the University of California, Berkeley (2021), “the term empathy is used to describe a wide range of experiences. Emotion researchers generally define empathy as the ability to sense other people’s emotions, coupled with the ability to imagine what someone else might be thinking or feeling”. In simpler terms, empathy is often described as “being able to walk in other’s shoes”. In current design and innovation approaches, participatory design, co-design and HCD tools are still desirable, valid, and widely used both in industry and academia. However, contemporary academic discourse and research also critique the “human-centric” design and innovation

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approaches, as part of an “anthropocentric” view, which is the root of many problems derived from the industrial revolution, such as environmental and complex problems of our times. Thus, researchers are proposing a “post-human” design and innovation approach, which is not human-centric, but focused on protecting all forms of life, including fauna and flora. While the critiques of anthropocentric approaches to human-centred design are valid and timely, especially around topics of sustainability and the depletion of natural resources, it could also be said that a human-centred approach to design is also in line with the social aspects of sustainability. Such approaches to design in healthcare also include salutogenic design (or design that focuses on a positive impact of the build environment in human health) and biophilic design, where people are connected to nature in the built environment, also to improve people’s health. Design Thinking and Healthcare Design thinking can be understood in different ways. From an academic perspective, as proposed by design theorists Nigel Cross (2011) and Brian Lawson (1997) among many others, design thinking is the “designerly” way in which design practitioners think. However, from a more applied perspective, design thinking has become an approach to innovation, popularised by design consultancies such as IDEO and institutions such as the UK Design Council. To explain design thinking, many models of the process have been developed, ranging in a number of steps or phases, from two steps to seven or more. In a widely cited 2008 Harvard Business Review article, Tim Brown describes the process as “iterative cycles of prototyping, testing and refinement” (Brown, 2008, p. 4). The cyclic nature of the process is widely described as the core of the design thinking process, and can be visualised as one overall cyclic, iterative process. Design thinking is also said to be a combination of both divergent and convergent thinking. Two of the most widely used models are five-step visualisations, popularised by IDEO and the Stanford d-school. We will not discuss these models here, as these have been widely discussed, and are not the focus of the paper. However, it is worth mentioning the development of numerous tools to facilitate the design thinking process, including the IDEO Method Cards, and the Stanford d.school bootleg. For the Design4Health Bootcamps described in this chapter, we used the Double Diamond model developed by the British Design Council in 2005 as a basis for the adapted model we will describe below. The Design Council “double diamond” model proposes four steps in the creative process: (1) discover, (2) define, (3) develop and (4) deliver (Design Council, 2005). In summary, different design thinking models and approaches are relatively similar and comparable in their phases, as well as the overall iterative process and divergent-convergent approaches. In healthcare, many papers report the use of design thinking, co-design, UCD, HCD and design with empathy focused on healthcare innovation around the world, especially since the first decade of the last century, and coinciding with some of the dates when the models described above were developed. Some of the most relevant topics as described through highly cited papers are social innovation (Brown & Wyatt, 2010), healthcare management (Roberts et al., 2016), and education for healthcare (Altman et al., 2018; Badwan et al., 2018; MacLoughlin et al., 2019), among others. Relevant to the Middle East region, which is the focus of our chapter, is the study by Traifeh and others (2021), which maps design thinking in

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the Arab world, tracing the first translation to Arabic language of the term “design thinking” to 2010.

LEARNING APPROACH TO DESIGN THINKING BOOTCAMPS In the early 1970s, Roskilde University in Denmark developed a program to modify the educational system, which at the time was based on traditional memorisation and rote learning. This new model, named Problem-oriented Project Learning (PPL), emerged to address complex problems in more broad multidisciplinary groups (Blomhøj & Kjeldsen, 2009; Olesen & Jensen, 1999; Olsen & Pedersen, 2008). Although sometimes interchanged, PPL is related, although not identical to Problem-Based Learning (PBL). In both methods, students learn by actively engaging with real-world problems which don’t have right or wrong solutions, but rather less or more adequate solutions according to the situation, time, context and user. Students also work in teams, and through relatively long periods of time that may vary from an intense week to a whole semester. These learning methods develop and require critical thinking, collaboration, communication, and problem-solving skills. More recently, Kolb (2014) has developed the idea of Experiential Learning (EL), explained through his cyclical model that illustrates how learners draw upon experience (feeling) that can be reflected upon (watching) to conceptualise a theory or model (thinking) before implementing said theory or model (doing). Experiential Learning can be achieved through educational techniques as such PPL, PBL, action learning and team-based learning. In our Design4Health Bootcamps, which will be described in more detail in the next section, we used EL, PPL and PBL techniques to enhance inter- and multidisciplinary collaborative co-design, to address challenges around Health and Digital Health, especially in the local context in Dubai. Real-life challenges were posed by local collaborators, such as hospitals and the health authorities, including institutions such as Mediclinic and Kings Hospital in the first bootcamp, and the Dubai Health Authority in the second bootcamp. Participants were provided with selected topics around Design4 Health, which were considered as relevant immediate problems by the collaborating institutions. They were then given the background and context of the issue by representatives of the institutions. Working in multidisciplinary groups, participants focused on specific areas, and conducted both primary and secondary research around the topics, for example benchmarking best international practices around a topic, and then conducting unstructured interviews with doctors and nurses who were facing a specific challenge in one of the collaborating institutions. In terms of the facilitation of the Bootcamps, the three main tutors (including the two authors) provided their specialised points of view (medicine, design, and computer science) as well as a structure for the Bootcamps through a pre-established curriculum which included short “knowledge capsules” as a theoretical basis for participants to engage in the design thinking process. While there were some short lectures involved and the tutors adopted an instructor role in the process (often related to passive learning), the main core of the experience was around active learning, where the tutors mainly acted as facilitators, mentors, and expert consultants.

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DESIGN4HEALTH BOOTCAMPS The first Design4Health Bootcamp in Dubai was initially designed in 2019 to introduce students to the concept of Health Innovation. To build on each other’s knowledge, experience, and skills, three universities from the United Arab Emirates collaborated, namely two new institutions, the Mohammed Bin Rashid University of Medical Sciences (MBRU), the Dubai Institute of Design and Innovation (DIDI), and the more established American University of Sharjah (AUS). The Design4Health bootcamp has been built on three main concepts: (1) Multidisciplinarity, (2) Immersion and (3) Impact. Multidisciplinarity has many advantages. First, it allows for viewing the world from different perspectives. Sharing opinions, assumptions and ideas creates opportunities for the students to debate and converge towards a single, but a common view. Second, multidisciplinary work allows participants to complement each other’s skills, competencies, strengths, and weaknesses. Design thinking requires a set of skills and abilities that cannot come from one single individual or discipline. Each participating team in the Design4Health Bootcamps was purposely assembled to include medical students, students in design and students in engineering. We believe that these three disciplines represent the core of what a design thinking project focusing on health, requires. Immersion: though there is no single format to teach design thinking, we believe that immersing students in an intensive programme provides the best outcomes for the following reasons: (a) students do not know each other and require some time to understand the specificity of each discipline and to build trust. (b) Challenges addressed with design thinking are very often weakly defined. Participants thus need time to capture information about the challenge and to navigate ambiguity. It would therefore not be appropriate to interrupt the participants in the middle of an interview and data analysis session. (c) To keep the momentum and the participants engaged, we have experienced that short but intense teaching modes are more appropriate. Impact: the choice of the challenges was also very important to maximise the outcomes. As the name suggests, the Design4Health Bootcamp has a strong emphasis on health. We always make sure that the challenges have a high impact, and the environments are easy to access, to enable participants to interview and observe stakeholders. We thus work with real-world local challenges through collaborations with local hospitals and health authorities. To better engage students and participants with the process of design thinking, we created our own model of the process, inspired by the Double Diamond model proposed by the Design Council (2019), mainly because of its clarity and an emphasis on iterations of both the divergent and convergent phases. While the Double Diamond model originally has four distinct steps, which we will call phases – Discover, Define, Develop and Deliver – we integrated additional visual elements to provide more support to design thinking learners. As shown in Figure 7.1, our design thinking process has two main spaces: (1) the problem space, which includes the steps Understand & Empathize and Define; and (2) the solution space, which is separated from the problem space by the step Problem definition. The solution space consists of three steps, which are Ideate, Prototype and Test. Following the Design Council’s model, our model has four phases, two are divergent and two are convergent. Additionally, we integrated two explicit iterations, visualised as arrows; one between the steps Define and Prototype and the other between Prototype and Test.

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Source: Adapted from the UK Design Council Double Diamond.

Figure 7.1

Design4Health Bootcamp design thinking process

2019 Bootcamp 1 In the first edition of the Bootcamp in 2019, 14 students formed three groups, each composed of two medical students, two computer engineers and one designer (except for one group that only had one engineer). Each team worked independently of the other teams, focusing on one challenge. Although three hospitals participated, two suggested the same challenge – i.e., decreasing the number of no-shows in outpatient clinics, while the other hospital asked a team to investigate how to increase satisfaction feedback from outpatients. The curriculum was planned around introductory content delivered through 10–20-minute mini-lectures, or knowledge capsules, followed by discussion and implementation sessions of at least 30 minutes. This initial Bootcamp was conducted over a week and planned to allow participants to experiment with roughly one phase of the Double Diamond design thinking model per day (during the first four days) and then allocate the final day for final presentations of solutions in front of experts in the fields of medicine, design, and engineering, as well as representatives from the participating hospitals. In this first iteration, participants were required to provide multiple solutions, based on effort and impact matrices for evaluation of solutions. All groups proposed low-effort high-impact solutions (short-term improvements); for example, minor improvements in text messages to patients to improve communication and avoid now-shows to previously booked medical appointments. Some of these solutions were so simple that representatives from the participating hospitals stated they would implement them shortly. The other, more interesting type of solutions were high-effort high-impact solutions (futuristic or long-term improvements), which included more exploratory, future-oriented, and potentially costly solutions, such as robotic hosts for hospitals to improve the patient experience.

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While a proposed second version of the Bootcamp was planned for 2020, due to the COVID-19 global pandemic and consequent lockdowns and restrictions, the face-to-face Bootcamp could not be conducted and was replaced by different efforts from the institutions, including a Hackathon around COVID-19 organised by MBRU, and an Agile Factory, a response to the pandemic by DIDI, where design students and faculty developed and produced personal protective equipment and other solutions to the challenges posed by the pandemic. 2021 Bootcamp 2 The latest edition of the Design4Health Bootcamp took place in August 2021 in Dubai, United Arab Emirates. It was conducted for two weeks and involved four teams with a final total of 15 students from four different universities: five medical students from MBRU, four design students from DIDI, three engineering students from AUS, one student in bioengineering from Trinity College in Dublin, one student in engineering from Imperial College London and one student in medicine from the same university. Each team worked on one of the following challenges: (1) support the mental health of frontliners involved in COVID-19 related activities; (2) improve the journey of preventive medicine in private clinics; (3) improve the management of beds across hospitals for critical COVID-19 cases; and (4) improve communication between frontliners and management in COVID-19 isolation centres. Unlike the first edition, the 2021 edition ran over two weeks. This duration aimed to provide students with more time for the collection of data as part of the “understand and empathize” phase and during the prototyping and testing phases. In total, the participants received 12 knowledge capsules, with varying topics, which ranged from conducting interviews to defining needs and problem statements, ideating, prototyping, and testing. To provide a deeper knowledge of practical aspects of digital health, the authors also sourced 11 talks from external speakers, including entrepreneurs, practitioners, and educators. This second Bootcamp was planned with an allocation of 30 hours for hands-on activities and 8 hours of field observation. The Bootcamp ended with the final presentations in front of the collaborating institutions who proposed the challenges. Following the design thinking process of iterations for continuous improvements, the second version of the bootcamp built on what we learnt from the first iteration. Because a lack of feasibility of many solutions was identified in the first Bootcamp, the second one included many talks by entrepreneurs in health, looking to better connect participants to the business and real-world of the health challenges explored. In view of the hybrid communication modes explored by all academic institutions during the 2020 pandemic, another improvement was to include international collaborators, who joined from the UK. Finally, one of the most important improvements in the second edition of the Bootcamp was the creation and use of the design thinking Journey Map, a methodology and toolkit to support the understanding and implementation of design thinking. Development of the Design Thinking Journey Map As presented above, design thinking is an approach that is often represented through a visual model of the process. The latter guides the design thinker along with a series of steps and

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helps them to know what is expected to be done. It is up to the design thinker to find a way to complete these steps. Without proper guidance or experience, this task can easily become cumbersome. To support design thinking learners, specialised companies and educational institutions have developed tools such as websites and kits that compile and describe tools and techniques that can be used. For instance, in 2003, IDEO developed Method Cards,1 and the Stanford d.school bootleg2 developed manuals that support the execution of design thinking by means of methods and cards. Although these different tools have proven to be very useful, we argue that their usability limits their impact. The cards are often not linked to the design thinking process’ steps, while some of the methods tend to be lengthy. For these reasons, the MBRU Design Lab, led by one of the authors, created the Design Thinking Journey Map (DTJM), a methodology that aims to help design thinkers plan and execute their design thinking projects. This tool was built with learners, academics, and practitioners, following the design thinking process of iterations of testing and refinement. The DTJM tool is based on the hybrid model of the design thinking process we used for the first Bootcamp, which integrates elements from the previous models discussed. The DTJM’s development was as described below. (1) Understand and empathize (1 month): During the last five years, we have delivered more than a dozen multidisciplinary Bootcamps and hackathons to introduce participants to human-centred design using design thinking. To understand what training materials are usually given to design thinking learners, we reviewed the work of three pioneering institutions in design thinking: Stanford d.school, IDEO and Hasso-Plattner Institute (HPI). (2) Define (1 month): Our observations revealed that without active guidance, participants had difficulties applying design thinking effectively. They were often lost in the process without knowing what to do. Existing research also demonstrates that participants have difficulties grasping what is expected from them throughout the design thinking process (Lor, 2017; Valentim et al., 2017). (3) Problem statement: The DTJM assists learners to effectively learn and apply design thinking so they can leverage HCD to address weakly defined challenges. (4) Ideate (2 weeks): After brainstorming many solutions, the idea of combining the concepts of board games and puzzles received the most attention. (5) Prototype (2 months): The prototype was made up of two elements. (1) A board, including six design thinking’s key steps – (a) understand and empathize, (b) define, (c) problem statement, (d) ideation, (e) prototype and (f) testing. For each step, we listed a set of requirements based on our experience. (2) A set of tools, designed as puzzle cards, which support the realization of the requirements. These tools were adapted and compiled from existing copyright-free toolkits. (6) Test (4 months): For an initial test, we gave the prototype to ten design thinking learners, five who had used design thinking one time, and five who had never heard of it. For the latter five, we introduced them to HCD through a 15-minute lecture. Individually, the participants were asked to plan a fictive design thinking project using our prototype. To test the usability of our prototype, we relied on the cognitive walkthrough evaluation technique and asked participants to think aloud about what they were trying to achieve and how they were using the prototype (Rieman et al., 1995). Feedback was very positive and supported our design.

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Although this first iteration allowed us to validate our concept, the conception of the final version required additional steps: we conducted interviews with five design thinking experts from academia and industry to validate and complement the steps’ requirements. In parallel, we investigated publicly available materials of IDEO, HPI and d.school to gather their requirements and learning materials. The number and types of tools were also reviewed and complemented from the expert feedback. After receiving feedback and being modified, the concept received an additional round of evaluation with end-users. The first evaluation involved 15 students (from different backgrounds, mainly medicine, engineering and design) who used the DTJM as part of the two-week, onsite, Design4Health Bootcamp described above. At the end of the two weeks, they filled in a survey to share their experience with the DTJM. When asked whether they enjoyed using the DTJM, they all replied positively, stating that it helped them organise their work and thoughts. One participant wrote that the “DTJM used a more structured way to use DT, compared to what I received in college”. Most of the participants also responded that the DTJM was easy to use and very intuitive. When asked if the DTJM helped reduce potential inconsistencies in the execution of DT, participants also all replied positively, citing that “it’s structured in a consistent way if you follow the steps, it’s difficult to make mistakes”; “because for each step you know what is expected, it helps”. One participant wrote “as much as it helped me, I believe sometimes there are other ways to help different people. No one way of anything will be perfect for everyone”. The final outcome is the DTJM. We chose this name as we argue that our tool supports design thinkers in planning and executing their design thinking projects. The DTJM is available as a physical box as well as a digital template that is available on the collaborative digital platform Mural. In its final version, the DTJM’s board is made up of six steps, each described using key requirements. The latter helps design thinkers know when the step is completed. Unlike many design thinking implementations, we created a specific step dedicated to the problem statement. The problem statement is the step that separates the “problem space” from the “solution space”. We then argue that it plays the role of delimitator and must be explicitly represented. Each step is represented by a colour that helps DTJM’s users with the selection of the tools that are available to support its completion. The tools for each step were explained in physical cards, which were built in a puzzle shape, to guide design thinkers to follow the flow of the design thinking’s steps. For instance, the cards “Define” cannot be connected with the cards “Ideate”. Only when a problem statement is defined, are the “Define” and “Ideate” cards connected. Apart from the sequence, the DTJM does not require the design thinkers to use any particular tools (i.e., cards) as it will depend on the nature of the challenge and the type of the project. As shown in Figure 7.2, the steps are displayed horizontally, while we recommend the users display the cards in the sequence by which they were executed within each step. The most recent evaluation of the DTJM involved 69 first-year medical students who took part in a course on innovation, as part of their curriculum. During six weeks, the students learnt the concept of human-centred design through a challenge aligned with the needs of the local population. For the year’s cohort, students were asked to design solutions focusing on weight management and obesity. The course took place on-site, where students worked in teams of six for two hours each week. The course followed the structure of the DTMJ. We opted for both quantitative and qualitative measurements through an online questionnaire that was distributed at the end of the course. With 58 answers received, the participation rate was 84%. Amongst

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Figure 7.2

Research handbook on design thinking

Extract of the board and cards of the Design Thinking Journey Map (DTJM)

the respondents, 54% had never heard of design thinking and 36% had heard about it but had never applied it. The respondents evaluated the DTJM as is detailed in Table 7.1.

RECOMMENDATIONS AND CONCLUSIONS Building on scientific evidence and prior experience, this section aims to reflect upon three years of bootcamps and hackathons in Dubai, to provide guidance for institutions that plan to integrate similar programs as part of their offerings. The first question that we faced when setting up the first edition of the Bootcamp was “How and where to start?” Looking at existing literature and amongst our network, we could not find much information when it comes to designing learning objectives, finding teaching materials and identifying suitable teaching modes. Due to the unavailability of resources, after running and evaluating one iteration of the Bootcamp, we decided to publish its structure along with its learning objectives for any institutions that want to embark on a similar journey (Boillat et al., 2020). Finding the appropriate timing to run the Bootcamp was the second challenge we faced. The intensity of

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Table 7.1

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Results of online questionnaire to evaluate the DTJM Strongly disagree

Disagree

Neutral

Agree

Strongly agree

I quickly learnt how to use the DTJM

0 (0%)

1 (2%)

9 (16%)

29 (30%)

17 (52%)

I did not notice inconsistencies with the design of the

3 (5%)

7 (13%)

8 (14%)

24 (43%)

14 (25%)

1 (2%)

0 (0%)

5 (9%)

30 (54%)

20 (36%)

0 (0%)

1 (2%)

9 (16%)

27 (48%)

19 (34%)

0 (0%)

1 (2%)

6 (11%)

29 (52%)

20 (36%)

1 (2%)

1 (2%)

5 (9%)

18 (32%)

31 (55%)

DTJM The DTJM has helped me understand and apply design thinking The DTJM helps to reduce potential mistakes linked to the execution of design thinking The DTJM improves the quality of the solutions my team and I designed The DTJM promotes teamwork and collaboration

the course (5–10 consecutive days) and its multidisciplinary aspect make its integration into existing curriculum very difficult. Each institution has its own academic calendar and list of events. As a result, the editions of the Bootcamp were organised during the second part of the summer break in the UAE, which is in August. This timing had an impact on the availability of students. As explained above, multidisciplinarity is one of the Bootcamp’s core pillars. From a participant’s perspective, working with students from other universities and disciplines can be overwhelming at first. It is thus critical to design ice-breaking activities, to allow participants the opportunity to get to know each other, as part of the creative activity. These activities required participants to collaborate, as working alone there was no chance to accomplish the activities. During the first edition, the participants were challenged to construct, in teams, the highest possible towers made of spaghetti and marshmallows (popularly known as the “Marshmallow Challenge”), while in the second Bootcamp, participants had to draw one personality trait, which their peers had to guess. To better guide participants along the design thinking process, we realised that methodological support was necessary. For this reason, we designed the Design Thinking Journey Map. Not only did it support instructors and participants, but it also became a common language, a facilitator of multidisciplinary dialogue between people with different backgrounds and ways of thinking, to collectively approach complex challenges and propose contextualised solutions. Although the majority of the participants found the DTJM easy to use and useful, some still faced some challenges to understand how to use it. As part of our future plans, we will create a series of video tutorials that explain in-depth the application of the methodology using use cases. Amongst the evaluation with medical students, only one had previously applied design thinking. It was interesting to read that the participant found the method helpful to develop solutions of higher quality and helped to bring more structure to the process. While from this single answer we cannot make any generalisation, it would be interesting to evaluate the DTJM with more experienced design thinkers. The DTJM also helped in rebalancing disparity with existing knowledge and understanding, mostly around design, a word that has different meanings for different people in varied contexts. While most design student participants were very familiar with the design process, even if coming from very early years of study, when medical practitioners hear about design

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as a general concept, it is usually interpreted as mostly “drawings”, or “making things look good”. In parallel, design is also a common word used in engineering. For engineers, design is a method to solve problems. Actually, “engineering design” is taught not only in many faculties of engineering at the tertiary level, but also in many primary and secondary schools (NAGB, 2021). We observed that the DTJM is also very effective in encouraging students to advance progressively through the design thinking steps, without jumping straight into obvious solutions. Participants with some knowledge of the process, but with limited experience, will often tend to jump very quickly into an early or pre-determined solution, without fully exploring the nature of the problems and the variety of possible solutions. In this case, sometimes we observed students and participants prototyping very early in the process, before realising that their solution did not match the identified needs, or was an obvious, and non-innovative solution. While the DTJM is accessible online as part of a collaborative platform, we also observed that its physical alternative is more adequate to nurture dialogue and collaboration. After experiencing a fully onsite and face-to-face Bootcamp for the first edition, and a hybrid mode for the second edition, we believe that the former is the most beneficial approach for students and collaborators. We base our recommendations on two observations: first, the nature of the challenges makes a hybrid or online version of the Bootcamp very challenging, unless the remote participants form teams based on their geographic locations. For instance, this year’s challenges were very specific to the Middle East. Therefore, if online is the only possible teaching mode, then the challenges must be chosen accordingly. Second, we realised that students from different disciplines have difficulties to collaborating with each other and keeping track of who is doing what. Regardless of the teaching delivery mode, it is important to give enough time to students to discover the DTJM and to use the appropriate cards depending on their challenges. As challenge facilitators or faculty, it is always good to know whether the participants have used design thinking in the past. Accordingly, the facilitators should adapt the amount of teaching and information to ensure that the participants stay curious and decide on their own what tools are the most appropriate. When comparing the first and second editions of our Bootcamp, it is relevant to consider duration, type of challenges and collaborators, participants, and types of outcomes. In relation to duration, the 2019 version was relatively shorter, as it was 1 week in duration, versus the 2-week program in 2021. However, the daily work was more intense in 2019 as the schedule was from 8:30 am to 4:30 pm, while in 2021 we worked from 10 am to 4 pm. While the original intention of the second bootcamp was to have more time for the in-situ observations and in-depth interviews with users, we believe this was not necessarily achieved, and think the ideal duration is still an intensive 1-week, as in our first bootcamp. In relation to the type of challenges and collaborators, in the first bootcamp we worked mainly with private institutions (hospitals) who had very specific challenges related to their business. The pros of this were that the challenges were specific and easy to understand, and this was reflected in the outcomes too, which in some cases were easily implementable solutions. The cons were mainly from a pedagogical perspective, where student participants did not have the possibility to explore broader, complex, and meaningful topics. In contrast, in the second bootcamp we worked with both private hospitals and governmental institutions, such as health authorities and public institutions. Here the challenges were more open-ended and

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complex, which was beneficial for student participants. However, some of the challenges also dealt with sensitive topics, for example the management by the government of the COVID-19 isolation centres for low-income foreign workers. In both cases, the results and engagement with collaborators also differed. In terms of results, we observed that the visualisations and proposals of the first bootcamp were more detailed and refined than the solutions by the participants of the second bootcamp. On one hand, the complexity of the challenges affects this. Simpler challenges meant simpler solutions, which were possibly easier to visualise, while more complex and open-ended challenges were more difficult to prototype, visualise and explain. The complexity and sensitivity of the topics addressed in the second bootcamp also influenced the perception of the solutions by the collaborators. In the first bootcamp, the collaborators were very happy with the broad array of ideas, many of them easily implementable, such as an app, or modifying a text message to make it easier for the patients. However, in the second bootcamp, the complexity of the challenges also meant that some participating student groups proposed some generic, or in-progress solutions, which required further thought and detail. In addition, due to the sensitivity of topics, solutions proposed by students were perceived by members of the collaborating institutions as a critique of their existing procedures. Therefore, to ensure that the students’ message is presented effectively, it is key to conduct an internal pre-presentation of solutions, before presenting them to externals. This works as another “test” and “refine” iteration of the solutions, which can benefit from peer feedback from all the participants, and can also avoid uncomfortable situations or miscommunications with collaborators. Finally, another consideration for future bootcamps and similar programs is the limited financial viability, lack of continuity, and implementation of solutions. While so far, some institutions have been interested in implementing some of the ideas, we have not had the opportunity to validate if they have actually done so, or to follow up on the further refinement and completion. Such activities would require additional support from one of the universities’ technology transfer offices, which has not materialised yet due to the recent founding of both institutions. While the main aim of this type of programme is around merging teaching and learning with applied research, it is desirable to try to extend the duration and further development of the initiatives, to create more impact in society. For example, a possible way forward can be, in the short term, to include students from a business school in a future iteration of the bootcamp. However, from a long-term perspective, ideally, this programme would be housed within the existing curricula of the respective universities, or further developed through research centres and/or entrepreneurship and incubation programmes.

NOTES 1. https://​www​.ideo​.com/​post/​method​-cards 2. https://​dschool​.stanford​.edu/​resources/​design​-thinking​-bootleg

REFERENCES Altman, M., Huang, T. T., & Breland, J. Y. (2018). Peer reviewed: Design thinking in health care. Preventing Chronic Disease, 15.

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Badwan, B., Bothara, R., Latijnhouwers, M., Smithies, A., & Sandars, J. (2018). The importance of design thinking in medical education. Medical Teacher, 40(4), 425–426. Blomhøj, M., & Kjeldsen, T. H. (2009). Project organised science studies at university level: exemplarity and interdisciplinarity. Zdm, 41(1), 183–198. Boillat, T., Tuffnell, C., Rivas, H., Aloul, F., & Montana, C. (2020). Design4Health Bootcamp: A design thinking approach to improve the 21st century skills of health, engineering and design students. In 2020 Advances in Science and Engineering Technology International Conferences (ASET) (pp. 1–5). IEEE. Brown, T. (2008). Design thinking. Harvard Business Review, 86(6), 84. Brown, T., & Wyatt, J. (2010). Design thinking for social innovation. Development Outreach, 12(1), 29–43. Cross, N. (2011). Design thinking: Understanding how designers think and work. Berg Dempsey, P. G., Wogalter, M. S., & Hancock, P. A. (2000). What’s in a name? Using terms from definitions to examine the fundamental foundation of human factors and ergonomics science. Theoretical Issues in Ergonomics Science, 1(1), 3–10. Design Council (2005). A study of the design process. https://​www​.designcouncil​.org​.uk/​sites/​default/​ files/​asset/​document/​ElevenLessons​_Design​_Council​%20(2)​.pdf Design Council (2019). What is the framework for innovation? https://​www​.designcouncil​.org​.uk/​ news​-opinion/​what​-framework​-innovation​-design​-councils​-evolved​-double​-diamond Accessed 20 September 2021. Dreyfuss, H. (1955). Designing for people. Simon and Schuster. New York Giacomin, J. (2014). What is human centred design? The Design Journal, 17(4), 606–623. Kelly, C. J., & Young, A. J. (2017). Promoting innovation in healthcare. Future Healthcare Journal, 4(2), 121. Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development. FT press. Ku, B., & Lupton, E. (2020). Health design thinking: Creating products and services for better health. MIT Press. Lawson, B. (1997). How designers think: The design process demystified. Architectural Press. Lor, R. (2017). Design thinking in education: A critical review of literature. In Conference Proceedings Asian Conference on Education & Psychology, Bangkok, Thailand, pp. 36–68. ISBN 978-986-5654-23-8. McLaughlin, J. E., Wolcott, M. D., Hubbard, D., Umstead, K., & Rider, T. R. (2019). A qualitative review of the design thinking framework in health professions education. BMC Medical Education, 19(1), 1–8. National Assessment Governing Board NAGB. (2021). Chapter 2, Areas of Technology and Engineering Literacy. https://​www​.nagb​.gov/​naep​-frameworks/​technology​-and​-engineering​-literacy/​2014​ -technology​-framework/​toc/​ch​_2/​design/​design2​.html Norman, D. (2013). The design of everyday things: Revised and expanded edition. Basic Books. Olesen, H. S., & Jensen, J. H. (1999). Project studies: A late modern university reform. Frederiksberg: Roskilde Universitetsforlag. Olsen, P., & Pedersen, K. (2008). Problem-oriented project work – A workbook. Roskilde University Press. Rieman, J., Franzke, M., & Redmiles, D. (1995). Usability evaluation with the cognitive walkthrough. In Conference Companion on Human Factors in Computing Systems (CHI’95). Association for Computing Machinery, New York, NY, USA, pp. 387–388. Roberts, J. P., Fisher, T. R., Trowbridge, M. J., & Bent, C. (2016, March). A design thinking framework for healthcare management and innovation. In Healthcare (Vol. 4, No. 1, pp. 11–14). Elsevier. Steen, M. (2011). Tensions in human-centred design. CoDesign, 7(1), 45–60. The Greater Good Science Center at the University of California Berkeley. (2021). What is Empathy? https://​greatergood​.berkeley​.edu/​topic/​empathy/​definition Traifeh, H., Abou Refaei, R., von Thienen, J., von Schmieden, K., Mayer, L., Osman, S., & Meinel, C. (2021). Mapping design thinking in the Arab world. In Design Thinking Research (pp. 41–60). Cham: Springer.

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8. Design thinking to improve student mental well-being Jane E. Machin INTRODUCTION Research from around the globe, including Europe (Cao et al., 2021; McCloud & Bann, 2019; Nurunnabi et al., 2021), Africa (Schreiber, 2018), Australia (Browne et al., 2017; Carter et al., 2017; Usher, 2020) and China (C. Wang et al., 2020) demonstrates that the growing number of students in higher education experiencing mental health problems is a worldwide issue. The COVID-19 pandemic has only exacerbated these trends (Araújo et al., 2020; Fried et al., 2021; Fruehwirth et al., 2021; Grubic et al., 2020; Nurunnabi et al., 2021; Savage et al., 2020; Son et al., 2020) as students grapple with remote learning, loss of routines, social isolation, health fears, and uncertainty about future job prospects. Poor mental health has significant negative consequences not only for individual students but academic institutions as a whole (Cunningham & Duffy, 2019; McMahon & Bonilla, 2020; Pilato et al., 2021; Price Waterhouse Coopers, 2021). However, the inadequate availability and efficacy of current campus mental health treatments have led to calls for more innovative approaches (Bravo et al., 2018; Browne et al., 2017; Cohen et al., 2020; Cullinan et al., 2020; Francis & Horn, 2017; Goodman, 2017; Hartrey et al., 2017; Holm-Hadulla & Koutsoukou-Argyraki, 2015; McMahon & Bonilla, 2020; Murphy, 2017). The lack of student voices has been identified as a key contributor to such poor outcomes (Pilato et al., 2021). Engaging students in the creation of solutions to improve their mental well-being is critical (Carette et al., 2018; Goodman, 2017; National Academies of Sciences, Engineering, and Medicine et al., 2021). Instead of making assumptions about what students need to improve their mental health, universities need to listen carefully to the perspectives of students themselves (Carette et al., 2018; Goodman, 2017). Students are, after all, best placed to know what is needed to support their mental health. They are frequently the first to witness worrying behaviour in their peers, and are often the first line of support (Martin, 2010; Prince, 2015; Reavley & Jorm, 2010). In fact, placing students as active collaborators in the fight against mental health issues parallels the notion of agency in psychotherapy, a key predictor for therapeutic success (Fried et al., 2021; Schreiber, 2018). Bringing students into the design process is critical to the co-creation of successful innovations that improve mental well-being and design thinking is an intrinsically collaborative approach to innovation (Brown, 2008). In iterative rounds of divergent and convergent thinking that focus first on understanding problems from multiple perspectives before ideating, prototyping, and testing solutions, design 142

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thinking is ideally suited to solving wicked problems – socially complex, highly ambiguous issues that have no clear solution (Crowley and Head, 2017) – such as student mental health. This chapter explores the implementation of a design thinking project to co-create novel solutions that improve the mental health of undergraduate students. First, we review the incidence, consequences, and current solutions to student mental health. In the second section, we report on the implementation of a design thinking project to generate an integrative understanding of how students conceive of mental health. In the third section, we identify and describe some of the novel solutions students designed to improve their mental health before the chapter concludes with a summary of the benefits of using design thinking to solve the wicked problem of poor mental well-being.

UNDERGRADUATE STUDENT MENTAL HEALTH The age at which most young people are in higher education is the age of peak onset for many mental health issues, with the first onset occurring before age 25 in 75% of cases (McGorry et al., 2011). Transitioning into adulthood and living away from home for the first time, college students often lack the life skills and support networks that are necessary for good mental health (Usher, 2020). Unfamiliar environments, novel learning experiences, unprecedented freedom (Francis & Horn, 2017; Iarovici, 2014; Usher, 2020), as well as rising tuition and student debt, exacerbate existing mental health concerns (Goodman, 2017; McCloud & Bann, 2019; Usher, 2020; Winzer et al., 2018). According to the American College Health Association (ACHA), over 60% of students felt overwhelming anxiety, and over 40% experienced depression so severe they had difficulty functioning (American College Health Association, 2018). Eating disorders such as bulimia, anorexia, and binge eating rapidly increase during the college years (Ganson et al., 2021; Kelly-Weeder, 2011; White et al., 2011), and the use of alcohol and illicit drugs, as well as the misuse of prescription medication, peaks in this timeframe (Arria et al., 2017; Charles et al., 2021; Ganson et al., 2021; Hefner et al., 2019). Prevalence of mental health issues is even higher among certain student subpopulations, such as first-generation students, non-native English speakers, students of colour, student-athletes, and sexual and gender minorities (Fruehwirth et al., 2021; Reavley et al., 2012; Santomauro et al., 2021; Thornicroft et al., 2016). The COVID-19 pandemic significantly exacerbated existing mental health concerns, leading many scholars to use the term “crisis” to characterize the challenges currently facing college students (Copeland et al., 2021; Fruehwirth et al., 2021; Grubic et al., 2020; Son et al., 2020; C. Wang et al., 2020; Zhang et al., 2020). Unfortunately, many students with poor mental health remain untreated due to the stigma that accompanies these disorders (Phelan & Basow, 2007; Phelan et al., 2000). Both public stigma and self-stigma are major obstacles to recovery as they prevent students from seeking help for fear of discrimination during their education and in subsequent employment (Eisenberg et al., 2007; Martin, 2010; Quinn et al., 2009). The penalties of not seeking help are severe, both for the student and the institution. Research clearly demonstrates that student mental wellness is critical for their success (National Academies of Sciences, Engineering, and Medicine et al., 2021; Pilato et al., 2021). Poor mental well-being affects a student’s energy level, concentration, dependability, and optimism, hindering academic performance (Martinez et al., 2018; Tembo et al., 2017). Many

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students with untreated mental health conditions struggle to meet university requirements (Hartrey et al., 2017) since mental health problems are associated with lower grade point averages, higher rates of absenteeism, breaks in education, longer times to graduate, and lower graduation rates (Eisenberg et al., 2007; Francis & Horn, 2017; National Academies of Sciences, Engineering, and Medicine et al., 2021; Prince, 2015). Dropout rates for students with a diagnosed mental health problem range from 43% to 86% (Iarovici, 2014; National Academies of Sciences, Engineering, and Medicine et al., 2021; Stones & Glazzard, 2019). Given the global drive to make student retention and degree completion a priority, it’s no surprise that university administrators increasingly recognize mental health services as an important element of their strategic and risk management plans (Cunningham & Duffy, 2019; Lipson & Roy, 2015; Pilato et al., 2021; Price Waterhouse Coopers, 2021). The financial costs of student attrition due to mental illness run to millions of dollars when lost tuition, fees and state/federal funds from each non-returning student are included (Raisman, 2013). On the other hand, investments in student mental health generate increased tuition revenues for institutions and higher earnings for students who attain a college degree (American College Health Association, 2018; Bruce-Sanford & Soares, 2019). In general, support for student mental wellness lies under the remit of student counselling services (National Academies of Sciences, Engineering, and Medicine et al., 2021), though university health centres, student unions, and disability resource offices provide supporting roles. While the efficacy of counselling varies depending on the type, length of treatment, and the type of disorder that is being treated, research points in general to successful outcomes (Conley et al., 2013; Francis & Horn, 2017; Quinn et al., 2009). The issue, then, is one of access (Francis & Horn, 2017) with increased student demand for counselling services (Thorley, 2017) forcing under-resourced centres to place limitations on services, such as shorter sessions, longer waitlists, and increased referrals to off-campus mental health providers (Prince, 2015). Nevertheless, simply bolstering access to counselling services is unlikely to be sufficient (National Academies of Sciences, Engineering, and Medicine et al., 2021) because the forces affecting student mental wellness extend beyond the purview and resources that university support services can offer. The numerous sociocultural and contextual causes and consequences of mental health require holistic, integrated solutions that not only treat mental illness, but also promote good mental health (Hill et al., 2020; National Academies of Sciences, Engineering, and Medicine et al., 2021; Pilato et al., 2021; Prince, 2015; Schreiber, 2018; Thorley, 2017). Good mental health is not merely the absence of mental illness (Galderisi et al., 2015). The World Health Organization defines mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community” (World Health Organization, 2004). This definition recognizes that mental disease etiology is more than biology and therefore solutions to improve mental wellness must also consider the broader social, environmental, psychological, behavioural, and emotional causes as well. Design thinking is a research process particularly well suited to tackling this multi-dimensional, highly complex problem (Buchanan 1992; McMillan and Overall 2016; Rittel and Webber 1973). The research reported in this chapter was conducted at a mid-sized university located in a small rural city in Appalachia, a cultural region in the eastern United States that stretches from New York state to northern Alabama and Georgia. The stigma associated with mental

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health is particularly strong in this region (Gore et al., 2016) and access to mental health services is worse than the national average due to a lack of mental health professionals (Hendryx, 2008). All student services that might influence mental health, from housing to fitness, fall under the remit of the Division of Student Affairs, a team of 80 full-time employees dedicated to improving the campus experience of approximately 8,000 undergraduate students. Primary responsibility for student mental health lies with the office of Student Counselling Services (SCS), which has six full-time licensed counsellors, two of whom also hold administrative roles, limiting the number of active patients they see. SCS also has four graduate trainees. Up to six hour-long individual therapy sessions are available for free to enrolled students. SCS also offers a drop-in service two hours a day, four days a week, when students can have an informal consultation with a counsellor without an appointment. Person-centred, solution-focused, and cognitive behavioural therapy are the dominant counselling approaches used. Medication evaluations, referrals and tele-behavioural health are also offered. The Substance Abuse office employs two additional licensed counsellors who provide programming specifically focused on alcohol and drug addiction. Limited mental health support is also offered by the Center for Accessibility Services (for neurodevelopmental disorders such as ADHD), the Center for Diversity and Inclusion, and the Military Resource Center (for intersectional mental health issues), and the professional staff and student assistants who oversee life in the 15 on-campus residence halls. Off campus, students have access to two community mental health centres and four private counselling practices. This institution recently formed an interdisciplinary task force with the objective of identifying ways to improve the mental health of the student population.

DESIGN THINKING PROCESS OVERVIEW The design thinking process was implemented by 150 students in an introductory creativity and innovation class. The overarching goal of the course is for students to be able to describe and apply design thinking as a creative problem-solving technique. Specific learning outcomes include the ability to identify, understand and frame problems; apply ideation techniques to generate novel solutions; and to design and build prototypes to evaluate and improve ideas. The course is required for marketing majors in the business college but is open to all students across campus. Seventy-two percent of students were from the business college, of whom 62% were marketing majors. The remaining 28% of students were from non-business disciplines, including biology, psychology, nursing, history, criminal justice, and cyber security. Less than 5% of students were design majors. Figure 8.1 provides a summary of student demographics. A majority received federal financial aid, data that are consistent with the overall student population of this university. All students knew at least one person currently experiencing a mental health disorder. The most commonly reported issue was substance use disorder (22%), followed by anxiety (19%), depression (17%) and eating disorders (13%), consistent with other research on the incidence of specific disorders in this population (Francis & Horn, 2017; Goodman, 2017; Iarovici, 2014). The semester-long project was led by a professor with a Doctorate in Marketing and a Master of Fine Arts in Design Thinking. One graduate assistant with design thinking experience assisted. During the initial four weeks, students practised designerly mindsets such as experimentation, collaboration, iteration, empathy, and optimism, and were introduced to

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Figure 8.1

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Student demographics

design thinking as a creative problem-solving process. While many models of design thinking exist, the process can be conceptualized broadly in two primary phases: first, understand the problem and second, generate solutions (Figure 8.2). Each phase combines divergent thinking (gathering information and ideas) with convergent thinking (finding patterns to narrow information and ideas using logical inference).

Source: Adapted from UK Design Council (2018) and Brown (2008).

Figure 8.2

Design thinking process to improve student mental health

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Phase 1: Problem Understanding To prepare for the design thinking challenge, guest speakers from the Substance Abuse office and Student Counselling Services briefed the class about mental health, and students analysed a comprehensive report on youth mental health (Mental Health America, 2021). Students then spent five weeks developing a deep, holistic, and empathetic understanding of the issue to narrow the broad challenge into a more focused problem statement used to launch the solution generation phase (Brown, 2008). To maintain individual accountability while also fostering peer learning, students completed each method on their own before sharing key insights with the whole class via the digital collaboration platform, Mural (https://​www​.mural​.co/​). Students began by identifying individual and institutional stakeholders that they felt influenced their mental health either positively or negatively. Family (e.g., siblings, parents, and children) and friends (from college and their hometowns or high schools) appeared on all lists, as did social media (e.g., TikTok, Instagram, and Snapchat) and other forms of entertainment (e.g., video games, television shows, music). University-affiliated individuals (e.g., professors, counsellors, advisors), services (e.g., the recreation centre, student housing, campus police) and policies (e.g., attendance and exam requirements, COVID-19 mask and social distancing procedures) were the second largest category. The marketplace, including brands, services, stores, advertising, and, especially, food service providers (e.g., campus dining services, Starbucks, chain restaurants, Uber Eats), affected student mental health. Companies (e.g., car insurance, credit card, utility), employers, and policies (e.g., loan repayment requirements) related to their personal finances were also identified as important stakeholders by most students. Other common categories included church, pets, and the physical campus environment. Students plotted these key stakeholders (one per virtual “sticky note”) according to the degree of influence (how much could the person or institution affect student mental health) and interest (how vested is the person or institution in student mental health) on the collaborative Mural board (Figure 8.3a). This helped to prevent duplication of identical stakeholders while broadening perceptions of the range of people and places that might influence student mental health. Students also colour coded their notes to reflect whether the influencers had a net positive effect (green), net negative effect (red) or neutral effect (yellow) on their health. University policies, social media, and part-time employment emerged as areas of high influence, but with little impact on students’ mental health. On the other hand, socializing with family and friends emerged as an important positive driver of mental health. Students used the stakeholder map to identify key individuals with whom to conduct empathy interviews, the next method used in the problem understanding phase. Empathy interviews are a qualitative research technique that seeks to gather stories. Interviews are conducted more as unscripted conversations than formal question and answer sessions, using open-ended, probing questions to foster a deeper understanding of the interviewee’s desires, struggles and opinions (Luma Institute 2012). Each student conducted an interview on their own and then shared key insights with the whole class on an empathy map, another design thinking tool used to capture what participants say, hear, see, do, feel and think (Ideo.org, n.d.). Over 260 unique insights were posted on the Mural board (Figure 8.3b). The instructor later helped to sort these into key clusters to simplify the information. Students also kept a photo journal for one week, capturing with their cell phones daily experiences they identified as either improving or harming their mental health. Photography is a participant-centred research approach

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Figure 8.3

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Screenshots of the mural collaborative discussion board posts for Phase 1: problem understanding

that provides an unbiased, first-hand perspective of a situation, helping to make the invisible, visible (Machin et al., 2021). Collectively, over 1,000 photographs were generated through this exercise, with each student taking on average 10 photographs. Students selected up to five photographs they felt had the greatest impact on their mental health and posted them on the shared collaboration board along with a hashtag or comment to summarize the image (Figure 8.3c). Approximately 200 photos were shared and sorted into logical clusters based on key themes (Luma Institute, 2012). It is interesting to note that most photographs students posted were of positive experiences. This could be because it is easier to photograph the presence of something positive than the absence of something negative, but also highlights the need

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for solutions that promote mental well-being, not just treat mental illness. Finally, students conducted a social listening exercise, a form of online observation (Li & Bernoff, 2011). An additional 160 insights, one per sticky note, were posted on an empathy map on the shared Mural board (Figure 8.3d). These were colour coded again to indicate whether the insight had a positive or negative net effect on mental health. It is noteworthy that most of the negative items aligned with psychological experiences internal to the mind (think and feel section of empathy map, dark squares), while most of the positive items aligned with tangible actions external to the mind (say or do section of empathy map, lighter squares). To synthesize insights from all this research, students created personas, or summary profiles representing different populations concerned with unique aspects of the broad mental health challenge (Ideo.org, n.d.). Ten unique personas were generated, including a freshman finding it difficult to make friends; a faculty member unsympathetic to student mental health concerns; a student barista who feels ill-equipped to deal with stressed-out customers; a student who binge drinks at least three days a week; a male student experiencing symptoms of depression but afraid to seek help; a parent worried about their first-generation student; and a senior with imposter syndrome. The persona profiles were used to brainstorm specific, actionable problem statements (Brown, 2008; Luma Institute, 2012), using the popular “How Might We…(HMW)?” question format to encourage inclusive, solution-focused ideation (Ideo.org, n.d.). A summary of the themes that emerged from all the research, together with examples of associated HMW statements is provided in Table 8.1. Phase 2: Solution Generation During the last five weeks of the semester students completed iterative cycles of ideation, prototyping, and testing. To kick-off Phase 2, students each chose one specific HMW statement to focus on and participated in three ideation exercises over sequential weeks to generate multiple potential solutions. Students first completed a Creative Matrix, a popular design thinking tool to spark new ideas at the intersection of discrete categories (Luma Institute, 2012). The matrix columns comprised five different persona profiles of the student’s choice, while five rows represented different potential categories of solutions. Students posted single-sentence descriptions of potential solutions at each intersection, resulting in 25 unique ideas. The second ideation tool adopted was Alternative Worlds (Luma Institute, 2012). Students identified different contexts, systems, spaces, or businesses that shared underlying attributes of their specific problem and used these analogues to inspire novel solutions. Students were required to brainstorm an additional 10 ideas using this technique. Finally, students used the popular SCAMPER technique to try to improve their solutions by substituting, combining, adapting, magnifying, putting to another use, eliminating or reversing various attributes of one of their earlier ideas (Serrat, 2017). After each weekly ideation session, students posted their favourite individual idea onto the shared Mural board and provided structured feedback on at least one peer idea each week. In the first week of ideation, the Rose, Thorn, Bud (Luma Institute, 2012) method was used to appraise ideas. Students described aspects of the idea they valued on pink sticky-notes (roses), concerns on blue sticky notes (thorns), and possible extensions on green sticky notes (buds). The technique encourages specific, constructive criticism rather than vague expressions of likes and dislikes (Luma Institute, 2012). Potential solutions from the second week of ideation

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Figure 8.4

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Screenshots of the mural collaborative discussion board posts for Phase 2, solution generation

were sorted according to novelty, ranging from boring (ideas that already exist) to innovative (ideas with great potential). Completely outrageous ideas were placed outside the selection grid (Figure 8.4a). Finally, students plotted ideas on a Feasibility Grid, a design thinking method used to identify ideas that were not only desirable but also potentially feasible and viable to produce (Luma Institute, 2012). Until this point, students had been asked to focus on the desirability of their solutions and temporarily ignore feasibility and viability constraints in the belief that, to generate truly novel ideas, it is easier to tame wild ideas than improve boring ideas (Figure 8.4b). Over 1,000 ideas were generated across the three weeks, which represented about 100 unique ideas after

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omitting duplicate ideas and combining similar ideas. Students turned their favourite idea into a paper prototype to solicit feedback from people outside of the class (Figure 8.4c). Feedback from this research was implemented into a final design solution, presented in the format of a magazine article reporting on the successful launch of the idea (Luma Institute, 2012).

DESIGN THINKING RESULTS Results were grouped according to five broad themes that emerged from the student research: (1) literacy, (2) socialization, (3) access, (4) policy, and (5) marketing. For each theme, we first discuss insights uncovered by students during their empathy interviews, field visits, and photo journals and examples of related HMW questions (Phase 1). Short descriptions of the most popular solutions are then presented (Phase 2). While the ideas have been organized according to the primary mental health theme they address, innovations frequently solve problems in two or more domains. Simplified descriptions of solutions are provided in Table 8.1 and examples of some student prototypes appear in Figure 8.5. Mental Health Literacy This theme explored student’s knowledge about mental health issues. For the most part, students demonstrated high levels of conceptual knowledge about mental health causes, consequences, symptoms, and solutions. They understood, for example, that toxic friendships were detrimental to their psychological well-being; that a lack of sleep could lead to depressed behaviour, while sleeping too much could be a sign of depression; that binge drinking was an unhealthy coping strategy; and that the university offered counselling. Nevertheless, there was a belief that if mental health education were more entertaining students might give it more attention, as expressed in the problem statement: HMW make learning about mental health more fun? Conceptual knowledge is of little use, however, without the motivation, ability, or opportunity to apply that knowledge in advantageous ways. For example, while students and faculty knew the theoretical symptoms of various mental disorders, both groups had trouble recognizing them in practice, or lacked the procedural knowledge of what steps to take if they did. Similarly, students lacked practical strategies to extricate themselves from unhealthy relationships or to refuse an alcoholic drink. They did not know where the university counselling centre was located or what costs were associated. Poor organizational skills limited the time available to prioritize self-care activities, including sleep. Problem statements, then, focused less on ways to acquire declarative knowledge and more on how to improve the motivation and ability to use existing knowledge. Several solutions, for example, integrated technology into a variety of common items, including a watch, headband, smart phone app, stress ball, eyeglasses, and even a hair tie, designed to help students recognize symptoms of poor mental health in themselves or others, in real time. Another set of solutions focused on ways to help students prioritize sleep. Mental Health Socialization The second theme to emerge from the student research concerned the importance of social contact for mental health. The COVID-19 pandemic has starkly exposed the devastating

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mental health consequences of limited social interaction (Fried et al., 2021; Lewis, 2020; X. Wang et al., 2020). The desire for interpersonal connection is a fundamental human motivation (Baumeister & Leary, 1995). Physically distanced from familiar support networks, then, students reported struggling with feelings of loneliness and isolation when beginning university. Most incoming freshmen are randomly assigned suitemates in a residential building that houses over 900 students – at an age when students are discovering their identities, finding a new friend group in such circumstances can be intimidating and exhausting. It is not surprising, then, that many problem statements and subsequent solutions focused on ways to make this transition easier. Several ideas focused on how to match students even before they arrived on campus. Other solutions offered ways to reduce the stress associated with approaching unknown peers in class. Recognizing the comfort and relief that can be found in family and old high school friendships, some ideas centred on ways to maintain these connections while away at college. Not all social experiences were positive for student mental health, however. Several worried about making friendships they might later have to terminate because they proved toxic to their psychological well-being. Severe anxiety was felt by many students, especially first-generation ones, about failing to live up to the expectations of excited family members. Other students, often the only child from a single-parent home, struggled to overcome perceived guilt for leaving family behind. And almost all students reported feeling pressure to go out and socialize when they had little money or knew they needed to be studying. Mental Health Access Insights in this area concern the perceived and actual accessibility of mental health solutions at the university. Access to on-campus mental health services is provided through Student Counselling Services (SCS). In person counselling sessions require advance appointments which can only be made after an initial consultation with a clinician. Appointments are typically scheduled for regular working hours, though limited after-hour sessions are available four days a week to students that demonstrate need (e.g., they work full time). All enrolled students are entitled to either six 50-minute sessions or 12 30-minute sessions per year. Tele-behavioural services are also available but again require an initial consultation. There was an approximately two-week wait for non-emergency initial appointments, though SCS provided emergency assessments during regular office hours if needed. All after-hour mental health emergencies are referred to a local community resource centre. Counsellors also hold informal, first-come, first-served walk-in hours from 3–5pm through a program called Let’s Talk. While students were aware that SCS existed, they had little sense of how to utilize its services. Students did not know the physical location nor how to make the initial appointment. No students were aware of the Let’s Talk program. Unsurprisingly, then, many problem statements and subsequent ideas focused on ways to improve both perceived and actual accessibility. These included using social media to communicate information such as hours and appointment procedures, holding sessions in locations closer to student residential buildings or dining halls, opening in times more consistent with student schedules and needs, and removing barriers to access such as proof of full-time employment to receive after-hours treatment. Other ideas focused on increasing the availability of counselling either through crowd-sourcing or artificial intelligence. Students also reported being deterred from seeking treatment by the intimidating sterile environment of SCS. Light, airy, modern spa-like envi-

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ronments were suggested as more welcoming surroundings to seek mental wellness. Finally, the stigma surrounding poor mental health discouraged many students from visiting SCS. Creative solutions included increasing service access visibility or destigmatizing help-seeking behaviour. This was particularly important for men, who worried more than women about being discriminated against for accessing mental health services. Mental Health Policy Student research identified policies at the micro (individual professors or classes), meso (departments or colleges) and macro (institutional) levels that hurt their mental health. Several described faculty members who believed students needed to “toughen up”, and refused to honour mental disability accommodations because “you won’t get them in the real world”. A less overt form of discrimination manifested in policies that inadvertently penalized anxious students. For example, participation grades based on time spent speaking in full class discussions, unclear or inconsistent grading criteria, poorly organized group projects, last-minute changes to syllabi, or tardy feedback on assignments. Concurrent class deadlines (e.g., final project presentation dates or mid-term exam dates) resulted in widely imbalanced stress levels over the semester. Work–life balance felt non-existent for many students, who expressed frustration with faculty who seemed oblivious, or indifferent, to competing obligations. Many students expressed a desire for mental health issues to be incorporated into all classes, rather like ethics, as part of a broader definition of diversity and inclusion. Policies about distance learning proved somewhat polarizing. Some students reported a preference for more flexible instruction, such as asynchronous options, to complement traditional schedules. Others, however, worried their education suffered in remote learning environments. Sudden changes in instructional policies, however, as prompted by COVID-19, uniformly stressed students. Worries about money, and debt after graduating, were an omnipresent source of anxiety. Some students reported consuming cheaper junk food, or skipping meals entirely, owing to financial constraints. Finally, student research revealed a universal desire for help with time-management skills. Mental Health Marketing Targeting is a central tenet of marketing management – the idea that no single product or service can, or should, meet the needs of everyone. However, students found that mental health services were very much “one-size fits all”. This led to problem statements and ideation sessions focused on unique groups. Some segmented the student population according to the type of mental health issue – for example, solutions for those experiencing depression versus those experiencing anxiety. Others segmented the market based on shared demographic characteristics. As noted, earlier, men suffered the stigma associated with mental illness more acutely than women and wanted solutions that acknowledged differences in how these disorders presented. Depressed or anxious men, for example, might be more likely to engage in violence rather than cry (Branney & White, 2008). Other identified demographics included non-traditional students, first-generation students, and student-athletes. Populations at the intersection of mental illness and a second stigmatized characteristic, such as a non-conforming gender identity or sexual orientation, felt in need of solutions tailored to their unique mental health concerns.

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Marketing communications, and particularly social media, share some of the blame for poor student mental health. Instagram, for example, has recently been criticized for allegedly knowing the app can contribute to eating disorders in young girls (Wells et al., 2021). Students developed solutions to curb the negative side of social media, while also using such apps to promote positive mental well-being. For example, students suggested changes to newsfeed algorithms that would promote positive wellness content and hide negative content. Other ideas included a warning feature if someone searched for concerning terms such as suicide or bulimia and adding automatic limits to minimize time spent engaging with social media sites. Finally, some students called on brands to use their vast marketing budgets to improve student mental health in meaningful ways, not just “blues washing”, meaning inauthentic marketing tactics that exploit mental health concerns for financial gain.

KEY LESSONS OF USING DESIGN THINKING PROCESS TO IMPROVE STUDENT MENTAL HEALTH While many diagrams portray the design thinking process linearly, with equal phases of divergent and convergent thinking, the reality is much messier. Feedback on student paper prototypes, for example, frequently revealed novel information about the target’s needs, but students were reluctant to circle back to the problem definition phase because they interpreted such an iterative step as a failure, rather than an integral part of the innovation process. Accustomed to the reasoned security of quantitative survey results, students struggled to trust insights that emerged from the qualitative research methods we used to empathize with the end-user. Identifying which jargon-laden design method to use at each stage also proved frustrating, especially as many methods are effective at both the problem understanding and solution generation phases of the design thinking process. We found the Luma Institute’s System of Innovation (2012), which categorizes methods by looking, understanding, and making, to be beneficial as they are relatively stage-agnostic. From an educator’s perspective, it was difficult to find time within a 12-week semester to allow multiple iterations of the design cycle to commence. Students required help identifying key themes from their research findings, while formulating HMW questions that were broad enough to allow for multiple solutions, but sufficiently focused to direct ideation efforts, proved especially problematic. Improving mental health is an example of a wicked problem – a multi-dimensional, unstructured situation that requires the involvement of numerous stakeholders, often with conflicting or shifting agendas (Rittel & Webber, 1973). Design thinking is frequently lauded as a problem-solving technique particularly suited to such problems (Buchanan, 1992; McMillan & Overall, 2016). In our experience, one unexpected difficulty emerged conducting a design thinking project in mental health. Unlike other wicked topic domains, mental health is an invisible disorder and therefore hard to observe or simulate. For participants without mental health issues, empathizing with someone with depression or anxiety is extremely difficult. People who are mentally healthy can grossly underestimate the severity of mental illness, attributing it to a lack of willpower (Borchard, 2016) and may therefore fail to adequately accommodate those suffering from mental illness when designing solutions. Of the three empathy exercises the students completed, social listening, a form of online observation (Li & Bernoff, 2011), proved the most valuable. Students, already familiar with social media, found inspiration in the memes, photos, and status updates of posters with mental health issues. In the future, virtual

symptoms

literacy

relationships

socialization

Cancel relationships

Maintain

Mental health

Start relationships

solutions

Knowledge of

Knowledge of

causes

Knowledge of

Key insights

Example simplified solution descriptions An escape room, where success depends on applying healthy tactics. A mood tracking app that provides a visual map of current well-being. Smart glasses that monitor others’ facial cues for signs of distress. Smart wearable tech that provides early warnings of anxiety triggers. A smartphone game that simulates friends’ concerning behaviours. Scooters to navigate across campus more quickly, freeing time for self-care. A game that rewards students for increasing the amount they sleep. Melatonin tea offered free by the campus dining facilities and local bars. Sleep incubators placed in unused areas (e.g., library) for quick naps. A Living Learning Community for minority student groups (e.g., LGBTQ+). An app that identifies other new students on campus with shared interests. An app that helps students within the same class identify shared study needs. Free bus service between campus and train station. A community green space that pairs students with local elderly residents. Dedicated “visitor floor” in residential buildings for friends to stay for free. An on-campus animal rescue centre to connect students with animals in need. An AI-driven app that helps students recognize toxic friends. An automatic excuse generator that provides reasons to avoid going out.

Example problem statements

HMW make learning about mental health fun?

HMW motivate students to prioritize self-care?

HMW improve faculty ability to identify at-risk students?

HMW help students recognize triggers in the moment?

HMW help students support crisis-adjacent friends?

HMW help students manage stress related behaviours?

HMW increase use of existing mental health resources?

HMW minimize use of unhealthy coping solutions?

HMW match students with underutilized resources?

HMW help new students find their friend group?

HMW help students meet people while social distancing?

HMW help classmates better connect with each other?

HMW help students connect with distant friends?

HMW reduce homesickness?

HMW balance time at school with time at home?

HMW relieve (perceived) pressure from family?

HMW help students avoid toxic relationships?

HMW manage peer pressure to socialize?

Themes, insights, example problem statements and solutions

Mental health

Theme

Table 8.1

Design thinking to improve student mental well-being 155

marketing

Mental health

policy

Mental health

access

Mental health

Theme

communication

Branded

communication

Conscious

communication

Targeted

University policies

College policies

Instructor policies

access

Destigmatized

Attractive access

Convenient access

Key insights

App that connects students with un-awarded scholarship funds. Consistent exam policy that offers an alternative for test-phobic students. A virtual reality game that prepares high school students for college life. A sports-themed counselling office with coaches instead of counsellors. A student-athlete comfort zone for dealing with sports-related stressors. A game that rewards players for vigorously shaking it to help relieve anger. A smart band that detects heart rate changes when user compares self to others. An app that restricts time spent on social media and cannot be overridden. An app that delays real-time posts and texts from socializing friends. Tasteful branded room décor distributed free to brighten up residential halls. A spa that is part nail salon, part hair salon, and part mental health salon. A sponsored cereal bar that provides free food and wellness resources.

HMW decrease the stress of remote learning?

HMW prepare students in high school better for college?

HMW help specific demographic groups (e.g., males)?

HMW help intersectional mental health groups?

HMW help specific disorders (e.g., depression, anxiety)?

HMW reduce unrealistic comparisons on social media?

HMW manage social media to promote mental health?

HMW reduce fear of missing out (FOMO)?

HMW use the brand resources to improve mental health?

HMW blend mental health within the marketplace?

HMW prevent brand “blues washing”?

Rewards program where faculty earn points for positive pedagogical policies.

HMW make faculty empathetic to mental health?

ProfTube with all their lectures posted for students to access at their leisure.

An app to help faculty coordinate exam and final project dates.

HMW help faculty coordinate inter-class work volume?

HMW reduce the cost of higher education?

A temporary tattoo that is a hidden patch dispensing anti-depressant drugs.

HMW destigmatize help-seeking behaviours?

Sticker system to help students avoid procrastination and meet deadlines.

App that connects students to peers with similar issues to share strategies.

HMW normalize talking about mental health?

HMW restructure classes to meet student schedules?

Smart mirror that “talks” to you about your mental health using AI responses.

HMW decrease visibility of students seeking help?

HMW promote class policies for good mental health?

Voice-activated smart speaker that specializes in mental health counselling.

HMW better incorporate mental health care in the home?

iBeacon technology that disconnects cell phones in class and study areas.

Human Library where people talk about managing their mental health issues.

HMW motivate use of campus mental health services?

College policy giving students mental health days to take each semester.

Self-care lounges with counselling-adjacent services (e.g., yoga classes).

HMW make mental health spaces more attractive?

HMW improve student homework–life balance?

Apps that use artificial intelligence (AI) to help counsel students in real time.

HMW crowdsource counselling?

HMW incorporate mental health into every classroom?

Locations and hours that better reflect student schedules (e.g., after 5 pm). Communication through student-oriented media options (e.g., Instagram).

HMW improve awareness of service accessibility?

Example simplified solution descriptions

HMW make services accessible in the moment?

Example problem statements

156 Research handbook on design thinking

Design thinking to improve student mental well-being

Figure 8.5

157

Examples of student prototypes

reality simulations, where participants figuratively walk in the shoes of others, offer promising opportunities to experience and empathize with the mentally ill (Machin et al., 2020). Campus administrators around the world are being forced to take the mental health of their students seriously (American College Health Association, 2018; Pilato et al., 2021; Price Waterhouse Coopers, 2021). The connection between mental health issues and student retention, particularly for students from historically marginalized groups, has implications for the economic well-being of students and institutions alike. Services that prioritize student mental health can help institutions differentiate themselves in the increasingly competitive higher education marketplace, and innovative approaches to mental health can also reduce the risk that students will drop out (Bruce-Sanford & Soares, 2019). Design thinking, with its focus on empathy, visualization, collaboration, and experimentation, is a particularly useful problem-solving approach for university administrators to consider. The active involvement of students throughout the ideation phase is of particular importance in designing solutions that will be adopted effectively. This chapter extends our understanding of the mental health experiences of Gen Z, the youngest and most ethnically diverse generation in history (Brown, 2018; Dimock, 2019). At the broadest level, our results confirm that optimal mental health is more than the absence of mental illness (Galderisi et al., 2015). While most students did not have a formally diagnosed disorder, they nevertheless reported levels of distress that hurt their ability to manage daily college activities. Unfortunately, to receive academic accommodations, class and institutional policies typically require official documentation. Our findings suggest this prevents students from addressing issues, which may lead them to stop attending class or even drop out.

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Incorporating mental health services into highly visible, and highly trafficked, areas such as dining halls or recreation hubs helps to normalize conversations around the topic. Solutions emerging from this research went beyond isolated treatment ideas, such as increasing counsellor availability, to identifying opportunities that destigmatize mental illness throughout the entire student experience. One idea was integrating mental health instruction into all classes, much like the push for curricula to cover ethics 40 years ago (Hosmer, 1988). Students had several ideas to make mental health education more engaging, ranging from escape rooms to VR experiences. However, declarative knowledge about mental health was not the primary concern. The real frustration emerged in a perceived lack of procedural knowledge and the motivation, opportunity, and ability to apply that knowledge. Solutions that helped students and faculty recognize, manage, or avoid activities that hurt their mental health were popular. Unsurprisingly for this tech savvy generation, these included a lot of technology-based solutions, such as apps or smart wearables, to help students prioritize sleep, avoid toxic relationships, or crowdsource the advice of mental health peer mentors. It is worth noting that students tailored many of these solutions to specific subgroups, such as first-generation students, men, or minority populations, recognizing the need for different approaches for different segments. Our research also demonstrates the value of design thinking as a research method to investigate the wicked problem of mental health (Milroy et al., 2021). Much existing research on student mental health comprises quantitative surveys examining the prevalence of mental illness (e.g., Arria et al., 2017; Santomauro et al., 2021), barriers to access (e.g. Cage et al., 2020; Eisenberg et al., 2007), or the efficacy of mental health interventions or satisfaction with mental health services (e.g. Conley et al., 2013; Hartrey et al., 2017). These studies remain rooted in the normative, pathogenic model of mental health, which emphasizes the treatment of mental illness. Student mental well-being, however, is a shared responsibility and cannot be limited to the counselling centre alone. A whole university approach that accounts for the social and emotional influences on mental wellness is required to decrease the stigma associated with seeking help. The qualitative, participatory research methods embedded in design thinking are ideally suited to understanding mental health as a holistic, psychosocial phenomenon (Galderisi et al., 2015) and to developing desirable innovations that promote wellness, not just treat sickness.

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Price Waterhouse Coopers. (2021). Managing risk in higher education: Higher education sector risk profile 2021. PwC. https://​www​.pwc​.co​.uk/​industries/​government​-public​-sector/​education/​managing​ -risk​-in​-higher​-education​.html Prince, J. P. (2015). University student counseling and mental health in the United States: Trends and challenges. Mental Health & Prevention, 3(1), 5–10. https://​doi​.org/​10​.1016/​j​.mhp​.2015​.03​.001 Quinn, N., Wilson, A., MacIntyre, G., & Tinklin, T. (2009). “People look at you differently”: Students’ experience of mental health support within Higher Education. British Journal of Guidance & Counselling, 37(4), 405–418. https://​doi​.org/​10​.1080/​03069880903161385 Raisman, N. (2013). The cost of college attrition at four-year colleges & universities—an analysis of 1669 US institutions [Report]. Educational Policy Institute. https://​vtechworks​.lib​.vt​.edu/​handle/​ 10919/​83250 Reavley, N., & Jorm, A. F. (2010). Prevention and early intervention to improve mental health in higher education students: A review. Early Intervention in Psychiatry, 4(2), 132–142. https://​doi​.org/​10​ .1111/​j​.1751​-7893​.2010​.00167​.x Reavley, N. J., McCann, T. V., & Jorm, A. F. (2012). Mental health literacy in higher education students. Early Intervention in Psychiatry, 6(1), 45–52. https://​doi​.org/​10​.1111/​j​.1751​-7893​.2011​.00314​.x Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169. https://​doi​.org/​10​.1007/​BF01405730 Santomauro, D. F., Herrera, A. M. M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D. M., Abbafati, C., Adolph, C., Amlag, J. O., Aravkin, A. Y., Bang-Jensen, B. L., Bertolacci, G. J., Bloom, S. S., Castellano, R., Castro, E., Chakrabarti, S., Chattopadhyay, J., Cogen, R. M., Collins, J. K., … Ferrari, A. J. (2021). Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet, 398(10312), 1700–1712. https://​doi​ .org/​10​.1016/​S0140​-6736(21)02143​-7 Savage, M. J., James, R., Magistro, D., Donaldson, J., Healy, L. C., Nevill, M., & Hennis, P. J. (2020). Mental health and movement behaviour during the COVID-19 pandemic in UK university students: Prospective cohort study. Mental Health and Physical Activity, 19, 100357. Schreiber, B. (2018). Mental health at universities: Universities are not in loco parentis – Students are active partners in mental health. Journal of Student Affairs in Africa, 6(2), Article 2. https://​doi​.org/​ 10​.4314/​jssa​.v6i2 Serrat, O. (2017). The SCAMPER technique. In O. Serrat (Ed.), Knowledge solutions: Tools, methods, and approaches to drive organizational performance (pp. 311–314). Springer. https://​doi​.org/​10​ .1007/​978​-981​-10​-0983​-9​_33 Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on college students’ mental health in the United States: Interview survey study. Journal of Medical Internet Research, 22(9), e21279. https://​doi​.org/​10​.2196/​21279 Stones, S., & Glazzard, J. (2019). Supporting student mental health in higher education. Critical Publishing. Tembo, C., Burns, S., & Kalembo, F. (2017). The association between levels of alcohol consumption and mental health problems and academic performance among young university students. PLoS One, 12(6), e0178142. Thorley, C. (2017). Not by degrees: Improving student mental health in the UK’s universities. IPPR: London, UK. Thornicroft, G., Mehta, N., Clement, S., Evans-Lacko, S., Doherty, M., Rose, D., Koschorke, M., Shidhaye, R., O’Reilly, C., & Henderson, C. (2016). Evidence for effective interventions to reduce mental-health-related stigma and discrimination. The Lancet, 387(10023), 1123–1132. https://​doi​.org/​ 10​.1016/​S0140​-6736(15)00298​-6 UK Design Council (2018). https://​www​.designcouncil​.org​.uk/​our​-work/​skills​-learning/​tools​ -frameworks/​framework​-for​-innovation​-design​-councils​-evolved​-double​-diamond/​ Usher, W. (2020). Living in quiet desperation: The mental health epidemic in Australia’s higher education. Health Education Journal, 79(2), 138–151. https://​doi​.org/​10​.1177/​0017896919867438 Wang, C., Cheng, Z., Yue, X.-G., & McAleer, M. (2020). Risk management of COVID-19 by universities in China. Journal of Risk and Financial Management, 13(2), 36. https://​doi​.org/​10​.3390/​ jrfm13020036

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Wang, X., Hegde, S., Son, C., Keller, B., Smith, A., & Sasangohar, F. (2020). Investigating mental health of US college students during the COVID-19 pandemic: Cross-sectional survey study. Journal of Medical Internet Research, 22(9), e22817. https://​doi​.org/​10​.2196/​22817 Wells, G., Horwitz, J., & Seetharaman, D. (14 September 2021). Facebook knows Instagram is toxic for teen girls, company documents show—WSJ. Retrieved October 19, 2021, from https://​www​.wsj​.com/​ articles/​facebook​-knows​-instagram​-is​-toxic​-for​-teen​-girls​-company​-documents​-show​-11631620739 White, S., Reynolds-Malear, J. B., & Cordero, E. (2011). Disordered eating and the use of unhealthy weight control methods in college students: 1995, 2002, and 2008. Eating Disorders, 19(4), 323–334. Winzer, R., Lindberg, L., Guldbrandsson, K., & Sidorchuk, A. (2018). Effects of mental health interventions for students in higher education are sustainable over time: A systematic review and meta-analysis of randomized controlled trials. PeerJ, 6, e4598. https://​doi​.org/​10​.7717/​peerj​.4598 World Health Organization. (2004). Promoting mental health: Concepts, emerging evidence, practice: Summary report. World Health Organization. Zhang, Y., Zhang, H., Ma, X., & Di, Q. (2020). Mental health problems during the COVID-19 pandemics and the mitigation effects of exercise: A longitudinal study of college students in China. International Journal of Environmental Research and Public Health, 17(10), 3722. https://​doi​.org/​10​ .3390/​ijerph17103722

9. From gas to green: designing a social contagion strategy for the energy transition in Rotterdam, the Netherlands Jesal Shah, Rebecca Anne Price and Jotte de Koning INTRODUCTION Due to global warming and frequent earthquakes in the gas-producing north Groningen region and the dependence on oil and gas from Russia, the Dutch government aims to completely discontinue the consumption of natural gas by 2050. Currently, about 90% of the homes in the Netherlands use natural gas for heating purposes (Beckman & van den Beukel, 2019). In 30 years, these 7 million residential homes and 1 million other buildings must transition to using greener energy alternatives such as solar power, wind power, residual heat from industries and geothermal energy. Technological innovation in the past two decades has ensured that these alternative technologies are now mature, viable and widely available. Yet, active adoption by citizens and associated changes in practices and beliefs within households are proving difficult to achieve. Adoption by society and institutionalisation of these technologies is essential to achieve the requisite impact (Geels, 2004). Given this background, the government and local councils in the Netherlands are faced with the question: ‘how to practically activate a critical mass of citizens to transition from gas to green energy sources?’. This study was carried out in 2020; however, the urgency of answering this question and the relevance of this project has increased due to current geo-political pressures. The challenge of activating citizens towards the energy transition falls within the purview of designers since design shapes peoples’ perceptions and behaviours, whether intentionally or unintentionally (Lockton, 2013). Additionally, as Ceschin & Gaziulusoy (2016) and Buchanan (2015) outline, methodological maturity in the field of design has led to an evolution in the nature of challenges designers now concern themselves with – from symbols, physical objects, services and processes to broader environments, systems and organisations. The application of design towards these broader environments is more commonly referred to as Design Thinking. There are other design movements that deal with these new challenges, such as ‘Systemic Design’, ‘Transition Design’ and ‘Design for Sustainability’. They bring forward a design approach to intervene in complex systems while striving for long-term sustainable change. The tools and methods available from these design movements combined with the general capability of a design thinking process to embrace ambiguity and complexity offer a different approach to tackling societal problems. In this study, we use a design thinking approach to 164

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conceive strategic ways of activating citizens to adopt sustainable energy alternatives towards the socio-technical challenge in the Dutch energy transition context. We specifically focus on how social influence or social contagion can activate residents to adopt greener energy alternatives and support the energy transition. The Reyerood neighbourhood in Rotterdam serves as our case study context. We follow a ‘research through design’ approach (Stappers & Giaccardi, 2017) wherein our process is inspired by the double-diamond approach of design thinking. This approach entails that the results from our case study are multi-layered. The first layer includes what we learn by embarking on a design process, in co-creation with the municipality, for social contagion and the energy transition. In this we have identified individual residents’ motivations and apprehensions towards the energy transition in Reyeroord and the relations between residents within the social networks. Second, the outcomes of the design process culminate in the development of the ‘Design for Social Contagion Framework and Toolkit’ which codify a design (thinking) approach for the municipality to create interventions and shape contagion processes to activate residents. This was preliminarily tested with the municipality of Rotterdam. Last, by applying design thinking in the context of the energy transition we learn about the value of such an approach which is highly relevant give the criticality of gas to green transitions in Europe for sustainability and geopolitical objectives.

RESISTANCE TO CHANGE There is increasing awareness amongst citizens about the need for and value of sustainable lifestyles, yet it does not reflect in behaviours and consumption patterns (Frederiks et al., 2015). The transition from gas to green requires citizens to invest time, money and effort to make changes and overcome the short-term inconveniences of a technology change. The return on this investment is marginal in the immediate future and long-term financial savings are often unclear. There is ambiguity too, as to who will make the decision (together) and initiate action to change: the tenants, house-owners, housing associations, housing corporations, the municipality or the national government. Even if citizens have a positive attitude towards gas discontinuation, achieving the ‘socio’ component of a socio-technical-system change is highly challenging given this perception of inconvenience and uncertainty. The challenges described above can be theorised as ‘lock-ins’ that reinforce accepted ways of thinking, doing and being (Klitkou et al., 2015) and hold citizens (and energy providers) in the current socio-technical gas system. To overcome lock-ins, governments often turn to top-down policy mechanisms to stimulate preferred behaviours. These policies are often met with public resistance. For example, when the city of Barcelona first introduced ‘superblocks’, where car traffic was permitted only on perimeter roads to curb pollution and car collisions in 2017, car owners and residents from surrounding neighbourhoods took to the streets to resist the change (O’Sullivan, 2017). Providing financial incentives is the other commonly opted route. However, a provision of financial incentives does not imply that citizens will actively opt for the gas to green transition (Frederiks et al., 2015). For instance, people in Boulder, Colorado did not actively purchase energy-efficient appliances even though the government provided free home energy audits, rebates, and other incentives (Simon, 2010). This highlights the need for novel strategies to overcome societal lock-ins and stimulate changes in citizens’ behaviours.

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Looking to transition theories and literature from the domains of technology and innovation diffusion, it is observed that there is limited practical guidance on how to activate a critical mass of citizens to adopt innovation at the micro-scale. Theories such as the Multi-Level-Perspective (Geels, 2002), Technology Innovation System theory (Hekkert et al., 2007) and Strategic Niche Management (Caniëls & Romijn, 2008) are analytic in nature and provide zoomed-out, meta-level understanding of the dynamics of transitions. These theories advocate the co-evolution of user practices, norms and technology, but do not offer concrete strategies to activate individuals and communities. From the domain of policy development and governance, Transition Management (Loorbach & van de Lindt, 2007) emphasizes the involvement of stakeholders through transition arenas. These arenas are focused on strategic activities, limited to select stakeholders and early adopters. The experimentation phase of transition management, where activation of citizens must happen, provides fewer tools for practically activating a critical mass. The diffusion of innovation theory by Rogers (1983) highlights how innovation can spread within communities through active influencers and front-runners. While this has merit when it comes to the diffusion of certain products and services, it does not always prove useful for complex behaviours that need to overcome entrenched routines and beliefs and require a considerable investment in terms of time, money and effort (Centola, 2018). Moving away from well-functioning technologies (systems) requires intrinsic and extrinsic motivation as well as changes in cultures and norms – which cannot be guaranteed simply by diffusion of knowledge of innovation.

DESIGN THINKING FOR SYSTEMS TRANSITIONS Today’s societal problems are characterised by the interaction of several socio-technical systems with high levels of complexity and unpredictability which cannot be controlled or altered by a single definitive solution. Tackling such problems or transitioning away from such systems requires an explorative way of working and the creation of a variety of interventions at different levels in a system (van der Bijl-Brouwer & Malcolm, 2020). Design thinking provides one such explorative approach towards complex challenges. Its abductive problem-solving capability and co-creative mandate can help in unveiling and deconstructing societal lock-ins and devising persuasive strategies for change. As Drew et al. (2021) highlight, the core capacities of design thinking include integrative thinking, perspective-taking, abductive reasoning, propositionality, reflexivity and synthesis-through-making. These are valuable not only to understand the existing paradigms and navigate/visualise system complexity, but also to reimagine futures. These can help in building the big picture, a shared understanding of desirable futures and states of our systems. At the granular level, given that the crux of design thinking lies in creativity and abductive thinking where, at the start of the process, both the ‘WHAT and ‘HOW’ of tackling a problem are unknown (Dorst, 2011), it is apt that design thinking be applied to shape interventions towards system change. As Norman (2020) highlights, this includes diving deeper into local cultures and communities and co-designing solutions with community members. Apart from their own design expertise, honing diffuse design capabilities (as Manzini (2015) terms it) and co-designing interventions enable designers to engage in and transverse across different levels of a system.

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Additionally, as Nelson & Stolterman (2012, p. 57) argue, designers are ‘able to create essential relationships and critical connections in their designs and between their designs and the larger systems in which they are embedded’. This is the very ethos of systemic design with its focus on how independent parts become an inter-dependent whole. Thus, design thinking is inherently systemic in nature and can prove valuable in shaping both, the big picture (interdependent whole) and the interventions (independent parts) towards the big picture for systemic change. This application of design thinking in shaping the future states and interventions towards the future states is captured in the emerging movements of ‘Transition design’ (Irwin et al., 2015) and ‘Systemic design’ (Drew et al., 2021) with a focus on deep transformation. These movements emphasise that deep transformation necessities a shift in mindset – a certain ‘way of thinking’ before ‘doing’ – which is inherent to design thinking. These domains advocate identifying ‘new ways of designing’ or ‘leverage points’ for intervention to steer change. One such leverage point for intervention we identify is ‘Designing for behaviour change’, which we explore in the next part of the chapter and our case study.

DESIGN INTERVENTIONS FOR BEHAVIOUR CHANGE Design inherently includes a process of change (towards preferable situations) which shapes practices. Design fulfils peoples’ needs while enabling them or prompting them to do or not to do something through the tangible and intangible aspects of a product-service system or socio-technical system. The affordances provided by design or the interactions and emotions evoked through design intervention influence peoples’ associations, attitudes, decisions and actions (Lockton, 2013), in turn shaping long-term routines, habits and cultural paradigms. There is increasing consciousness amongst designers about this (desirable or undesirable; and intentional or unintentional) impact of design on (consumer) behaviours; and, thus, an increased sensibility of intentionally and explicitly influencing behaviours. This is captured within the field of ‘Design for behaviour change’ (Lilley, 2007; Michie et al., 2011; Niedderer et al., 2014) which draws upon behavioural sciences such as psychology, sociology and neuroscience to inform design interventions and strategies to trigger certain behaviours. The interventions either aim at triggering a direct cognitive change in an individual or shaping contextual cues which indirectly influence one’s behaviour; or a combination of both (Niedderer et al., 2014). More recently, acknowledging the contribution of behaviours (of individuals and groups) towards social and environmental problems, ‘Design for behaviour change’ is being used by the private and public sectors alike to prompt healthy, sustainable and socially desirable behaviours. Examples include interventions to increase energy-saving, reduce littering, reduce crime, improve compliance with tax laws, continued adherence to medication, exercise routines and healthy diets (Lockton, 2013; Niedderer et al., 2014). While strategies of design and behaviour change are a good starting point to trigger sustainable behaviours (be it a one-time behaviour or a continued habit), a focus on shaping individuals’ behaviours or small groups of individuals is observed; constraining them in scale of impact. Scaling the behaviours of individuals to the communities they are embedded in is crucial to achieving a critical mass towards reaching the sustainable development goals. The

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need for devising (new) approaches to scale behaviour change beyond individuals while activating communities and networks is apparent.

SOCIAL CONTAGION TO SCALE BEHAVIOUR CHANGE Studies on pro-environmental behaviours and habits, energy use and consumption as well as technology adoption accentuate the role of culture (Lutzenhiser, 1992), social influence (Goldsmith & Goldsmith, 2011), social norms (Trudel, 2018), social proof (Nolan et al., 2008) and social diffusion (Costanzo et al., 1986) in positively affecting behaviours. This follows from people’s tendency to use shortcuts (heuristics) to reduce the effort in evaluating and comprehending choices while making decisions. One such shortcut is to align one’s choice with those of similar others; assuming that others have more knowledge, or that if the majority has chosen something it must be correct. Social influence also arises from people’s need to conform to and comply with social norms in order to achieve three self-goals, namely: to act effectively, to build and maintain social relationships and to manage the self-concept (Cialdini & Trost, 1998). People seek social comparison evidence (normative guidance), specifically from similar others to evaluate themselves in terms of the appropriateness of their abilities, behaviours and beliefs. When the attitudes and actions are shared with the comparison group, they are further reinforced. If there is a discrepancy, the attitudes and actions are altered (Marsden & Friedkin, 1993). People are strongly influenced by the (in)action of others, which implies that one would act only if several others have chosen to act (Buskens & Raub, 2013; Cialdini & Trost, 1998). This also applies to sustainable behaviours, as exemplified by Trudel (2018). He concludes that one’s ‘personal and social identities’ and ‘social influence through social norms’ are two key factors that ‘powerfully, predictably, and pervasively influence sustainable behaviours’. On the downside, this social influence gives rise to the impasse wherein people wait for others to act first, with no ultimate action. This is problematic as climate science describes a precipice for significant change to human activity now (Williamson et al., 2018). While this interdependency in decision-making is a hurdle, it is also an opportunity to gain traction towards the notion of designing for critical mass. Several ‘Design for behaviour change’ toolkits include ‘providing social proof’, ‘social traces’, ‘social support’ or ‘designing for collective use’ (Ploderer et al., 2014) as strategies for change amongst others. However, often these are focused on shaping individuals’ (or small groups of individuals) behaviours, e.g., providing neighbours’ energy-saving data to prompt conscious behaviour amongst families, or giving social proof (e.g. ‘90% of guests who stay in this room reuse the linen and towels’) to guests in a hotel to reuse linen. The inherent scaling power of social influence, which can be applied to social networks and communities, stays underexplored. Building on these strategies and expanding their scope of application, we explore how to leverage the potential of social influence and social contagion within social networks and communities, using complex contagion theory (Centola, 2018) and a design thinking approach. We try to understand how citizens can be activated not as individuals but as groups (communities) and how activation happens within groups in the context of the energy transition.

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CASE STUDY: GAS TO GREEN ENERGY TRANSITION IN THE NETHERLANDS Historical Context ‘Natural gas is to the Netherlands what oil is to the Gulf States’ (Rapid Transition Alliance, 2021). The discovery of large natural gas reserves in 1960s marked the beginning of the economic and industrial legacy of the Netherlands. Amid rising nuclear power, which could slash energy prices, and cheaper coal imports that rendered Dutch coal unprofitable, the exploitation of natural gas became a political priority. Through strong coalitions with private sector oil companies, extensive infrastructure and policy planning and exercise of power by the state, no alternatives could reach the market, any resistance was avoided, and the natural gas regime gained a monopoly in the Dutch economy (Correljé et al., 2003; Kemp, 2010; Beckman & van den Beukel, 2019). Dutch industries transitioned to natural gas and prospered. Export contracts with neighbouring countries not only gave rise to a positive trade deficit and profits, it gave the Netherlands a central position in the energy ecosystem in the European Union. Revenues from gas exports as well as internal supply fuelled the development of the ‘Dutch welfare state’ (Correljé et al., 2003, p. 16). Extensive campaigns and cost advantages were designed and citizens were sold the idea of increased convenience and comfort. The majority of the citizens readily bought into this since coal was tedious and dirty to use, houses were poorly heated, not insulated and uncomfortable. The post-war economic development gave rise to an increase in personal incomes and well-being, and a widespread sense of modernisation which also contributed to the spontaneous buy-in amongst citizens. Consequently, the Netherlands has become one of the few countries within the European Union with the highest natural gas consumption, especially in the residential sector. Nine in every ten homes in the Netherlands was heated by gas as of 2018; natural gas contributed 69.3% to the share of fuels in the energy consumption in the residential sector in the Netherlands in 2019, as compared with 38.8% in Germany (Eurostat, 2021). Policy Directives The Dutch government recognises that transitioning away from natural gas is a social, sustainable and now geopolitical challenge, and acknowledges that acceptance by citizens is a key condition to achieve the transition. It aims to employ a decentralised, district-oriented approach wherein local residents, businesses, civic bodies and other relevant stakeholders are involved in planning and executing the transition (Klimaatakkoord, 2019). It is important to note that although it is citizens who have to actively adopt greener alternatives, the municipalities and local councils must (and are) play(ing) a key role in facilitating, planning, managing and supporting the transition by bringing together stakeholders, defending the common interests of the public, ensuring viability and overcoming uncertainties. The government also identifies the affordability of greener technologies and feasible renovation in the built environment as two pillars to drive change. Hence, achieving housing cost neutrality (which entails that monthly financing costs for the transition do not exceed the savings on the energy bill for house owners/tenants) is a key point in the energy policy directive. The government aims to reduce costs by bundling supply and demand, working on

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digitization and innovation, combining transition activities with other neighbourhood development activities as well as re-designing price structures, energy taxes and subsidies. There are several funding schemes, loans with low-interest rates and mortgage options being made available for individual house owners or collective housing associations by the local and central governments to prompt change. The challenge here is that large-scale adoption is a necessary condition to regulate prices and have a viable business case for greener technologies, especially since there are insufficient resources to shape a fully state-funded gas to green transition. Additionally, the Netherlands and other European countries aim to become independent of gas imports from Russia. As of 2022, a quarter of the gas utilised in Europe is imported from Russia (Ministry Responsible Ministry of Economic Affairs and Climate Policy, 2022). Thus, the Netherlands is looking for ways to speed up the transition from gas to greener energy sources, e.g., by installing additional wind turbines in the North Sea, insulating more homes and increasing the use of green hydrogen gas. With this backdrop, the Dutch energy transition in the built environment forms an apt real-world context to apply the theory of social contagion to activate residents. Case Study Site: Reyeroord, Rotterdam To gain rich insight into people’s perceptions and their social networks to stimulate social contagion, we chose to use the neighbourhood scale for our case study. We approached the Rotterdam municipality to select a neighbourhood. The municipality has chosen five pilot neighbourhoods (differing in the types of houses, socio-economic backgrounds of residents and the most suitable type of alternative energy source) to experiment with and learn from. Reyeroord is one such pilot neighbourhood, chosen for its characteristic of having a majority of privately owned houses wherein the household incomes lie below average. Due to the proximity of Reyeroord to the Rotterdam port, and since a part of the heat network already exists there, district heating is chosen as the most feasible and viable alternative (municipality recommends it; residents can still opt for other alternatives). For a shift from gas towards district heating it is essential to get opt-in from a majority of the homeowners. Getting opt-in from each homeowner is tedious and difficult, compared with convincing a housing corporation. The municipality would like to learn how to overcome this challenge. The Reyeroord community has a diverse demographic composition. Its ethnic composition includes 63% native Dutch residents, 12% immigrants from eastern European countries and 25% non-western immigrants. Seventeen percent of the population comprises children between the ages of 0 and 14 (it is considered a ‘Children’s Kingdom’) and 20% consists of the elderly (Borgman, 2019). This age-wise composition is reflected in the social interaction spaces in the neighbourhood, which are mostly directed towards the children or the elderly. A majority of the people in Reyerood lead traditional lifestyles where they are focused on the status quo and strongly hold onto traditions and material possessions (ibid., 2019). They find the concept of sustainability vague and inconsequential to their lives (ibid., 2019). The municipality is in the initial stages of planning and developing a business case for district heating, and is faced with the overarching question: How to motivate the residents to actively participate in the upcoming transition to make Reyeroord natural-gas free?

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The municipality of Rotterdam is actively looking to use a ‘social design’ approach to identify residents’ needs and design interventions to motivate them. Since the municipality’s planning and efforts are at a further stage in Reyeroord as compared with other neighbourhoods, and the municipality is motivated to use social design in its approach within Reyeroord, it makes an apt case for this study. Case Study Methodology The defining feature of case study research is its focus on ‘how’ and ‘why’ questions (Schoch, 2020) and, hence, it is appropriate to answer our qualitative, exploratory research question – how can social influence/social contagion activate residents to adopt greener energy alternatives and support the energy transition in Reyeroord? We followed a ‘research through design’ approach (Stappers & Giaccardi, 2017) wherein the process was inspired by the double-diamond design framework with research and design cross-fertilizing iteratively. The process was guided by three sub-questions: 1. What are the individual resident’s motivations and apprehensions towards gas discontinuation? 2. What do the relations within the social networks of residents look like? How can the contagion unfold in the neighbourhood? 3. How can the municipality use social influence/ social contagion to activate residents to switch to greener energy alternatives? Table 9.1 gives an overview of the different methods used within the diverging and converging phases of our double-diamond design approach. Contextual inquiry The ‘Discover’ phase started with a literature review and secondary desk research on social contagion and the energy transition in the built environment in general. This included understanding the historical context of natural gas, the alternative technologies available, the government’s plans, strategies, policy directives, efforts until now; tools, frameworks and strategies being used by different actors, the process of transition for different types of houses and different information channels available for residents. This was followed by Reyeroord-specific contextual inquiries. Older reports and documents published by the municipality as well as other researchers and design agencies working in the neighbourhood were used to gain insight into the neighbourhood and its social structure. Fourteen semi-structured interviews were carried out with: (six) municipality officials, (one) technology provider, (two) other stakeholders who are involved in the energy transition in Reyeroord, (three) researchers/ designers who have previously worked in the neighbourhood and have in-depth knowledge about the residents and (two) experts on the transition. The participants were recruited by the authors based on the municipality’s advice and represent an expert sampling of key stakeholder groups. The interviews were conducted in English and accompanied by sensitizing exercises to aid the interviewees’ in formulating their answers. These helped to overcome the language barrier, since some interviewees found it difficult to articulate their thoughts in English – Dutch being their native language. The interviews were recorded and transcribed for further analysis.

1, 2

To answer sub-

 

from psychologya)

audio-recorded)

English)

Online via Zoom

participants’ office;

In person at

manually by authors)

Google

translated to

of interviews (done

– Verbatim transcription

(on printed templates)

during the interview

 

– Personas

– System mapping

– SWOT Analysis

analysed using models

(1–1.5 hours each;

– Sensitizing exercises

(specific data was

– Thematic Analysis

1, 2

data

transcripts, secondary

Analysis of interview

structured interviews

– 14 Semi-

resources

Dutch

– English;

language

– (Search

review

– Literature

research

1, 2

Primary research

 

– How-tos

– Reframing

3

goals, criteria

Defining design

& Miro

Online via Zoom

• Braindrawing

• Brainstorming

• How-tos

session:

– During each

hours each)

sessions (2–2.5

– Three ideation

3

Ideation

Develop

 

Deliver

develop the next iteration

toolkitsb

& Miro

Online via Zoom

iterations

with five total

of the toolkit –

were used to strategies and

each workshop

– Insights from

each)

(2–2.5 hours

workshops

– Five co-creation

3

validation

development,

Concept

(3 weeks)

change’

behaviour

‘Design for

with existing

– Comparison

analysis

– Thematic

3

Synthesis

(8 weeks)

 

workshop

co-creation

– One

presentations

– Four

Present outcomes

Notes: a Models used: Theory of planned behaviour (Ajzen, 1991); Integrated model of pro-environmental behaviour (Wilson & Dowlatabadi, 2007); MAO (Ölander & Thøgersen, 1995); Fogg’s behaviour change model (Fogg, 2009); Diffusion of Innovation (Rogers, 1983). b Design for Behaviour change toolkits used: Design for Intent toolkit (Lockton, 2013); Social influence strategies (Cialdini, 2007); The Brains, Behaviour and Design toolkit (2011); Behavioural intervention design toolkit for service design (van Lieren, 2017); Mindspace framework for behaviour change (Dolan et al., 2011).

Location

Methods used

– Desk

research

activity(s)

question(s)

Secondary

Key

framework

diamond design

Define (4 weeks)

Discover

(7 weeks)

Double

Overview of the methods used

Phase from

Table 9.1

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Thematic analysis was used in the ‘Define’ phase to analyse the secondary data sources and interview transcripts to understand: 1. The municipality and energy providers’ current efforts, approach and plan for the transition in Reyeroord as well as the different barriers, dilemmas, tensions they face. SWOT Analysis was used identify the strengths and weaknesses of the current approach. 2. Different residents’ profiles and their motivations, apprehensions and decision-making criteria towards gas discontinuation. These motivations and apprehensions found from the interviews were analysed using models from psychology to categorise them into recurring factors and themes, identify the underlying construction of these themes (as shown in Figures 9.1, 9.2) and define the relationships between these themes (Figure 9.3 shows one such example of the relationship between different themes). 3. Meta-level social identities of the population in Reyeroord based on their affiliations in the neighbourhood and the networks that ensue. Insights about residents’ social identities and networks were used to visualise how the contagion can unfold in Reyeroord building on complex contagion theory. System mapping techniques were used throughout to understand the relationships between different aspects and to visualise the complexity the energy transition in Reyeroord entails. Figure 9.4 shows an example of one such map capturing the tensions, dilemmas and interactions between key stakeholder groups. Design intervention Insights from thematic analysis were used to define design criteria and ‘how-to’ questions that were answered in the ‘Develop’ phase through three ideation sessions with different participants (Session 1: Six design students; Session 2: two design students, two expert designers; Session 3: Two expert designers). The sessions included a Reyeroord-specific brainstorm to ideate upon strategies regarding how social contagion can support the transition by overcoming residents’ apprehensions. Next, these Reyeroord-specific ideas and strategies were translated into a more generalized design approach (concept) to scale up behaviour change. It resulted in the design of the ‘Design for Social Contagion’ Framework and Toolkit which define a design (thinking) approach for the municipality to create interventions and shape the contagion process with a set of design principles, design components and inspiration strategies. The toolkit underwent five iterations, wherein each iteration was validated in-use during a 2–2.5-hour-long workshop, each with a combination of design students, expert designers, design teachers and municipality officials (in total 23 participants). Design students, teachers and experts were recruited by the authors from their own design practice networks; and municipal officials based on the municipality’s advice. The validation was carried out by organizing co-creation workshops wherein participants used the toolkit to design interventions to stimulate social contagion towards gas discontinuation – just how the toolkit is meant to be used in practice. The co-creation workshops helped to qualitatively test the usability aspects of the toolkit – ease of use, the structure and format of the toolkit, intuitiveness, whether it inspires new ideas and helps municipal officials come up with ways to activate residents and if they will use it in the future. As a part of the ‘Deliver’ phase, the outcomes were presented to different clusters within the municipality, to the ENRGISED consortium and at the Dutch

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Note: These factors are classified into Motivation, Ability and Trigger factors based on Fogg’s model for behaviour change to understand whether residents lack motivation or the ability to act towards gas discontinuation. These were used as input for ideation of interventions. Source: Fogg, 2009.

Figure 9.1

Residents’ motivations and apprehensions identified from interviews were simplified into key factors

Design Week 2020. A workshop was organised for a service design agency working on the transition to use the toolkit and brainstorm interventions. Limitations Our study is limited to one neighbourhood, hence future research must be done in different contexts (of energy transition and other areas of sustainable transitions) to further strengthen the notion of designing for social contagion towards sustainable lifestyles. Our study is

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Note: Seven key themes were identified. Maslow’s hierarchy of needs was used to further define which basic needs these themes cater to. The themes were used to identify the decision-making process of different resident profiles in Reyeroord. Source: Adapted from McLeod, 2020.

Figure 9.2

The factors derived from interviews, clustered together to identify recurring underlying themes that drive residents’ decision-making

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Note: Procrastination is the result of three interrelated themes (and their underlying factors) according to our findings – Trust, Awareness/Understanding and Loss/Risk perceptions.

Figure 9.3

Deconstructing ‘Procrastination’ towards gas discontinuation

a starting point to give a practical form to theory on social contagion and social influence. Future research should point out which strategies are more effective in achieving which type of sustainable transition goals, and how the strategies can be further customised. The study builds on complex contagion theory by Centola (2018); future research can explore other theories of contagion to strengthen the framework. We take a qualitative approach to intervene in social networks. This can be complemented by quantitative network mapping techniques to improve predictability as well as evaluation of the interventions. Since persuasive techniques, specifically social influence, can alter behaviours, designers must be highly reflexive about their intentions as persuaders. Ethical considerations and implications of using social influence strategies must be further researched.

RESULTS This results section is divided into three sub-sections, where each section answers one sub-question respectively. The first two sub-sections present the findings from the Reyeroord-specific contextual inquiry regarding the residents’ motivation and apprehensions

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Example of a system map showing causal relationships between the efforts and perspectives of the key stakeholders involved in the energy transition in Reyeroord

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and their social networks. The third sub-section outlines the results of our design intervention as to how municipalities can use social contagion to activate residents. Residents’ Motivations and Apprehensions towards the Energy Transition in Reyeroord The interviews show that key factors shaping the motivations and apprehensions towards the energy transition in Reyeroord are affordability, issues of trust, loss/risk perceptions, traditional lifestyles, concern for the environment and for their children’s future. Results reveal that affordability is the key constraint in this neighbourhood since average disposable income is €30,000 per annum; considered to be below average compared with other regions. Fifty-seven percent of households in the region fall in the low-income category with 13% being below the poverty live. Frugal living is a part of their ethos, which is also exemplified by people’s attraction to discounts, coupons, or offers. Since most people are busy making ends meet, their minds are preoccupied and other priorities take precedence. People are unwilling to even hear about the energy transition (e.g., they ignore any letters, mails sent to them). Specific personal situations further compound this hesitation – for example, when one is pregnant, or has just moved to the neighbourhood or plans to move out in the near future; or a common notion amongst the elderly being – ‘This is not going to happen in my lifetime’. The segment of the population with mid-level incomes (approximately 34%) which can afford the transition procrastinates decision-making. Apprehension amongst these residents stems from lack of trust either in the process, the energy alternatives or stakeholders involved. This lack of trust is due to negative past experiences – say in dealing with the municipality or a specific energy provider, or general scepticism. Lack of awareness and miscomprehension (also caused by the lack of a single point of personalised information) is another cause for procrastination. For example, some residents believe that soon they will get hydrogen gas through the existing natural gas infrastructure; or in a glimpse residents think that solar panels are the cheapest and best alternative for them, not knowing the hidden costs involved or infrastructure requirements for their house. Loss/risk perceptions also fuel procrastination of decision-making since residents do not want to be the first to make the change and want to learn from others’ (with similar socio-economic backgrounds or lifestyles) experiences. Residents have a perception that they are losing their freedom of choice. Currently, based on competitive pricing, residents can easily switch between different energy providers. However, with alternatives such as district heating only one provider shall cater to a specific neighbourhood, hence the notion that the energy providers shall have a monopoly and quote exorbitant prices. In reality, the providers are being kept under check by the municipality – a fact that residents are unaware of, which surfaced in the research. Some residents are waiting for gas discontinuation to become a law in order to have more certainty. Paradoxically, these residents also believe that the government cannot force them to change. A few residents consider it the governments’ responsibility to fund and realise the energy transition. Some residents in Reyeroord are ready to switch to greener alternatives immediately. For these enthusiasts, the key motivation is their concern for the environment and for their children’s future. This small percentage (approximately 9%) of residents with higher incomes can easily afford the transition and find it an opportune moment to avail themselves of the discounts and change infrastructure within their houses at lower costs. On the other hand, there

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is a group of residents for whom affordability constraints exceed their motivation. They are open to knowing the different options available, where some even go the extra mile of getting advice from the social department of the municipality to find means to fund the transition. If they find the finances, they will readily opt for gas discontinuation. It is observed that in previous interventions by the municipality in Reyeroord, residents often participated or were willing to participate only when their neighbours also participated or the neighbours invited them. Given that apart from affordability, residents seek certainty and credibility of change, where they want to learn from their peers’ experiences, we hypothesize that socially-driven interventions can play a key role in overcoming apprehensions and stimulating behaviour change. Resident’s Social Networks and the Contagion Process To understand how the social contagion can unfold in Reyeroord, we probed the residents’ social identities and the social networks that exist within the community. We find that people’s social identities in Reyeroord draw on their socio-economic background which has high correlation with the type of houses they inhabit. People with similar socio-economic backgrounds live in similar houses (individual bungalows, three-storey apartments, apartments for the elderly), in close vicinity and know each other well. This gives rise to neighbourhood micro-networks (based on the vicinity of the house) that can be used to seed the contagion and activate residents within neighbourhood clusters. Additionally, people’s social identities follow from the activities they pursue and their interests. These activities are related to the social spaces in the neighbourhood they visit – e.g., church, gym, park, centre for the elderly; or other routine habits, e.g., while walking their dogs, going for nature walks, picking their kids up from school, etc. Interactions amongst residents during these activities give rise to practice-based networks where people from different neighbourhood micro-networks interact. These practice-based networks can be used to spread the contagion across the different neighbourhood micro-networks. There are some influencers and active people in the community who can serve as the seed nodes to start the spread of the contagion. Once activated, specific interventions need to be designed to spark interactions between residents such that these seed nodes can spread and reinforce the credibility of the target behaviour to others within their neighbourhood micro-networks. Simultaneously, interventions need also to be designed to enable activated residents from neighbourhood micro-networks to spread and reinforce the behaviour to other residents they meet through their practice-based networks (spreading the behaviour across different neighbourhood micro-networks). Having identified how social contagion can unfold in Reyeroord, next we present results on how to design interventions to practically spark interactions and activate residents using social contagion. Design for Social Contagion Framework: Using Social Influence To Activate Residents Based upon the literature of complex contagions (Centola, 2018), our design process and findings from the Reyeroord case study, we have developed the Design for Social Contagion Framework presented in Figure 9.5. The framework outlines an overall process to shape con-

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tagions within a community, qualitatively. It shows three key elements that need to be defined (based on the context) to shape social contagions, namely: 1. The WHAT: includes defining the content or the target behaviour that needs to be spread amongst a population. In the case of gas discontinuation, it relates to the contagion of a positive attitude or decision towards shifting to greener energy alternatives. The WHAT can also be determined by understanding the residents’ specific apprehensions. 2. The HOW: refers to the means or mode of contagion – how the contagion of the target behaviour can unfold in a specific context. This includes visualising and defining the network dynamics of the contagion – the seed nodes (initiators of social influence), clusters (social networks of people), bridges, etc. The framework outlines an actionable six-step process as shown in Figure 9.5 to define the network dynamics. Note that this process is not explicitly listed or prescribed by Centola (2018). It is derived by the authors based on the examples provided by him. 3. The WHY STRATEGY (Persuasion): Apart from defining the network dynamics as to who will spread the target behaviour to whom and when, it is essential to define how people will spread the behaviour to their peers and what will activate the target behaviour. This happens in Steps 3, 4 and 5 of the process. It includes devising persuasive and tactical ways of inducing the behaviour (the strategy of contagion), building upon behaviour change strategies to enable the contagion within and across networks. The framework was used as a high-level compass to guide the design project for the case of Reyeroord. It is developed to guide design processes for municipality officials and designers who want to stimulate social contagion towards gas discontinuation in the future.

DESIGN FOR SOCIAL CONTAGION TOOLKIT Next to the framework, we have developed the ‘Design for Social Contagion Toolkit’, which aids designers in defining the WHY (Persuasion) Strategy element, specifically designing practical interactions and interventions for social contagion in Steps 4 and 5 of the framework. The toolkit builds on the ‘Anatomy of an intervention’ (as shown in Figure 9.6) which is the logic of designing interventions. The anatomy outlines that each intervention aimed at shaping social contagion needs to fulfil two design criteria, follow four design principles, and can be designed using four intervention components. This anatomy of an intervention is derived by analysing and generalising the concepts developed during ideation. The two design criteria an intervention must fulfil are: (1) enable the target behaviour, and (2) enable the contagion of the behaviour. The clear distinction between these two steps shows that the activation of the behaviour in people and the spreading of the behaviour (contagion of it) are different processes that both need to be designed for in different ways. Apart from the two criteria, each intervention must follow four design principles: (1) scale down: translate global to local; (2) three Ss: Simple, Slow and Steady win the race; (3) comparison is key: enable (sub)conscious comparison; (4) make it desirable, silly! These principles must be kept in mind while designing interventions and help to ensure that the interventions have impact on the target group. For example, since people find the concept of sustainability vague, it is important that the interventions highlight what this global phenomenon means for people’s daily lives, their kids, their surroundings. Only if people recognise and

Figure 9.5

Design for social contagion framework

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Figure 9.6

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Anatomy of an intervention aimed at stimulating social contagion of target behaviour

relate to something, they will act upon it. The design principles can also be used as qualitative evaluation criteria for the intervention. Last, the anatomy outlines four components that constitute an intervention and give form to the design principles and criteria: (1) Actions, (2) Spread mechanism, (3) Touchpoints, and (4) Incentives. Designing the Action and Spread mechanism help to fulfil the two distinct criteria stated above. These must be complemented with apt incentives and well-designed touchpoints that make the target behaviour or spreading the target behaviour more easy, intuitive and desirable. The four components are not mutually exclusive; however, they are specified as different components to guide the design process, and to ensure each aspect is explicitly thought about.

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The toolkit consists of an inspiration card deck (54 cards), a set of five design canvases and a handbook (as shown in Figure 9.7). The card deck includes a description of the criteria (2×), principles (4×) and components (4×). The card deck also includes a set of design for behaviour change persuasion strategies outlined under each design component (Action, Spread Mechanism, Touchpoint and Incentive). These provide inspiration for and examples of how to design specific components in an intervention. The canvases facilitate the process of using the inspiration cards to design interventions, from problem definition, brainstorming to conceptualisation, evaluation and detailing. These can be used by individuals or in group sessions. How to use the inspiration cards with the canvases is outlined in a handbook provided with the toolkit.1 The toolkit was validated with (seven) municipal officials in two different sessions each of 2–2.5 hours. This validation highlighted that the toolkit familiarises people with behavioural and social constructs of decision-making. It inspires them to think differently and incorporate social innovation in their approach. Initiating this change in mindset is crucial to live through and steer transitions. This points to a dual role of the toolkit: (1) it triggers a change in mindset amongst municipal officials, prompting them to be more empathetic, creative and experimental; and (2) it provides a foundation for municipality employees to better explore, understand, design and implement creative interventions to steer the requisite social transitions using the phenomena of social contagion.

DISCUSSION AND CONCLUSION The main research question for this study was – how can social influence/social contagion activate residents to adopt greener energy alternatives and support the energy transition in Reyeroord? Our research shows that social influence and social contagion can help municipalities in activating communities and networks of citizens using social interventions. Residents have many apprehensions and misconceptions towards the energy transition. Social contagion can help in overcoming these apprehensions, building a positive attitude and commitment towards gas discontinuation. However, in our study the actual implementation and effects of interventions have not yet been tested. Lying at the intersection of design (thinking), sociology and psychology, our research contributes to the emergent transition design and systemic design in two novel ways: 1. Theoretically, the proposal of using the phenomenon of ‘social influence and social contagion’ to steer transitions adds to the ‘theories of (scaling) change’ within the field of Transition design (Irwin et al., 2015). With a focus on the collective rather than the individuals, social contagion inherently is a systemic approach. Hence, our case study is an example where systemic thinking and design thinking come together. Our methodology pushes the boundaries of traditional design thinking applications towards what Drew et al. (2021) term system-conscious and system-shifting design. 2. As a practical contribution, we present insights into the social factors shaping the energy transition for a specific neighbourhood. We present the first version of a framework and toolkit to design context-specific interventions to activate networks of people to adopt greener alternatives using social contagion. This codified method adds to the limited practical tools and strategies to activate a critical mass by providing local councils and design-

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Figure. 9.7

Design for social contagion toolkit

ers with a way of designing, keeping scaling human behaviour in mind. The outcomes add to the qualitative approaches towards designing and intervening in social networks. Reflecting on the broader application of design thinking for societal transitions and systemic change, we see that in the past decade, design thinking has piqued the interest of the business, management community and public sector institutions alike. It is successfully being applied to

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drive innovation, develop value propositions and address open and complex problems faced by these organisations. However, it has been oversimplified and popularised as a step-wise formula for creativity or customer discovery. While creativity and abductive reasoning (the core of design thinking) are important, we need to find new ways to apply design thinking when dealing with complex societal challenges. Our study shows how design thinking is much more variant than a step-wise approach in its form and must be adapted to the context. (Systemic) Design thinking entails drawing on different theories and practices from other disciplines and translating this transdisciplinary knowledge into actionable tools and strategies. This is in line with Norman (2020), who highlights the importance of designers’ capability of combining the skills and knowledge of other disciplines into novel and powerful strategies to tackle societal problems. Our approach of building on social contagion theory from sociology and psychology to develop practical strategies for activation exemplifies how a design thinking approach can facilitate this combination of transdisciplinary knowledge. A key aspect of applying design thinking is ‘morphing to the context’, which involves engaging with the community. This includes identifying active, creative members within communities who understand the local culture, needs, and capabilities, and co-designing solutions with them. With respect to systemic change, Norman (2020) observes that community members often have ideas to overcome challenges. However, often these are focused on alleviating the symptoms rather than tackling the underlying causes. Here, expert design thinking can empower these community members to further utilise their creativity (diffuse design capability) to develop practical solutions to overcome the underlying causes of systemic problems. In our project, by relying on the theory of social contagion we try to use these diffuse design capabilities of the community members as well as the municipality officials and spark them to come up with contextually embedded strategies through our toolkit. Our expert design practice was combined with co-design methods to develop the strategies. This points to the need for different types of design thinking (expert, diffuse and co-design), as Manzini (2015) also argues, to come together to shape deliberate, equitable and just transitions. In our collaboration with the public sector, we observe that several local councils are inspired by creative ideas and are enthusiastic about using design thinking for their projects. While this highlights the ground for design thinking in the public sector, it needs to be taken seriously and deliberately (and sustainably) incorporated into the approach of local councils to achieve true impact. More often it is noticed that expert designers are brought in for a project to develop new ways of approaching problems. However, these ideas are seldom implemented in practice since the ‘implementers’ lack a sense of ownership with a ‘not invented here’ perception. Designing toolkits and processes (such as ours), which involve municipal officials through the whole process of designing and implementing ideas, are helpful in building a sense of ownership and ensuring implementation. We do recognise the irony and limitations of outsider designers (us, the authors) having designed this toolkit. This role of the toolkit in building ownership amongst municipal officials surfaced during validation, and aligns with the fact that only when ideas are generated by the people themselves, do they tend to be implemented. Close collaboration between expert designers and the public sector is essential, even while the latter adopts a design thinking mindset. Moreover, as Niedderer et al. (2014) recommend, it is essential that policymakers and designers focus on ‘design for behaviour change’ within the innovation process in order to promote ethical and sustainable practices. Our study provides

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one such practical example of application of ‘design for behaviour change’ to prompt sustainable practices at scale. With respect to expert design, we see ‘reframing’ (creation of novel standpoints from which a problematic situation can be tackled (Dorst, 2011)) as a core quality of design thinking practice while aiming for systemic change. Systemic problems entail a wide range of paradoxes and tensions, be it conflicting views and standpoints or conflicting considerations/requirements to overcome problematic situations. For example, within the energy transition context, a common paradox we observed is that on one hand residents procrastinate decision-making citing the reason that they will discontinue gas only when it becomes a law, whereas on the other hand they show resistance towards policy-driven, top-down efforts towards change. Uncovering such paradoxes, diving deeper into the latent aspects that drive these paradoxes and the causal relationships between these aspects, and then finding novel ‘frames’ or ‘leverage points’ to tackle these, is the core capability that design thinking brings to systems change. Upon uncovering the paradoxes in our study, our reframing process led to using ‘social contagion and peer networks’ as a means to activate communities and to seeing how activation can happen within communities. In sum, design thinking for transitions and systemic change is not a mere five-step formula, rather an integrative, abductive, perspective-taking practice that must be adapted to the context at hand. It involves explicating the implicit, understanding dynamics between various aspects, reframing and probing in systems by combining transdisciplinary knowledge and shifting between the big-picture and detail-oriented mindset. Design thinking can contribute to the strategic, tactical and operational activities of achieving systemic transitions. It is the pluralism of design thinking in application and approaches that makes it adept at tackling complex systemic challenges and contributing to the transition towards sustainable futures.

ACKNOWLEDGEMENTS We would like to thank Mr Jacco Kwakman from the Rotterdam Municipality for helping us gain access to the Reyeroord neighbourhood, giving us the relevant information, connecting us to the required stakeholders and actively participating in the co-creative process.

NOTE 1.

To know more details about the toolkit or to get a physical/digital copy, contact us at jesalshah92@​ gmail​.com.

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Irwin, T., Kossoff, G. & Tonkinwise, C. (2015). Transition design provocation. Design Philosophy Papers, 13, 3–11. 10.1080/14487136.2015.1085688. Kemp, R. (2010). The Dutch energy transition approach. International Economics of Resource Efficiency, 187–213. doi: 10.1007/978-3-7908-2601-2_9 Klimaatakkoord (2019). Klimaatakkoord. Retrieved from: https://​www​.klimaatakkoord​.nl/​ Klitkou, A., Bolwig, S., Hansen, T., & Wessberg, N. (2015). The role of lock-in mechanisms in transition processes: The case of energy for road transport. Environmental Innovation and Societal Transitions, 16, 22–37. https://​doi​.org/​10​.1016/​j​.eist​.2015​.07​.005 Lilley, D. (2007) Designing for behavioural change: Reducing the social impacts of product use through design. Doctoral thesis, Loughborough University, Department of Design & Technology. Lockton, D. (2013). Design with intent: A design pattern toolkit for environmental & social behaviour change. PhD thesis, Brunel University, School of Engineering & Design. Loorbach, D.A, & van de Lindt, M. (2007). From theory to practice of transition management: The case of sustainable living and housing in Flanders. Leuven Conference MOPAN, 28-29 June 2007. Retrieved from http://​hdl​.handle​.net/​1765/​34982 Lutzenhiser, L. (1992). A cultural model of household energy consumption. Energy, 17(1), 47–60. doi: 10.1016/0360-5442(92)90032-u Manzini, E. (2015). Design, when everybody designs. Cambridge, MA; London, England: MIT Press. Marsden, P. V., & Friedkin, N. E. (1993). Network studies of social influence. Sociological Methods & Research, 22(1), 127–151. doi: 10.1177/0049124193022001006 McLeod, S. (2020). Maslow’s hierarchy of needs. Simply Psychology, 29 December. Retrieved 10 October 2021, from https://​www​.simplypsychology​.org/​maslow​.html. Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. Implementation Science: IS, 6(April), 42. https://​doi​.org/​10​.1186/​1748​-5908​-6​-42 Ministry Responsible Ministry of Economic Affairs and Climate Policy. (2022). Reducing dependence on Russia. Less gas from Russia. 12 April. Government.nl. Retrieved 30 May 2022, from https://​www​ .government​.nl/​topics/​gas/​reducing​-dependence​-on​-russia Nelson, H. G., & Stolterman, E. (2012). The design way: Intentional change in an unpredictable world. 2nd ed. MIT Press. Niedderer, K., Cain, R., Clune, S., Lockton, D., Ludden, G., Mackrill, J., Morris, A., Evans, M., Gardiner, E., Gutteridge, R., & Hekkert, P. (2014). Creating sustainable innovation through design for behaviour change: Summary report. University of Wolverhampton, CADRE. Nolan, J. M., Schultz, P. W., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2008). Normative social influence is underdetected. Personality and Social Psychology Bulletin, 34(7), 913–923. https://​ doi​.org/​10​.1177/​0146167208316691 Norman, D. (2020). To create a better society: The 2020 MP Ranjan Memorial Lecture, 22 November. jnd.org. Retrieved 24 January 2022, from https://​jnd​.org/​to​-create​-a​-better​-society/​ O’Sullivan, F. (2017). Barcelona’s car-taming ‘superblocks’ meet resistance. Bloomberg CityLab, 20 January. https://​www​.bloomberg​.com/​news/​articles/​2017​-01​-20/​barcelona​-s​-superblocks​-expand​-but​ -face​-protests. Ölander, F., & Thøgersen, J. (1995). Understanding of consumer behaviour as a prerequisite for environmental protection. Journal of Consumer Policy, 18(4), 345–385. https://​doi​.org/​10​.1007/​bf01024160 Ploderer, B., Reitberger, W., Oinas-Kukkonen, H., & van Gemert-Pijnen, J. (2014). Social interaction and reflection for behaviour change. Personal and Ubiquitous Computing, 18(7), 1667–1676. https://​ doi​.org/​10​.1007/​s00779​-014​-0779​-y Rapid Transition Alliance. (2021). The dash away from Gas: How the Netherlands kicked a BIG fossil fuel habit, 2, June. https://​www​.rapidtransition​.org/​stories/​the​-dash​-away​-from​-gas​-how​-the​ -netherlands​-kicked​-a​-big​-fossil​-fuel​-habit/​. Rogers, E. M. (1983). Diffusion of innovations. New York: Free Press. Schoch, K. W. (2020). Chapter 16: Case study research. In G. J. Burkholder, K. A. Cox, L. M. Crawford, and J. H. Hitchcock (eds), Research design and methods: An applied guide for the scholar-practitioner. SAGE Publications, Inc. https://​us​.sagepub​.com/​en​-us/​nam/​research​-design​-and​ -methods/​book262895​#contents)

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Simon, S. (2010). Even Boulder finds it isn’t easy going green. The Wall Street Journal, 13 February. https://​www​.wsj​.com/​articles/​SB1​0001424052​7487043201​0457501592​0992845334 Stappers, P.J. and Giaccardi, E. (2017). ‘Research through design’. In The encyclopedia of human-computer interaction, 2nd ed. (Ch. 43). Interaction Design Foundation. Retrieved from: https://​www​.interactiondesign​.org/​literature/​book/​the​-encyclopedia​-of​-human​-computer​-interaction​ -2nd​-ed/​research​-throughdesign Trudel, R. (2018). Sustainable consumer behavior. Consumer Psychology Review, 2, 85–96. van Lieren, A. (2017). Rational override; influencing behaviour beyond nudging: A service design approach towards creating behavioural interventions. Retrieved 29 June 2020, from https://​repository​ .tudelft​.nl/​islandora/​object/​uuid:​234307dd​-42e4​-43f3​-80a8​-210240d3325c van der Bijl-Brouwer, M., & Malcolm, B. (2020). Systemic design principles in social innovation – a study of expert practices and design rationales. She ji – The Journal of Design, Economics and Innovation, 6(3), 386–407. Williamson, K., Satre-Meloy, A., Velasco, K., & Green, K., (2018). Climate change needs behavior change: Making the case for behavioral solutions to reduce global warming. Arlington, VA: Rare. Available online at rare​.org/​center Wilson, C., & Dowlatabadi, H. (2007). Models of decision making and residential energy use. Annual Review of Environment and Resources, 32(1), 169–203. https://​doi​.org/​10​.1146/​annurev​.energy​.32​ .053006​.141137

10. Method case study – A design thinking toolkit for framing market conditions Ilya Fridman, Robbie Napper, Amrik S. Sohal and Sairah Hussain INTRODUCTION Companies struggle to manage technological transitions due to the difficulty and uncertainty in understanding upcoming market disruptions (Kaplan, 2008). This stems from the way in which managers ‘frame’ a situation as it determines the strategy for how a company responds (Kaplan, 2008, p. 736). A ‘frame’ can be described as someone’s understanding of a situation or a problem to be solved (Schön, 1984, p.132). These perspectives can also form a ‘collective meaning’ and coordinate action when shared with others within the organisation (Kaplan, 2008, p. 732). In order to address the ambiguities of a technological transition, it is important for managers to create collective frames that recognise current market conditions and enable future possibilities to be explored. Frame creation – also known as framing – is suggested to be at the ‘core of design thinking’ (Dorst, 2011, p. 521). This is typically done early within the process during the problem definition stage where known information is analysed and synthesised to identify the ‘core problems’ of a given situation (Dam, 2021, para. 7). Once created, frames provide a ‘common ground’ for people to ‘discuss the problem and possible solutions’ (Dorst, 2015, p. 64). They can also allow the situation to be reconceptualised or reframed to ‘explore alternative ways for approaching the problem’ (Dorst, 2015, p. 78). This is a critical stage within design thinking as it lays the foundation for creative idea generation; however, it is also a difficult one to achieve for non-designers who struggle with the complexity of ‘defining the project’ and ‘seeing the problem in an unfamiliar context’ (Mosely et al., 2018, p. 184) – findings which are also supported by experiences from this chapter’s lead author, who has been teaching design thinking to non-designers for over five years. A better understanding is required for how to support businesses with tools and practices that guide them through this stage of the design thinking process. Framing market conditions and future possibilities would provide managers with insights that enable them to plan accordingly during a technological transition. This case study describes a collaborative research process between business and design researchers at Monash University working with Australia’s largest bus manufacturer, Volgren. Through this process, the team developed a design thinking toolkit that was used to explore barriers and opportunities in Australia’s transitioning public transport bus industry. Research during this project was conducted through a Monash University Seed Fund Grant scheme 190

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in combination with funding from the Bus Association of Victoria and the City of Greater Bendigo. The project’s methods and outcomes demonstrate how a design thinking toolkit can provide managers at a large manufacturing company with the ability to frame market conditions and collectively explore different opportunities presented by a technological transition within their industry. Through this example, it is proposed that design thinking tools and practices can provide a unique contribution to knowledge development that supports business decision making.

OVERVIEW OF THE TECHNOLOGICAL TRANSITIONS IN AUSTRALIA’S BUS INDUSTRY Australia’s public transport sector is at a unique moment in time as it begins to transition away from combustion engines powered by fossil fuels and towards zero-emission electric vehicle (EV) technologies powered by renewable energy (Napper et al., 2022). Bus services are an important part of this technological transition because they are essential to all public transport networks across Australia’s major cities and regions. In some places, buses are the only public transport available. EV technologies bring many potential benefits, including the ability to shed our dependency on imported oil and associated market fluctuations by promoting the local generation of renewable energy (Garnaut, 2019); creating local jobs within Australia’s developing energy market (Carroll, 2020); reducing noise and exhaust pollution in residential and commercial areas where bus services typically operate (Honnery et al., 2016). EVs improve passenger travel experience with smoother ride quality through quieter operation and reduced vibration (Hildén et al., 2016). At the same time, EV technologies pose numerous challenges and uptake barriers due to the risks associated with investing in new systems and their potential to disrupt well-established industry practices (International Association of Public Transport Australia/New Zealand, 2020; Napper et al., 2022). The Australian bus industry has developed and evolved over many decades through a close relationship with the internal combustion engine (ICE). This has led to the development of a robust industry in which supply chains, infrastructure and approaches to service provision are all set up around ICE-driven vehicles, commonly powered by diesel fuel. Even current contractual agreements stipulate a vehicle’s daily service time, allowable dwell time and expected travel distances that have all been modelled on ICE performance (Fridman, 2016). This well-established relationship means that it is both difficult for alternative fuel technologies to enter the Australian market and that any technological change could result in significant industry-wide disruptions. EV buses require new supply chains to be established for components such as electric motors and battery energy storage systems. They demand the deployment of new infrastructures such as charging stations and associated electrical network upgrades. Consideration must also be given to new operational approaches that balance a vehicle’s driving distance and charging time (Napper et al., 2017). These present a unique challenge for businesses due to the uncertainty of how these technologies will impact the local market. Understanding and planning for these technologies becomes critical, particularly for large companies such as Volgren who are at the forefront of this transition.

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Design thinking processes can support public transport businesses by enabling managers to create frames that incorporate technological and economic considerations allowing market opportunities to be explored conceptually prior to implementation. Fridman and Coxon (2021, p. 19) have provided an example of how ‘visual conflict framing’ can support designers during new public transport product development; however, within the current case study a more collaborative and interactive approach was required to facilitate non-designers through the frame creation process. It was determined that a design thinking toolkit would be more suitable for this task.

DEVELOPING A DESIGN THINKING TOOLKIT TO SUPPORT FRAMING MARKET CONDITIONS Toolkits are often used in design workshops to facilitate the collaborative generation of ideas and the development of project insights (Sanders & Stappers, 2014). Within these contexts, they provide a structure that allows participants to creatively engage with the content and each other by developing a ‘concrete common language’ to discuss specific issues under consideration (Muller et al., 1995, p. 137). A design thinking toolkit was developed as part of this research project to facilitate the process of framing current market conditions and creatively exploring different scenarios for implementing EV buses. The toolkit consisted of a combination of both commonly available and original elements that served specific purposes in supporting creative exploration. These included pens, Bostik Blu-Tack, Lego Duplo blocks, multiple lengths of red and blue ribbon, and four card sets (Figure 10.1). In order to develop the toolkit, business researchers first conducted a literature review to understand the implications for EV bus component supply chains and business partnerships by analysing existing case studies and industry reports from other regions. This review established a foundation of existing knowledge by identifying potential pathways and considerations for implementing EV buses in Australia. Business and design researchers then discussed the literature review findings and synthesised them into four key categories that could describe an EV bus market. These were players, products, relationships, and functionalities (Figure 10.2). These categories were then translated by the design researchers into four card sets as part of a design thinking toolkit. Player cards (Figure 10.1, No. 1) represented various stakeholders such as vehicle manufacturers, energy providers and state road authorities. Product cards (Figure 10.1, No. 2) represented various products both tangible and intangible such as batteries, bus stops and subsidies that could be incorporated as part of an EV bus market. Relationship cards (Figure 10.1, No. 4) represented the different types of relationships that stakeholders could have with one another, such as lease, purchase and partnership agreements. Functionality cards (Figure 10.1, No. 3) represented the value exchange between stakeholders, such as inputs and outputs that resulted from certain partnerships. Most cards were pre-filled with specific terms that could encourage participant discussion; however, each set also included blank cards and pens that participants could use to introduce new aspects that researchers had not yet discovered. Halskov and Dalsgaard (2007, p. 203) refer to these types of contributions as ‘external sources of inspiration’ that may generate ‘emergent’ project insights that would otherwise be unconsidered.

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Photograph of the design toolkit components

Lego Duplo blocks (Figure 10.1, No. 7) were selected as part of this toolkit to stimulate creative thinking by bringing a sense of playfulness within the workshop when prototyping different market scenarios. Scharge (2013, p. 70) suggests that playful prototyping is especially important in innovation contexts where it can stimulate creative thinking by relaxing the rules to ‘explore alternatives’ and ‘give rise to new realities’. Workshop participants could work with the blocks and construct them in any desired configuration to represent various elements of a bus market including products, infrastructure and stakeholders. Multiple lengths of ribbon (Figure 10.1, No. 8) were provided in two different colours to establish connections between different market elements, such as the connection between bus operator and energy provider. The blue ribbons were intended to represent functionalities such as value inputs, while red ribbons were intended to represent relationships such as lease agreements. Participants could attach the corresponding relationship and functionality cards to each ribbon using the Bostik Blu-Tack provided within the toolkit.

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Key categories of an EV bus market

Through these materials, the toolkit embodied information discovered through the literature review and provided necessary elements for framing market conditions. This would generate an understanding around key challenges associated with implementing EV bus technologies, as well as enable them to be reframed by physically manipulating individual system elements to explore new market opportunities.

IMPLEMENTING THE DESIGN THINKING TOOLKIT DURING A RESEARCH WORKSHOP The toolkit was used to facilitate a research workshop that was conducted with representatives from Volgren and researchers from Monash University. There were seven workshop participants in total: two representatives from Volgren’s management team covering Engineering and Sales, two design researchers specialising in EV technologies, two business researchers specialising in Supply Chain Management and a representative from Monash Energy Institute’s senior leadership team. The workshop was conducted under ethics approval from Monash University Human Research Ethics Committee number 9648. Workshop participants used the toolkit to model the current diesel bus market based on research findings and Volgren’s industry expertise. By doing so, they created a collective frame of existing market conditions that encompassed bus infrastructure, supply chains and business partnerships including value flows and operational agreements (Figure 10.3). This represented the problem identification stage of a design thinking process enabling current conditions to be analysed through discussion that identified key implementation challenges related to EV technologies. It also established the foundation for creative synthesis, which was

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Figure 10.3

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Photograph of toolkit materials in use during the research workshop

achieved as participants remodelled the frame to explore alternative pathways towards establishing an EV bus market. This was done alongside discussions of potential market barriers that would impede this transition and opportunities that can be leveraged during the process (Figure 10.4). Frames created during the workshop provided a ‘common ground’ for people to ‘explore alternative ways for approaching the problem’ (Dorst, 2015, pp. 64, 78). One example of this was when participant discussion focused on the possibility of transport service providers to enter the energy market by trading energy stored in a vehicle’s batteries. This shift in thinking from transport provider to energy trader created a new frame through which the market could then be viewed. Dorst (2015, p. 78) suggests that once framed and understood, situations can be reframed by considering a simple statement: ‘If the problem of... was approached as if it was a problem of..., then the solution should be…’. In a similar manner, the toolkit enabled workshop participants to realise that if the problem of additional investment (view of a transport provider) was approached as if it was a problem of storage capacity (view of an energy trader), then the solution should be to design vehicles that balance battery capacity for both vehicle traction and energy trading in order to create a more dynamic market. Through interactions with the toolkit, Volgren managers engaged collaboratively with Monash researchers to generate a greater shared understanding of the technological transition while contributing a speculative industry perspective into the research. This differed from the typical research approach that would have otherwise relied on stakeholder interviews to generate primary data, which would have been communicated back to Volgren in a static written and visual format. On the contrary, the design thinking toolkit encouraged proactive engagement

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Photograph close-up of workshop outcomes

with the information by asking participants to construct and reconstruct it to expand understanding and develop new research insights through the process.

PUBLIC TRANSPORT INDUSTRY FINDINGS Outcomes from the research workshop found that unique opportunities existed for establishing new business models and business partnerships to support a transition to EV technologies in the Australian bus industry. These findings build on what had already been discovered by business researchers through literature review and provided a local industry perspective through the contributions from Volgren’s managers. Through workshop discussions, it was identified that Volgren could build buses for Australian operators through three different approaches that depended on establishing partnerships with technology and infrastructure original equipment manufacturers (OEMs). It was also identified that bus operators could enter the energy market as part of a distributed electrical storage network by using their vehicle’s batteries for energy trading while the vehicle was stationary at the depot. Within this model, operators would store electrical energy in their vehicle’s batteries and then sell this back to the market during times of peak electricity demand, which would create an additional revenue stream that helped them recuperate their investment in the technology. This additional energy storage would also help to reduce the strain on electricity generation within the state. A further opportunity was presented in the second-hand battery market as vehicle batteries could be on-sold for use as stationary energy storage as part of the electricity grid once they were no longer suitable for traction application on board

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a vehicle. These strategies would require operators and manufacturers, such as Volgren, to plan for a vehicle’s energy storage requirements and specify component suppliers accordingly in order to enable this type of trading while also fulfilling transport service demands. They support the importance of establishing early partnerships between OEMs, bus builders, bus operators and energy companies in order to leverage financial opportunities presented by this transition. Workshop discussions also brought up the potential to consider new ownership models and lease agreements for EV technologies where certain high-value components such as batteries or charge stations could be co-owned by multiple businesses or leased from one business to another. Business partnerships could help to mitigate the risks of investing in new technologies and generate collective benefits by leveraging that technology’s capabilities. These models did not currently exist in the local public transport market so they had to be imagined and created by workshop participants through the speculative and collaborative opportunities offered by the design thinking toolkit. Ideas like these were modelled using the toolkit and thus tested out instantly, rather than being ‘noted’ as might be the case in more traditional approaches. This process aligned with suggestions that design thinking should be applied to support collaborative ‘big picture thinking’ by enabling ideas to be envisioned in a ‘hands-on way’ (Bjögvinsson et al., 2012, p. 101).

Figure 10.5

Visualisation of workshop outcomes

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Workshop outcomes were documented and translated into an illustration (Figure 10.5) that represented the various connections between infrastructure, supply chains and businesses as part of a future EV market. These were used to communicate future market opportunities discovered through the research project.

A NOVEL APPROACH TO FRAMING MARKET CONDITIONS This case study has presented the development and application of a novel design thinking toolkit that can help managers frame market conditions and explore new opportunities within the public transport industry. This approach has demonstrated how research knowledge may be synthesised into tangible tools that enable collaborative frame creation and has provided insights for how non-designers may be guided through this process. The toolkit addressed challenges associated with the problem definition stage of the design thinking process where non-designers can struggle with ‘defining the project’ and ‘seeing the problem in an unfamiliar context’ (Mosely et al., 2018, p. 184). This is evidenced by how the toolkit enabled workshop participants to generate a shared understanding by framing the public transport bus market, discussing the key challenges of implementing EV technologies and then reframing them to explore possible alternatives. In doing so, the toolkit assisted problem definition, as well as creative ideation through framing and reframing during the workshop. This highlights how design thinking tools and practices can support business decision-making by enabling complex, interconnected problems to be explored collaboratively to generate a better understanding of emerging markets and identify opportunities that may be leveraged. Australia’s transport sector is not alone in facing a technological transition to EVs. While some countries are at more advanced transition stages others are in a similar position and could benefit from applying this type of approach to frame market conditions and explore emerging opportunities. Beyond public transport, this type of approach may also be applied to other industries facing technological transitions that include complex stakeholder relationships, such as warehousing and logistics (Cano et al., 2021). Toolkit development and research outcomes would differ depending on which industry was selected as the focus, which stakeholders were involved and given that local market factors differ in each region; however, the advantage of using a design thinking toolkit such as the one described here is the flexibility of its development and its ability to allow locally relevant knowledge to be constructed through a design thinking process.

REFERENCES Bjögvinsson, E., Ehn, P., & Hillgren, P. A. (2012). Design things and design thinking: Contemporary participatory design challenges. Design Issues, 28(3), 101–116. https://​doi​.org/​10​.1162/​DESI​_a​ _00165 Cano, J. A., Salazar-Arrieta, F., Gómez Montoya, R. A., & Cortés, P. (2021). Disruptive and conventional technologies for the support of logistics processes: A literature review. International Journal of Technology, 12(3), 448–460. https://​doi​.org/​10​.14716/​ijtech​.v12i3​.4280 Carroll, S. (2020). The electrical industry’s role in a sustainable future. Electrical Connection, (Summer 2020), 12–15. https://​search​.informit​.org/​doi/​10​.3316/​informit​.642353069781639 Dam, R. F. (2021). 5 Stages in the design thinking process. Interaction Design Foundation. https://​www​ .interaction​-design​.org/​literature/​article/​5​-stages​-in​-the​-design​-thinking​-process

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Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. https://​doi​.org/​10​.1016/​j​.destud​.2011​.07​.006 Dorst, K. (2015). Frame innovation: Create new thinking by design. MIT Press. Fridman, I. (2016). Battery-electric route bus: A platform for vehicle design [Unpublished Doctoral dissertation]. Monash University, Australia. Fridman, I. & Coxon, S. (2021). Visual conflict framing in public transport innovation. In S. Coxon & R. Napper (eds), Advancing a design approach to enriching public mobility (pp. 19–34). Springer. Garnaut, R. (2019). Superpower: Australia’s low-carbon opportunity. La Trobe University Press. Halskov, K., & Dalsgaard, P. (2007). The emergence of ideas: The interplay between sources of inspiration and emerging design concepts. CoDesign, 3(4), 185–211. https://​doi​.org/​10​.1080/​ 15710880701607404 Hildén, E., Ojala, J., & Väänänen, K. (2016, October 23–27). User needs and expectations for future travelling services in buses [Paper presentation]. 9th Nordic Conference on Human–Computer Interaction, NordiCHI’16, Gothenburg, Sweden. https://​doi​.org/​10​.1145/​2971485​.2996733 Honnery, D., Napper, R., Fridman, I., & Moriarty, P. (2016, November 16–18). Spatially differentiated energy and environment comparison of diesel and electric buses [Paper presentation]. Australasian Transport Research Forum, ATRF 2016, Melbourne, Australia. https://​www​.aust​ralasiantr​ansportres​ earchforum​.org​.au/​papers/​2016 International Association of Public Transport Australia/New Zealand. (2020). Zero Emissions Bus Forum, report and key findings. International Association of Public Transport. https://​cms​.uitp​.org/​ wp/​wp​-content/​uploads/​2021/​02/​UITPANZ​-ZEB​-Forum​-Report​-2021​.pdf Kaplan, S. (2008). Framing contests: Strategy making under uncertainty. Organisation Science 19(5), 729–752. https://​doi​.org/​10​.1287/​orsc​.1070​.0340 Mosely, G., Wright, N., & Wrigley, C. (2018). Facilitating design thinking: A comparison of design expertise. Thinking Skills and Creativity, 27, 177–189. https://​doi​.org/​10​.1016/​j​.tsc​.2018​.02​.004 Muller, M. J., Tudor, L. G., Wildman, D. M., White, E. A., Root, R. W., Dayton, T., Carr, R., Diekmann, B., & Dystra-Erickson, E. (1995). Bifocal tools for scenarios and representations in participatory activities with users. In J. M. Carroll (ed.), Scenario-based design: Envisioning work and technology in system development (pp. 135–163). Wiley. Napper, R., Coxon, S., Fridman, I., & del Canto, J. L. (2022). Transitioning Victoria’s bus industry to zero emission buses: Handbook for operators. Bus Association Victoria. Napper, R., Fridman, I., & Reynolds, J. (2017). Balancing level of service for a battery-electric university intercampus shuttle bus [Paper presentation]. Australasian Transport Research Forum, ATRF 2017, Auckland, New Zealand, 27–29 November. https://​www​.aust​ralasiantr​ansportres​earchforum​.org​.au/​ papers/​2017 Sanders, E. B. N., & Stappers, P. J. (2014). Probes, toolkits and prototypes: Three approaches to making in codesigning. CoDesign, 10(1), 5–14. https://​doi​.org/​10​.1080/​15710882​.2014​.888183 Scharge, M. (2013). Crafting interactions: The purpose and practice of serious play. In L. Valentine (Ed.), Prototype: Design and craft in the 21st century (pp. 77–105). Bloomsbury. Schön, D. A. (1984). Problems, frames and perspectives on designing. Design Studies, 5(3), 132–136. https://​doi​.org/​10​.1016/​0142​-694X(84)90002​-4

PART III

Perspectives on Design Thinking as a Practice

11. The fragility of design thinking: applying symbolic interactionism to promote shared meaning Jan Jervis and Jeffrey E. Brand INTRODUCTION In 1992, the Design Thinking Research Symposium explored the connection between design research and methods from the viewpoint of design thinking. Dorst (2011) points out that various design thinking models appeared thereafter. However, it was not until 2008 that Harvard Business Review (HBR) published an often-cited article, Design Thinking, by Tim Brown, the CEO and President of IDEO, introducing a public audience to the details of the design thinking process and its benefits and applications for business. Fourteen years after Brown’s article, design thinking lacks prominence in businesses; after all, what company has an officially labelled ‘Design Thinking Office’? Indeed, some business managers have retreated from design thinking because they have failed to control or measure the creative process (Nussbaum, 2011). Similarly, some designers claim that design thinking is not a complete and applicable design method and have distanced themselves from it (Jen, 2018; Liedtka, 2018). Nevertheless, Liedtka (2018) examined 50 business projects over seven years for evidence of changes brought about by design thinking. The results were predominantly positive, showing that the general methodology of design thinking was helpful to business managers who, as probable non-designers, were required to adjust to unfamiliar activities required by the method. These new skills helped organisations innovate by avoiding group biases and overcoming issues caused by workplace politics (Liedtka, 2018). Design thinking also helps businesses formalise a process that places more stakeholders at the centre of business decisions (Brown, 2008; Buchanan, 1992; Liedtka, 2018; Nussbaum, 2011). However, unfamiliarity with the term can result in people discarding it or choosing not to apply it for fear of ‘getting it wrong’ (Jervis, 2021). The theory of symbolic interactionism is a lens through which we can examine our understandings of ‘things’. Blumer (1969) describes ‘things’ as anything we name and give meaning to, whether it be a chair, cloud or concept. Our understanding of things illustrates our view of the world and is thus the foundation of our behaviour. Symbolic interactionism explains how we assign meanings to our inherently personal and unique social experiences and histories. Therefore, our understanding of design thinking emerges from our interactions with those around us (Blumer, 1969). 201

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Nevertheless, it seems incongruous to associate design thinking, and its history of more than 40 years, with the notion of ‘fragility’. However, research shows that people are more likely to accept a concept if they share an understanding of it (Barnett, 2005; Conklin, 2005). The authors argue that symbolic interactionism provides a way to develop this acceptance and strengthen knowledge of design thinking in the collective mindset.

SYMBOLIC INTERACTIONISM Symbolic interactionism is a perspective that places human communication as the central element of social structure (Blumer, 1969; Mead, 1934). It seeks to explain human actions, meanings, and beliefs. It illustrates the relationship between the meaning we give to something and how our understanding relates to the people we interact with from the different societies to which we belong. Words symbolise the physical and abstract objects and ideas of our world, and these words combine to form our languages, with which we communicate and interact with others. Symbolic interactionism has authentic connections to scholars from the Universities of Chicago, Iowa, and Indiana (Carter & Fuller, 2016). The theory emerged in the early 20th century from distinguished American pragmatists such as Cooley (1902) and Dewey (1910). It was a radical departure from positivist and quantitative thinking that, at the time, dominated social research. However, George Herbert Mead, from the University of Chicago, is credited with progressing and raising the profile of symbolic interactionism in the early 20th century (Benzies & Allen, 2001). Mead’s work was published posthumously from student lecture notes and writings under Mind, Self and Society: From the Standpoint of a Social Behaviorist (Mead, 1934). A 2015 anniversary printing of the book pays special tribute to Mead and his contribution to the development of social philosophy for more than 80 years (Morris, 2015). Symbolic interactionism argues that our interactions, i.e., the language we use in communication, form the foundation of our thinking and behaviour, which affects the societies we create. Fundamentally, it is a ‘bottom-up’ approach to building communities which contrasts with widely held positivist thinking that focuses on a top-down approach to social structure (Carter & Fuller, 2016). According to Burke (2003), symbolic interactionism acknowledges society and individuals while simultaneously recognising their different identities. The theory produces ‘insightful accounts of human interaction in natural settings’ (Huber, 1973, p. 274). To this day, symbolic interactionism maintains a respected academic presence (Carter & Fuller, 2016). Premises of Symbolic Interactionism Symbolic interactionism reached a peak in the 1920s, but it was not until 1937 that Herbert Blumer, a former student, and colleague of Mead, named the theory. Blumer (1969) was an essential contributor to symbolic interactionism. He created three premises to define the view, published in Symbolic Interactionism: Perspective and Method, which have helped keep the tradition active for more than 50 years (Fine & Tavory, 2019). However, even though Blumer’s premises had united the theory for more than 50 years, Fine and Tavory (2019) argue that symbolic interactionism needs to consider the technological

The fragility of design thinking

Table 11.1

  1

2

3

203

Premises of symbolic interactionism by Blumer (1969), Fine and Tavory (2019)

Blumer (1969, p.2)

Fine and Tavory (2019, p.458)

Premises of symbolic interactionism

Revised premises of symbolic interactionism

Human beings act toward things on the basis of the

People act upon meanings while participating in distinctive

meanings that the things have for them.

communities that, in turn, depend on shared meaning.

The meaning of such things is derived from, or arises out of, the social interaction that one has with one’s fellows.

Meanings depend on continuing and self-reflexive interaction, as such interaction refracts actors’ pasts, presents, and anticipated futures.

These meanings are handled in and modified through an

Situations are linked in patterned ways. They change or

interpretative process used by the person in dealing with

further ossify as participants recognize this patterning and

the things he encounters.

the structures that support these meanings.

changes and ‘non-human’ interactions applicable to the 21st century. To this end, they offer three revised premises for symbolic interaction. Table 11.1 displays the original three premises of Blumer (1969) and their revisions by Fine and Tavory (2019). The first symbolic interactionist premise is that people behave towards things based on the meanings they have for them. The second premise is that we absorb implications based on interactions with others in different social settings. For instance, our understanding of something can come from communications and interactions at home, the workplace, or other social groups, but fundamentally, other people influence the meaning we give something. Blumer (1969) considered the third premise the most important for symbolic interactionism: we modify our understanding of something, and our thoughts about it, based on changing interactions and situations with people. Fine and Tavory’s (2019) revision of symbolic interactionism adds the focus on relationships and institutions with a move away from the individual’s viewpoint. Fine and Tavory (2019) acknowledge the expanded communications of physical and virtual communities that are part of the present day. At the same time, although the theory of symbolic interactionism has a respected foundation, longevity, and invigorated status, the theory is not without critics.

SYMBOLIC INTERACTIONISM AND DESIGN THINKING IN BUSINESS Design thinking is a process that encourages problem-solving through cross-disciplinary collaborations. The concept places ‘people’ at the centre of the problem-solving process and is therefore closely associated with human-centred design (Buchanan, 1992; Dorst, 2011; Norman, 2018; Ratinum, 2020). According to IDEO, a not-for-profit Design consultancy, Embracing human-centered design means believing that all problems, even the seemingly intractable ones like poverty, gender equality, and clean water, are solvable. Moreover, it means believing that the people who face those problems every day are the ones who hold the key to their answer. Human-centered design offers problem solvers of any stripe a chance to design with communities, to deeply understand the people they’re looking to serve, to dream up scores of ideas, and to create innovative new solutions rooted in people’s actual needs. (IDEO.org, 2015, p. 9)

204

Research handbook on design thinking

At present, design thinking has supporters who champion the method (Brown, 2008; Buchanan, 1992; Cross, 2011), those who feel it is just a ‘buzzword’ (Jen, 2018), and many professionals who do not know what design thinking means or what to do with it (Jervis, 2021). A ‘wicked problem’ is a label given to problems that are not easily defined and have no ‘end’ (Rittel & Webber, 1973); the more exploration of a wicked problem, the more it reveals additional concerns. For instance, providing help to people living in poverty is a wicked problem because it has no obvious ‘solution’ (IDEO.org, 2015). A ‘tame’ problem, on the other hand, has a solution realised through a linear process. Complex variables related to the problem or solution are not considerations for a tame problem (Rittel & Webber, 1973). The three premises of Symbolic Interactionism show us that we are responsible for the meanings we give to things, and our behaviours reflect those meanings. However, we also apply definitions to things based on our interactions with others in the communities where we live and work. Because individual experiences differ, we cannot assume another person shares our knowledge or experience with design thinking. We cannot assume shared understanding (Blumer, 1969; Fine & Tavory, 2019; Jervis, 2021). As we move through different cultures and have different interactions, we can amend our understanding. It is compelling to test the boundaries of our understanding by purposefully and routinely engaging with others, to manage communication breakdowns and the consequent fragility of meanings. To illustrate the mission at hand for design thinking and to show how breakdowns in communication occur from different meanings of design thinking in the workforce, the authors adapted Cossette’s ‘Model for understanding from a symbolic interactionist stance’ (Cossette, 1998, p. 1363). Figure 11.1 illustrates how a symbolic meaning of design thinking in a workplace can emerge from an interactive situation such as a staff meeting discussing design thinking or a workshop involving design thinking methods. Staff attitudes towards design thinking will relate to their personal experiences, emotional state, grammatical understanding, their first spoken language and the environment in which the interactive meeting is held (Cossette, 1998). The example of a staff meeting illustrates how communication breakdowns occur between professionals in the workplace (Figure 11.1). During the meeting (Interactive Situation), one manager (Person 1) promotes design thinking as a strategic and problem-solving method in the organisation. Participants include management executives, full-time staff, and a group of self-employed contractors to the company. As the discussion about design thinking begins, one of the contractors (Person 2), unfamiliar with the term, assumes design thinking must be associated with design. He begins to feel frustrated that he was required to attend the meeting and take time from his business to talk about ‘design’. In his mind, he does not work in design, so the concept of design thinking does not apply to him. This contractor makes an excuse to leave the meeting; another follows suit and leaves shortly after. Unfortunately, in this staff meeting, the fragility of design thinking is not apparent. The manager, who was feeling excited about presenting a design thinking initiative, has trouble understanding the attitude of the contractors as, in his mind, design thinking is about problem-solving, which applies to every person connected to the business. The staff meeting closes with no resolution about how design thinking can benefit the business or how it can be applied.

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205

Source: Adapted from a ‘Model for understanding language from a symbolic interactionist stance’ (Cossette, 1998, p. 1363).

Figure 11.1

Understanding design thinking in business through the lens of symbolic interactionism

The example described above came from a real-world business situation described by a manager to one of the authors. Figure 11.1 can also apply to other scenarios, such as the design professions. Jervis (2021) found that professionals in design, people who work in the different design disciplines, disagreed on whether design thinking offered value to the business community. The authors argue that for design thinking to be sustainable, we must first acknowledge its connection to the word design and any implications that association creates.

THE LEXICAL AMBIGUITY OF DESIGN AND DESIGN THINKING Thus, the fragility of design thinking is predicated in the ambiguity of design itself. Business managers oscillate between having abstract interpretations of design or dismissing it as frivolous (Boland, 2004). Nussbaum (2007) claims a breakdown between design and business is caused by ignorance about design, while Rae (2013) says problems stem from business managers’ inability to manage and measure design functions. Even the design disciplines do not agree on a universal definition of design (Bryant & Wrigley, 2014). Subsequently, a designer does not command the same professional respect as an engineer or architect (Smith, 2005). Design can be a noun, verb, and adjective in English. It does not have an academic or publicly recognised definition (Julier, 2008). Therefore, it is possible to view design thinking not

Research handbook on design thinking

206

as a compound noun but as an adjective modifying a noun, i.e., ‘design the thinking’ instead of ‘design thinking’. Dewey (1910) devoted the first chapter of his book How We Think to the various definitions people associate with ‘thinking’ and ‘thought’. He summarised human thought processes as: Everything that comes to mind ‘goes through our heads’. (p. 2) We think (or think of) only such things as ‘we do not directly see, hear, smell, or taste’. (p. 2)

Dewey’s (1910) research aligns with the premises of symbolic interactionism in that we use ‘evidence’ from our social interactions to give meaning to things. The meaning (of ‘thinking’ or ‘thought’) is limited to beliefs that rest upon evidence or testimony. Furthermore, our meanings change as we ‘think’ about any new information we receive. It hardly surprises that the meaning of design thinking is confusing when there is so much lexical ambiguity surrounding the words design’ and ‘thinking’. This state of affairs begs for investigation and understanding to strengthen the status of design and design thinking in business. Symbolic interactionism can help us see how breakdowns in communication reduce the reliability of the words we use to share meaning with others. For instance, improved interactions between the manager and the contractor in Figure 11.1 could result from awareness of how the other person might be thinking. Jervis (2021) established a disconnect between the meaning of design for management, business, and design professionals. Furthermore, Jervis (2021) found a lack of shared understanding of design thinking. She used symbolic interactionism to show why the fields of design and business are not supportive of each other despite evidence that design delivers substantial economic advantages for businesses (Martin, 2007). To this end, the following sections present findings from Jervis (2021). In addition, one of Jervis’s (2021) studies, examining how employers write about design thinking in their job ads, is replicated to provide new data. The findings presented in the following sections are important knowledge contributing to a communication bridge for design thinking in professional settings. A discussion of the findings leads to a model of shared understanding of design thinking through a symbolic interactionist lens.

PREMISES OF SYMBOLIC INTERACTIONISM ASSOCIATED WITH DESIGN THEMES Jervis (2021) used symbolic interactionism as the foundation for three studies examining communication and design and design thinking in business. She found that despite substantial research into the misunderstandings between design and business professionals, there is little investigation into the vocabulary used to create shared understanding. The findings were summarised into ten ‘critical points’ and aligned with the premises of symbolic interactionism by Blumer (1969) and Fine and Tavory (2019) in Figure 11.2. Five design themes were used to group the ten critical points. Consequently, two critical point findings align with the theme, Design Confusion; two appear under the second theme, Design Frustration, one under Design Ingenuity, three under Design Manifestation and two under Design Translation.

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207

Source: Jervis, 2021, p. 188.

Figure 11.2

Premises of symbolic interactionism associated with design themes

The themes of Design Confusion, Design Frustration and Design Ingenuity link to the first premise of symbolic interactionism for Blumer (1969) and Fine and Tavory (2019), which posits that we behave towards something based on the meaning it has for us.

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Research handbook on design thinking

The second premise of symbolic interactionism is that meanings of things are derived from our interactions with people in our past, present and possible future; all can be associated with all five themes: Design Confusion, Design Frustration, Design Ingenuity, Design Manifestation and Design Translation. The third premise, however, refers to our understanding of how we modify our thinking based on recognising different encounters. The third premise relates to the themes of Design Ingenuity, Design Manifestation and Design Translation. The following sections briefly describe each theme emerging from the Jervis (2021) study. Design Confusion The Design Confusion theme registered misunderstandings around design. For instance, people use the word design in general without connecting it to the professional discipline of design. Alternatively, they struggled to explain the concept and had more than one meaning for it. In some cases, design spoken as a verb or adjective had no connection to professional design. Indeed, Jervis’s (2021) second study content-analysed more than 18 years of published articles in business and management journals. She discovered that business authors, writing in those journals, do not discuss design as though it were a profession. The word design rarely appeared without another adjective or descriptive word attached. Unexpectedly, a similar result emerged from published articles written by design authors in professional design journals. The primary descriptors for all three areas of business, management, and design were: design activity, design process, product design, and conceptual design. From a symbolic interactionist perspective, these results invoke Blumer’s first premise in that these writers are acting towards design based on the meaning it has for them (Blumer, 1969): that is, design is not necessarily professional. Smith (2005) claimed that unless the public understands design and what designers do, the word will not invoke the prestige associated with a profession. Therefore, the fragility of design thinking in the workplace is associated with the impoverished meaning of design. Design Frustration The Design Frustration theme emerged from the saturated use of the word design in general conversation and its vagueness for many people who compare design with art (Jervis, 2021). In other words, this theme represents the frustration many professional designers feel when business and management devalue design as purely an aesthetic discipline. A clear division between design and the other two fields of business and management emerged in the publications analysed. For instance, business and management had common word combinations: business enterprise, industrial management, management research, organisation effectiveness, personnel management, and strategic plan. Tellingly, there were no word combinations business and management shared with design. Similarly, design publications did not publish shared word combinations with either business or management. The only connection between the three areas was the keyword product design, which does not consider the many distinctions associated with the design profession.

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209

The lack of connection between design, business and management continued with associated words such as design thinking, creativity, and innovation. In professional design articles, design cognition substantially overshadowed design thinking. Indeed, design thinking had a limited presence in business articles and was completely absent from management publications. The term creativity appeared more often in design articles, whereas innovation had a more substantial presence in business and management categories. The theme of Design Frustration aligns with the revised first premise of symbolic interactionism by Fine and Tavory (2019). They argue that distinctive (professional) communities impact how people act towards the meaning of something. In this instance, design, business and management are distinctive professional communities that impact how people act towards the meaning of design. Design Ingenuity The theme Design Ingenuity represents our human imagination’s unlimited possibilities and creative ability. Changeable thinking and the creative process are directly related to interpretation. Blumer (1969) states, in the third symbolic interactionist premise, that we interpret the meaning of things as we encounter them. This meaning cannot be assumed. As people recognise patterns and structures in their encounters, they can adjust their thinking in turn (Fine & Tavory, 2019). Jervis (2021) summarised findings under this theme as the different ways people understand design, design thinking, human-centred design, creativity, or innovation. Therefore, ingenuity emerging from design thinking is unpredictable. Design Manifestation The theme Design Manifestation represents a realisation of something real or imagined; it is an expressed demonstration. For example, product design is the manifestation of an idea. Product design was the only design concept that linked published articles in all three publications covering professional business, management and design. In business, the terms product design and industrial design are contextually similar. In particular, industrial design is considered an economic driver (Margolin & Margolin, 2002; Smith, 2005). The business and management sections of the Jervis (2021) study related to a product outcome in which industrial design was the most common connection. However, no specific recognition was afforded for design on its own in business. In other words, design is considered a manifestation of an industrialised profession. These findings speak to the first and second premises of symbolic interactionism for Blumer (1969) and Fine and Tavory (2019). Professional boundaries can define the meaning of design thinking for both business and design. Design Translation The theme Design Translation is an established association for design. In Design Across Disciplines, Daly (2008) stated that Design Translation was an “organised translation from an idea to a plan, product, or process that works in a given situation” (p. 80). Jervis (2021) defined

210

Research handbook on design thinking

design translation as the “ideas, views or things, translated and interpreted to enable shared understanding” (p. 204). Design Translation connects to symbolic interactionism’s second and third premises. Blumer (1969) and Fine and Tavory (2019) explain that people interact with each other in social situations and determine meaning for things such as design thinking from these interactions. We then think about things and adjust our thinking as we recognise patterns and behaviours that support new interpretations. Fundamentally, Jervis (2021) discovered that design translation  was relative to the vocabulary professionals use in business, management, and design.

ASSOCIATIONS OF DESIGN, BUSINESS AND MANAGEMENT VOCABULARY The central research question for Jervis (2021) was, how do professionals in design and business communicate their meaning of design? The results demonstrated that business and management struggle to connect with design because they do not share an understanding of the topics the other side deems essential, or they do not have a vocabulary to communicate shared meaning. Jervis found that professionals in design, that is, people who work in design-related professions, focused on design procedures and methods, but generally they had limited status in business and management. Business and management seemed more accepting of design if it was associated with industrial and product outcomes. Surprisingly, she also found that design professionals are remiss in their communication in design fields, business and management; they do not place sufficient value on design disciplines, so they do not communicate successfully with business or management. The disconnect between design, business and management is evident in a study of word combinations from professional publications. Jervis (2021) compared the top 20 word combinations in three academic journals (one each for design, business and management). The findings demonstrate how communication gaps occur. In Table 11.2, Jervis’s list of word combinations (2021, p. 132) shows only six shared words between business and management: Business enterprise, Management research, Industrial management, Personnel management, Strategic planning, and Organizational effectiveness. Design had no in-common words with business or management and vice versa. The numbers in Figure 11.3 show the level of importance for the word in the publications. For instance, ‘Strategic plan’ ranked number one in business but number 13 in management and not at all for design. The words ‘Design think’ appear at number 15 under the design-related word combinations, but not specifically design thinking. Design cognition is listed at number eight for design but does not appear for business or management.

HOW DO BUSINESSES WRITE ABOUT DESIGN THINKING IN JOB ADs? It is clear that words used to communicate design thinking in business affect how it is perceived and its success based on Jervis’s (2021) past findings. The conclusion is extended when studying how businesses communicate their needs to future employees through job ads.

The fragility of design thinking

Table 11.2

211

A list of design, business and management’s top 20 word-combinations

Design

Business

Management

1

Design process

Strategic planning

Organizational behaviour

2

Design

Business plan

Business enterprise

3

Conceptual design

Business enterprise

Management research

4

Engineer design

Competitive advantage

Job performance

5

Design education

Business model

Organizational structure

6

Product design

Economic aspect

Social aspect

7

Design activity

Industrial management

Organizational change

8

Design cognition

Corporate culture

Industrial management

9

Design research

Organizational effectiveness

Personnel management

10

Collaborative design

Chief executive

Management science

11

Design team

Marketing strategy

Job satisfaction

12

Architectural design

Ability management

Organizational sociology

13

Design practice

Executive ability

Strategic plan

14

Design problem

Management research

Social network

15

Design think

Executive ability management

Work environment

16

Design theory

Long term

Organizational effectiveness

17

Design knowledge

Personnel management

Firm performance

18

Problem solve

Supply chain

Research organizational

19

Design tool

Chief executive officer

Employee attitude

20

Design method

Executive officer

Human capital

Arguably, the vocabulary used in job ads sits at the intersection of communication between professionals and businesses. As a study of job ads by Jervis (2021) contributed to the results discussed in the previous sections, we sought new insights into how businesses use the term design thinking during recruitment. We focused on the third premise of symbolic interactionism (Fine & Tavory, 2019), which states that repetitions (or patterns) are a recognisable way to support meanings. Thus, we looked for the frequency and relationships for design thinking in the job ad wording. The online job portal, seek.com.au, provided the job-seeking situation (sample frame) and the occurrences of design thinking in the job ads represented a possible patterned structure (dependent variable). The new study examined explicit use of design thinking. For instance, if the words design and thinking appeared in the body text of the job ad but did not relate to design thinking, we excluded that job ad from the results. The 2021 study examined 18 variables in a systematic random sample of job ads taken from the website. Design Thinking in 2013 and 2019 Job Ads In 2013 and 2019, Jervis (2021) analysed how businesses communicated the word design in their job ads on the seek.com.au online job portal. In 2013 an initial search for design-related jobs returned more than 15,000 listings, and in 2019 the number was 19,000.

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Table 11.3

The frequency of Design Thinking (DT) and Human-centred Design (HCD) in 2013 and 2019 job ads

Year

Job title

DT/HCD

Geographical location

2013

Senior Executive Designer

DT

Canberra

2019 2019

Associate Director – Customer, Brand & Marketing Advisory Manager, Customer Experience Delivery

Job classification Consulting & Strategy Information &

DT/HCD

Canberra

Communication Technology

DT

Melbourne

Education & Training Information &

2019

Scaled Agile Business Analysts

DT

Melbourne

Communication Technology

2019

Senior Marketing Manager

DT

Sydney

Marketing & Communications

2019

User Experience & Interface Designer

DT/HCD

Regional Victoria

Government & Defence

2019

UX Designer

DT

Canberra

Design & Architecture

2019

Digital Product Manager

HCD

Melbourne

Marketing & Communications

In the 2013 study, none of the job titles referred to design thinking. However, the term was present (explicitly stated) in the body text for a government position seeking a Senior Executive Designer to apply design thinking and innovation. In 2019, design thinking was still not appearing in job titles. However, it was present in the body text in six job ads from a sample of 330. One of the listings under Marketing & Communications referenced human-centred design, not design thinking. Again, a government position requested human-centred design and design thinking knowledge. Overall, the listing classifications for design thinking spread across Design & Architecture, Education & Training, Government & Defence Information & Communication Technology and Marketing & Communications. Table 11.3 displays the results by Jervis (2021) with an overview of the job titles, geographical locations, job classifications, and their request for either design thinking (DT), human-centred design (HCD) or both (Jervis, 2021).

DESIGN THINKING STUDY REPLICATION: DATA COLLECTION AND SAMPLE In early March 2021, the authors replicated Jervis’s 2013 and 2019 job ad studies (Jervis, 2021). The starting point was an initial search for jobs using the keyword ‘design’. The 2021 search returned 28,145 possible listings for design-related jobs and 3,839 for design thinking. Due to the dynamic nature of the website, these numbers are subject to change minute by minute, and the seek.com.au portal provides a maximum of 200 pages; in reality, the site offers access to 4,000 job ads at any one time. Each page of the seek.com.au website displayed the titles and previews of approximately 20 job ads, but the first 20 pages also included two fixed, feature job ads. These feature job ads were not part of the website’s dynamics and did not extend for the entire 200 pages, so they did not become part of the analysis.

The fragility of design thinking

213

The aim was to analyse 10% of the job listings. A systematic random sample (SRS) of every 17th and 20th job listing for each of the 200 pages provided the target sample of n=400 job ads. The analysis was quantitative and qualitative and utilised a combination of computer-assisted and human coding. Analysis The codebook replicated the one used by Jervis (2021) for the 2013 and 2019 job analysis studies. For this chapter, the analysis of the job ads involved machine-coding six variables: Job Title, Date of the job post, Geographical Location (State), Work Type (Full, Part-time, Casual, Hourly Rate), First Job Classification, and Second Job Category. Explicitly stated words ensured the answers were as reliable as possible. Human coding was necessary to search the body text for design thinking and human-centred design instances. Thus, the results were a mix of quantitative and qualitative findings. In 2021, an apparent increase in human-centred design led the authors to look more closely at how often that term was associated with design thinking. Design Thinking in 2021 Job Ads The number of 2021 job listings relating to design thinking increased substantially from the 2019 study by Jervis (2021) from six ads in 2019 to 17 in 2021 – a marked increase in two years. Requests for human-centred design and design thinking knowledge appeared in eight of those 17 job ads and an additional 11 listings asked for human-centred design without design thinking. Thus, a total of 28 jobs specifically mentioned design thinking or human-centred design in the 2021 sample. Twenty-seven of the 28 job listings appeared under the main classifications of: Information & Communication Technology, Design & Architecture, Marketing & Communications, Consulting and Strategy, Education and Training, and Government and Defence. One job ad for human-centred design, but not design thinking, was listed under Accounting. Table 11.4 presents an overview of the 17 job listings explicitly stating design thinking (DT) and human-centred design (HCD) in the authors’ 2021 study. Table 11.5 lists eleven job ads that asked for human-centred design without mentioning design thinking. The 2021 study was the first to find a job title explicitly stating design thinking: Agile BA with Design Thinking experience. Two other job ads, not included in the study, implied human-centred design through their wording but did not use the term. Another job ad, under Design and Architecture, asked for MVP (minimum viable product) and DVP thinking (desirability, viability and feasibility). Although, this job request did not mention design thinking or human-centred design, both acronyms connect to the concepts (Orton, 2017). Unusually, there was one job title with the single word Designer. Classified under Government & Defence and categorised as a Government-State job, it did not mention design thinking. However, it referred to a ‘human-centred product development team, who values your happiness, growth and personal development’. Table 11.5 presents the job ads requesting human-centred design knowledge but not design thinking.

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Table 11.4

Year 2021

Job ads in 2021 that include Design Thinking (DT) and Human-Centred Design (HCD)

Job title Agile BA with Design Thinking experience

DT/HCD

Geographical location

DT

Sydney

2021

Senior Consultant – UI Design

DT/HCD

Sydney

2021

User Experience Designer

DT/HCD

Brisbane

2021

User Researcher

DT

ACT

DT

Sydney

DT/HCD

Sydney

2021 2021

UX/UI Designer | Digital Banking | Lead Experience Designer Lead UX Designer

Newcastle, Maitland

Job classification Information & Communication Technology Information & Communication Technology Information & Communication Technology Information & Communication Technology Information & Communication Technology Design & Architecture Design & Architecture

2021

User Experience Designer

DT

2021

UI/UX Designer

DT

Sydney

Design & Architecture

DT/HCD

Sydney

Design & Architecture

DT

Sydney

Marketing & Communications

& Hunter

User Experience Designers, Visual 2021

Designers, UX/UI Hybrid & Service Designers

2021

Customer Experience Design Specialist

2021

Service Designer

DT/HCD

2021

UI/UX Product Designer

DT

2021

Instructional Designer & Product Manager

DT/HCD

Newcastle, Maitland & Hunter Brisbane Mornington Peninsula & Bass Coast

Marketing & Communications Marketing & Communications Consulting & Strategy

2021

Senior Consultant – Customer Design

DT/HCD

Sydney

Consulting & Strategy

2021

IT Designer

DT

ACT

Government & Defence

DT

ACT

Government & Defence

DT/HCD

Melbourne

Education & Training

2021 2021

Senior & Junior Graphic Designers – Noetic Group Senior Design Lead

The following discussion section reviews the 2021 study findings through the lens of symbolic interactionism. The discussion aligns the premises of symbolic interactionism with evidence of understanding of design thinking (Blumer, 1969) and any patterns emerging from the findings that connect to the meaning of design thinking (Fine & Tavory, 2019).

DISCUSSION The 2021 study of job ads from seek.com.au indicates that professional interests in design thinking and human-centred design are increasing. According to symbolic interactionism, our meaning of design thinking reflects the understanding we gain from interactions with others (Blumer, 1969; Fine & Tavory, 2019). Thus, the increasing number of job ads explicitly asking

The fragility of design thinking

Table 11.5 Year

215

Job ads in 2021 that included only Human-Centred Design (HCD)

Job title

DT/HCD

Geographical

Job classification

location 2021

Graphic Designer

HCD

Melbourne

Design & Architecture

2021

Service Design Lead | Global firm

HCD

Melbourne

Design & Architecture

2021

Service Design Lead | Global firm

HCD

Sydney

Design & Architecture

2021

Designer

HCD

NSW

Government & Defence

2021

Architectural Graduate

HCD

Melbourne

Design & Architecture

2021

CX / UX Service Designer

HCD

Sydney

Accounting

2021

Senior Interior Designer

HCD

Melbourne

Design & Architecture

2021

UI/UX Product Designer

HCD

Brisbane

Design & Architecture

2021

UX/UI Product Designer

HCD

Melbourne

Information & Communication

2021

Customer Experience Manager

HCD

Brisbane

Education & Training

2021

Journey Expert – User Experience

HCD

Melbourne

Marketing & Communications

Technology

for design thinking indicates that knowledge and understanding of design thinking are spreading through encounters with the process (Blumer, 1969). The number of jobs for design thinking has grown from one job listing in 2013, six job listings in 2019 (Jervis, 2021), to 17 job listings in 2021. The increase in jobs associated with human-centred design indicates growing public awareness of the term as well. However, the findings show inconsistent connections between job requests for design thinking and human-centred design in the role. Indeed, the jump in numbers for the two years between 2019 and 2021 is significant as it may show increasing public awareness of the concept. Table 11.5 shows that titles for jobs with design thinking and human-centred design have associations with digital production and employment related to human interaction on a digital platform. Positions linked to user experience and user interface design dominate the advertised titles. Similarly, Web Development & Production or Web & Interaction Design job categories reinforce the application of design thinking and human experiences in digital transformation. One difference in the job categories between design thinking and human-centred design was the additional classification of Accounting. However, the job title was CX/UX Service Designer, referring to customer experience and digital user experience design. Four titles out of nine for the human-centred design jobs asked for user experience, customer experience or user interface skills and roles. This connection between design thinking and digital interactions is a development trend that has emerged over the two years. Unsurprisingly, in light of the global pandemic, digital interactions are increasingly crucial to the function of our lives. Jervis (2021) found that most references to design were associated with other descriptive words, for instance, Interior Designer. So, it was unexpected to see one job title in the 2021 study with the single word Designer associated with human-centred design and classified as a Government and Defence industry role. The wording did not mention design thinking but cited a ‘human-centred product development team, who values your happiness, growth and personal development’. Symbolic interactionism’s first and second premises show us that our different social circles and interactions within them give meaning to design thinking. The authors’ 2021 replication

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study points to an association between design thinking, human-centred design and digital interactions. The fragility here is that our understanding of design thinking may solidify as people associate their meaning of design thinking with this additional vocabulary as predicted in general terms by Fine and Tavory (2019). The third premise of symbolic interactionism highlights the repeated patterns that create our meanings. There was inconsistent use of vocabulary in the job ads. For instance, descriptive wording includes Human Centred Design Thinking, Design Thinking, HCD Methodologies, or Human-centred Design. More than one job ad required the applicant to be an ‘advocate’ for design thinking, suggesting there may not be universal acceptance of the process in the company. Does this matter? Yes. Human-centred design is an opportunity to approach all human problems believing that ‘the people who face those problems every day are the ones who hold the key to their answer’ (IDEO.org, 2015, p. 9). To that end, design thinking is a method all professionals can use, whether design or non-design professionals, to devise innovative solutions to wicked problems (Brown, 2008). While the increase in job requests is encouraging, inconsistencies in the job listings point to the need for each organisation to define design thinking and its relationship to human-centred design with the mission and values of its organisation. To facilitate that aim, the following section offers a new model for promoting a shared understanding of design thinking through the lens of symbolic interactionism in a professional setting.

SHARED UNDERSTANDING OF DESIGN THINKING THROUGH A SYMBOLIC INTERACTIONIST LENS A model for shared understanding from a symbolic interactionist perspective builds on the research summary of Jervis (2021) and the new information presented in this chapter. We are labelling it the Model of Acceptance, Vocabulary and Acknowledgement applied to Design Thinking, or the ‘AVA’ model. The AVA model is not an attempt to create a global shared understanding of design thinking. As symbolic interactionism points out, our experience with other people constantly changes how we understand the world around us; therefore, achieving a single definition of design thinking is highly unlikely (Blumer, 1969). Instead, the AVA model shows that we must consider the importance of shared understanding to achieve the best outcomes for design thinking and human-centred design within interactive situations, such as business meetings. AVA applies symbolic interactionism by envisioning a cyclical process and three main stages. The first stage of AVA is Acceptance of the different possible meanings that could exist in an interactive situation involving design thinking. This Acceptance reflects an organisation’s culture and willingness to change and accept that even if people are engaged and contributing, they may not understand design thinking (Jervis, 2021). Acceptance that people have different understandings around design thinking must apply from management downwards. The second stage is Vocabulary. Jervis (2021) detailed that design, business, and management professionals do not share the same vocabulary. In a study of common word combinations in professional publications, design had nothing in common with business or management. Business and management have some wording in common, but each measures

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the importance differently (see Table 11.2). A narrow vocabulary suggests the organisation is not open or receptive to other ideas and concepts. In this model, it is up to each organisation to recognise the vocabulary they use to describe design thinking. The third stage represents Acknowledgement of the previous two areas. This stage is an opportunity to encourage contributions and determine if all people in the interactive situation, for example, a business meeting, understand what design thinking means to the business. As Blumer (1969) and Fine and Tavory (2019) show in their respective three premises, our actions come from the meanings we give something, which arises from our interactions with other people and the communities we share. However, we constantly modify and interpret these meanings as we encounter different situations and recognise patterns and structures that further explain the implications. Figure 11.3 illustrates the Acceptance, Vocabulary and Acknowledgement (AVA) model for design thinking.

Figure 11.3

Acceptance, Vocabulary and Acknowledgement model for design thinking

AVA represents an iterative process in which participants establish shared understanding and produce a shared meaning of design thinking. Codification is the opportunity to formalise design thinking meaning and process for participants. We believe in the codification moment – when there is formal recognition of design thinking through acceptance, vocabulary, and acknowledgement – design thinking will become a core part of the business.

CONCLUSION This chapter establishes that our conceptions of design thinking and its root concept, design, are fragile because we fall short of sharing the common meanings these ideas have for us. If we stopped at this jarring juxtaposition, it would be concerning enough. However, this chapter applies the lens of symbolic interactionism to present the surprising, if disturbing, conclusion that design thinking and its root, design, are fragile within professional communities where, by virtue of professional standards, this should not be the case.

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Earlier research demonstrated that the disparity between the vocabulary used by design professionals and business and management professionals raised concerns (Jervis, 2021). However, new research reported in this chapter raises further concern that the role of design thinking in business and society requires the urgent reflection and resurrection argued throughout this volume generally. The Acceptance, Vocabulary and Acknowledgement (AVA) model for design thinking provides an easily understood and readily applied solution to strengthen the place of design thinking in the professions. Explored and applied, AVA may standardise responses to design thinking and see it used in the rapidly changing workforce and the post-pandemic reality of organisations. We believe that future research to apply and validate AVA holds promise for design, business and management professionals to work with the academic community in a way that better positions design and design thinking for productive use.

REFERENCES Barnett, K. (2005). Creating meaning in organizational change: A case in higher education [Doctoral dissertation, Louisiana University]. LSU Doctoral Dissertations. 2362. https://​digitalcommons​.lsu​ .edu/​gradschool​_dissertations/​2362 Benzies, K. M., & Allen, M. N. (2001). Symbolic interactionism as a theoretical perspective for multiple method research. Methodological Issues in Nursing Research, 33(4), 541–547. https://​onlinelibrary​ .wiley​.com/​doi/​epdf/​10​.1046/​j​.1365​-2648​.2001​.01680​.x Blumer, H. (1969). Symbolic interactionism: perspective and method. University of California Press. Boland, R. J. Jr. (2004). Design in the punctuation of management action. In R. J. Boland Jr. & F. Collopy (Eds), Managing as designing (pp. 267–276). Stanford University Press. Brown, T. (2008, June). Design thinking. Harvard Business Review, 86(6), 85–95. https://​hbr​.org/​2008/​ 06/​design​-thinking Bryant, S., & Wrigley, C. (2014). Driving toward user-centered engineering in automotive design. Design Management Journal, 9(1), 74–84. https://​doi​.org/​10​.1111/​dmj​.12007 Buchanan, R. (1992). Wicked problems in design thinking. Design Issues, 8(2), 5–21. https://​www​.jstor​ .org/​stable/​1511637 Burke, P. J. (2003, February). Commentary on ‘whither symbolic interaction?’ Symbolic Interaction, 26(1), 111–118. https://​www​.jstor​.org/​stable/​10​.1525/​si​.2003​.26​.1​.111 Carter, M. J., & Fuller, C. (2016). Symbols, meaning, and action: The past, present, and future of symbolic interactionism. Current Sociology Review, 64(6), 931–961. https://​doi​.org/​10​.1177​ %2F0011392116638396 Conklin, J. (2005). Dialogue mapping: Creating shared understanding of wicked problems. John Wiley & Sons Ltd. ISBN: 978-0-470-01768-5 Cooley, C. H. (1902). Human nature and the social order. Scribner Cossette, P. (1998). The study of language in organizations: A symbolic interactionist stance. Human Relations, 51(11), 1355–1377. https://​doi​.org/​10​.1177/​001872679805101102 Cross, N. (2011). Design thinking: Understanding how designers think and work. Berg Publishers. Daly, S. R. (2008). Design across disciplines [Doctoral dissertation, Purdue University]. http://​docs​.lib​ .purdue​.edu/​dissertations/​AAI3343992 Dewey, J. (1910). How we think [Digitised 2007]. Internet Archive. https://​ archive​ .org/​ details/​ howwethink00deweiala Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. https://​doi​.org/​10​.1016/​j​.destud​.2011​.07​.006 Fine, G. A., & Tavory, I. (2019). Interactionism in the twenty-first century: A letter on being-in-meaningful-world. Symbolic Interaction, 42(3), 457– 467. https://​doi​.org/​10​.1002/​symb​.430 Huber, J. (1973). Symbolic interaction as a pragmatic perspective: The bias of emergent theory. American Sociological Review, 38(2), 274–284. https://​www​.jstor​.org/​stable/​2094400

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IDEO.org. (2015). The field guide to human-centred design (1st ed.). https://​www​.designkit​.org/​ resources/​1 Jen, N. (2018, March 19). Natasha Jen: Design thinking is bullsh*t [Video, 6:31 minutes]. The 9th 99U Conference, 7–9 June, New York City. YouTube. https://​youtu​.be/​_raleGrTdUg Jervis, J. S. (2021). I see what you mean: A three method symbolic interactionist study of design and business [Doctoral dissertation, Bond University]. https://​research​.bond​.edu​.au/​en/​studentTheses/​i​ -see​-what​-you​-mean​-a​-three​-method​-symbolic​-interactionist​-study​Julier, G. (2008). The culture of design (2nd ed.). SAGE. Liedtka, J. (2018). Why design thinking works. Harvard Business Review, 96(5), 72–79. https://​hbr​.org/​ 2018/​09/​why​-design​-thinking​-works Margolin, V. & Margolin, S. (2002). A ‘social model’ of design: Issues of practice and research. Design Issues, 18(4). https://​www​.jstor​.org/​stable/​1511974 Martin, R. (2007). Design and business: why can’t we be friends? Journal of Business Strategy, 28(4), 6–12. DOI: https://​doi​.org/​10​.1108/​02756660710760890 Mead, G.H. (1934). Mind, self, and society from the standpoint of a social behaviorist. University of Chicago Press. Morris, C. W. (Ed) (2015). George Herbert Mead. Mind, self, and society: The definitive edition. The University of Chicago Press. Norman, D. (2018, August 10). Principles of human-centered design (Don Norman) [Video]. NNgroup. YouTube. https://​youtu​.be/​rmM0kRf8Dbk Nussbaum, B. (2007). CEOs must be designers, not just hire them. Think Steve Jobs and iPhone. Bloomberg Business Week, 28 June. https://​www​.bloomberg​.com/​news/​articles/​2007​-06​-27/​ceos​ -must​-be​-designers​-not​-just​-hire​-them​-dot​-think​-steve​-jobs​-and​-iphone​-dot Nussbaum, B. (2011). Design thinking is a failed experiment. So what’s next? Fast Company, 5 April. https://​www​.fastcompany​.com/​1663558/​design​-thinking​-is​-a​-failed​-experiment​-so​-whats​-next Orton, K. (2017). Desirability, feasibility, viability: The sweet spot for innovation. Innovation Sweet Spot, 29 March. https://​medium​.com/​innovation​-sweet​-spot/​desirability​-feasibility​-viability​-the​ -sweet​-spot​-for​-innovation​-d7946de2183c Rae, J. (2013). What is the real value of design? Design Management Review, 24(4), 30–37. https://​ onlinelibrary​.wiley​.com/​doi/​epdf/​10​.1111/​drev​.10261 Ratinum, M. (2020, August 10). Human-centred design and the public sector [Video]. Department of Premier and Cabinet Victoria. YouTube. https://​youtu​.be/​8VNkmb​_​_gEA Rittel, H. W. J., & Webber, M.M. (1973). Dilemmas in general theory of planning. Policy Sciences, 4, 155–169. https://​doi​.org/​10​.1007/​BF01405730 Smith, G. (2005). Misunderstood and mysterious: How design and designers are perceived by design professionals, design educators and the public [Doctoral dissertation, Swinburne University of Technology]. Swinburne Theses Collection http://​hdl​.handle​.net/​1959​.3/​26050

12. Dealing with the difficulties of policy formulation in policy design: the merits and demerits of the application of design thinking to the policy realm Michael Howlett INTRODUCTION: POLICY FORMULATION AND POLICY DESIGN Policy design is an activity undertaken in the process of formulating policies. It involves the conscious effort to learn best practices and apply lessons from past policy successes and failures to the crafting of policy alternatives (Howlett and Mukherjee, 2017, 2018; Howlett et al., 2009). This form of “design-oriented thinking” involves thinking about formulation processes in a “design” way: that is, as a calculated method of problem resolution in which the various tools at the disposal of government are systematically evaluated in terms of their ability to “get the job done” (Colebatch, 2017; Howlett, 2019). This instrument-oriented way of thinking about policy formulation design differs from other forms, such as recent efforts to promote “design thinking” in the policy sphere, although the two terms are often incorrectly treated as synonyms and used interchangeably (Clarke and Craft, 2019). “Design thinking”, including in the policy realm, is usually thought of as approximating “thinking outside the box” (Considine, 2012). That is, to be a form of creative, innovative alternative generation and problem re-thinking which allows problems to be addressed in a new way, whether that problem is a consumer need or a product, a production process or a policy one (Bason, 2014; Blomkamp, 2018). It is a process of problem-solving which involves reasoning backwards from the value to be created by an endeavour of any kind – a new product, an old one, or a new policy or programme – without being locked into the manner in which problems and solutions have been matched in the past (Brown and Wyatt, 2007; Dorst, 2011). Hence, for many observers, participants and erstwhile practitioners, design-thinking is synonymous with an open-ended re-thinking of past practices and current problems which often involves the re-framing of problems and the articulation of new solutions to old or new problems (Dorst, 2011). This approach has been lauded in product design, for example, in the creation of new apps and businesses built around them, such as Uber or Google, which re-think and re-structure traditional forms of activities such as ride-hailing or internet searching. And it has been suggested that many of the same kinds of benefits might flow to policymaking through the adoption of similar modes of thinking and acting (Bason, 2014; Blomkamp, 2018). Many recently estab220

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lished policy “labs”, for example, profess to follow design-thinking inspired approaches to policy problems and issues and stress the advantages of re-conceptualizing and implementing policy problems and solutions through activities such as workshopping, prototyping or crowdsourcing (McGann et al., 2018; Wellstead et al., 2021). However, as argued below, while this form of (re)problematization and invention is indeed characteristic of design thinking in many technology-oriented and creative spheres of activity – from product to graphic design – in the policy realm it typically does not capture the full range of activities that go into the crafting of feasible policy alternatives, which are both more wide-ranging and more complex than many product launches, often being highly contested and uncertain, politicized and conflict-riddled in a way most product or service designs are not (Clarke and Craft, 2019). As a result, applying design thinking principles and practices in the policy world may not result in the kinds of path-breaking and highly successful disruptions that have been achieved in other sectors and areas of application but rather, instead, can generate unrealistic or infeasible options which fail to attract decision-makers or lead to effective implementation. The reasons for this and the possible solutions to this dilemma are discussed in what follows.

THE POLITICAL NATURE OF POLICY FORMULATION AND THE CHALLENGES IT POSES TO POLICY DESIGN Policy formulation is a step in the policy process whereby alternatives and options are generated to possibly resolve issues or problems which have made their way onto the government agenda (Howlett et al., 2020). It is by no means always or ever a neutral or technical activity but rather often is a highly contested process in which multiple actors lobby, cajole and coalesce in order to have their preferred solution adopted by a government (deLeon, 1992; Thomas, 2001; Kingdon, 1984). This basic point about the political nature of policy formulation is often forgotten in the design-oriented literature (Clarke and Craft, 2019). Of course, the extent to which this affects the generation and support for particular policy options or directions varies. Depending on the circumstances, policy formulation processes can take many forms, from fairly closed expert-driven “technocratic” or legal analyses which take past practices and precedents seriously, to more wide-open public participation processes which may feature emotionally, partisan or uninformed debate – rather than the knowledge-driven policy discourses present in the former case (Hoppe, 2010; Howlett and Mukherjee, 2017). Although many of these different kinds of processes may share the same desire and orientation towards the matching of means and ends or tools and goals in a policy programme in an effective way, and therefore take into consideration the evaluation of alternatives which, through reason and experience, can reasonably be expected to achieve desired results, many do not. And even those that do may differ dramatically in what kinds of tools or instruments emerge as the preferred means to accomplish any given task (Simons and Voss, 2017; Taylor et al., 2019). And, of course, many highly politicized formulation processes – such as legislative bargaining or “log-rolling” – are scarcely recognizable as such from a traditional “design” perspective. These “non-design” formulation efforts are very common in policymaking, however, and often feature such familiar political and partisan elements such as the exchange of favour,

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corrupt or clientelistic promotion of alternatives, or bureaucratic politics and budget maximization efforts and considerations (Hartley and Howlett, 2021; Howlett and Mukherjee, 2014; Mortati, 2019; Newman and Widdi Nurfaiza, 2020). These activities and processes colour efforts at formulation and lead to outcomes from choices and choice processes which neither resemble the “classic” kinds of design work highlighted by Schon and others (Schon, 1984, 1988; Waks, 2001) nor are addressed at all in most discussions urging the application of design thinking to their study and practice.

POLICY DESIGN VERSUS DESIGN THINKING IN THE POLICY SPHERE “Design” is commonly espoused by many observers, practitioners and scholars as a preferable method of formulation to these kinds of “non-design” practices – usually because it is argued more evidence-driven technical approaches are more likely to achieve a successful resolution of a problem than more overtly political and less knowledge-based ones (Grabosky, 1995; Gunningham and Sinclair, 1999; May, 1981; Rose, 1993, 2005; Schneider and Ingram, 1997, 2005; Weimer, 1992). Neither the traditional policy design approach nor the more recent “design thinking” one, however, is synonymous with formulation, which can be carried out in many different ways (Howlett and Mukherjee, 2014). “Policy Design” is only one of the several ways in which policy alternatives can be formulated and placed before decision-makers as options for dealing with a problem and “Design Thinking” is only one form of policy design. In themselves, the characteristics of policy formulation highlighted above limit the potential for “design thinking” to have a major impact on policymaking or its outcomes since this approach is fundamentally unsuited to the resolution of major issues featuring, for example, political, partisan, ideological or religious debate or disputes. Nevertheless, in some cases, the “design space” or circumstances in which formulation takes place may be constructed as one which would allow some innovations to be promoted and adopted (Chindarkar et al., 2017). And in such propitious circumstances a design thinking orientation may prove fruitful if there is a moment in time when a new policy is being developed or an old one reformed and radical innovations and re-conceptualizations are needed or appreciated by those involved in policymaking (Turnbull, 2017). However, such design spaces are not common and when such a propitious moment is absent, efforts in this direction may be stimulating but ineffective or simply not very useful. The difficulties the vagaries of the policy formulation process pose to the emergence, application and effectiveness of any kind of design orientation in policymaking were well recognized by the pioneers of policy design research in the 1980s and 1990s. These authors noted that, like design activities in other fields, policy design involves knowledge of the basic building blocks or materials with which actors must work in constructing a policy and the elaboration of a set of principles regarding how these materials should be combined in that construction. But in the policy realm, especially, they noted it also requires a third element often missing or less significant in other areas of design application, that is, a clear understanding of the processes by which a policy alternative becomes translated into reality and of the many barriers that exist between concept and realization (Barzelay and Thompson, 2010).

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In other words, while not all problems and contexts might be amenable to some kind of design-oriented process, an even smaller number is amenable to more open-ended and creative design processes. These very different contexts need to be taken into account by anyone who might like or want to influence policy choices. Of course, this is also true to a certain extent in many other fields, such as architecture, where an architect or designer must consider not only the formal elegance of a design but also its costs, likelihood of its acceptance by a client, zoning and other regulations, view cones and corridors, and the desire of city councils to promote or restrain certain types of buildings and enterprises in certain places and times. But these problems are magnified in policymaking, and their neglect by many analyses of policy design, including those which stem from or promote a design thinking orientation (Clarke and Craft, 2019), is not helpful. How the different approaches to policy design deal with these basic elements of policymaking are set out in more detail below.

THREE ASPECTS OF POLICY-MAKING AFFECTING THE APPLICATION OF A DESIGN LOGIC TO POLICYMAKING The policy studies literature is replete with case studies and examples of successful and unsuccessful policy designs, and an approach that neglects these lessons is in danger of merely repeating these errors. While “design thinking” has its place in the pantheon of policy formulation types of varieties, the emphasis of the “policy design” approach rooted in traditional policy studies upon the character and content of policy tools and processes (Howlett, 2017) means it is more likely to allow the articulation of policy solutions likely to effectively match problems than a more open-ended and less historically-minded approach. This can be seen in the context of how these different approaches to policy design deal with the fundamental challenges of policy formulation. Knowledge of the Basic Building Blocks or Materials with which Actors must Work in Constructing a Policy In a policy context the most important aspect of “design” involves understanding the kinds of implementation tools governments have at their disposal in attempting to alter some aspect of society and societal behaviour. These “policy instruments” are the techniques used in policy designs and involve the utilization of state authority or its conscious limitation in the pursuit of government aims (Howlett, 2019). The study of policy instruments has a long history in the policy sciences, having been undertaken by economists, political scientists and others (Dahl and Lindblom, 1953; Edelman, 1964; Kirschen et al., 1964) for over 50 years. “First-generation” economists studying the tools of government were concerned largely with the study of business–government relations; with the effects of state regulation and economic policy formation on business efficiency and with the ability of specific kinds of tools to correct specific kinds of market failures (Bator, 1958; Breyer, 1979; Zeckhauser et al., 1968; Zerbe and McCurdy, 1999). These currents promoted a somewhat Manichean view of instrument options and often led to simplistic recommendations for tool selection such as always preferring market-based tools over government-based ones (Howlett, 2005; Le Grand, 1991; Wolf, 1987, 1988). Not all early

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studies shared these characteristics, of course, and some presented more complex and nuanced models and analyses of tools and instrument selection criteria. By the early 1980s, attention began to be focused on more precisely categorizing policy instruments in order to better analyse the reasons for their use (Salamon, 1981). Careful examination of instruments, and instrument choices, was expected to lead to better insights into the factors driving the policy process and the characterization of long-term patterns of public policymaking, and would also allow practitioners to more readily draw lessons from the experiences of others with the use of particular techniques in specific circumstances (Woodside, 1986). Building on the base of case studies and insights developed in these works, “second generation” students of instrument choice attempted to improve on early models and introduced more complexity and subtlety into policy instrument analysis and considerations around tool choices and designs, especially better understanding their sources of support in society and government which are either or both attractive and difficult to change (Bressers and O’Toole, 1998; De Bruijn and Hufen, 1998; Van Nispen and Ringeling, 1998). More recently, students of instrument choices have focused on the use of multiple tools or “policy instrument mixes” rather than upon single instrument choices (Flanagan et al., 2011; Gunningham and Sinclair, 1999; Gunningham and Young, 1997; Gunningham et al., 1998; Rogge and Reichardt, 2016). This new emphasis has raised to the forefront not only older questions such as why specific tools were adopted, but many more design-oriented ones such as why specific kinds of mixes exist at present and whether and to what extent such mixes can be designed to be optimally effective. Generally speaking, very little of this literature is referenced by studies of “design thinking” in the policy realm. Such exercises generally rely on the expertise or “wisdom” of crowds or the public in fashioning instrument alternatives (Blomkamp, 2018) even though the public, generally, has little knowledge of the wide variety of possible tools which could be deployed or their strengths and weaknesses. The Elaboration of a Set of Principles Regarding how these Materials should be Combined in that Construction The aim of applying a design orientation to policymaking is not only better description but better prescription and, in order to accomplish this, it is necessary to elevate the discussion of policy tools from simply a taxonomical level to the articulation of some basic principles of policy design that erstwhile designers can and should follow. In design thinking the principles to be followed generally are to be open-minded and attempt to re-frame problems in such a way that new alternatives for their solution can emerge and be “prototyped” (Bason, 2014). Very little, in general, is said about exactly what kinds of alternatives should be deployed and when or why. In more traditional policy design studies, this is not the case. Rather, several key principles for effective policy designs based on the “character” or innate characteristics of policy tools have been clearly articulated. A very early and oft-cited rule in this area, for example, is that the optimal ratio of the number of tools to goals in a policy is 1:1 (Knudson, 2009), an axiom first put forward by the Nobel Prize-winning economist Jan Tinbergen in 1952. Assuming that utilizing more instruments costs more than utilizing fewer, and that redundancy is not a virtue,

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this maxim translates easily enough into a basic efficiency principle for the selection of tools to meet policy ends. This emphasis on parsimony means utilizing in a mix only the number of tools required to reasonably attain the number of policy goals expected to be achieved (and achieve compliance with government wishes) and no more or less than that. While it may not be clear at the start what is that number, beginning with a smaller number and adding tools as needed to ensure compliance and monitoring the impact and effect of each additional tool can help identify that “sweet spot”. This highlights a second principle found in the older literature on policy design, which was not only to be parsimonious in the number of instruments chosen at a specific point in time to attain a goal, but also dynamically or sequentially. In the mid-1970s and early 1980s, for example, Bruce Doern, Richard Phidd and Seymour Wilson argued that different policy instruments varied primarily in terms of the “degree of government coercion” each instrument choice entailed (Doern, 1981; Doern and Phidd, 1983; Doern and Wilson, 1974; Tupper and Doern, 1981). They argued that tool choices should only “move up the spectrum” of coercion from minimum towards maximum if and when necessary according to the degree to which earlier tools achieved expected outcomes, or not. This rationale is again based on a cost–effort calculation given the character of specific tools but is also linked to considerations of the context of tool deployment, in this case an appreciation of the preferences of (mainly) liberal-democratic governments for limited state activity (Howlett, 2017). Preferring “self-regulation” as a basic default, for example, Doern and his colleagues argued governments should first attempt to influence overall target group performance through exhortation and then only add instruments as required in order to compel recalcitrant societal actors to abide by their wishes, eventually culminating, if necessary, in the fully public provision of specific kinds of goods and services. A third, more recent, principle has involved the articulation of criteria such as “consistency” (the ability of multiple policy tools to reinforce rather than undermine each other in the pursuit of policy goals), “coherence” (or the ability of multiple policy goals to coexist with each other and with instrument norms in a logical fashion), and “congruence” (or the ability of goals and instruments to work together in a uni-directional or mutually supportive fashion) as important measures of optimality in policy mixes (Howlett and Rayner, 2007; Kern and Howlett, 2009; Lanzalaco, 2011). This recognizes that policies are composed of several elements and some correspondence across these elements is required if policy goals are to be integrated successfully with policy means (Cashore and Howlett, 2007). Much work on policy design and policy mixes has focused on the need for the various parts of a mix or portfolio to be integrated for maximum effectiveness (Briassoulis, 2005a, 2005b). This approach ties closely to a fourth principle, which is to maximize complementary effects while minimizing counter-productive ones in such mixes. Work on “smart regulation” in the late 1990s, for example, underlined the importance of this principle (Gunningham et al., 1998). As Grabosky (1995) and others suggested, some tools counteract each other – for example, using command and control or state-driven coercive regulation while also attempting to encourage voluntary compliance – while, as Hou and Brewer (2010) argued, other tools complement or supplement each other – for example, using command and control regulation to prevent certain behaviour deemed undesirable, accompanied by financial incentives to promote more desired activities, such as jail sentences for unsafe driving combined with lower insurance rates for accident-free or ticket-free drivers.

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“Smart” design implies creating packages which take these precepts into account in their formulation or packaging (Eliadis et al., 2005; Gunningham and Sinclair, 1999; Gunningham et al., 1998) and is something at which traditional policy design studies excel. A Clear Understanding of the Processes by which a Policy Alternative becomes Translated into Reality and the Barriers that Exist between Concept and Realization Knowledge of the character of individual tools and mixes is important in developing principles for policy design but, as noted above, understanding of the policy design context and understanding the process are equally important (deLeon, 1988). Again, this aspect of policymaking is lacking in many works inspired by design thinking, which assumes, whether explicitly or implicitly, that any combination of tools is possible in any circumstance (Clarke and Craft, 2019). That is, decision-makers have unlimited degrees of freedom in their design choices. Empirical studies in the traditional policy design orientation, however, have noted this kind of freedom in combining design elements is only found in very specific and generally rare circumstances – what Thelen (2003) terms “replacement” or “exhaustion” – when older tool elements have been swept aside or abandoned and/or when a new mix can be designed or adopted de novo. Policy design studies have always noted the difficulties with policymaking processes, which tend to lock-in policies and inhibit change. Kirschen et al. (1964), for example, noted very early on that the key determinants of the policy choices they examined were the economic objective pursued and the structural and conjunctural context of the choice. That is, while the choice of a specific instrument could be made on essentially technical grounds, according to criteria such as efficiency, cost or effectiveness, it is also affected by the political preferences of interest groups and governments, institutional vetoes and barriers, and by a variety of sociological and ideological constraints which would also inform tool choices and preferences. Lasswell (1954) noted the extent to which governments could affect these aspects of policymaking through their instruments and argued that a principal task of the policy sciences must be to understand the nuances of these situations and calibrate their actions and their effects accordingly (Doern and Phidd, 1983; Doern and Wilson, 1974; Lasswell, 1954, 1971). That is, traditional policy design work recognizes that design choices emerge from and must generally be congruent with both long-term contextual factors such as the governance modes or styles practised in particular jurisdictions and sectors (Howlett, 2009) as well as with the more medium or short-term preferences of key policy, social and economic actors if they are to be “workable” or feasible. Works on “policy styles” and administrative traditions (Freeman, 1985; Kagan, 2001; Knill, 1998; Richardson et al., 1982) have identified common patterns and motifs in the construction of typical policy designs in different jurisdictions reflecting such concerns (Howlett, 2009; Kiss et al., 2013) and leading to preferences for particular kinds of tools which make their design and adoption simpler than non-traditional ones. Design thinking needs to take such contextual constraints into account in articulating programme options. “Goodness of fit” between tool and context is thus a key concern in contemporary policy design considerations and can be seen to occur at several different levels (Brandl, 1988). Policy designs need to consider both the desired governance context and the actual resources available to a governmental or non-governmental actor in carrying out its appointed role. Thus, for example, planning and “steering” involve direct coordination of key actors by gov-

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ernments, requiring a high level of government policy capacity to identify and utilize a wide range of policy tools in a successful policy “mix” or “arrangement” (Arts et al., 2000, 2006). In addition to the requirements of “goodness of fit” with prevailing governance modes with respect to the logic of policy design there are also constraints imposed on designs by existing trajectories of policy development. As Christensen et al. (2002) have argued, the issue here is the leeway or degrees of freedom policy designers have in developing new designs given existing historical arrangements, path dependencies, policy legacies and lock-in effects. As Christensen et al. note, “these factors place constraints on and create opportunities for purposeful choice, deliberate instrumental actions and intentional efforts taken by political and administrative leaders to launch administrative reforms through administrative design” (Christensen et al., 2002, 158). Determining how much room to manoeuvre or degrees of freedom designers have to be creative (Considine, 2012) or, to put it another way, to what degree they are “context-bound” in time and space (Howlett, 2009) is key for contemporary design studies, and this third element of policy formulation, again, is typically ignored in studies advocating “design thinking”.

CONCLUSION: THE CONTRIBUTION OF DESIGN THINKING TO POLICY DESIGN Governments grapple daily with complex problems involving situations in which they must deal with multiple actors, ideas and interests in complex problem environments, which typically evolve and change over time. This means there is often a high level of uncertainty in policymaking, and the modern policy studies movement began with the observation that public policymaking not only commonly results from the interactions of policymakers in the exercise of power rather than knowledge, and also with the recognition that this does not always guarantee effective policies or the attainment of desired results (Arts and Van Tatenhove, 2004; Lasswell, 1958; Stone, 2008). Recently, an emphasis on “design” has re-emerged in the policy sciences, in part in the resurrection of an older tradition of thinking in this orientation from the 1980s and 1990s (Howlett and Lejano, 2013) and in part from an effort to apply the kinds of design thinking that have been so successful in other fields to policymaking (Lindgaard and Wesselius, 2017). As the discussion above has shown, it is clear that traditional policy design studies exhibit a much wider range of concepts and examples, and have developed a sounder appreciation for both the tools of policy implementation and formulation processes. Notwithstanding this, “design thinking” in this field does have its place and, in the right circumstances, can contribute to policy invention and innovation in a way that more traditional techniques may not. In any such comparison, however, it must be reiterated that not only, as Clarke and Craft (2019) have pointed out, are these two approaches to policy design not synonymous, but as Howlett and Mukherjee (2014, 2018) have stressed, neither do they exhaust the field of the varieties of policy formulation, many of which do not involve either, or any, kind of design orientation. Nevertheless, both these approaches have proven fruitful in helping to open up the “black-box” of policy formulation; an activity which, although pivotal to policymaking, still has not received its due share of attention (Howlett and Mukherjee, 2017), and they also help us understand how this stage of policymaking operates and what is needed for it to work to

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produce policies more likely to resolve important social problems and concerns in a concise and efficient way.

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13. The weakest link: the importance of problem framing in design thinking Martin Meinel, Tobias T. Eismann, Sebastian K. Fixson and Kai-Ingo Voigt DESIGN THINKING AND THE ROLE OF PROBLEM FRAMING While the roots and origins of design thinking reach way back into the 20th century, its rise over the past two decades has been quite remarkable. The evolutionary path of design thinking involved two domains – including the tensions within and across these domains – which helps explain both its remarkable success and its challenges. The broad rise of design thinking across industries over the past 20 years was fuelled primarily by the domain of practice. Practitioners, i.e., designers who applied their skills to an increasing array of tasks, from initially product-oriented tasks such as architecture, industrial design or graphic design, to process-oriented design tasks such as service design or user-interaction design, to increasingly abstract tasks such as organization design, drove the emergence of design thinking as a powerful method of innovation. The common language underneath this expansion of design became labelled design thinking, to a large degree through design consultancies who grew their own business through this expansion. One specifically visible example of this trend was the US firm IDEO, whose then-CEO Tim Brown published a widely-cited article entitled “Design Thinking” in Harvard Business Review (Brown, 2008) followed by a widely-sold book “Change by Design” the following year (Brown & Katz,, 2009). But this rapid rise of design thinking as a moniker for design practices applied to a wide array of problems, often by non-designers, also led to a backlash among some of the professional designers who saw their craft cheapened and devalued, labelling design thinking as bullshit (Jen, 2018) or even as a contagious disease (Vinsel, 2017). Similarly, design thinking received both supportive and critical treatment in the second domain: academia. Here, design thinking has been described as a learning process (Beckman & Barry, 2007), a competitive advantage (Martin & Martin, 2009), and how designers think and work (Cross, 2011). Researchers from various disciplines have viewed design thinking through different lenses: as a reasoning pattern in design (Dorst, 2011), as a method for an innovation process (Seidel & Fixson, 2013), as a concept to be enacted (Carlgren et al., 2016), as an approach interacting with organizational culture (Elsbach & Stigliani, 2018), and as a collection of attributes and tools (Micheli et al., 2019). In addition to these multidisciplinary viewpoints, some scholars have critiqued design thinking – at least the version they describe

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as managerial – as superficial and lacking sufficient anchoring in intellectual tradition (Johansson-Sköldberg et al., 2013). Consequently, various definitions and conceptualizations of design thinking can be found across practice and academia. Tim Brown, the IDEO CEO, defines design thinking as “a discipline that uses the designer’s sensibility and methods to match people’s needs with what is technologically feasible and what a viable business strategy can convert into customer value” (Brown, 2008, p. 86). In a more general sense, design thinking is often understood as a creative problem-solving approach (Beverland et al., 2015; Brown, 2008; Liedtka & Ogilvie, 2011). These wide-ranging debates notwithstanding, most authors agree that, when viewed through the process lens, design thinking includes a set of activities to learn about what it is that is asked to be designed, activities to generate solution options, and testing and experimenting with some of these solutions to develop one that addresses the originally identified problem or opportunity. In the literature different descriptions of this process have emerged, varying in granularity and labels of the individual steps. Figure 13.1 presents some examples.

Figure 13.1

Examples of process figures

The processes in Figure 13.1 are displayed in a linear fashion for simplicity, but most authors agree that design thinking should be generally conceptualized as set of iterative activities. In fact, in the design community, there is a strong view that problems and solutions co-evolve (Dorst, 2019; Dorst & Cross, 2001). Differences in naming and scope notwithstanding, most descriptions begin with activities aiming to better understand what needs to be solved. This phase often recommends the use of data collection techniques borrowed from ethnography such as interviews and detailed observations. In particular, user focus is strongly emphasized among researchers (Brown, 2008; Michlewski, 2008; Owen, 2007) when trying to “convert need into demand” (Brown & Katz, 2009, p. 39). Empathizing with the user and therefore developing a detailed understanding of the user supports the identification of hidden and latent needs (Michlewski, 2008) and consequently creating better fitting solutions (Brown & Katz, 2009). The second phase focuses on generative activities, to create solution options that may address the identified problem. Numerous methods and tools have been proposed to help with this process (Kumar, 2012). Finally, the third set of activities aims at selecting, merging, and

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refining solution options, sometimes fragments, and testing them to explore their usefulness. For this phase a variety of techniques is also available. It is the transition from the first to the second phase (see shaded areas in Figure 13.1) where in practice the often unexplained occurs. Somehow, out of the sea of un- and semi-structured data from interviews, observations, secondary research, user contributions and own experiences and personal and organizational values, a point of view emerges that in the best case provides a well-chosen jump-off point from which subsequent solution generation activities (ideation, creation, etc.) can tremendously benefit. It is this aspect of the design thinking process, the framing of problem statements, which is the focus of our chapter. Also often referred to as “synthesis” (Kolko, 2010), the act of transforming user insights into a problem statement that is worth solving remains an act of (often intuitive and iterative) abstraction and deduction until arriving at a satisfying state (Cramer-Peterson et al., 2019; Dorst, 2011; Kolko, 2010). Even expert designers often describe their point of arrival at a satisfying problem statement as “I know it when I see it” (Tanner & Landay, 2019). Interestingly, the design thinking literature offers only little support for the framing of problem statements in terms of techniques and practices but integrates intuition as a useful design practice (Martin, 2010). For example, design thinking suggests looking at the data gathered and trying to derive journey maps, personas, and so-called “how might we?” questions (Beckman, 2020; Kolko, 2010; Micheli et al., 2019). That is why the formulation of good problems is a challenge, especially for design thinking students, because they still have little experience and intuition to fall back on. Hence, we want to elaborate on some perspectives from design research that might serve students and teachers as support for answering the question of whether they have arrived at a “good” problem or not.

WHAT MAKES A GOOD PROBLEM? Problems differ in their complexity and solvability. At one end, there are simple, obvious, or tame problems that typically exhibit a clear cause-and-effect relationship and have a right answer, i.e., solutions already exist for these types of problems. At the other end of the problem–difficulty spectrum lies the world of “wicked” or “chaotic” problems. Many big societal problems fall into this category (e.g., how to deal with a pandemic). They lack constraints, exhibit no clear cause-and-effect relationships, and often appear in a turbulent and unknown context (Buchanan, 1992; Snowden & Boone, 2007). In this chapter, we focus on complex problems, where cause-and-effect relationships are discoverable but not immediately apparent (Snowden & Boone, 2007). Although the totality of problems is unknown at any given point in time, the bulk of problems that many organizations face seems to appear in the middle of the difficulty spectrum as complex problems, especially well-suited for a design-inspired approach, because they deal with “known unknowns” (Wrigley & Straker, 2017). To provide design thinkers with stronger support in the transition phase from inspiration to ideation, i.e., to frame “better” problems, the following section synthesizes the existing literature and distils four distinct perspectives that might be useful to consider when solving complex problems.

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PROBLEM SPACE Facing a certain task or problem should entail identifying an adequate abstraction level before searching for possible solutions (Kalogerakis et al., 2010). A problem definition that is too narrow or specific reduces the number of possible solution options and runs the risk of leaving out possible solution options at the very beginning of the problem-solving process (Kalogerakis et al., 2010). For example, narrowing down an initial “long-distance travelling” problem framing to “the buttons on the entertainment system on long-distance flights are too small to handle” might only offer few directions to take when searching for novel solutions. On the other hand, if the problem statement is too broad and unspecific, one faces a vast solution space that makes the search very difficult, due to the availability of an almost endless number of solution paths that could be pursued (Kotovsky & Simon, 1990; Newell & Simon, 1972). For example, describing the long-distance traveller’s problem as “insufficient entertainment programmes” might overwhelm especially novice design thinkers leaving them too much space to tap into, even if the chance of creative outcomes increases (Kalogerakis et al., 2010). Of course, one can constantly shift back and forth between the problem space and the solution space during design thinking, but sufficient narrowing of the problem space can signal the next change into the solution space especially for design thinking novices. On the other hand, if the team is stuck on the solution side, expanding the problem space again or increasing the level of abstraction can make sense to get the project unstuck. In this way, a larger problem space could provide new avenues of thought. Thus, while we may not know exactly where the adequate level of abstraction lies in each situation, our experience suggest that most novices start with a problem statement that is too narrow. Therefore, we recommend starting with a problem statement that is slightly more abstract than most find comfortable, before becoming more concrete, and, if necessary, raising the abstraction level again. This iterative procedure can be supported by using methods such as abstraction laddering, i.e., switching back and forth between a high degree of abstraction and the most concrete problem statement possible. In this way, the novice can develop a better understanding of which problem formulations might be too abstract and which ones might be too narrow.

SOLUTION FOCUS One important strategy for successfully managing the early stage of problem-solving is “staving off solutions” (Bardwell, 1991) or avoiding “solution-mindedness” (Maier, 1958). It responds to the human tendency of trying to create a solution too quickly, before investigating what the problem really entails (Choo, 2014; Dery & Mock, 1985; Stadler, 2011). In business practice, often people “converge” too early, resulting in creative developments that are rather small evolutions, i.e., refinements of what is already existing. This tendency for converging quickly is both a human trait and an often-observed organizational effect. Most people and most organizations strive to reduce uncertainty as quickly as possible. The initial investigation of this phenomenon occurred in the context of group-level problem-solving where the groups had been given the problem by their superiors. To reach high-quality decisions – and manage anxiety about not doing so – superiors tend to be highly “solution-minded” and, therefore, influence their subordinates to adopt the same mindset (Maier & Hoffman, 1960).

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The inadequate focus on the problem statement might lead to the formulation of the problem in terms of a solution that impedes generating alternatives and developing novel ways of solving it (Bardwell, 1991; Dery & Mock, 1985; Spradlin, 2012). An example based on the long-distance travel experience with a very high solution focus could be as follows: “People who need silence during long-haul flights cannot rest when seated next to a family with young children. By placing guests with the same interests in different areas of the aircraft, conflicts can be reduced and potentially interested parties can exchange ideas with like-minded people”. In this way, including a particular solution in the problem statement decreases the effectiveness of problem-solving (Maier & Hoffman, 1960), and creates an obstacle in the problem-solving process (Stadler, 2011). In contrast, efforts to counteract the tendency to instantly pursue an obvious solution improve the quality of group problem-solving efforts (Maier & Solem, 1962). But where do these solution ideas come from? When confronted with a customer problem to be solved, developers and engineers often try to be efficient and search for solutions that can be generated using existing components, often without thinking about the customer perspective. Hence, there is a risk that a solution focus would be adopted, from which it is no longer possible, or at least harder, to break with in the solution space. Design thinking helps here, because it includes interviews with customers to ensure that at least the user perspective is included. However, it may also be that in these interviews, customers are proposing a particular solution that might impede creativity, because the customer might not be able to recognize the entirety of technological possibilities and developments. There are plenty of examples that illustrate that not always the customer or user knows what is best for innovation. Often cited stories, such as Henry Ford’s remark that customers would have asked for faster horses instead of cars, illuminate this phenomenon. That is one reason that can make it a challenging task for design thinkers not to be overly influenced by proposed solutions during the design thinking approach. A phenomenon related to solution focus is “design fixation” (Jansson & Smith 1991), which Crilly and Cardoso (2017) categorize as a cognitive bias. It connotes a designer’s tendency to blindly adhere to existing concepts (e.g., previous solutions) and reluctance to use different approaches to discovering and solving a design problem (Condoor & LaVoie, 2007; Crilly, 2015; Jansson & Smith, 1991). Chrysikou and Weisberg (2005) describe design fixation as a “negative transfer from examples that may significantly affect problem-solving” (p. 1145). Consequently, design fixation predetermines outcomes, limits creativity, and can result in inferior solutions (Condoor & LaVoie, 2007; Jansson & Smith, 1991). The general opinion within design-fixation research is that it constitutes a real problem in design practice (Crilly, 2015), and multiple studies show how pictorial and verbal sample solutions can impede creative performance (Chrysikou & Weisberg, 2005; Condoor & LaVoie, 2007; Jansson & Smith, 1991; Purcell & Gero, 1996). For these reasons, it is important to focus on separating problem exploration from solution generation, although both processes are mutually dependent. For example, when formulating user insights or “how might we” questions, you should take care not to include reference to existing or future solutions. Another way to move away from solution ideas is to use the “5-why” method. This method works in such a way that you question the initial problem definition with regard to its relevance at least five times with the question “why?”. In this way you achieve a deeper problem understanding, which is usually even more strongly oriented to the user with his or her needs as the cause of the problem.

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USER FOCUS User focus is a key aspect of design (Dorst, 2015a, 2015b; Dreyfuss, 1955; Hekkert & van Dijk, 2011; van der Bijl-Brouwer & Dorst, 2017), design thinking (Brown & Katz, 2009; Meinel et al., 2020), and innovation management (Chesbrough, 2003; Gassmann, 2006; Kelley & Littman, 2001; Meinel & Leifer, 2011; Osterwalder & Pigneur, 2010), in which gaining deep customer insight and applying strong human-centeredness are fundamental elements or principles for creating value. When problem-solvers “seek meaning” by identifying and analysing needs and values, they investigate underlying themes of people the problem situation affects, to better understand their motivations, behaviours, and needs (Dorst, 2015a). Therefore, designers are encouraged to attend to the human dimension of the problem (Dorst, 2015a), an essential part of problem discovery and a designer’s problem-solving activities (Dorst, 2011, 2015a, 2015b). Dorst (2011, 2015b) also offers examples that show a deeper understanding of involvement in a problem leading to new and potentially more satisfying solution directions. In one of his case studies, designers were asked to solve the problem that an entertainment district had with drunkenness, violence, and other minor criminal activities. In previous attempts, the problem owner (local government) tried to solve these problems by improving security measures such as increasing CCTV-surveillance and police presence, which did not improve public safety and unfortunately led to a less comforting atmosphere through the extra visible security. By investigating the users in this field (the party goers), designers realized that most of these people were non-criminals who simply wished to have a good time but got frustrated as the nights progressed due to long queues at bars and clubs or inadequate transportation options for leaving this district at night. Looking through the lens of these users, designers reframed the original problem into “how to improve the music festival experience”, which created multiple viable solution paths for decreasing criminal and misconducted behaviour in the district. This example shows that understanding people’s goals, motives, and behaviours constitutes a fruitful source for creating new solutions (Hekkert & van Dijk, 2011). Applying a user-centred approach to problem-solving, designers use observation, listening, and dialogue, to uncover hidden needs and motivations (Michlewski, 2008). Designers also use storytelling in problem statements to increase the problem-solver’s empathy (McGinley & Dong, 2011). Moreover, a clear user focus also leads to solutions that users are more likely to adopt (Veryzer & Borja de Mozota, 2005). Including the user perspective explicitly in the problem statement can ensure that the team will continue to focus on the customer in the subsequent phases as well. An example from the long-distance travel perspective with strong user focus could be: “Otto, a retiree, is planning a trip to America to visit his son living there. He is 72 years old, divorced, has two sons and does not speak English, which makes travel planning and execution very challenging for him.” Generally, you can think of lots of different users: Is it an individual or is it a group? Is it a specific niche group? Have I thought about whether the problem is also relevant for other users and not just for the one I interviewed? And if so, how can we transfer this individual case to a user group that is large enough so that tackling the problem is more worthwhile? In any case, weaving the user closely into the problem statement aims for a more empathic solution finding process. In contrast, “getting luggage safely and easily from A to B regardless of the means of transportation”, is an example for a problem statement that does not explicitly include a user.

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Without a user perspective being included in the problem statement, there is a high risk of getting lost in technical solution options and rather being fixated on your own wants and needs. Thus, both conceptual and moral arguments for a problem-statement focus on the user have garnered empirical research support. Hence, we suggest focusing on the user throughout the whole problem-solving process. First, key insights that you gain through user research should be noted in a way that includes the concrete user or a specific user group, their wants and needs, and their specific obstacles regarding the problem. Second, we suggest always including the user in formulating the problem statement in the form of “how might we” questions. Because user research often produces needs and insights from several different users, the challenge is often which of the users to focus on. For example, in an industrial context, the problem of unexpected errors occurring on the machine may affect the machine builder, the machine user, the programmer, and the end customer who buys the product produced by the machine. In such situations of complex system interdependencies, we especially recommend design thinking novices to focus on the user on whom they themselves have the strongest influence and the chance of changing the situation seems highest. Starting from an initial solution, further solutions can usually be added. Third, when generating solution options afterwards, one should regularly ask yourself whether the generated and preferred ideas actually address the user’s problem or whether you have already moved too far away from the initial problem during the process.

ORIGINALITY At the beginning of most design projects is the briefing process. In the briefing, the client ideally discloses all relevant information the designers need to realize the planned project (Hansen & Vanegas, 2003; Paton & Dorst, 2011). The briefing can also go beyond the client’s initial project formulation and involve several interactions between the parties, to create a collective comprehension of the project’s intention (so-called “framing” activities). When designers are partially or totally uninvolved in the project formulation but face a strictly defined brief in a way that the problem at hand already indicates the necessary solution, they are almost unable to propose more actionable, more original frames. Interviewed designers label these projects as “typical”, quite the opposite of “innovative” projects that actively involve designers in shaping the problem statement, building a shared understanding of the project, and offering generative potential (Paton & Dorst, 2011). In fact, there is a strong relationship between originality, creativity, and problem-solving (Csikszentmihalyi & Getzels, 1971). Researchers agree that originality embodies an integral part of creativity itself (Runco & Charles, 1993). Hence, measuring creativity often includes originality as a dimension. However, used in various contexts, the term originality often appears as a synonym for novelty, individuality, or uniqueness, and describes a characteristic of a problem, an idea, a solution, and/or even human beings. Originality (in ideas) requires two attributes: rarity and imaginativeness (Dean et al., 2006). Thus, a problem’s originality should be much more than just its novelty; something can be novel but not original at the same time if it lacks generative potential. Hence, the more original the problem is, the more likely it is that the discovery of solutions to this problem can also lead to more original ideas. The more original the problem is, the more likely it is that no one has looked at this problem before and no one has solved it yet – so it

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contains untapped potential in the solution space. An example for an original problem statement from the long-distance travel field might be “ensuring the uninterrupted and accurate cooling of insulin during a daily travel/hiking of about 6–8 hours”. An unoriginal problem instead would be one that lots of people easily come up with and problems that lots of people encounter. Think, for example, of “boredom during the time periods between individual travel stages”. A problem like this might be worth solving because it probably involves a large market. But the question is: why hasn’t anyone solved it yet? Are the existing solutions sufficient? Would competition be greater here because many others are also tackling this problem? Arriving at an unoriginal problem could also be a sign that the problem space has not yet been sufficiently explored and that you are still scratching the surface. To increase the chance of arriving at more original problem statements, we suggest including observations into your user research and not solely relying on interview feedback. Through direct observations of the users’ behaviour in the problem situation, the observer can arrive at insights that the users themselves are not able to articulate – insights that have greater generative potential. In addition, observations help to avoid misinterpretations that may arise during interview situations. Similarly, we recommend introducing originality as an evaluation criterion in the convergent decision-making process of settling on a problem with which to enter idea generation. For this decision step, novices often use the classification of user insights or problem statements in matrices along two axes or criteria such as the risk of inaction or the strength of the user gain. The originality of a problem statement is also a suitable criterion in this context and should be assessed by a team. Table 13.1 provides an overview of all four perspectives that we propose to look at when framing problems during synthesis in design thinking.

PROBLEM FRAMING: THE FUTURE PATH FOR DESIGN (THINKING)? In the previous section, we looked at problem framing, often referred to as synthesis in design thinking, from different perspectives. We also discussed the different manifestations of these perspectives and their significance for the design thinking process. In doing so, we have elaborated the perspectives based on the extant literature, especially the design literature. Hence, we contribute to a better understanding of problem framing and, in particular, provide design thinkers with suggestions and support in exactly the process step in which design thinking literature has so far offered the least support. Beyond that, it is becoming increasingly clear how digitalization is changing the innovation process, for example by re-arranging tasks or combining and linking them along the value chain (Marion & Fixson, 2021). Here, we want to focus on one development from the digital world and its impact on problem definition: Artificial intelligence (AI). Designers are already using AI technology along the design process. A recent overview of relevant practical examples and application fields of AI in design thinking is provided by Verganti et al. (2020). With the increasing possibilities offered by the integration of AI and data science, various opportunities for integrating AI in the design thinking process exists. In the inspiration phase, AI can help to sort large data sets and identify patterns, possibly leading to understanding users through the pattern of their behaviour, perhaps even without contacting them individually. Consider, for example, Stitch Fix, an online personal styling

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Table 13.1

Perspective Problem Space

Framework of problem framing perspectives, exemplary literature, and practical advice Description

Exemplary references

Advice for design thinkers and novices • Start with a problem statement that is slightly

The level of

Brown (2008), Goel and Pirolli

abstractness. Is

(1992), Jaarsveld et al. (2010), Newell

the problem space

(1980), Simon and Newell (1971),

narrow or broad? Is

Welter et al. (2017)

the problem space

• Do abstraction laddering, i.e., switching back and forth between a high degree of abstraction and the most concrete problem statement

domain-dependent? Solution Focus

more abstract than most find comfortable.

seem possible.

Does the problem

Bardwell (1991), Choo (2014),

statement contain

Chrysikou and Weisberg (2005),

or hint at any

Condoor and LaVoie (2007), Crilly

solutions? Do these

(2015), Dery and Mock (1985),

hints limit the

Jansson and Smith (1991), Luchins

creative process?

(1942), Maier (1958), Maier and

• Focus on separating problem exploration from solution generation. • Do not include reference to existing or future solutions. • Question your initial problem statement with

Hoffman (1960), Maier and Solem

the 5-why method: ask yourself five times

(1962), Paton and Dorst (2011),

“why?” (consecutively).

Purcell and Gero (1996), Spradlin (2012), Stadler (2011) User Focus

Does the statement

Evans (2011), Gralla et al. (2016),

contain a specific

McGinley and Dong (2011), Salunkhe

user story? Is

and Kadam (2018)

the statement

• Focus on the user throughout the whole problem-solving process. • Note your key insights in a way that includes the concrete user or a specific user group,

written in an

their wants and needs, and their specific

empathy-enhancing

obstacles regarding the problem.

manner?

• Include the user in the problem statement in form of how might we questions. • When there is more than one interesting user group, focus on the user on whom you have the strongest influence and the chance of changing the situation seems highest. • When generating solution options afterwards, ask yourself regularly whether the generated and preferred ideas actually address the user’s problem. Originality

The rarity and

Csikszentmihalyi and Getzels (1971),

novelty. Does the

Dean et al. (2006), Isaksen et al.

problem statement

(1993), Mackworth (1965), Runco

occur frequently?

and Charles (1993), Schaffhausen and

Does the statement

Kowalewski (2016)

hold generative potential? Does it surprise?

• Include observations into your user research. • Introduce originality as an evaluation criterion in the convergent decision-making processes.

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service that uses recommendation algorithms and data science to personalize clothing based on size, budget, and style preferences provided by the customer. Next, in the ideation phase, the variety of design options and the speed with which different design options can be represented is increased through AI (Lewrick, 2022). For example, approaches such as generative design allow the exploration of much larger solution spaces via algorithms than human designers ever could. Using the popular game “battleships” as an analogy, the former CEO of Autodesk, Carl Bass, described this effect as follows: When you play battleships, you normally make point estimates, by stating a letter–number combination, e.g., B4, indicating a specific position on the agreed-upon battlefield. Stating a specific letter combination is running a local test, which provides a binary response: either a hit (i.e., at least a part of a ship is there) or a miss (i.e., there is no ship at this location). But what if, he posited, you could test all possible positions at once? In practice, these generative design tools are used in complex designs tasks to find solutions that are optimized for additive manufacturing processes by using the least amount of material. Finally, in the implementation phase, AI or digital tools might be able to select more creative or better solution options than humans would do. Against this view, one could ask whether the introduction of AI into design thinking will make designers obsolete just as photography has done this with most (realistic) painters. The ideas and examples shown above indicate that some of the design (thinking) processes can be replaced or at least supported by AI. These are especially the disembodied, cognitive processes that are necessary for effective information processing in problem solving. But complementary to these are the sense-making processes emerging from embodied practices of designers. These practices are shaped by a designer’s cultural development and education through studio culture (Kolb & Kolb, 2005) and are therefore hard to imitate. Hence, it is exactly the transition phases, especially the one between inspiration and ideation, that require too much of a designer’s sensibility to be replaced by AI. While AI might be able to gather and structure information on a user, it is (so far) not capable of replicating the experiential and cultural aspects that underlie the creative practice of designing (Rylander Eklund et al., 2021). Therefore, we share Verganti et al.’s (2020) view, who predict a change in the role of the designer in terms of a greater focus of human design activities on problem-finding and problem-formulation processes instead of the so-far focus on problem-solving processes. But what does the integration of AI in design thinking mean for students learning how to do design thinking? First, design students should be provided with more support in the transition phase of between inspiration and ideation, for example with a checklist regarding the different perspectives that can be taken as presented in this book chapter. This checklist might help them to resist converging too early, and instead reveal information on the problem that was otherwise hidden from them. This novel and unexpected information may help design students arrive at a problem formulation that they find (more) worth solving and that an algorithm may find original solutions for. Second, students could be provided with large data and algorithm sets they could tinker with. For example, you could experiment with different problem formulations and see how different algorithms arrive at different possible solution spaces. In other words, such a simulation would allow students to explore the relationship between their variations in problem statements, and the solution generating capability of an AI system. Third, students should be provided with support in the use of digital technologies during design thinking. For example, the use of augmented and virtual reality technologies can make a unique contribution when design thinkers put themselves in the user’s shoes during

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information gathering and synthesis. For example, an immersive and spatialized experience of a problem situation, especially through virtual reality technology helps to gain a deeper understanding of particularly complex problems (Earle & Leyva-de la Hiz, 2020). Through virtual experiences, designers increase their emotional engagement with their work which, in turn, increases the quality of a design (Rieuf et al., 2017). In this way, designers might arrive at more original problems that they would not otherwise have imagined. Although research on the role of AI for problem framing is still in its infancy and practical examples remain scarce, this technological development promises great potential for designers. If AI and other digital technologies become more like a force multiplier – for better or worse outcomes – then the starting direction becomes increasingly important. Because of its leverage function, problem framing, when done well, has the potential to change the world. Our four perspectives allow us to assess the general quality of the problem frames, and the outcome of problem framing activities. Future research should investigate which processes support directionally to arrive at higher value problem statements.

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Simon, H. A., & Newell, A. (1971). Human problem solving: The state of the theory in 1970. American Psychologist, 26(2), 145–159. Snowden, D. J., & Boone, M. E. (2007). A leader’s framework for decision making. Harvard Business Review, 85(11), 68. Spradlin, D. (2012). Are you solving the right problem? Most firms aren’t, and that undermines their innovation efforts. Harvard Business Review, 90(9), 84–93. Stadler, C. J. (2011). How innovation traits in members of advertising agency teams propel the creative process: The professional opinion (Published thesis). University of Oregon, Eugene, OR. Tanner, K., & Landay, J. (2019). “I Know It When I See It”: How Experts and Novices Recognize Good Design. In Design Thinking Research (pp. 249–266). Springer, Cham. Van der Bijl-Brouwer, M., & Dorst, K. (2017). Advancing the strategic impact of human-centred design. Design Studies, 53, 1–23. Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212–227. Veryzer, R. W., & Borja de Mozota, B. (2005). The impact of user-oriented design on new product development: An examination of fundamental relationships. Journal of Product Innovation Management, 22(2), 128–143. Vinsel, L. (2017). Design thinking is kind of like syphilis — it’s contagious and rots your brains. Retrieved from: https://​sts​-news​.medium​.com/​design​-thinking​-is​-kind​-of​-like​-syphilis​-its​-contagious​ -and​-rots​-your​-brains​-842ed078af29 Welter, M. M., Jaarsveld, S., & Lachmann, T. (2017). Problem space matters: The development of creativity and intelligence in primary school children. Creativity Research Journal, 29(2), 125–132. Wrigley, C., & Straker, K. (2017). Design thinking pedagogy: The educational design ladder. Innovations in Education and Teaching International, 54(4), 374–385.

14. Factor structure, validity, and reliability of an instrument for assessing design thinking Elena Novak and Ilker Soyturk INTRODUCTION A recent survey of business leaders from around the world lists creativity as one of the top skills for tomorrow’s workforce and calls for schools to invest more time and education in this skill development (Dell, 2021). Many educators and policymakers consider creativity and innovation among critical 21st-century thinking skills. Nevertheless, practising these skills in a K-12 classroom remains a challenging task due to insufficient emphasis of these skills in today’s curricula and lack of appropriate educator training and resources (Adobe, 2018). Several prominent researchers have argued that teacher preparation programmes should devote more attention to enhancing their graduates’ creativity and design thinking (DT) skills, because DT can help teachers think more creatively about diverse, multifaceted, human-centred educational problems that require non-linear, complex solutions on a day-to-day basis (Henriksen et al., 2017; Mishra & Mehta, 2017). DT originated in engineering and design professions as a framework for supporting an iterative and interactive process where designers “experiment, create and prototype models, gather feedback, and redesign” (Razzouk & Shute, 2012, p. 330). Research in this area focused primarily on the cognitive processes that novice designers apply to solve design challenges as compared with experienced designers (e.g., Nagai & Noguchi, 2003; Owen, 2007; Stempfle & Badke-Schaube, 2002). Recent research has extended the application of DT to non-design professions as an approach for developing an individual’s confidence to think and act creatively (Wrigley & Straker, 2017). As a result, DT has been closely associated with innovation and creativity, as an increasingly practised approach for improving communication, innovativeness, and success (Cross, 2007; Royalty et al., 2014). It is considered a cognitive style (Kimbell, 2011) or a third way of thinking that is different from the humanities and sciences, due to “extensive experimentation and exploration resulting from an iterative process” (Hokanson & Nyboer, 2018, p. 1). More broadly, it is grounded in a philosophy of design that is different from other formulations of philosophy, such as a philosophy of science, for example, because a philosophy of design has different aims, e.g., use creativity to create something innovative, make an intentional change (Nelson & Stolterman, 2013). A recent literature review on DT highlighted a lack of research that examines the impact of DT on organizational and individual performance as well as assessments of DT skills (Micheli et al., 2019). One of the reasons for the scarcity of research in this area is a lack of validated measures that assess DT skills. Moreover, a large body of research examined DT 246

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in engineering, design, and business professions (Magistretti et al., 2021; Nakata & Hwang, 2020; Razzouk & Shute, 2012). Considerably less research examined DT in K-12 and teacher education. To address these research gaps, this study (1) examined DT in prospective teachers using Royalty et al.’s (2014) scale that measures the outcome of teaching DT, hence referred to as the Design Thinking Scale (DTS), and (2) explored the DTS’s factor structure, validity, and reliability. The DTS is an 11 items five-point Likert scale that asks participants how confident they are that they can exhibit various behaviours related to DT. However, the scale has not been validated and its underlying factors are yet to be explored.

DESIGN THINKING MINDSET DT is viewed as both a mindset and a process or a set of tools (Groeger et al., 2019; Wrigley & Straker, 2017). As a process and a set of tools, DT has been applied to solve “wicked problems” that require designers to solve ill-defined challenges (Buchanan, 1992). A typical DT process involves empathizing with end users, defining the problem, brainstorming radical ideas, prototyping, testing to refine the solution and collect data, and assessing project work (Stanford d.school, 2010). As a mindset, DT entails the underlying values, cognition, and behaviours that influence organizational culture and people’s beliefs about innovativeness and creativity (Groeger et al., 2019). The “design state of mind” (Beverland et al., 2017) is rooted in the fixed and growth mindset dichotomy (Dweck, 2012) that explains how an individual’s ability to innovate and think creatively connects to their mindset and not necessarily to the employed DT processes and tools, because it is the mindset that is critical for achieving desired innovation objectives (Liedtka, 2011). A vast majority of DT literature has focused on the design thinking processes, tools, and methods as a way of making “the practices of designers accessible and meaningful to managers” (Johansson-Skoldberg et al., 2013, p. 128). However, equating DT with a skillset or toolset without understanding the DT mindsets (nuances of applying and practising DT) has resulted in DT often being misrepresented, demonstrating very little evidence of success (Collins, 2013; Howard et al. 2015; Nussbaum, 2011). Literature on DT as a mindset refers to attitudes, sensibilities, or stances that underpin a professional approach to design thinking. Although, DT researchers and professional designers agree that design mindset plays a critical role in DT, the literature on how design mindset is developed and applied is limited (Howard et al., 2015). Several attempts have been made to conceptualize attributes of a DT mindset using a variety of research approaches. For instance, Carlgren et al. (2016) identified three major dimensions of DT (i.e., principles/mindsets, practices, and techniques) based on 61 interviews with companies that have extensive experience applying DT. Russo (2016) conducted a comprehensive analysis of 70 articles to investigate DT characteristics. The analysis revealed 17 commonly cited characteristics of DT that were broadly classified as a mindset, process, method, and attitude. Schweitzer et al. (2016) identified 11 characteristics of a DT mindset based on interviews with innovation managers: (1) empathetic towards people’s needs and context; (2) collaboratively geared and embracing diversity; (3) inquisitive and open to new perspectives and learning; (4) mindful of process and thinking modes; (5) experiential intelligence; (6) taking action deliberately and overtly; (7) consciously creative; (8) accepting of uncertainty and open to risk; (9) modelling behaviour; (10) desire and determination to make a difference; and

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Table 14.1

Principles of design thinking education

Human-centred

Design thinking is a human-centred process. The focus is on making people the source of inspiration and direction for solving design challenges.

Mindful of process

A critical mindset in design thinking is being “mindful of process” or having metacognitive awareness.

Empathy

Empathy is the intellectual identification with or vicarious experiencing of the feelings, thoughts or attitudes of others. Empathy develops through a process “needfinding” in which one focuses on discovering peoples’ explicit and implicit needs.

Culture of

The mindset of creating and maintaining a “culture of prototyping” focuses on being highly experimental,

prototyping

building to think, and engaging people with artefacts.

Show don’t tell

As a mindset, “show don’t tell” takes traditional visualization one step further, as it includes sketching and traditional prototyping, digital communication and storytelling.

Bias toward action

“Bias toward action” is a focus on action-oriented behaviour rather than discussion-based work. A “bias toward action” mindset utilizes all modalities of learning.

Radical collaboration

This mindset is built upon the idea that radically diverse multidisciplinary teams will lead to greater innovations than teams that come from the same discipline. Examining and confronting team dynamics is an essential component.

Source: Rauth et al., 2010.

(11) critically questioning. Stanford University’s Design School Bootcamp Bootleg (Stanford d.school, 2010) mentions the following seven mindsets: human-centred design, being mindful of the process, empathy, a culture of prototyping, show don’t tell, bias toward action, and radical collaboration. These mindsets were adapted from Rauth et al.’s (2010) seven basic principles of design thinking education (Table 14.1). However, there is no information about how these principles were developed. Overall, despite the variety of different research approaches, settings, and target audiences used to study DT characteristics, the literature provides quite consistent findings regarding the commonly cited characteristics of a DT mindset. These characteristics reveal DT scholars’ efforts to translate cognitive processes involved in DT into observable behaviours.

MODELS FOR TEACHING DESIGN THINKING The recent increased interest in DT as an approach for supporting innovation and success in business and industry has drawn attention to DT from non-design communities. For instance, the d.school at Stanford University and the d.school at the Hasso Plattner Institute of Germany are among the leading institutions in the field that work with students from all disciplines to enhance their creativity and innovation through a design process (Royalty et al., 2014). They use a design thinking framework to increase students’ creative confidence (Kelley & Kelley, 2013), which is believed can support students’ ability to act and think creatively. The concept of creative confidence is rooted in Bandura’s (1994) work on self-efficacy. Self-efficacy reflects an individual’s belief in their ability to succeed in a particular domain (Bandura, 1994). In DT education, the domain can be viewed as creative problem-solving. According to Bandura (1982), self-efficacy is part of a broader construct, agency – a means by which “people can effect change in themselves and their situations through their own efforts” (p. 1175). Agency involves beliefs about the world, cognitive, behavioural and social states, physical settings, context and other factors. Royalty et al. (2014) view creative agency as

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“individuals’ capacity to effect change in themselves and their situations to support successful creative problem-solving” (p. 82). Several models and frameworks were proposed for teaching DT. For instance, Rauth et al.’s (2010) model for teaching creative confidence (Figure 14.1) postulates that repeated practice of DT fosters the development of design mindsets and processes. Students become more confident in their creative problem-solving and ability to act with creative confidence: Different competencies are developed, such as prototyping skills, emotional skills, capability of adopting perspectives, empathy and a certain mindset. The development of these creative competencies culminates in the acquisition of creative confidence, which assures the students of their own ability of acting and thinking creatively. (Rauth et al., 2010, p. 7)

The model suggests a hierarchy of skills and competencies as a series of steps that lead to the development of creative confidence, which is the goal of DT education.

Source: Adapted from Rauth et al., 2010.

Figure 14.1

Model of creative confidence

In contrast to Rauth et al.’s (2010) hierarchical approach, Nelson and Stolterman (2013) used a quadrant with dichotomies to represent skills and competencies that are foundational to design learning. They view design learning across four domains: (a) design character, (b) design thinking, (c) design knowing, and (d) design action or praxis (Figure 14.2a). These four domains correspond to the four design competency sets that are essential in the process of becoming a designer: (i) mindset, (ii) knowledge set, (iii) skillset, and (iv) toolset (Figure 14.2b). Figure 14.2 demonstrates the connections between the competency sets and design domains. Design character and design thinking are manifested through mindsets and

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knowledge sets, while design knowing and design action are manifested through skillsets and toolsets. Nelson and Stolterman (2013) argued that design knowledge and design knowing cannot be captured using typical knowledge hierarchies that represent knowledge as an outcome of training or learning (e.g., Bloom’s (1956) taxonomy of learning, Russell Ackoff’s (1989) knowledge hierarchy), because “wise action and not just evaluated understanding is a demonstration of design wisdom” (Nelson & Stolterman, 2013, p. 229). They view the outcome of design education as being “a process of managing competency sets that are interrelated among the quadrants formed by the crossing axis of familiar dichotomies such as concrete reality and abstract thinking, and the individual contrasted to social collectives” (Nelson & Stolterman, 2013, pp. 229–230; Figure 14.3). Nelson and Stolterman’s (2013) hierarchy of design-learning outcomes presents the following progression: design capacity (facts, skills, understandings), confidence (do/act – create change), capability (make/produce – excellence), competence (to learn – to not know, to know), courage (creative and innovative), connection (interrelated and interrelating), and character (personal wholeness). Thus, for instance, design capacity is valuable only if a designer has the confidence to act. The competence to learn in designing matters only if the designer has the courage to be innovative and creative. This hierarchy suggests that DT mindset is foundational for learning design skills, facts, and tools.

Source: Adapted from Nelson and Stolterman, 2013.

Figure 14.2

Interconnections of DT domains and sets

Despite these differences, Rauth et al.’s (2010) and Nelson and Stolterman’s (2013) models share many similarities. For instance, both models identify mindset, DT skills, and methods/ tools among the essential design competency sets. In addition, both models view mindset as a more complex competency set than the skillset in terms of knowledge acquisition, as mindset is inherently more abstract than a concrete skillset. At the same time, Nelson and Stolterman (2013) cautioned that their schemas (Figures 14.2 and 14.3) are not enough for explaining

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Source: Adapted from Nelson and Stolterman, 2013.

Figure 14.3

Mediation of DT sets and the process of establishing and maintaining design competency sets

design expertise because a designer’s knowledge is influenced by numerous external factors such as predispositions, aptitude, environment, societal norms, laws, clients, and stakeholders. More recently, Wrigley and Straker (2017) proposed the Educational Design Ladder to guide the learning process of Design Thinking in Design and Business fields. The Educational Design Ladder (Figure 14.4) was developed based on a review of 51 courses on Design Thinking as well as various academic programmes centred on creativity and innovation from 28 universities around the world. The analysis of the content being taught in these courses as well as how it was taught in terms of assessments and instructional approaches revealed the following five themes: “(i) theories, methods and philosophies, (ii) product focus, (iii) design management, (iv) business management, and (v) professional development” (p. 377). These themes represent a progression in Design Thinking knowledge and therefore can be viewed as “pedagogical stages in the development of Design Thinking” (p. 379). Wrigley and Straker (2017) combined the identified themes with Biggs’ SOLO taxonomy (the Structure of the Observed Learning Outcome; Biggs, 1996) to provide a hierarchical framework for designing

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a curriculum that integrates Design Thinking and Business across the five stages of DT. The Educational Design Ladder model offers an approach for developing a multi-disciplinary curriculum in DT. However, it is unclear to which extent the model can be applied in non-business fields such as education, for example.

Source: Adapted from Wrigley and Straker, 2017.

Figure 14.4

Educational design ladder pedagogy

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MEASURING OUTCOMES OF TEACHING DESIGN THINKING Despite the growing popularity of integrating DT in non-design fields, little is known about how the acquisition of DT skills can be assessed (Micheli et al., 2019). Most of the research in this area is primarily qualitative. An initial quantitative attempt to measure the outcomes of teaching DT was undertaken by Royalty et al. (2014) who conducted a series of surveys and interviews to identify key DT competencies in successful d.school alumni (The Hasso Plattner Institute of Design at Stanford University). These studies led to the development of the Design Thinking Scale (DTS), validation of which is the focus of the present study. The DTS includes 11 DT competencies in the area of creative problem-solving such as creative idea sourcing, comfort with ambiguity, openness, building creating environments, anti-perfectionism, prototyping, perseverance after failure, creativity facilitation in others, mastery and knowledge of creative process, and successful problem-solving. Our examination of the literature on a DT mindset suggests that these DT competencies are reminiscent of those identified in the extant literature (Carlgren et al., 2016; Russo, 2016; Schweitzer et al., 2016), thus suggesting the DTS’s DT competencies can be found in various settings, audiences, and domains of practice.

RESEARCH GOALS We collected responses of prospective teachers to a battery of assessments of DT, creativity, and innovation to examine the factor structure, validity, and reliability of the DTS. Specifically, two studies were conducted to pursue the following research goals: 1. 2. 3. 4.

Examine the DTS’ factor structure using Exploratory Factor Analysis (EFA; Study I). Examine the DTS’ internal consistency and reliability (Study I&II). Confirm the EFA results using Confirmatory Factor Analyses (CFA; Study II). Examine aspects of the DTS’ validity: i. Convergent validity was examined by correlating DT with Creative Achievement (Carson et al., 2005) and Innovation scores (Chen et al., 2017). Positive associations of DT with Creative Achievement and Innovation scores were hypothesized (Study II). ii. Discriminant validity: One of the common variables that may threaten to confound self-reporting measures such as DT is social desirability bias, i.e., participants’ socially desirable responses to questions. To examine the extent to which participants are likely to provide socially desirable responses to the DTS items, Marlowe–Crowne Social Desirability scores (Crowne & Marlowe, 1960) were correlated with DT scores (Study II).

STUDY I: EXPLORATORY FACTOR ANALYSIS AND INTERNAL CONSISTENCY OF THE DTS The goal of the first study was to examine the DTS’s factor structure using EFA. In addition, it examined the DTS’s internal consistency.

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Method Participants and procedures Undergraduate students majoring in education (N = 191; 185 females, six males; M age = 21.75, SD = 2.26) from a large mid-western university in the United States participated in the study. Participants were recruited from undergraduate Science Methods and Educational Technology courses. They completed a background questionnaire (age, gender, educational background, etc.) and the DTS online for research credit participation in their course. Design thinking scale (Royalty et al., 2014) The original DTS includes 11 five-point Likert-type items that gauge respondents’ beliefs about their ability to act with creative confidence. The response categories include: “Not at all confident” (1), “A little confident” (2), “Moderately confident” (3), “Very confident” (4), and “Completely confident” (5). Before administering the instrument to the study participants, we conducted several informal interviews with prospective teachers and their course instructors to get a better understanding of how they interpret the items and the scale relevancy to teacher education. Based on the interviews, Item 3 wording (“Change the definition of a problem you are working on”) was changed to “Consider a problem from different perspectives”. DT scores were calculated by averaging the 11 items of the scale. The minimum possible score was 1 and the maximum possible score was 5. Table 14.2 presents descriptive statistics of the 11 DTS items. Data analysis The factor structure of the DTS was investigated using EFA, which helps determine how items are related to constructs if a theory model is not provided (Brown, 2006; Thompson, 2004). Principal Axis Factoring (PAF; Schumacker & Lomax, 2010) along with Direct Oblimin (DO) rotation was used in EFA. Internal Consistency of the factors was measured using Coefficient Alphas for each factor and the total scale. Results Factor structure After screening the data and verifying relevant assumptions, the potential factor structure of the DTS was examined using EFA with PAF extraction with DO rotation. The Kaiser’s criterion for the eigenvalue greater-than-one rule and the scree plot (Figure 14.5) suggested a two-factor solution for the DTS (Table 14.3). The first factor included five items (#4, #8, #9, #10, #11) that can be viewed as Creative Agency – an “individual’s capacity to effect change in themselves and their situations to support successful creative problem-solving” (Royalty et al., 2014, p. 82). The second factor included five items (#2, #3, #5, #6, #7) that describe Design Dispositions such as being mindful of the design process, a culture of prototyping, action-oriented behaviours, and collaboration (Rauth et al., 2010). Item #1 was excluded because it did not fit the meaning of Factor 2. These factor loadings are consistent with the theoretical perspectives underlying the DT construct (Rauth et al., 2010; Royalty et al., 2019).

Factor structure, validity, and reliability of an instrument for assessing design thinking

Table 14.2

255

Descriptive statistics for the design thinking scale items (N = 186)

How confident are you that you could…

M

SD

1. Find sources of creative inspiration not obviously related to a given problem.

2.89

0.935

2. Effectively work on a problem that does not have an obvious solution.

2.89

0.908

3. Consider a problem from different perspectives.

3.68

0.826

4. Shape or change your external environment to help you be more creative.

3.43

0.899

5. Share your work with others before it is finished.

3.66

1.024

6. Try an approach to a problem that may not be the final or best solution.

3.32

0.942

7. Continue to work on a problem after experiencing a significant failure.

3.30

1.052

8. Help others be more creative.

3.86

0.945

9. Identify and implement ways to enhance your own creativity.

3.60

0.966

10. Explicitly define or describe your creative process.

3.23

1.025

11. Solve problems in ways that others would consider creative.

3.33

1.022

Note: SD = Standard deviation.

Table 14.3

Final exploratory factor analysis for the DTS with factor loadings (10 items) Items

Factors 1

2

9.

Identify and implement ways to enhance your own creativity.

0.948

11.

Solve problems in ways that others would consider creative.

0.820

8.

Help others be more creative.

0.780

10.

Explicitly define or describe your creative process.

0.726

4.

Shape or change your external environment to help you be more creative.

0.581

6.

Try an approach to a problem that may not be the final or best solution.

0.878

5.

Share your work with others before it is finished.

0.690

7.

Continue to work on a problem after experiencing a significant failure.

0.559

2.

Effectively work on a problem that does not have an obvious solution.

0.402

3.

Consider a problem from different perspectives.

0.355

0.357

Note. Factor 1 = Creative Agency; Factor 2 = Design Dispositions

Internal consistency The DTS and its two sub-scales had a high level of internal consistency (Cronbach’s αCreativeAgency = 0.884; Cronbach’s αDesignDispositions = 0.807, Cronbach’s αDTS = 0.882). Discussion The DTS factor structure suggests that DT skills as assessed by the DTS can be viewed along two dimensions: Design Dispositions and Creativity Agency. Using an EFA, five DTS items

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Figure 14.5

Research handbook on design thinking

Scree plot for the DTS

are loaded on the Creative Agency factor and the other five DTS items loaded on the Design Dispositions factor. The Design Dispositions subscale includes items that describe how people approach work on design-led innovations. These design dispositions reflect the basic principles of DT education (Rauth et al., 2010) and are similar to Schweitzer et al.’s (2016) attributes of a DT mindset. For example, Item 10 (Share your work with others before it is finished) implies working with others and gathering feedback, which is similar to Schweitzer et al.’s (2016) DT mindsets of empathy, collaboration, and openness to new perspectives. Item 6 (Try an approach to a problem that may not be the final or best solution) describes what Schweitzer et al. (2016) define as an experiential intelligence mindset – a preference for experimenting with different ideas and transforming “tangible ideas into tangible outcomes” (Schweitzer et al., 2016, p. 79). Item 7 (Continue to work on a problem after experiencing a significant failure) and Item 2 (Effectively work on a problem that does not have an obvious solution) reflect a mindset of accepting uncertainty and being open to risk (Schweitzer et al., 2016) as well as a mindset of a culture of prototyping and action-oriented behaviour (Rauth et al., 2010). Designers often need to create solutions for a future that is different from the present. Tasks like these require an ability to embrace ambiguity and the risk of failure.

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The DTS’s Creative Agency dimension focuses on the conscious creative mindset that highlights an understanding of a creative process and the work one does to produce innovative ideas and solutions (Schweitzer et al., 2016). In order for people to have a creative agency, they need to understand what it takes to be creative, as well as conditions and processes that enable creativity in themselves and others (Kelley & Kelley, 2013; Royalty et al., 2014; Schweitzer et al., 2016). Sample items in this subscale include “Identify and implement ways to enhance your own creativity”, “Help others be more creative”, “Explicitly define or describe your creative process,” and “Shape or change your external environment to help you be more creative”.

STUDY II: CONFIRMATORY FACTOR ANALYSIS, VALIDITY, AND TEST–RETEST RELIABILITY OF THE DTS The goal of Study II was threefold. First, we investigated whether a confirmatory factor analysis (CFA) supported the two-factor solution identified in Study I. Second, we examined the DTS’s convergent validity by correlating DT with Creative Achievement (Carson et al., 2005) and Innovation scores (Chen et al., 2017), and the DTS’s discriminant validity by correlating DT with Marlowe–Crowne Social Desirability scores (Crowne & Marlowe, 1960). Third, we explored the DTS’s test–retest reliability. Method Participants and procedures Education majors (N = 179; 154 females, 25 males; M age = 20.94, SD = 3.30) enrolled in an undergraduate Educational Technology course in the same mid-western university in the United States participated in Study II that took place in the following semester after Study I. Participants completed a series of online questionnaires: (1) background questionnaire, (2) DTS (Royalty et al., 2014), (3) Creative Achievement (Carson et al., 2005), (4) Innovation Stance (Chen et al., 2017), and (5) Marlowe–Crowne Social Desirability Scale (MCSD; Crowne & Marlowe, 1960). Students received course credit for participating in the study. Two weeks later, a sub-sample of 27 students (one male; 26 females) completed the DTS again (post-test) for additional course credit. The pre- and post-test took place before and after one of the course projects that required students to create a Scratch story – a web-based tool for teaching coding and computational skills in K-12 education. Instruments Design Thinking Scale (Royalty et al., 2014): Ten DTS items that were retained after conducting the EFA in Study I were used to assess participants’ DT. DT scores were calculated by averaging the 10 items of the scale. The minimum possible score was 1 and the maximum possible score was 5. Creative Achievement questionnaire (Carson et al., 2005) is a self-report measure that assesses creative personality across 10 artistic and scientific domains. The participants were asked to mark all items describing their accomplishments. The items in each domain are weighted from 0 to 7. A total creative achievement score was calculated by summing all items (minimum score = 0, maximum score = 47).

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Innovation Stance (Chen et al., 2017) includes 12 four-point Likert-style items that ask participants to rate their comfort taking risks, appreciation of new ideas, entrepreneurial spirit, and desire to do something different or unique. Innovation scores were calculated by averaging the 12 items (minimum score = 0, maximum score = 4). Marlowe–Crowne Social Desirability Scale (MCSD; Crowne & Marlowe, 1960). A short version of the MCSD (Reynolds, 1982) was used in the present study. It included 13 items that assessed participants’ tendency to tailor their responses to appear socially acceptable. The items are keyed True (Coded 1) or False (Coded 2) to describe either very socially desirable but untrue for most people or very socially undesirable but very common behaviours. The scale includes five reversely coded items (i.e., Items 5, 7, 9, 10, and 13); the range of total scores is between 13 and 26. Data analysis CFA was conducted to examine whether the hypothesized model of factors in EFA was related to the set of items, and whether the sample confirms the model (Schumacker & Lomax, 2010). Chi-square, root mean square error of approximation (RMSEA) of 0.06 or less, standardized root mean residual (SRMR) of 0.08 or less, and comparative fit index (CFI) and Tucker–Lewis index (TLI) greater than 0.90 and 0.95, respectively, were used for model testing (Bentler, 1990; Hu & Bentler, 1999). A chi-square difference test was used for comparison across models. Mplus 7.0 was used for CFA. The associations between the two DTS sub-scales (i.e., Creative Agency and Design Dispositions), creative achievement, innovation, and Marlowe–Crowne Social Desirability scores were examined using a Pearson’s correlation coefficient. Results Confirmatory Factor Analysis (CFA) The two-factor structure of the DTS was confirmed using CFA with the Study II data. CFA was performed using a diagonally weighted least square estimator. The results (see Table 14.4 for more detail) indicated that the two-factor solution had a close fit. All standardized factor loadings were statistically significant (p < 0.01) ranging from 0.446 to 0.830. Additionally, the correlation between the factors was 0.71. Standardized factor loadings for the two DTS factors (i.e., Creative Agency and Design Dispositions) ranged from 0.446 to 0.633 and from 0.587 to 0.83, respectively. The results suggested adding error covariances between Item 5 and Item 6, which resulted in the greatest decrease in χ2. A further examination of these two items in wording and context showed that these items belong to the same latent factor and share some commonality. After adding error covariances between Item 5 and Item 6, the model significantly improved (Δχ2= 7.93, Δdf = 1, p