Design Thinking: Creativity, Collaboration and Culture [1st ed.] 9783030565572, 9783030565589

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Ju Hyun Lee Michael J. Ostwald Ning Gu

Design Thinking: Creativity, Collaboration and Culture

Design Thinking: Creativity, Collaboration and Culture

Ju Hyun Lee Michael J. Ostwald Ning Gu •

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Design Thinking: Creativity, Collaboration and Culture

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Ju Hyun Lee UNSW Built Environment University of New South Wales Sydney, NSW, Australia

Michael J. Ostwald UNSW Built Environment University of New South Wales Sydney, NSW, Australia

Ning Gu UniSA Creative University of South Australia Adelaide, SA, Australia

ISBN 978-3-030-56557-2 ISBN 978-3-030-56558-9 https://doi.org/10.1007/978-3-030-56558-9

(eBook)

© Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

In its simplest form, “design thinking” refers to the reasoning processes that occur during the act of creating a product. It encapsulates the cognitive strategies and behaviours of people who are engaged in developing innovative solutions to problems, or identifying new opportunities in a complex marketplace or ecosystem. Importantly, design thinking offers a valuable counterpoint to “scientific thinking”. The former is typically characterised as user-centred, inventive and even productively disruptive, while the latter is regarded as methodical, logical and reductive. Despite the simplicity of this characterisation, design thinking is fundamentally concerned with developing creative or original responses. At a time when the world’s headlines are dominated by grand challenges such as ecological dilemmas, economic crises and resource shortages, the capacity to develop innovative solutions to problems and sometimes even re-define the problems has never been so critical. Because design thinking is primarily used to solve “real-world”, “ill-defined” or “wicked” problems, it rarely follows a linear path to its destination. Instead, the cognitive paths taken by designers are more likely to include spiralling off-ramps, intricate intersections, elaborate cul-de-sacs and opportunistic shortcuts. Novice designers, and those from disciplines more accustomed to linear processes, typically struggle with complex, real-world problems. Not only are there a myriad of factors to consider, but the designer may also face the prospect that an optimal solution does not exist. In a sense, the labyrinthine pathways that confront the novice designer not only do not contain clear signposts to freeways; they sometimes don’t even lead to the desired destination. In contrast, expert designers develop cognitive strategies to assist them to navigate between different starting points and alternative destinations. They critically review and repeatedly question both the purpose of their travels and possible routes to its conclusion. Expert designers expect to navigate winding paths and discover dead ends while looking for hidden tunnels or fortuitous bridges to their journey’s end. They combine convergent and divergent thinking along with deductive and inductive reasoning to propose creative solutions which satisfy their goals.

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Preface

Designers, whether novice or expert, are not without resources to assist them to navigate the maze of possibilities that is the design process. In the context of our book, four of the most powerful resources for design thinking are computational platforms, creative thinking, collaborative processes and diverse teams. The first of these encompasses advanced computational systems and digital platforms, which is a new addition supporting design thinking with the recent advancements of Information and Communication Technology (ICT). These digital design environments support rapid modelling, testing and evaluation of alternative options. They also enable the development of enhanced cognitive strategies and skills. The second resource is arguably the reason design thinking is so sought after. Certain cognitive strategies in design have been linked to creative responses or solutions. Creativity is the capacity to find alternative, novel or surprising paths to a destination. The third resource arises from the realisation that designers rarely work alone and is currently also associated with the ways computational platforms can be used to support collaboration and to leverage collective intelligence in the design process. The cognitive operations of the crowd are increasingly being used to develop collaborative solutions to problems. The final resource reflects on the fact that most designers operate in teams, and some of the most effective teams are diverse ones. The utilisation of digital tools and networked technologies has made teamwork across geographical and cultural boundaries occurred more frequently and readily. Whereas a cognitively homogeneous team will tend to blaze a pre-determined trail to a finite destination, a multi-cultural team is more likely to assess unconventional paths and question collective assumptions. It is not surprising then that diverse teams are often linked to more creative solutions. There are, however, challenges and opportunities for design thinking when different cultures and languages are combined in the design process. Design is not a universal language and design thinking both shapes, and is shaped by, the language used to communicate it. Collectively, these themes in design thinking—computational, creative, collaborative and cultural—define the scope of this book. The first theme sets the context for the book, and the latter three shape its content. The focus of this book is on design thinking in digital or computational environments. Within this general context, the book examines three themes: creativity, collaboration and culture. The catalyst for Part I in this book is the claim that certain cognitive strategies or approaches to design thinking are more likely to result in creative outcomes. Part I uses evidence derived from empirical studies to develop an understanding of the way computational environments shape creative design thinking and may lead to more inventive outcomes. Part II considers the cognitive dimensions of the operations of design teams, crowds and collectives. Collaborative design thinking has received relatively limited attention in past research, and there are even fewer frameworks available to understand the way collective intelligence might operate in design. Drawing on a combination of a critical literature review, conceptual frameworks and empirical evidence, Part II expands our knowledge of design cognition in a collaborative context. Finally, Part III delves into territory that has only rarely been considered by design researchers, the impact of culture on design

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thinking. While, in a pragmatic sense, linguistic and cultural differences may disrupt communication in a design team, at a more fundamental level they may also shape the way people think. Part III contains some of the most advanced research undertaken into this last dimension of design thinking. This book has been written for designers, students and scholars who are interested in understanding the cognitive operations that occur in design thinking. The primary methods used for this purpose are explained in Chap. 1, and experimental results are presented throughout the book to support discussions about and reviews of past research. The authors of this book have backgrounds in architecture, interior design and urban design, and all three have expertise in computational design. We have separately completed major design projects in different sectors of the economy and in multiple countries. Significantly, we have different native languages (Korean, English and Chinese, respectively) and have also worked throughout our careers in multi-lingual and multi-cultural teams. These factors have shaped our approaches to several themes in this book and our interests in the role of collaboration and culture in design thinking. An additional factor shaping the content of this book is that we approach design thinking from a dual perspective: First, as qualified, experienced designers and second, as researchers who have conducted extensive formal studies in the field. This combination is not as common as readers might think. Many designers resist the idea that their cognitive processes can be studied, insights revealed and patterns identified in experimental settings. Conversely, a surprising number of researchers in the field of design thinking have only limited experience designing. This dual perspective—designers and researchers—shapes the way we interpret several themes in this book. It helps us to “reality test” our own experimental results, and to be productively sceptical about claims and theories. It means, for example, that we provide several alternative definitions of key concepts, rather than advocating for a singular or emphatic one. We also draw from and acknowledge past research in the field, and in other pertinent fields, even though we will not always agree with it. Finally, the new empirical research presented in this book, along with much of the literature reviewed for it, was developed from studies in the traditional design fields of architecture, industrial design and interior design. These are core disciplinary backgrounds in design cognition research. Nevertheless, the lessons and findings of the research in this book are applicable to design thinking across other fields including engineering, business, management, science and the arts. This is a key principle of the new discipline of design thinking that the lessons learnt in the traditional design domains offer significant opportunities for the world. Or, to return to the analogy used earlier in this preface, design thinking can open up new pathways to innovation, regardless of the disciplines or fields involved. Sydney, Australia Sydney, Australia Adelaide, Australia

Ju Hyun Lee Michael J. Ostwald Ning Gu

Acknowledgements

This book has evolved out of research undertaken over the last decade by the authors, and it has been supported by many people whose contributions we wish to acknowledge. We would especially like to thank our colleagues and research assistants, Ji Suk Lee, Maria Roberts, Chris Burns, Katie Cadman, Dr. Sue Sherratt, A/Prof. Julie Jupp, Prof. Richard Tucker, Prof. Anthony P. Williams, Prof. Jane Burry, Prof. Mary L. Maher, Prof. Marc A. Schnabel, Prof. Mark Taylor, Prof. Mi Jeong Kim and Prof. Robin Drogemuller. The ideas contained in this volume were also shaped by the generous responses of the editors and anonymous referees of the following journals, conferences and book projects: International Journal of Technology and Design Education, Architectural Science Review, International Journal of Design Creativity and Innovation, International Journal of Architectural Computing, Collaboration and Student Engagement in Design Education, Partners for Preservation, Design Computing and Cognition (DCC), Computer-Aided Architectural Design Futures (CAADFutures), Design Research Society (DRS), ACM Conference on Creativity and Cognition, Architectural Science Association (ASA/ANZAScA) and Computer-Aided Architectural Design Research in Asia (CAADRIA). Some sections of this book are derived from data and manuscripts that were previously published and have been substantially revised, expanded or updated for the present work. Specifically, Chap. 2 includes a revised section from two publications: Lee, Ju Hyun, Ning Gu, and Anthony P. Williams. 2014. Parametric design strategies for the generation of creative designs. International Journal of Architectural Computing 12 (3); Lee, Ju Hyun, Ning Gu, and Michael J. Ostwald. 2015. Creativity and parametric design? Comparing designer’s cognitive approaches with assessed levels of creativity. International Journal of Design Creativity and Innovation 3 (2). Chapter 4 draws on material published in Lee, Ju Hyun, and Michael J. Ostwald. 2019. Measuring cognitive complexity in parametric design. International Journal of Design Creativity and Innovation 7 (3). Chapter 6 revises a model and builds on materials published in Lee, Ju Hyun, and Ning Gu. 2019. Historical Building Information Model (BIM)+: sharing, preserving and reusing architectural design data. In Partners for Preservation: Advancing Digital ix

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Acknowledgements

Preservation through Cross-community Collaboration, ed. Jeanne Kramer-Smyth. London: Facet Publishing. Chapter 8 includes revised sections and extended ideas originally presented in Lee, Ju Hyun, Michael J. Ostwald, and Ning Gu. 2016. The language of design: Spatial cognition and spatial language in parametric design. International Journal of Architectural Computing 14 (3). Chapter 9 revises and expands material published in two publications: Lee, Ju Hyun, Ning Gu, and Michael J. Ostwald. 2019. Cognitive and linguistic differences in architectural design. Architectural Science Review 62 (3); Gu, Ning, Michael J. Ostwald, Ju Hyun Lee, and Maria Roberts. 2019. Developing Pedagogical Solutions to Linguistic and Cultural Barriers in Design Education Supporting Asian Architecture Students. Canberra, Australia: Australian Government Department of Education and Training, Skills and Employment. We gratefully acknowledge the support of the Australian Government Department of Education and Training, Skills and Employment, UNSW Scientia Fellowship Program, University of New South Wales, Sydney, and University of South Australia. All of the design experiments reported in this book were the subject of formal Human Research Ethics Approvals: No. H-20110313 and No. H-20160219 (University of Newcastle).

Contents

1

Introduction: Exploring Design Thinking . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Defining Design Thinking . . . . . . . . . 1.1.2 Research into Design Thinking . . . . . . 1.1.3 The Digital Anthropocene . . . . . . . . . 1.2 Creativity, Collaboration and Culture . . . . . . . 1.2.1 Creativity . . . . . . . . . . . . . . . . . . . . . 1.2.2 Collaboration . . . . . . . . . . . . . . . . . . . 1.2.3 Culture . . . . . . . . . . . . . . . . . . . . . . . 1.3 Research Method I: Protocol Analysis . . . . . . 1.3.1 Studying the Design Process . . . . . . . . 1.3.2 Protocol Analysis . . . . . . . . . . . . . . . . 1.3.3 Coding Scheme . . . . . . . . . . . . . . . . . 1.4 Research Method II: Expert Panel Assessment 1.4.1 Design Product . . . . . . . . . . . . . . . . . 1.4.2 Evaluation Criteria . . . . . . . . . . . . . . . 1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part I 2

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Creativity

Design Strategies and Creativity . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . 2.2 Study I . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Research Procedure . . . . . . . . . 2.2.2 Coding Results . . . . . . . . . . . . 2.2.3 Sketch-Based Design Strategies 2.3 Study II . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Parameter and Rule . . . . . . . . . 2.3.2 Research Method . . . . . . . . . . .

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2.3.3 Results . . 2.3.4 Parametric 2.4 Design Strategies 2.5 Conclusion . . . . . References . . . . . . . . . .

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Creative Micro-processes in Parametric Design . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Four Parametric Design Activities . . . . . . . . . . 3.2.1 Changing Parameters . . . . . . . . . . . . . . 3.2.2 Perceiving Geometries . . . . . . . . . . . . . 3.2.3 Introducing Algorithmic Ideas . . . . . . . . 3.2.4 Evaluating Geometries . . . . . . . . . . . . . 3.3 Creative Micro-processes in Parametric Design . 3.3.1 Patterns of Parametric Design Activities 3.3.2 Creative Micro-processes . . . . . . . . . . . 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4

Measuring Cognitive Complexity . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Measuring Cognitive Complexity . . . . . . . . . . . . . . . . . . 4.4 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Entropy Measure . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 A Coding System for Content Complexity . . . . . 4.4.3 Linkography Measures for Structural Complexity . 4.5 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Content Complexity . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Structural Complexity . . . . . . . . . . . . . . . . . . . . 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part II 5

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Collaboration

Collaborative Design: Team Cognition and Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Cognition and Representation for Teamwork . 5.2.1 Team Cognition . . . . . . . . . . . . . . . . . 5.2.2 Cognitive Representation . . . . . . . . . . 5.2.3 Design Team Cognition (DTC) Model 5.3 Design Cognition . . . . . . . . . . . . . . . . . . . . . 5.3.1 Problem and Solution Spaces . . . . . . . 5.3.2 Geometric and Algorithmic Modes . . .

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5.4

Design Communication . . . 5.4.1 Design Information 5.4.2 Spatial Language . . 5.5 Conclusion . . . . . . . . . . . . References . . . . . . . . . . . . . . . . .

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6

Design Thinking and Building Information Modelling . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Digital Design Collaboration and BIM . . . . . . . . . . . . . 6.2.1 Design Collaboration Through Digital Platforms 6.2.2 BIM Collaboration Essentials . . . . . . . . . . . . . . 6.3 An Advanced BIM Knowledge Framework . . . . . . . . . 6.3.1 A BIM Knowledge Framework . . . . . . . . . . . . . 6.3.2 Six Phases of the BIM Knowledge Framework . 6.3.3 Five Modules of the BIM Platform . . . . . . . . . . 6.4 BIM Futures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7

Design Thinking and the Digital Ecosystem . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 A Digital Design Thinking (DDT) Framework . . . . . . . 7.2.1 Types of DDT Processes . . . . . . . . . . . . . . . . . 7.2.2 Key Functionalities of Interactive and Collective Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Detailed Examples and Analysis . . . . . . . . . . . . . . . . . 7.3.1 Mobile Platforms . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Situated Platforms . . . . . . . . . . . . . . . . . . . . . . 7.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Part III 8

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Culture

Design Thinking Across Borders . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . 8.2 Research Procedure . . . . . . . . . . . . . . . 8.3 Design Cognition . . . . . . . . . . . . . . . . 8.3.1 Coding Results . . . . . . . . . . . . 8.3.2 Linkographic Analysis . . . . . . . 8.4 Cognitive and Syntactical Complexities 8.4.1 Cognitive Complexity . . . . . . . 8.4.2 Syntactical Complexity . . . . . . .

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8.5 Spatial Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 8.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 9

The Language of Design Thinking . 9.1 Introduction . . . . . . . . . . . . . . 9.2 Design Thinking and Language 9.3 Methodological Procedure . . . . 9.3.1 Design Experiment . . . . 9.3.2 Analysis Procedure . . . 9.4 Results . . . . . . . . . . . . . . . . . . 9.4.1 Coding Results . . . . . . 9.4.2 Correlation . . . . . . . . . 9.4.3 Comparison . . . . . . . . . 9.5 Discussion . . . . . . . . . . . . . . . 9.6 Conclusion . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . .

Part IV

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Conclusion

10 Conclusion: Three C’s of Design Thinking 10.1 Introduction . . . . . . . . . . . . . . . . . . . 10.2 Creative Design Thinking . . . . . . . . . 10.3 Collaborative Design Thinking . . . . . 10.4 Cultural Design Thinking . . . . . . . . . 10.5 Conclusion . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Authors

Dr. Ju Hyun Lee is a Scientia Fellow and Senior Lecturer in the Faculty of the Built Environment, UNSW Sydney. He has made significant contributions to the fields of architectural and computational design, since his first lecturing position (Digital Design) in 2003, South Korea. He was invited to become a visiting academic at the University of Newcastle in 2011, where he as a senior lecturer completed 5 years of post-doctoral studies in design computing and cognition. He was a Senior Research Fellow at the University of South Australia in 2018. He is co-author with Michael J. Ostwald of Grammatical and Syntactical Approaches in Architecture (IGI Global 2020). He led two externally funded research projects, supported by OLT and DFAT, in Australia. Prof. Michael J. Ostwald is Associate Dean of Research and Professor of Architecture at UNSW, Sydney. He has a Ph.D. in architectural history and theory and a D.Sc. in design mathematics and computing. He is Co-Editor-in-Chief of the Nexus Network Journal: Architecture and Mathematics (Springer) and on the editorial boards of ARQ (Cambridge) and Architectural Theory Review (Taylor and Francis). He is Co-editor with Kim Williams of Architecture and Mathematics from Antiquity to the Future (Springer 2015), Co-author with Josephine Vaughan of The Fractal Dimension of Architecture (Birkhäuser 2016), Co-author with Michael J. Dawes of The Mathematics of the Modernist Villa (Birkhäuser 2018) and co-author with Ju Hyun Lee of Grammatical and Syntactical Approaches in Architecture (IGI Global 2020). Dr. Ning Gu is Professor in Architecture at UniSA Creative, University of South Australia (UniSA). He is Deputy Director of Australian Research Centre for Interactive and Virtual Environments (IVE). His most significant contributions have been made towards research in design computing and cognition. The outcomes of his research have been documented in over 180 peer-reviewed publications. His

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About the Authors

research has been supported by prestigious Australian research funding schemes from Australian Research Council, Office for Learning and Teaching, and Cooperative Research Centres. He is an Associate Editor of Architectural Science Review (Taylor & Francis) and has guest-edited/chaired major international journals/conferences in the field.

Abbreviations

AEC AI ANOVA AR ASE BIM C CAD CAT CD CI CM CPSS CSCW DDT DMM DMS DT DTC F FBS FM GIS GPS H HC HS I-C-L ICT IFC

Architecture, Engineering and Construction Artificial Intelligence Analysis of variance Augmented Reality Analysis–Synthesis–Evaluation Building Information Modelling Content-based functionality or Clause Computer-Aided Design Consensual Assessment Technique Collective Design Collective Intelligence Critical Move Creative Product Semantic Scale Computer-Supported Cooperative Work Digital Design Thinking Distributed Mental Model Document Management System Divergent Thinking Design Team Cognition Functionality or Frequency Function–Behaviour–Structure Facility Management Geographic Information System Global Positioning System Entropy value as a level of complexity Cognitive complexity Structural complexity Interacting–Collecting–Leveraging Information and Communication Technology Industry Foundation Classes

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IM IoT L LBS LOD LTM MAR MM MR P PCP PDA SD Sig. SMM SNS STM T TM TMM TTCT TUI U UMPC VPE WPS

Abbreviations

Interactive Media Internet of Things Location- and time-based functionality Location-Based Service Level of Development Long-Term Memory Mobile Augmented Reality Metal Model Mixed Reality Probability Personal Construct Psychology Personal Digital Assistant Standard Deviation Statistical Significance Shared Metal Model Social Network Service Short-Term Memory T-unit Transactive Memory Team Mental Model Torrance’s Tests of Creative Thinking Tangible User Interface User-based functionality Ultra-mobile PC Visual Programming (language) Environment Wi-Fi Positioning System

Chapter 1

Introduction: Exploring Design Thinking

Abstract This chapter provides a background to the concept of “design thinking”, as it is defined and used in the field of design research. Thereafter, the chapter introduces three themes in design thinking—creativity, collaboration and culture— which have become increasingly important in the last decade. It briefly describes the content and structure of the present book, which includes detailed explanations, explorations and developments of design thinking in terms of creativity, collaboration and culture. In addition, throughout this book, empirical studies of these themes are typically presented using data derived from protocol analyses of experiments, sometimes coupled with expert assessment. Thus, this chapter also outlines these methods and their capacity to support exploration of design cognition, processes and activities.

1.1 Introduction In the last two decades, “design thinking” has grown in global significance, placing the design process and its products at the forefront of strategic thinking and planning around the developed world. Design thinking has been praised for promoting innovative solutions to complex, multi-variable problems in society and industry (Brown 2009; Martin 2009; Neumeier 2009). It has also gained considerable popularity and credibility in business and management because it supports the creation of new ideas that can present a unique competitive advantage in a market, or even potentially create a new market. Design thinking has not only been incorporated into business strategy (Brassett and O’Reilly 2015) but into organisational and social structures. In a complex world, under increasing economic, cultural and political pressure, design thinking appears to offer “a fruitful balance between intuitive thinking and analytical thinking” (Martin 2009, p. 137). This book has a particular focus on design thinking in a world where digital and computational processes are omnipresent. The origins of design thinking are often traced to research conducted from the 1960s to 1990s, and much has changed in the world since then. Moreover, many of the major theories of design thinking were developed from interviews with designers who were educated in the 1930s and © Springer Nature Switzerland AG 2020 J. H. Lee et al., Design Thinking: Creativity, Collaboration and Culture, https://doi.org/10.1007/978-3-030-56558-9_1

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1940s. They used hand-sketching, tracing paper, materials almanacs, slide rules and physical models to develop and test their ideas. Over time, however, such tools and methods have diminished in significance in the design professions, being replaced with Computer-Aided Design (CAD) drawings and Building Information Modelling (BIM) models, rapidly fabricated prototypes, Virtual Reality (VR) and Augmented Reality (AR) simulations (Gu and Ostwald 2012; Ostwald 2012). The contractual conditions governing design as a process have also changed in the last few decades and so too have liabilities and responsibilities. Even design pedagogy has evolved throughout this period, rejecting Beaux Arts and master-apprentice models in favour of structured-didactic programs, student-centred learning, inquiry-based approaches and flipped classrooms. Contemporary education uses recorded lectures, online discussion boards and “live” portfolios. First-year design students are immediately introduced to the software and processes they will need when they graduate and commence work. Today’s students are also “digital natives”, more at home with a laptop than pencil and paper. They are less inclined to use sketchbooks, tracing paper or cardboard models, and have never heard of materials almanacs or scale rulers. Considering all of these developments in design practice, is it reasonable to assume that design thinking is still the same? There are multiple reasons why the design process may be different today because of the advent of digital tools and systems. For example, classic models of the design process always set aside time for reflection and evaluation (Schön 1983), which is now potentially diminished because of the efficiency of new technology. The modern three-dimensional (3D) computer model is not just an advanced form of hand drawing. In a narrow representational sense, a hand-drawn elevation of a design could look almost identical to one produced in a computer; however, the similarities end there. The computer model can be tested for structural, environmental and economic performance and optimisation. A change in the computer model flows through all of these software simulations, updating the environmental or manufacturing properties and showing the designer the cost implications of a decision. The hand drawing and the BIM model are definitely not the same in their capacity to model, test or adapt a design. More significantly, contemporary CAD tools like parametric software have automated multiple stages of the design ideation, visualisation and testing processes (Woodbury 2010). The designer’s role in a parametric process is to define the limits, controls and functions that are required of an object or building, along with identifying the evaluation criteria for it to be successful (Jabi 2013). Thereafter, the designer’s role is to let the software generate compliant solutions, before taking responsibility for the process once more (Ostwald 2010). Regardless of whether we call the current era the “digital Anthropocene” or the “post-digital era” (because the ubiquity of digital processes effectively renders them invisible), the modern world’s operations and systems are shaping design thinking in ways that were not anticipated a decade ago. This book considers three themes in design thinking, all of which relate to the impacts of digital technology. The first of these is creativity, which continues to be a major motivation for many disciplines to adopt design thinking. Despite the ongoing fascination with creativity, parametric design, Artificial Intelligence (AI)

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and machine learning are all now being linked to increased creativity, raising questions about the design process and its outcomes. The second theme is collaboration. In the past, design thinking has tended to be presented or discussed as if it is an individual process. While some design process models acknowledge co-design or consultation, the majority of the well-known theories rarely mention that design has always been a team process. This is especially the case today, where digital tools in the design fields are innately collaborative, allowing or even requiring multiple people from different backgrounds to simultaneously participate in the design process. Design process models based on the “crowd” and the “swarm” also raise significant questions for the field of design thinking (Phare et al. 2016, 2018). Finally, in an increasingly globalised world, design teams have members drawn from different nations with different languages and cultures. This poses a different set of problems and opportunities for design thinking and cognition. Furthermore, design is not, as later chapters in this book reveal, a universal language, and this signals the need for a better understanding of culture and language in design. This book examines these three themes—creativity, collaboration and culture—in design thinking. The present chapter introduces design thinking as a concept and a field of research. It also describes the context of the book, which is motivated by gaining a better understanding of the impacts of advances in technology on design thinking. Thereafter, it outlines the three themes which structure the book and the content of its chapters. Finally, because the results of design experiments by the authors are used to ground this book, the two major research methods used are also introduced: protocol analysis (Cross and Cross 1995; Goldschmidt 1995) and expert panel assessment (Amabile 1983a; Besemer and O’Quin 1987). Subsequent chapters introduce specific methodological factors including the different coding schemes used to explore the three research themes.

1.1.1 Defining Design Thinking Despite being widely used today, the phrase “design thinking” doesn’t have an agreed definition or use. Further complicating this matter, there is a growing field of research and pedagogy called Design Thinking that, surprisingly for some, doesn’t necessarily have a close connection to the classic design disciplines of architecture, industrial design and graphic design. Attempts to explain what is meant by design thinking often resort to discussions about the difference between a process and a product. Such explanations typically explain that a shift has occurred over time, from “design” being a discipline-specific product to being a trans-disciplinary cognitive process. The reality, however, is more complex and it helps to understand that these two positions are not mutually exclusive. De-coupling design as a process from design as a product is not a simple thing to do, even though it is occasionally useful for researchers to consider one or other in isolation. Part of the difficulty with defining what is meant by “design thinking” is that the word design is both a verb and a noun, with its context in a sentence assisting the

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reader to understand its role (Glanville 1999). In the former usage as a verb, “design” refers to the process of conceptualising, refining, testing and documenting an object or outcome. In the latter usage as a noun, it refers to the product of this process, being a detailed representation or plan of an as yet unrealised object or outcome. Design is therefore, both a product and a process, and the difference between the two is contingent on its context or use. Importantly, the process of design necessarily includes stages where the products of design are created and reviewed. Of equal significance, the designed product is never static or complete; it is always part of a larger process that includes approvals, revisions, manufacturing, post-occupancy or post-user variations and repurposing. The classical recursive model of the design process features a series of stages, which cycle through ideation, problem-solving, representation and testing, before returning to ideation once more to start a new cycle. This sequence is repeated multiple times throughout the design process, each loop resulting in a more refined or compliant product. As such, the grammatical meaning of “design” shifts restlessly between noun and verb, spiralling between process and product and back again. It is certainly possible to talk about the “design process”, or the “designed product” in isolation, but from a larger perspective the two states are innately temporary. The phrase “design thinking” also has multiple potential meanings, even before specific definitions are considered. It could, for example, refer to the process of thinking creatively and how this occurs. It could also describe the product of creative thinking and its characteristics. The context in which the phrase is used is similarly significant as, for example, Rowe (1987) uses “design thinking” to describe the procedure used by architects when responding to a client’s brief or program. For Rowe, “design” is a noun and “thinking” is an adverb, and the phrase “design thinking” refers to the thought process underpinning a proposition. In contrast, Buchanan (1992) defines “design thinking” as a cognitive process which can be used to solve a particular type of complex or “wicked” problem. For Buchanan, “design” is a verb and “thinking” is a noun, and the phrase “design thinking” describes the generalised thought process of the designer. Such critical differences of opinion about the meaning of “design thinking” are often forgotten in books and courses about the topic, although the field of design thinking more commonly defines its scope as being focussed on the cognitive process. This book does not propose a new definition of “design thinking” or attempt to question the validity of its use to delineate a field. Instead, it takes a more inclusive approach, accepting that new knowledge about design thinking is developed by closely and critically observing the interactions between design as a process and as a product. Indeed, despite design thinking’s notional focus on process, it often positions the resultant product as evidence of the validity of the underlying cognitive operation. Furthermore, the awards and recognition to a designer or product are commonly used to argue for the inherent quality or innovation of the underlying process (Schön 1983). As such, separating process and product in design is not only difficult; it might even be counterproductive. A further factor to consider when approaching the topic of design thinking is that all past empirical research into the topic relies on evidence that is at least one

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step removed from the process of design. As the following section discusses, such evidence is typically presented in the form of interview transcripts, survey data and activity logs developed from observational studies. This evidence is invaluable to the field, but it must be generalised into models of design cognition in order for it to be applied. The effectiveness of these models also requires a level of validation, which inevitably returns to an assessment of the designed product and its functional or innovative properties. Thus, both the process and the product may need to be considered to develop robust or significant findings of design thinking. All of these factors have shaped the way design thinking is positioned and described in this book. The decision to avoid framing a finite definition is a practical and logical one. The purpose of this book is to develop and test new knowledge about design thinking in its broader sense, to allow readers to make their own decisions about its relevance to other fields and practices.

1.1.2 Research into Design Thinking The origins of design thinking are typically traced to the works of the early twentiethcentury philosophers of science, who formulated various logic models to explain the world and our understanding of it. They created frameworks or systems to study a wide range of processes and also to question how we construct knowledge about them. Following the footsteps of Karl Popper and Thomas Kuhn, and shaped by structuralist and post-positivist thinking, designers and architects soon became interested in developing what Wallas (1926) calls a “scientific explanation” of thinking. By the 1960s, an increasing number of designers had proposed scientific, mathematical and philosophical theories to explain design as a process (Alexander 1964; Asimow 1962; Gordon 1961; Osborn 1963). Soon thereafter, cognitive scientists led by Simon (1969), and engineers like McKim (1973), began to treat design as a type of science. In the following decade, Cross (1982) and Lawson (1980) formulated many key concepts in design thinking, noting that designers use different problem-solving techniques to non-designers. Various behaviours like “satisficing” and “solutioncentred” thinking soon began to be used to characterise what was becoming known as “designerly ways of knowing” (Cross 2011, 2018). In the 1990s, the volume of research produced about design thinking increased rapidly and multiple models were soon developed for explaining the design process. Most of these models conceptualise the “creative design process” as a series of actions or steps that take place in the mind of the designer. Thus, the design process is innately cognitive and is also described as “design cognition”, even though the actual mental activities that take place during design are still largely unexplored (Chan 2015). As an example of a design model, Brown and Wyatt (2010) propose a systems approach with three overlapping spaces: inspiration, ideation and implementation. A five-stage design thinking model developed by the Hasso Plattner Institute of Design at Stanford (2010) is also widely applied to teach design in various domains. Its stages, which

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partially mirror those in the classic architectural process model of the mid-twentieth century, are as follows: empathise, define, ideate, prototype and test. By the twenty-first century, design thinking had become a field of research in its own right. Its focus also shifted during this time, away from applications in the traditional creative design disciplines to those in business, management or engineering. Design thinking, in this new field, is no longer the sole domain of design professionals, but all who seek solutions to complex problems. As such, design thinking is often viewed today as a type of problem-solving process that aims to produce innovative outcomes and to address ill-defined or “wicked” problems. During this period, research into design thinking, intelligence and behaviour soon expanded to include six disciplines—psychology, linguistics, neuroscience, computer science, anthropology and philosophy (Miller 2003). This breadth of application is evidence of the growing importance of the field but is also a reflection of the origins of the methods that have been used to try to understand design as a cognitive process. Research into design thinking and cognition is typically undertaken using one of five methods (Cross 2011). The first, and also one of the earliest, is interviews with designers. The second is observation of designers and case studies of their work. The third is experimental studies in controlled contexts. The fourth is simulation and finally, reflection and theorising. Furthermore, some of the most effective research studies both design cognition and design activity, identifying key characteristics, patterns and strategies in both the process and product of design (Atman et al. 2005; D’Souza and Dastmalchi 2016; Georgiev and Georgiev 2018; Ho 2001; Kim and Maher 2008; Kruger and Cross 2006; Lee et al. 2015, 2019; Lee et al. 2014a; Lee and Ostwald 2019; Yu et al. 2013). Through research published in the journal Design Studies and the Design Thinking Research Symposium (DTRS) series (Cross 2018; Dorst 2018), design cognition has become the focus of sustained attempts to understand its operations and activities.

1.1.3 The Digital Anthropocene One of the most famous books about the challenges associated with the rise of the modern, technological world was Marshall Berman’s (1982) All That is Solid Melts into Air. In this book, Berman observes that, paradoxically, progress is also a destructive force, as every technological advance effectively erases the ones that preceded it. This was certainly the case for early twentieth-century industrial designers and architects. The early Modern movement promoted a new, machine-driven process of production, allowing designers to create technically superior products that surpassed the performance and durability of the handmade or crafted equivalents of the previous era. By the 1960s, however, these machine-made products were being replaced by those of the next generation of designers, using electronic components and synthetic materials. In the 1990s, bespoke production systems allowed designers to create unique, digitally enhanced objects which soon surpassed the performance or desirability of those of the mid-century modern movement. In this sense, the trajectory of

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the world is innately forward or upward, forever seeking new ideas or opportunities, while simultaneously leaving the past behind. The past, viewed in this way, is that which “melts into air”, as the ideas, techniques and processes used by designers, and which shape the world we live in, are all slaves to progress. Even the most tangible or “solid” concepts and theories from the past are always being challenged. Design thinking is a progressive field that acknowledges the impacts of new technology and ideas. For example, as noted previously, parametric design appears to offer an innovative way of generating new design solutions based on algorithmic thinking (Lee et al. 2015). Mobile and pervasive computing enables interactive and collective design thinking, continuously constructing and sharing creative processes and products (Lee et al. 2013b). The design process also adopts, as needed, innovative design tools and operates in a globalised design ecosystem (Singh and Gu 2012). These new paradigms of design for a digital Anthropocene are encapsulated in this book under three headings: Creativity, Collaboration and Culture of the “three C’s” (Fig. 1.1). Creativity as a theme in design thinking highlights computational design employing various strategies and complex cognitive activities. Collaboration in design thinking refers to interactive, collective thinking, teamwork and networked design collaboration. Culture in design thinking is concerned with designing and thinking in multicultural contexts, encompassing the language of design and cognitive and linguistic differences in design thinking. These themes are explored in the present book through a series of empirical studies of design cognition. The purpose is to develop new knowledge and ideas, not necessarily to erase old ones, although some existing theories in design thinking need to be questioned before they too “melt into air”, as progress is made in the field.

CreaƟvity

Computational design thinking Design strategies Cognitive complexity

DESIGN THINKING in the digital era

CollaboraƟon

Culture

Interactive and collective thinking Cognition and teamwork Networked design collaboration

Multi-cultural design thinking The language of design Cognitive and linguistic differences

Fig. 1.1 Themes in design thinking for the digital era

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1.2 Creativity, Collaboration and Culture 1.2.1 Creativity Creativity and novelty are often understood as closely related concepts in design, much like the reciprocal relationship between aesthetic and technical values. Each pairing requires both capacities and properties to be completely successful. For example, Kryssanov et al. (2001) define creativity as the combination of “novelty” and “appropriateness”. Creative ideas are not just new or unexpected, but provide solutions that are useful, efficient and valuable (Georgiev and Georgiev 2018). In addition, complementary models of creativity also define psychological creativity and historical creativity (Boden 2004), as well as human creativity and computational creativity (Maher 2010). For example, psychological creativity refers to “a surprising, valuable idea that’s new to the person who comes up with it” (Boden 2004, p. 2), whereas computational creativity is “expressed in the formal language of search spaces and algorithms” (Maher 2010, p. 22), addressing the joint products of humans and computers. Iordanova et al. (2009) also argue that as generative modelling tools replace those of previous eras (for example, as parametric modelling replaces hand drawing and traditional CAD modelling), new types of creativity are enabled. Similarly, generative design tools are often presented as supporting creativity, because they allow for rapid exploration of design alternatives at an early stage of the process (Blosiu 1999; SHoP/Sharples Holden Pasquarelli 2002; Spiller 2008). In such examples, creative potential is linked to increased capacity to generate options that fulfil functional parameters. Multiple theories exist about the role of creativity in design thinking. For example, Gordon’s (1961) theory of synectics proposes that creative processes can be systematised and learnt, and that people are more effective if they understand the creative process. Lawson (1980) postulates that creativity emerges from the overlap between convergent and divergent thinking. Convergent thinking leads to a singular, appropriate solution, while divergent thinking produces multiple ideas and alternatives. Blosiu (1999) suggests the use of a laterally integrated design model, which not only generates design alternatives but also develops creative capacity Bucciarelli (2001) also highlights analytic and synthetic design thinking in engineering education, drawing on Gordon’s synectics. A creative design can also potentially be generated using specific computational processes. For instance, Rosenman and Gero (1993) identify the processes of combination, mutation, analogy and first principles, and Gero (2000) later revises this to include their computational equivalents. Such computational models of design thinking, and their support for creativity through design exploration and evaluation, are considered in more detail in the present book. Specifically, design thinking with parameters and rules in parametric design is examined for its capacity to generate and evolve innovative or original ideas. Sternberg and Lubart (1999) categorise six scientific approaches to investigating creativity: (i) pragmatic, (ii) psychodynamic, (iii) psychometric, (iv) social–personal,

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(v) cognitive and (vi) confluence approaches. Their first, the pragmatic, addresses commercial and social benefit, while their second, the psychodynamic approach, is concerned with the role of psychology. Since Guilford’s (Guilford 1950) Divergent Thinking (DT) test first measured individual fluency, originality, flexibility and elaboration, psychometric approaches have been widely used to measure creativity. Several versions of the DT test also exist, such as the Structure of the Intellect (SOI) and Torrance’s Tests of Creative Thinking (TTCT), and have dominated the field of creativity assessment for several decades (Runco and Acar 2012). The TTCT consists of four measures, two figural and two verbal, and is highly respected in education as well as in industry (Kim 2006). Recently, Jung and Chang (2017) show how the Something About Myself (SAM) test can be used to assess creativity, along with the Latent Ability Detection (LAD) test to examine multiple intelligences. They also examine creative-convergence design and reveal that spatial and linguistic intelligences are positively related to creativity. However, such paper-and-pencil tests of the psychometric approach are unable to investigate design exploration using richer visual representations and the creative design processes it supports. This is, in part, because sketches and drawings often play a central role in models of the design process (Kokotovich and Purcell 2000; Goldschmidt and Smolkov 2006; Schön and Wiggins 1992; Suwa and Tversky 1997). Thus, the psychometric approach to assessing creativity is limited to divergent thinking inside an individual mind. Sternberg and Lubart’s (1999) fourth approach, the social–personal, emphasises the significance of social contexts or personalities in creativity, whereas the fifth, the cognitive, is the most important for understanding creative design processes (Chan 1990; Kim et al. 2007; Kruger and Cross 2006; Lee et al. 2013a; Lee et al. 2017; Suwa et al. 1998). Sternberg and Lubart’s (1999) last approach to creativity, the confluence approach, has growing acceptance across many disciplines (Katz 2002). This book employs both the cognitive and confluence approaches to investigate creative design thinking. One established model of creativity is Rhodes’ (1961) four P’s—person, process, press and product—which has been used as a multifaceted framework to investigate creativity (Askland et al. 2012; Williams et al. 2010). The person is the designer, the process is the cognitive operation and the product is the outcome of the design. As such, it replicates three components of design thinking discussed previously. “Press” in Rhodes’ model refers to environmental or contextual factors which shape creativity. This factor is probably the least researched of the four P’s in design thinking. For example, multiple studies address person, process and product in creativity (Hasirci and Demirkan 2007; Simonton 2003), while investigating both design processes and designed products has become more common over time (Goldschmidt and Smolkov 2006; Lee et al. 2015). Furthermore, exploring the design process can also involve the identification of individual design strategies and preferences, which indicate a person’s approach to creativity. The creative design process is often explored cognitively using protocol analysis (employing various coding schemes), while creativity in the design product is measured using a confluence approach, such as expert panel assessment (e.g. Consensual Assessment Technique or CAT). Press, however, is only rarely investigated in design thinking experiments, although the present book’s interest in culture and language does begin to address

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some environmental or contextual factors. Part I of this book is focussed on creativity and design thinking in the digital Anthropocene.

1.2.2 Collaboration The oldest models of creativity are typically traced to Plato and the belief that an artist’s vision and inventiveness are dependent on the existence of a transcendent muse or direct connection to the gods. Philosopher Immanuel Kant’s views have some similarities as he maintains that creativity is an innate and valuable gift bestowed on an individual (Sawyer 2006). These, so-called “romantic” views of creativity, all emphasise that creativity is an inborn or natural characteristic of some people and not of others (Williams et al. 2010). The alternative perspective on creativity, the “rationalist” view, holds that creativity can be taught or learnt by anyone. Drawing on the work of Aristotle, rationalist attitudes to creativity provided the foundations for the first attempts to understand design thinking in the twentieth century (Guilford 1950; Sawyer 2006). One of the few commonalities between the romantic and the rationalist models of creativity is that both focus on the role of the individual. This is not surprising, given the way society valorises individuals in creative processes, even though they rarely, if ever, work alone. Standard twentieth-century management theory even emphasises that creative problem-solving requires brainstorming rather than group thinking. Despite this, collaboration in the design process, and especially as enabled using digital tools, is an area that requires further research. In order to understand this need, a brief review of past research into collaboration in design thinking and cognition is useful. Furthermore, this review also touches on the topic of communication, which is relevant to the third theme in this book, culture. Collaborative design processes and communication in design teams have been examined in past experimental research using protocol analysis (Cross and Cross 1995; Dong 2005; Goldschmidt 1995; Stempfle and Badke-Schaub 2002; Valkenburg and Dorst 1998). The products of team design processes have also been evaluated or analysed using expert panels (Mulet et al. 2016; Karlusch et al. 2018). Unlike the singular “person” aspect of creativity in Rhodes’ model, team members’ thinking styles and preferences can be measured using several tests. Three examples of these tests are the State Action Part Phenomenon Input oRgan Effect (SAPPhIRE) model of causality (Mulet et al. 2016; Sarkar and Chakrabarti 2011), the Herrmann brain dominance test (Herrmann 1991) and Kirton’s adaptor–innovator test (Kirton 1994; López-Mesa and Thompson 2006). Dong (2005) also employs a Latent Semantic Analysis (LSA) method to investigate design team communication. In one of the first practical protocol studies in the field of design, Cross and Cross (1995) examine a social process of design with a focus on six points of view: “roles and relationships”, “planning and acting”, “information gathering and sharing”, “problem analysing and understanding”, “concept generating and adopting” and “conflict avoiding and resolving” through an in-depth analysis of a teamwork experiment. Valkenburg and Dorst (1998) expand Schön’s theory of reflective practice (Newman 1999; Schön

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1983, 1984) in design teams to address four activities: naming, framing, moving and reflecting. Stempfle and Badke-Schaub (2002) also suggest the use of four basic cognitive operations—generation, exploration, comparison and selection—to understand the collective design process. D’Souza and Dastmalchi (2016) identify smaller creative events potentially connected to big creative leaps in a collaborative design process. They further suggest that a balance between individual performance and team dynamics is essential to a successful multi-disciplinary team process. Research also proposes that individual thinking and reasoning styles can impact on knowledge-sharing capabilities (Mulet et al. 2016). Previous experimental and theoretical studies of cognition and team design emphasise four main approaches: (i) design space, (ii) design strategy, (ii) design productivity and (iv) spatial representation (Lee et al. 2017). These approaches are significant for the way teams operate as part of a creative design process. In the first of these approaches, design is regarded as a “co-evolutionary” process, which needs to shift backwards and forwards between problem and solution spaces for a creative product to be produced (Lee et al. 2015; Maher and Poon 1996). The way in which members of teams move between these two spaces would appear to be key to understanding and improving their collective, creative performance. To explore team communication in terms of problem and solution spaces, Stempfle and Badke-Schaub (2002) present four basic cognitive operations, “generation” and “exploration” to widen a problem space and “comparison” and “selection” to narrow a problem space. The second approach, design strategies, deals with the definition of sub-goals which limit or enable certain operations, like working-forward and working-backward strategies. These cognitive strategies are linked to personal preferences and habits and contribute to defining designers’ thought processes (Lee et al. 2014a). The significance of this is that it confirms that there are different teamwork strategies involved in the average design team. The third approach, design productivity, is associated with the effectiveness and efficiency of a cognitive process that is either individually or collectively undertaken (Goldschmidt 1995). Such is the importance of this way of conceptualising the effectiveness of design cognition that mathematical methods have been developed for interpreting linkograph data (Kan and Gero 2008; Lee and Ostwald 2019). Linkograph is a graph that illustrates how one idea, action or event interlinks with another (Goldschmidt 1995). These cognitive themes are related to how creativity is accommodated and supported in collaborative design processes. The last cognitive approach, spatial representation, is related to the capacity to conceptualise and communicate spatial information. It is associated with linguistic complexity and fluency, and is also a factor in multi-cultural design communication. The “collaboration” theme in the present book also considers interactive and collective design thinking, which are linked to “social creativity” in the “digital ecology”. For example, mobile devices like smartphones produce active relationships between people, objects and locations, supporting mobile social interaction beyond Greif and Cashman’s “Computer-Supported Cooperative Work” (CSCW) (Grudin 1994). Recent information technologies embed microprocessors in everyday objects, promoted the spread of wireless technologies and advanced the use of sensor

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networks and the internet (Lee et al. 2014b). Beyond human collaboration and office automation, the “internet of things”—“ubiquitous computing” or “pervasive computing”—allow for a diverse range of information communication processes. This level of mass interaction is potentially the catalyst for a new type of creativity (Phare et al. 2016, 2018). This “mobile creativity” involves interaction and collaboration with other individuals and is more strongly related to social creativity than individual creativity (Lee et al. 2013b). Thus, new collaborative, creative design processes evolve with these interactive and collective surroundings beyond physical environments. In this context, virtual workplaces, including BIM platforms, have attracted the attention of many researchers. BIM has been used as an innovative collaboration tool in building design, construction and management. Gu et al. (2015) highlight the significance of BIM-related products, processes and people in collaboration, and Mulet et al. (2016) compare the thinking and reasoning styles of design teams in a face-to-face environment and a virtual working environment. Mulet et al.’s (2016) results suggest that design teams in virtual environments produce a slightly higher degree of novelty in their design outcomes. Tang et al. (2011) also compare a traditional design environment (face-to-face, pen-and-paper based) with a digital sketching environment (emulating the traditional sketching environment). They examine the design processes through protocol analysis using a Function– Behaviour–Structure (FBS) coding scheme. Their results show no significant differences between design environments in terms of design process, suggesting that the higher level cognitive activities can be effectively supported in digital environments and may not be influenced by specific tools. Part II of this book examines these new collaborative design environments in terms of interactivity and collectiveness in designing.

1.2.3 Culture The third theme in this book, culture in design thinking, is probably the least well developed in past research. A few pioneering projects have identified the importance of multi-cultural education and practice in an increasingly globalised world (Lee et al. 2019; Santandreu Calonge and Safiullin 2015; Karlusch et al. 2018). Santandreu Calonge and Safiullin (2015), for example, argue that multi-national or multi-cultural teams are more creative than intra-national teams, because individuals from different cultural and linguistic backgrounds in a team can develop a wider variety of perspectives and points of views. That is, diversity is a “synergy enabler” to produce positive outcomes. Karlusch et al. (2018) also identify the importance of lateral thinking and incorporated intelligence in fostering creativity in diverse teams. Design naturally requires engagement with multiple complex processes and conceptual configurations that arise from visual and verbal communication. Design expertise is often spread across project teams and different locations and uses collaborative design management processes, where design communication is supported by

1.2 Creativity, Collaboration and Culture

13

representations such as sketches and drawings (London and Singh 2013). Nonetheless, the experimental results of past research indicate that cognitive and linguistic design abstraction is related to individual linguistic and cultural experiences and preferences (Lee et al. 2019). That is, for international architects and designers, language is not just a spoken or written system, it also shapes how they use and understand design representations. The relationship between thought and language, or cognition and culture, is a complex one. For example, psychological, social and semiotic researchers repeatedly note that ideas are intrinsically tied to the language in which they are both constructed and expressed (Bonvillain 2010; Lewis 2012). Structural linguists and post-structuralist philosophers agree on this one point, although for different reasons. For example, de Saussure (1959) argues that language is the fundamental basis on which we understand and represent the world, and therefore the structure of our language is the structure of our world. Derrida (1976) famously questions the “logocentric” hierarchy in de Saussure’s philosophy and semiotics wherein thought is argued to be superior to speech, which is in turn superior to writing. However, a central message in Derrida’s work is that language is never a transparent, stable means of communication. In contrast, Chomsky (1965), extending and also questioning de Saussure’s ideas, identifies common structural threads between languages across cultures. His research proposes that human language is universal or innate. Pinker (1994) also presents a compelling argument that there are thought processes at work long before people develop language skills. That is, thought comes before language. For example, as humans can acquire complicated linguistic skills from the earliest age without explicit lessons, people must be born with the ability to learn and reason. Thus, thought could be, to a certain extent, independent of language, or precede it developmentally. Furthermore, Arnheim’s “visual thinking” argues that “the cognitive operations called thinking are not the privilege of mental processes above and beyond perception but the essential ingredients of perception itself” (Arnheim 1997, p. 13). He also highlights the cooperation of verbal language and imagery and proposes that “language tends to suggest functional rather than formal categories and thereby to go beyond more appearance” (p. 239). Artistic expression is potentially another form of communicative reasoning, often related to “non-verbal thought” although it has been claimed that visual perception is an intelligent act with no relation to language (Ware 2008). These opposing arguments illuminate a core disagreement about culture and cognition that is pertinent to design thinking. In the discipline of linguistics, it is accepted that language shapes both the way ideas are presented or explained and the thought processes used to develop them. Wittgenstein summarises this idea as, “the limits of my language mean the limits of my world” (1922, p. 74). Significantly, linguistic researchers have observed that this phenomenon includes the construction and communication of spatial and formal relationships and reasoning (Herskovits 1986; Tenbrink and Ragni 2012; van der Zee and Slack 2003). For example, Chinese speakers do not typically follow a counterfactual story structure as they use different spatiotemporal metaphors (Casasanto 2008), while Korean speakers lack a system of particles and prepositions comparable

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1 Introduction: Exploring Design Thinking

to words like in, on, up and down in English (Choi et al. 1999). Nonetheless, the realisation that design language might shape design itself, and vice versa, has been less commonly observed (Dong 2009; Lee et al. 2016; Lee et al. 2019). The problem is not only that the globalised design environment is so reliant on linguistic skills, but also that language as a system is a reflection of the way people think and of their sociocultural values, both of which are central to the process of design (Gleitman and Papafragou 2005). Part III of this book explores multi-cultural design communication through protocol studies employing both cognitive and linguistic coding schemes.

1.3 Research Method I: Protocol Analysis 1.3.1 Studying the Design Process Empirical research into design thinking requires a method that can capture and compare aspects of cognitive processes. There are multiple models of the creative process that can be experimentally tested. One of the oldest examples, from Wallas (1926), proposes the use of a four-stage model of the creative process—preparation, incubation, illumination, and verification—to formally investigate creativity in human thought processes. Guilford’s (1967) Structure of Intellect (SI) theory identifies five types of mental operations—cognition, memory, convergent thinking, divergent thinking and evaluation—that can be examined in cognitive experiments. More recently, Hayes (1989) also categorises creative cognitive processes into five acts— preparation, goal setting, representation, searching for solution and revision—which facilitate creativity in the design process to be studied. Dacey and Lennon (1998) argue that cognitive processes can be researched and understood using two sets of theories. The first set comprises the combination and expansion of three early creativity models, associationism, Gestalt and cognitivedevelopmental approaches. These theories develop various aspects of the conceptual combination of ideas and suggest that creative cognition is more than problemsolving, highlighting selective and evaluative stages in the creative process. The second set of theories involves metaphors, analogies and mental models. Metaphors shift the interpretation from one conceptual understanding to a new point of view, a so-called “ontological shift”, while analogy is a cognitive process referring to conceptual parallels. Choi and Kim (2017) also suggest that analogical and metaphorical reasoning can be used to develop creative design thinking in tertiary education. This set of theories and the other cognitive models mentioned in this section have facilitated formal studies on creativity across multiple disciplines. In many cases, the methodological approach used to develop the empirical evidence from design experiments to support the development of these models and theories is protocol analysis.

1.3 Research Method I: Protocol Analysis

15

1.3.2 Protocol Analysis Protocol analysis is a method for analysing recorded actions, behaviours, verbalised responses or written information in a rigorous and repeatable way. A typical use of this method would be to analyse a recording of an experiment in accordance with an agreed or accepted set of rules (the “protocol”). Each time a particular action occurs, or a word is used in the recording, it is coded, counted and its timing noted. This process extracts rich data from the experiment that can be directly compared with the results of other participants in the experiment to identify similarities or differences and test theories about human cognitive behaviours. Protocol analysis became popular in cognitive psychology and design research in the 1980s and 1990s because it offered a way of methodically counting and comparing human processes. Since then protocol analysis has become the most widely accepted cognitive research technique for analysing the design process (Chai and Xiao 2012; Coley et al. 2007). It provides a previously unavailable level of scientific or mathematical rigour to the assessment of complex human operations. The strengths and weaknesses of protocol analysis can be readily understood by comparing it with a standard Likert-scale survey method. Surveys can engage a large number of participants for a short amount of time, generating data that is straightforward to extract. If the response rate is high enough, the results may be statistically generalisable to a larger population even though they are often grossly simplified. In contrast, protocol analysis engages a small number of participants for an extended period of time. The results are analysed in great detail and depth, often involving multiple simultaneous data collection and assessment processes. The results are time-consuming to develop but they are rigorous and rich. They cannot, however, be easily generalised to a larger population. Depending on the topic being investigated, both surveys and protocol analysis are valuable methods for researchers. However, for studying design cognition, depth, subtlety and richness are of paramount importance and thus protocol analysis has become the standard approach for many studies. Protocol analysis necessarily requires recruitment of participants to take part in experiments. Sometimes volunteers can be sought, whereas often incentives (typically gift vouchers) are used to compensate people for the time and effort. In most countries, formal human ethics clearances are required before the experiments may commence. These clearances cover pre-experiment disclosures, participant consent paperwork, briefs for participants and their rights, and information about data use and storage. Participants are generally anonymous and agree to being identified only by number or coding. In its typical use in design cognition or design thinking research, protocol analysis has five components which are needed to meet a specific aim or objective. They are (i) experimental settings, (ii) verbalisation, (iii) transcription and segmentation, (iv) encoding with coding schemes and (v) arbitration and intercoder reliability. Most protocol studies possess these same five elements, each of which are explained hereafter, even if they may vary for particular applications.

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(i)

1 Introduction: Exploring Design Thinking

Experimental settings are the parameters or limits for a study. These might include the location where the experiment takes place, the scenario or test participants will undertake, the hardware or tools provided and a time limit. For example, in a typical experiment in design thinking a participant is given a design brief and a selection of design media (for example, pen-and-paper or CAD tools), a specific outcome or goal and a time limit. The type of participants required (for example, novice or expert) and experiment venues (for example, simulated or real workspace) must be carefully considered to meet the scope and feasibility of the research. Figure 1.2 shows an example of a design experiment undertaken by the authors in a university computer laboratory simulating a parametric workstation in a design office. Each session of the experiment is video recorded with one camera giving a clear view of the designer’s overall activities and the other focusing on the computer screen to capture representation activities. For this experiment, participants were given a design brief (for a high-rise building), software (Rhino and Grasshopper) and one hour to complete the task. Only participants with a sufficient fluency or expertise in operating on the software were allowed to take part. (ii) Verbalisation describes the methods used to develop audio protocol data. There are two common techniques, concurrent and retrospective verbalisation. For the former, participants are asked to verbalise their thoughts and actions while designing (concurrent) and for the latter, immediately afterwards (retrospective). There are benefits to both concurrent and retrospective verbalisations (Coley et al. 2007). The former approach, often called “think-aloud”, could interfere with the design process, distracting the participant from acting normally. Participants in the latter, retrospective approach, often forget details or have incorrect recall. A mixed technique with both a think-aloud verbalisation during designing and a post-experiment interview can be the most effective combination (Lee et al. 2015). Most commonly, the verbalisation is recorded using the same video recording equipment used for capturing actions and behaviours. This allows the researcher to match actions to words, providing an added level of assurance when coding the data. The experiments described in the present book were video recorded, with a parallel “think-aloud” audio recording, and a post-experiment interview, to

Fig. 1.2 An example of experimental settings

1.3 Research Method I: Protocol Analysis

17

provide retrospective data to explain the participant’s thoughts and activities. Before each design experiment, a researcher also explained a design brief and undertook a short (for example, five-minute) “practice-run” of think-aloud verbalisations with each participant. (iii) Transcription and segmentation are the processes of extracting data from the recordings and breaking them down into smaller fragments. The audio data are typically transcribed in full, although this may not be necessary for some experiments or research questions. After transcribing the audio, each transcript is divided into smaller segments, sometimes called “episodes”. The episode timing can be determined using either set times—every 30 s or every minute— or using content. In the latter case, segmentation by content, there are two methods (Suwa et al. 1998). The first uses pauses or syntactic markers as breaks, and the second reviews the participant’s spoken content to determine where an idea has started and ended. It is also possible to use both time-based and content-based segmentation processes simultaneously, examining the relationship between time and content. Professional services often transcribe and segment (most often time-based) audio data for cognitive research projects. Such formal and rigorous transcriptions are valuable for analysing results as well as for supporting open research with peers and further dissemination. (iv) Encoding with coding schemes is arguably the most important procedure in protocol analysis. There are two parts of this component, the coding scheme and the coding process. The “coding scheme” is a guideline, schema or reference identifying precisely which activities are being captured. Each category in the coding scheme must be clearly defined, preferably with examples, of what constitutes evidence of an observed action being classified in a particular way. For many uses, it is better to adopt an existing “tried and tested” coding scheme. This also allows for comparisons to be made between experiments by different teams, for benchmarking. Previous coding schemes in design research fall into two types, targeting the “content-oriented” aspects of designing and its “process-oriented” aspects (Coley et al. 2007). For example, Suwa et al.’s coding scheme (1998) for coding designers’ cognitive actions is content-oriented, while Gero’s FBS coding scheme (Gero 1990; Mc Neill et al. 1998) is process-oriented. Most cognitive studies use only a single coding scheme, although a few employ multiple coding schemes simultaneously, to better compare different aspects of the data. For example, Mc Neill et al. (1998) use a four-dimensional coding scheme: level of abstraction, FBS, micro- and macro-strategies. Lee et al. (2019) present a triple-coding system consisting of design cognition, design information and spatial language coding schemes. The coding process is where a researcher views (and listens) to a recording, and identifies every action, behaviour or verbalisation listed in the coding scheme as it occurs to allow quantification of the design data. This is an exacting and timeconsuming process, where the increased level of scientific and mathematical rigour is provided to the analysis.

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(v) Arbitration and intercoder reliability are processes used to ensure the reliability of the coding. There are two approaches to this, arbitrated and self-arbitrated. In the arbitrated version, two or more coders undertake the encoding process (and sometimes, transcription as well) in parallel. Then the multiple sets of coded data are compared, and any disagreements are reviewed and resolved. In the self-arbitrated version, the same coder takes two or more “runs” coding the data, separated by a period of time (typically 2–3 months). When completed the different versions are compared and any disagreements resolved to ensure the reliability of the encoding. Methods also exist for assessing Intercoder Reliability (ICR), percentage agreement being the simplest one. Holsti’s coefficient, Krippendorff’s alpha and Cohen’s kappa (κ) indexes are also used for this purpose although there is no consensus on the single best measure (Cho 2011; Krippendorff 2018; Mouter and Vonk Noordegraaf 2012). “The selection of proper index will depend on the levels of coding, number of coded categories if coded nominal, number of coders, and number of coded units” (Cho 2011, p. 344). Research using protocol analysis should always address and describe these five aspects of the experiment. Relevant chapters in this book record this information for specific experiments accordingly.

1.3.3 Coding Scheme To conduct protocol analysis, a coding scheme must be selected, modified or designed to suit the specific research aims and objectives. If modified or designed, the scheme needs a clear underlying logic, preferably linked to established theories or models. Table 1.1 is an example of a coding scheme which supports the description and identification of cognitive thinking and activities for evaluating creativity in parametric design processes. This example coding scheme starts with four “levels” of creativity, representation, perception, goal setting and searching for solution, selectively developed from Hayes’ (1989) creative acts model. The representation level encapsulates modelling activities in parametric design. This level is similar to Suwa et al.’s physical actions (1998) and Kim and Maher’s three-dimensional modelling actions (2008). The perception level addresses the process of seeing relationships between elements (Flowers and Garbin 1989) or components, referring to the activities of visual imagery in the creative process. Perception is also related to the “incubation” stage of Wallas’ creative model (1926). The goal-setting level describes the activities of “problemposing” or “problem-formulation”, while the searching for solution level highlights divergent and convergent thinking processes.

1.3 Research Method I: Protocol Analysis

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Table 1.1 A coding scheme to investigate creativity in parametric design process Level

Category

Subclasses

Description

Representation

Geometry

RG-Primitive

Make primitive geometries

RG-Change

Change existing geometries

RG-Transformation

Perform deformations (or morphing of forms)

RG-Variation

Make variations (or generate forms)

RA-Parameter

Make initial parameters

RA-ChangeParameter

Change existing parameters

Algorithm

Perception

Geometry

Algorithm

Goal setting

Searching for solution

Formulating Goal

Evaluation (Geometry)

RA-Rule

Make initial rules

RA-ChangeRule

Change existing rules

RA-Reference

Retrieve or get an internal/external reference

PG-Geometry

Attend to existing primitive or changed geometries

PG-Transformation

Attend to transformed geometries

PG-Variation

Attend to variations (or generated forms)

PA-Parameter

Attend to existing parameters

PA-Algorithm

Attend to existing algorithms

PA-Reference

Attend to existing reference data

SF-Initial Goal

Introduce an initial goal

SF-Previous Goal

Introduce new ideas extended from a previous idea (or goal)

SF-Knowledge

Introduce new ideas derived from knowledge (or experience)

SE-Geometry

Evaluate primitive or changed geometries

SE-Transformation

Evaluate transformation

SE-Variation

Evaluate variations (or generated forms) (continued)

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1 Introduction: Exploring Design Thinking

Table 1.1 (continued) Level

Category

Subclasses

Description

(Algorithm)

SE-Parameter

Evaluate existing parameters

SE-Algorithm

Evaluate existing algorithms

SE-Reference

Evaluate existing reference data

SA-Geometry

Adopt new solutions to primitive or changed geometries

SA-Transformation

Adopt new solutions to transformation

SA-Variation

Adopt new solutions to variations (or generated forms)

SA-Parameter SA-Parameter

Adopt new solutions to parameters

SA-Algorithm

Adopt new solutions to algorithms

SA-Reference

Adopt new solutions to retrieve or get internal/external reference

Adopting Solution (Geometry)

(Algorithm)

Within each level, there are two categories (except for goal setting), in this example one for standard CAD environments (geometry) and the other for parametric environments (algorithm), and within each category there are further subclasses. The categories and subclasses for detailed actions in this example are adapted from established coding schemes for design actions (Suwa and Tversky 1997) and cognitive actions (Kim and Maher 2008). Because this example coding scheme is for comparing parametric and standard design processes, geometry and algorithm categories are used to capture the cognitive activity in each. For example, “Geometry category” includes general modelling activities (line or shape drawing) in software. In contrast, “algorithm category” represents generative algorithms that involve “parameter” and “rule” as well as “reference”. This coding scheme is an example of an adapted scheme, drawing on aspects of three common and accepted models, and then modifying them to suit the focus of the experiment. Chapters in this book record and briefly explain the coding schemes used for specific experiments, all of which follow this pattern.

1.4 Research Method II: Expert Panel Assessment

21

1.4 Research Method II: Expert Panel Assessment 1.4.1 Design Product Thus far in this chapter, most of the content has focussed on design as a verb or process, denoting an activity that might lead to creativity (Glanville 1999). It is difficult, however, to talk about creativity without considering design as a noun or product. This is because the creativity implicit in a product is often more apparent than in a process, and a creative product is also a common form of evidence for a creative process. From the earliest architectural treatises, the completed building has been viewed as encapsulating the structural, functional and innovative dimensions of the work. For example, in approximately 20–30 BC, Vitruvius, a Roman architect and military engineer, argued that a successful building should have three properties—firmitas, utilitas and venustas—which are translated as either firmness, commodity and delight or soundness, utility and attractiveness, respectively (Kuiper 2010; Rowland and Howe 1999). These three interrelated components have since been integrated into many research frameworks in architecture where the product’s success is assessed in both utilitarian and aesthetic terms. Rowe (1987), for example, treats design as both a form of fine art and a technical science, and Chan (2015) argues that design is characterised as the creation of satisfactory solutions or beautiful artefacts that fulfil certain functions. In addition, Vitruvius’s delight (venustas) can refer to an observer’s intellectual, emotional or psychological response. That is, design is not only about the function of a building, garment or other object, but also satisfying people’s emotions and desire for meaning or value (delight). Whereas protocol analysis can be used to interrogate the cognitive process of design, it cannot be used to directly assess the creativity that occurs in this process that often requires a review of the product. The creativity implicit in a product is best assessed by experts or judges in accordance with a set of evaluation criteria. Teresa Amabile (1983a), for example, identifies four components for any creative response: domain-relevant knowledge and abilities, creativity-relevant skills, intrinsic task motivation, and the social environment (Amabile 1983a; Hennessey and Amabile 1999). Amabile’s Consensual Assessment Technique (CAT) has been used for assessing both artistic and verbal creativities and been widely applied in research in education, arts, business and advertising. In particular, CAT has been used to assess the creativity evident in a designed product (Amabile 1983a; Christiaans 2002; Thang et al. 2008). A more general version of this approach is the expert panel assessment, where the creativity of an artefact is measured using the combined assessment of experts in that field. The following is a simple example of how expert panel assessment works in design thinking research. First, let us assume that a design experiment has occurred, with 20 participants each producing a design for a simple house. A group of ten experienced architects (the “expert panel”) are then presented with the 20 house designs to assess. They are also given a detailed set of evaluation criteria, covering various

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functional and aesthetic requirements. The expert panellists then independently view and assess each of the house designs against the provided criteria, scoring them on a five-point scale. At the completion of this, the scores provided by experts are averaged (representing a consensus view) and analysed mathematically (to identify outliers and trends). The resultant data provides an effective measure of creativity, innovation or other criteria for each house design, which are also able to be linked to the process that developed the design. Thus, expert panel assessment provides a level of information about the product, whereas protocol analyses are used to capture and encode the process. In combination, they allow researchers to connect creative outputs to cognitive operations.

1.4.2 Evaluation Criteria The primary evaluation criteria for an expert panel to assess creativity typically comprise three main categories: novelty, usefulness and aesthetics, or variations of these. The subscales within these categories are often selectively adopted from either CAT (Amabile 1983b) or the Creative Product Semantic Scale (CPSS) (Besemer and O’Quin 1987) or a combination of both that is most useful for the purposes of the specific research question. CAT requires an analysis of inter-judge reliability in the ratings on each dimension (Amabile 1983a). Factor analysis on the various dimensions of judgement, including several subjective dimensions, should be conducted to determine the degree of independence between one and the other dimensions. The various versions of the CAT have several dimensions of criteria such as the creativity, technical and aesthetic dimensions. The CPSS also uses three dimensions—novelty, resolution and elaboration and synthesis—to rate creative design products and 70 bipolar subscales. Iordanova et al. (2009) add indicators such as abundance, flexibility, evolution and originality of ideas and dynamic factors can also be included in the criteria (Liu and Lim 2006). The total number of subscales of the evaluation criteria should also be carefully considered. A large set of subscales might be important for comparing design outcomes in detail, while the relatively small set is more useful for correlating results with other data, like that derived from protocol analysis. Although it is acknowledged that the large set can also be used for the correlation through the mean values of factors using factor analysis, it would be too time-consuming for measuring and analysing it. As an example, the evaluation criteria for measuring the creativity of a parametric design outcome are developed in three categories: Novelty, Value and Aesthetics (Table 1.2). The subscale criterion combines CAT (Hennessey and Amabile 1999) and CPSS (Besemer and O’Quin 1987), while subscales for parametric-design-specific elements, like “well transformation”, are added after a review of literature and models for parametric design. The subscales in Table 1.2 are also developed from the results of correlation and factor analysis of related works (Besemer and O’Quin 1987; Christiaans 2002; Hennessey and Amabile 1999). The validity of evaluation criteria can

1.4 Research Method II: Expert Panel Assessment Table 1.2 An example of evaluation criteria for creativity in parametric design

Novelty

23 Value

Aesthetics

Originality (Idea) Function

Aesthetic form

Complexity

Usefulness

Elegance

Evolutiona

Understandable form Well transformationa

a Specific

criteria referring to parametric design evaluation

be constructed by the analysis of inter-judge reliability and factor analysis (Amabile 1983a). In this example set of evaluation criteria, Novelty consists of “originality (idea)”, “complexity” and “evolution”. Originality refers to the degree to which the design itself displays a novel idea (Amabile 1983a). Complexity not only refers to the degree to which the design shows a level of intricacy (Amabile 1983a), but also relates to the features of a parametric design. Evolution is a specific subscale for parametric design that refers to the degree to which the design shows evolutionary and progressive features. The category Value is measured using three subscales: “function”, “usefulness” and “understandable form”. Function refers to the degree to which the outcome fulfils or demonstrates the function of a given design task. Usefulness represents the degree to which the design shows the quality of being of practical use (Besemer and O’Quin 1987). Understandable form refers to the degree to which the model makes sense and is sufficiently coherent or lucid in terms of its stated purpose. Aesthetics also includes three subscales: “aesthetic form”, “elegance” and “well transformation”. Aesthetic form refers to the degree to which the design is tasteful or appealing (Amabile 1983a). Elegance represents the degree to which the design is attractive or stylish (Besemer and O’Quin 1987). Well transformation is derived from the CPSS’s “well crafted” (Christiaans 2002) and refers to the degree to which the model has been technically transformed and is more suited in the parametric design environment. To employ these evaluation criteria, experts should be familiar with the specific design domain and the techniques for producing the design. A series of assessment tasks are also suggested to evaluate the design product. For example, in addition to the criterion-based assessment using the evaluation criteria, an “independent noncriteria-based assessment” and a “comparative non-criteria-based assessment” can be considered. The latter one, evaluating design models relative to one another, is frequently used in design research. Most of the experimental design protocols described in this book employ protocol analysis and expert panel assessment in parallel to explore design cognition and creativity. However, each study uses its own coding schemes and evaluation criteria to investigate particular research aims and objectives.

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1.5 Conclusion This book is intended to explore new ways of investigating and promoting design thinking in the digital Anthropocene. A triangular model comprising the themes of “creativity”, “collaboration” and “culture” is used to improve our integrated understanding of new ways of design thinking in the globalised design ecosystem. Each theme is explored through empirical studies that have been conducted by the authors over the last 10 years. The volume of protocol data and experimental results reported in this book are also significant, as too are the advanced and innovative methodological approaches presented, which are especially useful for the future generation of researchers in the field. Finally, this chapter commenced by proposing that design as verb and noun cannot or should not be easily separated, and the closing sections of the chapter have demonstrated why, in a methodological sense, this is also the case.

References Alexander, Christopher. 1964. Notes on the Synthesis of Form. Cambridge, MA: Harvard University Press. Amabile, Teresa M. 1983a. The Social Psychology of Creativity. Springer series in social psychology. New York: Springer. Amabile, Teresa M. 1983b. The social psychology of creativity: A componential conceptualization. Journal of Personality and Social Psychology 45 (2): 357–376. https://doi.org/10.1037/00223514.45.2.357. Arnheim, R. 1997. Visual Thinking. Los Angeles: University of California Press. Asimow, Morris. 1962. Introduction to Design. Englewood Cliffs, NJ: Prentice-Hall. Askland, Hedda Haugen, Michael J. Ostwald, and Anthony Williams. 2012. Assessing Creativity: Supporting Learning in Architecture and Design. Sydney: Office for Learning and Teaching. Atman, C.J., M.E. Cardella, J. Turns, and R. Adams. 2005. Comparing freshman and senior engineering design processes: An in-depth follow-up study. Design Studies 26 (4): 325–357. Besemer, S.P., and K. O’Quin. 1987. Creative product analysis: Testing a model by developing a judging instrument. In Frontiers of Creativity Research: Beyond the Basics, ed. S.G. Isaksen, 341–357. Buffalo, NY: Bearly Limited. Blosiu, J.O. 1999. Use of synectics as an idea seeding technique to enhance design creativity. In1999 IEEE International Conference on Systems, Man, and Cybernetics, 1999. IEEE SMC ‘99 Conference Proceedings. Tokyo. Boden, Margaret A. 2004. The Creative Mind: Myths and Mechanisms. London, New York: Routledge. Bonvillain, Nancy. 2010. Language, Culture and Communication: The Meaning of Messages. New York: Pearson. Brassett, Jamie, and John O’Reilly. 2015. Styling the future. A philosophical approach to design and scenarios. Futures 74: 37–48. https://doi.org/10.1016/j.futures.2015.07.001. Brown, Tim. 2009. Change by Design, How Design Thinking Transforms Organizations and Inspires Innovation. New York: Harper Collins Publishers. Brown, Tim, and Jocelyn Wyatt. 2010. Design thinking for social innovation. Stanford Social Innovation Review (Winter). Bucciarelli, Louis L. 2001. Design knowing & learning: A socially mediated activity. In Design Knowing and Learning: Cognition in Design Education, ed. Charles M. Eastman, W. Michael McCracken, and Wendy C. Newstetter, 297–314. Oxford: Elsevier Science.

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Part I

Creativity

Chapter 2

Design Strategies and Creativity

Abstract This chapter uses the results of two studies to develop an understanding of different types of design strategies and their connection to creativity in design. Two sets of experimental data are used to capture these strategies and then correlate them to readings of novice or expert practices, and the production of conventional or creative designs. The first study identifies three effective design strategies during the conceptual design stage: drawing-reflection, graphical-goal forwarding and textualgoal forwarding. The second study identifies two generative strategies in parametric design for developing creative solutions and products: problem-forwarding and solution-reflecting. The chapter explains these strategies and links them to past research about design cognition and creativity.

2.1 Introduction This chapter is primarily concerned with two themes. The first is the design process, viewed in cognitive and strategic terms. This encompasses the tactics, processes or sequences of activities designers employ to solve problems. The second theme is creativity, which refers to the level of innovation or originality implicit in the product of a design process. These two themes are often connected in past research, because one sign of a successful design process is that its products are demonstrably creative. Furthermore, a core value of design thinking is its capacity to support creativity. Given the importance of this connection, it is not surprising that multiple empirical studies have attempted to link broad design strategies or specific problemsolving techniques to creative outcomes (Goldschmidt and Smolkov 2006; Kokotovich 2008; Lee et al. 2015). Despite such research into the connection between design thinking and creativity (Amabile and Pillemer 2012; Batey 2012; So and Joo 2017), evidence about the precise strategies obor techniques to achieve this connection remains limited. In response, this chapter reports the results of two studies using protocol analysis and Consensual Assessment Techniques (CAT) to identify design strategies that may enhance creativity. The first study in this chapter is concerned with the cognitive processes of designers working in “traditional” pen-and-paper environments, using sketching as © Springer Nature Switzerland AG 2020 J. H. Lee et al., Design Thinking: Creativity, Collaboration and Culture, https://doi.org/10.1007/978-3-030-56558-9_2

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their main tool. By examining these processes, different cognitive and problemsolving strategies are identified and discussed. The study identifies that three of these cognitive strategies––drawing-reflection, graphical-goal forwarding and textual-goal forwarding—correlate to expert design and problem-solving processes. The results are analysed through reference to past theories and studies of problem-solving and goal-setting behaviours in design (Weth 1999). The second study in this chapter is about the cognitive processes of designers working in parametric design environments. Using protocol analysis, this study assesses the types and levels of creativity present in design outcomes. This allows for a direct correlation to be made between cognitive processes and creative products. The successful strategies identified in this study are problem-forwarding and solution-reflecting. Both studies in this chapter examine cognitive strategies in design, considering the complex and diverse combinations of tactics and techniques used by designers. Before the two studies are explained and the data presented, this introduction briefly outlines some key concepts and ideas. In design thinking, problem-solving is the process where solutions are found to the needs of clients, which are typically encapsulated in the functional performance requirements of products. Problem-solving involves “a sequential search, making small successive accretions to the store of information about the problem” (Simon 1978, p. 274). It can be thought of as the process of moving from one problemsolving state to another, by the way of evaluation functions (Goel and Pirolli 1992). Problem-solving involves combinations of analytic and synthetic thinking, divergent and convergent thinking or intuitive and lateral thinking. Newell and Simon’s (1972) information-processing theory defines problem-solving as the interaction between an information-processing system, the problem-solver and the task environment. The task environment is conceptualised in recent models as the “problem space” because it is where the problem is first defined or understood. The problem space can also be explained as the volume of cognitive activity that is bounded first by divergent and later convergent, thinking. In practice, undertaking a specific design task for a client or user involves understanding the design brief (the problem space) and formulating an effective response to this (the solution space). Multiple problem-solving approaches and techniques have been identified in the fields of psychology, cognitive science, computational intelligence, and design. Some of the most well-known of these use analogies, heuristics, synectics, lateral thinking and directional thinking. Each of these approaches is briefly summarised hereafter. • Problem-solving by analogy involves the adoption or adaption of solutions developed for analogous situations, treating these situations as a body of knowledge to draw on. This approach is common in both traditional and computational design processes (Choi and Kim 2017; Gero 2000; Rosenman and Gero 1993). • Heuristic problem-solving uses self-discovery or pragmatic “rules of thumb” to identify obvious or pragmatic solutions (Wang and Chiew 2010). Such solutions are rarely optimal or creative, although they can be refined and improved over time.

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• Synectic, collaborative and brainstorming problem-solving techniques all require groups to be actively involved in the process (Gordon 1961). Members of the groups may use analogical or heuristic problem-solving techniques; however, by working in a group the weaknesses inherent in these approaches are easy to overcome. “Idea seeding” (Blosiu 1999) is a variation of collaborative problemsolving, where a group of people are involved, but initially work separately proposing, developing and refining solutions. These solutions are swapped between other group members at regular intervals, before they finally work together to rank options and select the right solution. • Lateral problem-solving, like lateral thinking, uses reasoning, situational thinking or concepts that are not immediately obvious, as a catalyst for solution-finding. Lateral thinking approaches can lead to creative solutions, and especially when working in teams, although the solutions they generate are not always optimal (Karlusch et al. 2018). • The “working-forward” problem-solving techniques start with a detailed and directed consideration of problems, before proposing and testing solutions. In practice, they often combine goal and sub-goal setting and problem segmentation or decomposition. For example, the “hill-climbing” technique involves hierarchically setting sub-goals (arrayed from larger to smaller scale) and solving them, step-by-step, to reach an overarching solution. The “divide-and-conquer” technique decomposes a whole problem into a set of sub-problems which are then prioritised and solved (Wang and Chiew 2010). In this approach, priority may be given to sub-problems with obvious or parsimonious solutions (the “Occam’s razor” model), leaving more complex or wicked sub-problems to later. • The “working-backward” problem-solving technique starts with solutions that are then tested to see if they solve the problem. This is sometimes called a “nondirected” approach. One example of a non-directed approach is “trial and error”, which is sometimes called “backward reasoning”. Such strategies are often found in novice design processes (Ahmed et al. 2003; Lee et al. 2015). However, the combination of “hill-climbing” and “working-backward” approaches, known as a “means-ends analysis” (Simon 1981), is common and effective in some fields, including Artificial Intelligence (AI). The processes of defining and setting goals are often subsumed in problem-solving models, even though they are significant in themselves (Weth 1999). Furthermore, goal segmentation or the setting of sub-goals can be an effective approach to complex problems. The process of defining and sequencing sub-goals can be undertaken hierarchically (“most to least” important or “biggest to smallest” scale) or sequentially (“first to last”, as defined in a project brief). A “design strategy” is made up of the (i) type, (ii) combination and (iii) sequence of techniques and activities that occurs during the design process. Potentially, each and every designer has their own individual strategy that is unique to them (Lee et al. 2014). Designers from different backgrounds, cultures and environments will also approach problems differently (Lee et al. 2019). Similarly, designers with diverse levels of experience will often adopt different strategies. Past research has begun

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to qualitatively and quantitatively map some of these design strategies to types of outcomes and levels of experience. Kruger and Cross (2006), for example, demonstrate that design strategy is linked to both design quality and creativity, and they specifically investigate problem- and solution-driven strategies. They argue that the problem-driven strategy often produces a better balance between creativity and quality, while the solution-driven strategy tends to result in a more creative, but lower quality outcomes. Ho’s (2001) study of expert and novice designers’ strategies identifies multiple differences in their applications of problem decomposition, working-forward and working-backward strategies. Ahmed et al. (2003) also reveal that novice designers often rely on “trial and error” and are rarely aware that they possess, or are using a design strategy. The two studies that make up the core of this chapter examine design strategies used by participants in design experiments. They specifically identify the (i) types, (ii) combinations and (iii) sequence of activities in the design process. In this way, they characterise various strategies in terms of their problem-solving, goal setting, solution-finding and other characteristics. From there, it is possible to differentiate expert and novice strategies, and map specific strategies to creative outcomes.

2.2 Study I The first study highlights the results of a protocol analysis of designers’ sketching activities in a “traditional”, manual (non-digital) design environment. The study uses the protocol data to explore two important components of a creative design process: “problem-structuring” and “problem-solving” activities. This introduction to the first study provides a brief background as to why sketching is significant and why problem-solving and structuring are important processes. As previously noted, design is not a straightforward, linear and predictable process. It is often subjective, recursive and heuristic. It requires designers to engage with multiple complex cognitive processes and the concepts that arise through the processes of visually and verbally communicating an idea or proposition. As such, design cognition can be thought of as a product of the interplay between two distinct subsystems, visual imagery and verbal systems (Paivio 1971). The former subsystem comprises the representational language of sketches, drawings and models, whereas the latter is the spoken or written word. Probably, the most basic example of design representation is the sketch, and it is not surprising that it has been the regular subject of past research in design thinking (Tovey 1989). For example, Suwa and Tversky (1997) argue that sketches support the design process by crystallising ideas and concepts. Goldschmidt and Smolkov (2006) uncover the role sketching plays in design problem-solving, and Mathias (1993) highlights the importance of drawings and models as representations of the stages in design thinking. Importantly, the sketches produced in the design process provide a basis for empirical investigation of design using protocol analysis (1993). This is why the first study in this chapter is focussed on sketch-based design processes.

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Mathias (1993) identifies four categories or classifications of drawings and models in the design problem-solving process. The first, “drawing for analysis and initial ideas”, deals with the preliminary assembly of concepts and the rapid exploration of ideas through free-hand drawing. The second, “drawing for synthesis”, is concerned with detailed considerations to aid decision-making. The next, “style and nature of the drawing”, is concerned with the shift in representational content from concept to format that can occur in a drawing. The last, “the use of drawing to support thinking”, covers representations intended to model and test ideas. Mathias (1993) also presents a framework for considering design strategy, which considers modes of thinking (convergent, divergent, holistic, serial, analytic, synthetic) and levels of capacity or ability (novice or expert). This framework, which supports the interpretation of various modes of thinking in design stages, has parallels with several other design models, including two that emphasise the strategies adopted by designers. Jones’ (1963) ASE framework distinguishes three stages in the design process— analysis, synthesis and evaluation—to capture both logical and creative thought patterns. Akin (1978) identifies eight staged information-processing mechanisms used in developing design solutions. They are (i) information acquisition, (ii) problem interpretation, (iii) problem representation, (iv) solution generation, (v) solution integration, (vi) solution evaluation, (vii) perception and (viii) sketching. Akin also acknowledges that not all solutions arise from an analysis of all relevant aspects of a problem. As such, Akin (1979) questions the sequential assumptions in ASE and also argues that “analysis” can be a part of all phases of the design process. Gero also proposes that in the early stages of the design process “formulation” is a better description of the cognitive process, and “analysis” is more correctly “used to refer to a precursor of evaluation” (Gero 1999, p. 49). Gero’s division between formulation and analysis is also instrumental in several related versions of the design process modelling. For example, Hay et al. (2017) suggest that the conceptual design stage can be understood as being focussed on search and exploration. They argue that the “design as search” strategy addresses solution-seeking and problem-structuring behaviours, while the “design as exploration” strategy involves (i) co-evolutionary design, (ii) visual reasoning processes, (iii) cognitive actions and (iv) unexpected discovery or situated invention. The design and problem-solving models of Jones, Akin, Gero and Hay all connect design strategies to processes and outputs. Significantly, several of these design strategies have also been tied to the production of creative solutions (Kokotovich 2008). The first study in this chapter looks at the cognitive and behavioural strategies of a small group of designers. It uses protocol analysis to differentiate approaches to problem-solving and structuring activities in a traditional design environment, and comments on the alignment between strategies and theorised levels of expertise and creativity.

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2.2.1 Research Procedure The first study in this chapter has six participants (Ko1–Ko6), all Masters-level students in the Department of Interior Architecture Design at Hanyang University in South Korea. All participants had successfully completed at least one major designstudio project and had sufficient English skills to clearly understand a design brief written in English, as well as to enable general written communication. Despite all being enrolled in the same level of study, the participants could not be classified as either “experts” or “novices”, with all six having some professional design experience. Before each experiment was conducted, a research coordinator explained to all participants the design brief (Box 2.1) and project site for the design (Fig. 2.1), which provide the stimulus for the study. All participants were also given time before the design experiment started to practice “thinking aloud”. The experiments were conducted serially, one after the other, with a gap between each.

Fig. 2.1 Building site

2.2 Study I

Box 2.1 Design Brief for Study I Multi-Generational Housing Design This project asks participants to examine a vacant block in a typical suburban street (drawings and support documents attached) and explore how to accommodate a multi-generational house. Participants are asked to illustrate their conceptual ideas for the design of a multi-generational house for inhabitants who range from young children to seniors. The concept design should suggest how a multi-generational housing community would better support sustainable living for inhabitants. Design Brief Design a house to accommodate and meet the needs of a multi-generational family in a vacant suburban block. Considerations • Environments should be created to be accessible for all users—including those who are challenged with mobility, dexterity or sensory/cognitive processing. • Environments should be considered to be safe for all users—including those who are very young or senior occupants. • Environments should balance both communal living requirements and the need for privacy. • Planning considerations to maximise limited space • Indoor environments should have physical and visible connection to outdoor environments. Occupants • • • •

Couple aged 70–90 with limited mobility Couple aged 35–45 2 children aged 0–12 Occupants can be from diverse linguistic and cultural backgrounds.

Deliverable: Sketch(s) of four design stages/components of the multigenerational housing design: 1. 2. 3. 4.

Initial conceptual design Site layout(s) of the multi-generational house Plan(s) Elevation(s) and/or section(s)

Time limit: 45 min.

39

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2 Design Strategies and Creativity

Camera 1

Camera 2

Fig. 2.2 Perspectives of two cameras

(i)

Experimental setting. Participants were asked to take part in a 45-min experiment in their own studio space. They undertook a specified design task, “multigenerational housing design”, with pencil-and-paper tools on a normal desk. While participants were allowed to use their own tools, they were all provided with yellow tracing paper, A3 white paper and pencils. Two video cameras were installed: one (Camera 1) focusing on the desk to capture sketch activities or processes, and the other (Camera 2) on the participant from the side, to give a clear view of the designer’s activities (see Fig. 2.2). One voice recorder was also installed to capture the designer’s “thinking aloud” data. Participants were able to stop their design session at any time, but they were encouraged to complete at least one of the deliverables in the 45-min period. The deliverables were initial conceptual design(s), site layout(s), plan(s), elevation(s) and/or section(s). After each design experiment, all deliverables were collected. (ii) Verbalisation. Participants were asked to verbalise or describe their thoughts and activities using their own language (Korean). The only time the research coordinator spoke during a session was to remind participants, if needed, to “please keep talking” in order to achieve effectively “think-aloud”. (iii) Transcription and segmentation. Two native Korean speakers transcribed and segmented the recorded data, according to cognitive activities (codes) specified in the coding scheme (Table 3.1). The first coder, who had over 5 years of experience in art and design practice, transcribed Camera 1’s video recording data with the support of voice recordings and segmented the data for analysis. The second coder having over 7 years of experience in protocol analysis in design undertook segmentation of the transcribed protocol data by the first coder, mainly using Camera 2’s video recording to capture further changes in cognitive activities. Consequently, the second coder produced more segments for each protocol. (iv) Encoding with a coding scheme. The coding scheme (Table 2.1) used for this study is a modified version of Suwa et al.’s (1998) scheme, consisting of five categories (representation, perception, function, evaluation and goal setting). The representation category consists of three physical design activities, Rdrawing, R-writing and R-label, which often occur during a sketch-based design

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Table 2.1 Coding scheme for exploring the traditional design process Category

Subclass

Representation R-drawing R-writing Perception

Description Make drawings (geometries) and/or geometric ideas Write texts

R-label

Make labels (or titles)

P-drawing

Attend to drawings

P-brief

Attend to a design brief or its summary or a (given) site

P-writing

Attend only to participant’s own writing

Function

F-interaction Explore the issues of interactions between artefacts, people and nature F-reaction

Consider psychological reactions of people

Evaluation

E-drawing

Evaluate existing drawings

E-writing

Evaluate existing texts

Goal setting

E-label

Evaluate existing labels

G-initial

Introduce goals (ideas) based on a given design brief

G-sub

Introduce new goals extended from a previous one

G-repeat

Repeated goals from a previous segment

process. The perception category consists of three subclasses, P-drawing, Pbrief and P-writing. In particular, P-brief captures reviewing a design brief as well as any site information made available, which is sometimes followed by introducing new goals (ideas). In the function category, F-interaction is related to exploring the interactions between artefacts, people and nature, while Freaction records activities associated with the psychological and emotional reactions of people. The evaluation and goal-setting categories are related to Suwa et al.’s conceptual level of cognitive actions. Both categories facilitate identification of cognitive patterns and design strategies. If a segment can be encoded using more than one code, it is encoded using the lower category code, because processing at an upper level is based on that at a lower level (Suwa et al. 1998). (v) Arbitration and intercoder reliability. The final protocol data used for the analysis were developed through an arbitration process where both coders agreed on the segmentation and coding of each protocol. Table 2.2 describes the results of segmentation and intercoder reliability of each protocol. The average time duration of a design protocol was 42 min 29.4 s, and the average number of segments was 173.67. The average time per segment was 15.03 s. The average score of percent agreement in the table is slightly lower than the overall target of 80%. Krippendorff’s α values—measuring intercoder reliability for nominal data with missing value—are in the range of “tentative conclusions” for items with α values between 0.80 and 0.67 (Krippendorff 2011, 2018). While it is customary to seek results were α ≥ 0.800, the scores in this first study are

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Table 2.2 Results of segmentation and intercoder reliability of each protocol Protocol code

Time duration

Num. of segments

Time per segment (second)

Intercoder reliability Percent agreement (%)

Krippendorff’s alpha

Ko1

45 min 1 s 137

19.72

76.83

0.677

Ko2

30 min 2 s 136

13.25

77.17

0.590

Ko3

44 min 52.8 s

178

15.13

77.40

0.676

Ko4

46 min 9.3 s

227

12.20

78.35

0.670

Ko5

46 min 1 s 159

17.36

73.17

0.686

Ko6

42 min 50 s

205

12.54

87.65

0.826

Average

42 min 29.4 s

173.67

15.03

78.43

0.688

common for a cognitive protocol analysis identifying individual design strategies. As Stevens et al. (2014) note, in many studies Krippendorff’s upper and lower standards must be reduced due to the presence of items that are either highly discrete (0.70 and 0.58) or highly distributed (0.40 and 0.33). (vi) Normalisation of Time Duration. Although participants were notionally given 45 min to complete the design task, one designer (Ko2) took less time, and two (Ko4 and Ko5) took longer time (Table 2.2) To accommodate these differences, the coding results refer to the percentage of the frequency weighted by time duration of each code (Table 2.3).

2.2.2 Coding Results The protocol data for the first study reveal that, on average in the design experiment, representation activities account for 53.6% (R-drawing: 45.4%, R-writing: 3.4%, Rlabel: 4.8%); perception activities account for 23.0% (P-drawing: 16.3%, P-brief : 4.7%, P-writing: 2.0%); function activities account for 12.3% (F-interaction: 11.4%, F-reaction: 0.8%); evaluation activities account for 6.8% (E-drawing: 6.5%, Ewriting: 0.4%); and goal-setting activities account for 4.3% (G-initial: 1.7%, G-sub: 2.2%, G-repeat: 0.3%) (Table 2.3). All participants produced relatively large amounts of representation (or physical) activities (particularly, R-drawing, 45.4%) and only relatively small amounts of goalsetting (4.3%) activities. On average, they also produced smaller volumes of the lower category of cognitive activities than the upper category in the scheme hierarchy. This pattern might seem significant, but individual cognitive allocations are quite different in Table 2.3. For example, Ko1 produced the largest amount of both R-drawing and F-interaction and the smallest amount of P-drawing (the fifth-ranked subclass in

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43

Table 2.3 The distributions of the coding results (the percentage of time duration) Category

Subclass

Designer Ko1

Representation R-drawing

Perception

Function Evaluation

Goal setting

Sum

Mean SD Ko2

Ko3

Ko4

Ko5

Ko6

54.5

53.3

51.7

49.2

23.2

40.6

R-writing

2.4

0.0

2.5

4.1

9.6

1.9

3.4

3.3

R-label

6.7

3.4

6.7

5.0

2.9

3.8

4.8

1.7

P-drawing

6.3

19.1

14.6

19.7

12.1

26.1

16.3

6.9

P-brief

2.1

0.7

6.2

4.0

10.5

4.7

4.7

3.4

P-writing

0.0

0.0

0.0

0.0

12.0

0.0

2.0

4.9 4.3

F-interaction

45.4 12.0

16.4

15.3

7.9

5.1

12.6

11.2

11.4

F-reaction

1.2

0.9

0.0

0.0

1.6

1.3

0.8

0.7

E-drawing

7.0

5.3

2.6

11.7

3.4

8.8

6.5

3.4

E-writing

0.0

0.0

0.0

0.0

2.1

0.0

0.4

0.9

E-label

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

G-initial

1.4

0.5

2.2

0.5

4.2

1.6

1.7

1.4

G-sub

1.8

1.4

3.6

0.7

5.8

0.0

2.2

2.1

G-repeat

0.0

0.0

2.0

0.0

0.0

0.0

0.3

0.8

100.0

100.0 100.0 100.0 100.0 100.0 100.0

–

her protocol), while the others generally focussed on drawings (the second-ranked subclass in their protocols, except for Ko5). The results indicate the presence of multiple cognitive patterns and preferences, or different thinking styles and design strategies.

2.2.3 Sketch-Based Design Strategies The results in study one indicate that half the participants (Ko1, Ko3 and Ko5) adopted specific design strategies, while the remainder used ad hoc or more idiosyncratic methods. For example, Ko1, Ko3 and Ko5 produced more goal-setting activities than the other three (see Table 2.3). Goal-setting activities are identified in both Simon’s (1973) and Jones’ (1963) models as being part of a conventionally structured design strategy. Notably, experts often establish sub-problems or reformulate problems in the design process, while novices tend not to employ this approach (Lindström 2006). As such, it could be argued that Ko1, Ko3 and Ko5 exhibit signs of a more advanced or experienced design strategy. In contrast, Ko2, Ko4 and Ko6 spent more time “attending” to their drawings (P-drawing). A relatively small proportion of the perception code for “attend to drawing” can be associated with “incubation” moments, when contemplating a drawing leads to creative insights. However, too much P-drawing activity may indicate a “trial-and-error” strategy or a “lack of confidence” in a decision, both behaviours typical of novice designers (Ahmed et al.

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2003). Thus, the remainder of this study focusses largely on the sketch-based and deliberative design strategies of Ko1, Ko3 and Ko5, as examples of problem-solving strategies and cognitive activities during the design process. Figure 2.3 depicts the three selected designers’ representations over time for the design sessions. In the data Ko1 adopts a design strategy that commences with drawing a plan and then develops solutions and refinements through iterative graphical reflection (“drawing-reflection”). Where novice designers (Ko2, Ko4 and Ko6) also drew and contemplated or evaluated drawings (P-drawing, E-drawing), they did so without goal setting or clear direction. In contrast, Ko1 continuously evolved

Ko1

Ko3

Ko5 Fig. 2.3 Designers’ evolving representations over time

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45

her designs and produced sequential deliverables at each step. Ko3’s data initially develop a bubble diagram encapsulating initial and sub-goals and subsequently generate detailed solutions (“graphical-goal”). He also develops a further graphicalgoal (G-sub) for a site layout at the end of his design session. Ko5’s cognitive strategy commences with a written summary of goals which he continues to use to inform the generation of designs (“textual goals”). He also uses bubble diagrams to develop design solutions, rather than for goal-setting purposes. It is also informative to review the data describing the behaviours and cognitive strategies of these designers (Fig. 2.4). Ko1, for example, continuously explores the interactions between artefacts, people and nature (F-interaction) in plan layouts focussing on related deliverables until her design session ends. She exhibits lower proportions of perception and evaluation activities, but they are well distributed across the session. Her initial goal also evolves over time. Thus, her protocol

Ko1

Start

Time

End

Time

End

Time

End

Ko3

Start

Ko5

Start

Fig. 2.4 Individual patterns of cognitive activities of different categories over time

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produces the highest percentage of representation” (63.7%) and function (17.7%) and the lowest percentages of perception” (8.4%) (Table 2.3). She adopts a so-called “drawing-reflection” strategy and continuously generates sequential design solutions. Thus, her design strategy is close to an expert’s “drawing-reflection forwarding strategy” which differs from a novice’s less directed “drawing-reflection behaviour”. Both Ko3 and Ko5 develop clear goals at the beginning of the design session and continue to use initial goals for the generation of designs. Both designers also subsequently generate detailed designs based on “graphical goals” or “textual goals”, respectively. Their cognitive patterns show that they start by exhibiting more goal-setting and perception activities—mainly G-initial and P-brief , respectively (Fig. 2.4). They then produce more function and evaluation activities in the middle and towards the end of their sessions. Thus, Ko3’s strategy is characterised as a “graphical-goal forwarding strategy”, while Ko5’s is a “textual-goal forwarding strategy”. By comparing not just the frequency of behaviours, but also their timing, it is possible to construct a picture of different design strategies and cognition processes (Fig. 2.4). For example, across all three strategies (as exhibited in Ko1, Ko3 and Ko5, respectively) there is a relatively consistent level of representation activities, and perception activities also take place throughout all three protocols. There is, however, a different density or frequency of perception activities, with Ko3 being more reliant on this behaviour, and Ko1, far less. Despite being able to clearly identify patterns in cognitive activity in this data, and categorise these as expert or novice, the results cannot be directly tied to creativity. Rather the link to creativity is by way of past research, outlined previously in this chapter, which connects particular problem-solving strategies to potentially innovative outcomes.

2.3 Study II The “environment” for the first study was a traditional design context, involving “pencil and paper” on a desk in a studio. For the second study, the environment is parametric design, or algorithmic scripting in a computer using software. In a traditional design environment drawn objects only have shape and are only seen and manipulated as shapes. Thus, for example, a sketch of a rectangle might represent a door, but the sketch itself has only its visible properties, few of which are actually associated with the door. In contrast, in a parametric design environment objects are defined by innate rules, properties, configurations and dependencies which are visible and able to be manipulated (Monedero 2000). For example, a parametric object may be coded as a door, being at least human height and width, having an associated frame, hinges and handle position, all of which will alter if the basic dimensions of the door are changed. The fact that the door has a rectangular shape is just a by-product of the rules that define its parameters. As such, objects are defined in this environment in terms of mathematically formulated properties, rather than shapes on a page. The major software tools used for this purpose in parametric design are Grasshopper (a

2.3 Study II

47

plug-in for Rhino), CATIA and Generative Components. Each of the programs uses “scripting” to enable design (Holzer et al. 2007). Scripting, sometimes called “algorithmic activity”, is the process of writing or graphically programming parameters, rules and their topological relationships (Salim and Burry 2010; Burry 2011). In the parametric design, process scripting replaces sketching as a mode of design production, although its impact is more profound than this. A script is a formulation of the parameters that need to be employed to create an effective design. Authoring a script potentially emphasises the problem-definition part of the design process. A script can also automatically generate many hundreds of variations of a compliant design solution, providing unprecedented opportunities for design exploration. It is not surprising, given these properties, that parametric design has been repeatedly linked to creativity. It supports a potentially innovative approach to designing, and especially insofar as it is capable of generating creative design solutions at the conceptual design stage (Iordanova 2007; Lee et al. 2015). However, parametric design activities and their impacts on the design process and product are not well understood. Furthermore, relatively little is known about the generative and algorithmic strategies used by parametric designers and their impact on creativity. The second study in this chapter responds to this gap in our knowledge by analysing the activities and strategies used by designers in a parametric environment. It then correlates the outcomes of these strategies to expert assessments of the levels of creativity apparent in the products of these strategies. In this way, the study examines the relationship between design strategies and creative outcomes. Before describing the experiment and its results, the following section briefly considers some theories connecting design thinking, parametric design and creativity.

2.3.1 Parameter and Rule In design thinking, a divergent approach is one that widens the scope or breadth of consideration of an issue, encompassing more factors, possibilities and responses. In contrast, a convergent approach is one that narrows the scope or breadth of possibilities being considered, focussing on potential solutions. In parametric design, there are aspects of both of these cognitive dimensions embedded in the scripting processes used by designers. In a script, for example, the “parameter” component promotes and acknowledges divergence, whereas the “rule” supports and even necessitates convergence. These divergent and convergent processes, in design thinking and in parametric design, are tied to different aspects of creativity and problem-solving. A “parameter” is a factor defining a range of variation (Monedero 2000; Cardenas (2008). While the concept of the parameter originates in mathematics, in design it has been used to describe the scope or extent of design possibilities being investigated. This is especially the case when considering the generation of design variations, where a divergent approach is often associated with creativity (Kolarevic 2003; Iordanova 2007). Parametric design not only makes this divergent generative activity

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relatively effortless (Roberto 2006), it allows designers to rapidly explore different ideas without reliance on their own drawing skills (Lawson 2002). The capacity to generate variations is not only seen as a precursor to pursuing creative solutions, but also to extending the boundaries of knowledge (Gero 1996; Liu and Lim 2006). The generated variations, however, can be abstract and difficult to grasp, and identifying a creative outcome from hundreds of alternatives remains a challenge in the field (Hanna and Turner 2006). A “rule” defines the relations between elements that must be maintained when generating variations (Lee and Kim 1996; Sutherland 1963). As such, rules can be conceptualised as tools supporting convergent strategies as they serve to narrow the set of possible solutions. A rule also implies the presence of knowledge, reflecting an understanding of the operations or dependencies implicit in a relationship. Knowledge is one of the important factors supporting creativity in design (Li et al. 2007). For example, encoded architectural knowledge, linked to rules pertaining to structure, climate and composition, provides a new way of exploring options and using architectural expertise (Iordanova 2007). The associative modelling, for example, considers the parametric constraints of user needs, functional requirements and structural demands (Park et al. 2004). While such rules in the traditional design environment might hinder creativity, in a parametric environment they may support it through control of variations. The parameters and rule functions in parametric design are akin to Roberto’s (2006) variable and fixed attributes, and have parallels to Jones’ (1992) observation of the coexistence of intuition and rationality in a design process. These three conceptual pairings—parameter/rule, variable/fixed attributes and intuition/rationality—point to ways the strategies used by parametric designers may be understood or interpreted. Significantly, whereas in a traditional design environment there is a level of interpretation required to differentiate such pairs, in a parametric environment the script actually requires the identification of the two. This means parameters and rules are relatively straightforward to detect, encode and analyse. Figure 2.5 illustrates exam-

Parameters

Rules

Parameters

Rules

a. Graphical algorithm editor (Grasshopper)

b. Text-based algorithm editor (Python)

Fig. 2.5 Examples of parameters and rules in two different parametric design environments

2.3 Study II

49

ples of parameters and rules in two different parametric design environments (a graphical algorithm editor and a text-based algorithm editor).

2.3.2 Research Method The second study in this chapter combines protocol analysis with the Consensual Assessment Technique (CAT) to explore individual design strategies and creativity. Protocol analysis is used in a similar way to the first study in the chapter, to identify the behaviours, activities and strategies of participants taking part in a design experiment. CAT is then used to assess the creativity present in the products of the experiments. This study is significant because the vast majority of claims made about the creativityenhancing properties of parametric design have little empirical evidence to support them.

2.3.2.1

Protocol Analysis

Four postgraduate architectural design students in Australia—two with over 5 years’ parametric design experience (Au1 and Au3) and two with only 1 year of experience (Au2 and Au4)—took part in this study. Each of the participants was given one hour (although the actual timing was flexible) to undertake a specified design task (see Box 2.2). The task was the conceptual design of a high-rise building that was required to meet performance requirements for building function, floor area, height, structural expression and visual expression. Deliverables of the experiment were design representations of the high-rise building to satisfy the brief, including (i) threedimensional model(s) and (ii) three rendered or captured images clearly representing the design concept and building. Before each experiment, a research coordinator explained the design brief to participants and all were given time to practice “thinking aloud”. Box 2.2 Design Brief for Study II Design Brief: Conceptual Design of a High-rise Building You are asked to provide a conceptual design of a high-rise building. This is a form generation design task for a landmark high-rise building. The following can be considered: (1) The high-rise building will have two main functional areas: Office and hotel. (2) Maximum floor area is 2,500 m2 (50 m × 50 m) per floor. (3) The high-rise building will have over 40 storeys, and it will be designated as a regional landmark.

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2 Design Strategies and Creativity

(4) You can reflect transformations of structural forces using external data. The basic structural design may be represented in the conceptual design (e.g. columns at the exterior or interior of the building). You should consider Novelty (e.g. originality, complexity and evolution), Value (e.g. function, usefulness and understandable form) and Aesthetics (e.g. aesthetic form, elegance and well transformation) for the conceptual design. A new creative architectural vision depends on you. At this early design phase, no site or construction constraints have been stipulated, with a client seeking a highly creative quality outcome as a priority. Deliverable: Design representation(s) of the high-rise building that should satisfy the brief and produce (1) three-dimensional model(s). In order for you to clearly represent the conceptual design, (2) three rendered or captured images showing the strength of your design should be saved on your desktop. Timeline: One hour.

(i)

Experimental setting: Each design session was video recorded using two cameras, one providing a view of the student’s overall activities and the other recording the computer screen. Participants were allowed to choose their own parametric modelling tools. Two (Au1 and Au2) of the four participants chose Grasshopper, a graphical algorithm editor, while the other two (Au3 and Au4) used a text-based editor (Python and Maya script editor, respectively). The design models produced in each session were collected at its conclusion. (ii) Verbalisation: A concurrent verbalisation (“think-aloud”) method was used. After the experiment, each designer also participated in a post-experiment interview leading to a retrospective protocol, to explain their thoughts and activities in the experiments. (iii) Transcription and segmentation: Each video session was directly transcribed using NVivo software and segmented into smaller episodes. (iv) Encoding with a coding scheme: One coder encoded each protocol using the coding scheme (Table 2.4). For this study, three levels from Suwa et al.’s (1998) scheme (physical, perceptual and conceptual) were adapted and revised to be more suitable for a parametric design environment. Designing in parametric environments involves both geometric and algorithmic activities, which are used as two categories to inform the physical and perceptual levels in the coding scheme. The physical level in the coding scheme represents generative activities and activities in parametric design. Geometry activities are the modelling activities for generating digital forms, while algorithmic activities represent the generative processes that describe the parameters and rules used, as well as the “reference”. The perception level represents the activities related to visual imagery in the design process. This level also has two categories of geometry

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51

Table 2.4 Coding scheme for exploring the parametric design process Level

Category

Subclasses

Description

Physical

Geometry

G-Geometry

Create geometries without an algorithm

G-Change

Change existing geometries

Algorithm

A-Parameter

Create initial parameters

A-Change Parameter

Change existing parameters

Perceptual

A-Rule

Create initial rules

A-Change Rule

Change existing rules

A-Reference

Retrieve or get references

Geometry

P-Geometry

Attend to existing geometries

Algorithm

P-Algorithm

Attend to existing algorithms

F-Initial Goal

Introduce new ideas (or goals) based on a given design brief

Conceptual Problem-finding

F-Geometry Sub Goal Introduce new geometric ideas F-Algorithm Sub Goal Introduce new algorithmic ideas Solution-generating G-Generation

Make generation (or variation)

Solution-evaluating E-Geometry

Evaluate primitives or existing geometries

E-Parameter

Evaluate existing parameters

E-Rule

Evaluate existing rules

E-Reference

Evaluate existing references

and algorithm. The conceptual level consists of three categories of activities: problem-finding, solution-generating and solution-evaluating (Gero and Neill 1998). This final level is closely related to problem-solving processes (Dorst and Dijkhuis 1995; Ho 2001) as well as selection and evaluation (Dacey and Lennon 1998), which may support design creativity. The solution-generating category is also essential for capturing the generative aspects of parametric design. (v) Arbitration and reliability: To ensure consistency and validity, the coder segmented and encoded each protocol twice with a 3-month interval between the processes. A final protocol coding was achieved using a process of arbitration. There was little conflict between the first and second coding results because most subclasses in the coding scheme developed for this study represent only algorithmic or geometric activities that are easy to identify and interpret. There was also a high degree of clarity in activities captured by the video which could be readily interpreted with the assistance of the designer’s verbalisations.

52

2.3.2.2

2 Design Strategies and Creativity

Consensual Assessment Technique

While the protocal analysis results can be used to identify cognitive patterns and strategies in each participant’s design process, a different technique must be used to measure the creativity arising from these processes. The technique used in this study is CAT. For this study, a panel consisting of seven expert judges provided assessment of the four outputs. Each expert had more than 10 years’ experience as a designer and assessor of design in a tertiary institution (university). All assessments were undertaken individually, and all design outputs were “blinded”. Participants in the experiments were not made aware of the identities of the expert judges. Each design was presented to the experts as a collage of images on A4 size paper, all design products being similarly scaled for consistency of evaluation (Fig. 2.6). The judges assessed the designs using three evaluation frameworks: (i) independent noncriteria-based assessment of creativity; (ii) comparative non-criteria-based assessment of creativity; and (iii) criteria-based assessment of creativity using novelty, usefulness, complexity and aesthetics. Each assessment used a seven-point Likert scale (from 1 to 7, with 7 being the highest).

Fig. 2.6 Four design outcomes generated by the participants

2.3 Study II

53

2.3.3 Results 2.3.3.1

Coding Result for Each Protocol

The average value of the number of segments of the four protocols is 297.8 (Au1: 220, Au2: 319, Au3: 364, Au4: 287). Over 90% of each protocol was encoded using the coding scheme, regardless of the two types of applications: graphical algorithm editor or text-based algorithm editor. This confirms that the coding scheme is effective for capturing the protocol data. Only Au1 completed the design in less than the given time (48 min), while the others each took longer (approximately 90 min). The overrun in time was allowed because of the need for additional troubleshooting processes required by participants Au2, Au3 and Au4. Parametric design tools also require the user to wait for the design to be generated, a factor which differs from one computer to another. For example, there were 17 segments of “waiting” in Au3’s protocols, contributing to the need for extra time.

Table 2.5 The percentage of coding results Level

Category

Subclass

Au1

Au2

Au3

Au4

Mean

SD

Physical

Geometry

G-Geometry

2.0

6.6

0.0

1.3

2.5

2.9

G-Change

0.5

9.3

0.0

3.0

3.2

4.3

A-Parameter

4.9

2.6

0.1

2.9

2.6

2.0

A-Change Parameter

10.0

1.7

4.7

1.0

4.4

4.1

A-Rule

24.7

20.5

20.0

19.0

21.1

2.5

A-Change Rule

6.0

4.3

12.2

26.9

12.4

10.3

Algorithm

A-Reference

0.0

0.0

0.2

2.8

0.8

1.4

P-Geometry

2.8

1.3

1.8

0.4

1.5

1.0

Perceptual

Geometry Algorithm

P-Algorithm

3.2

2.9

4.0

8.6

4.7

2.7

Conceptual

Problem-finding

F-Initial Goal

3.0

0.9

0.2

1.1

1.3

1.2

F-Geometry Sub Goal

7.0

8.7

2.5

2.3

5.1

3.2

F-Algorithm Sub goal

4.1

4.0

4.4

1.0

3.4

1.6

Solution-generating

G-Generation

4.0

2.0

13.0

3.0

5.5

5.1

Solution-evaluating

E-Geometry

19.4

18.8

17.3

8.8

16.1

5.0

E-Parameter

0.8

0.0

0.0

0.0

0.2

0.4

E-Rule

7.6

16.3

19.5

17.9

15.3

5.3

E-Reference

0.0

0.0

0.1

0.0

0.0

0.1

100

100

100

100

100

–

Sum

54

2 Design Strategies and Creativity

Table 2.5 shows the percentage of the frequency weighted by time (calculated by the time of the duration of each coded protocol). This allows for the time devoted to each component of the design strategy to be determined, as well as time devoted to each level of design thinking for each participant. On average, physical behaviours account for 46.8% (geometry: 5.7%, algorithm: 41.1%), perceptual account for 6.3% and conceptual account for 46.9% (problem-finding: 9.8%, solution-generating: 5.5%, solution-evaluating: 31.6%). Solution-evaluating processes dominate in the conceptual level, while algorithmic activities are dominant in the physical level for each protocol. A-Rule (writing an algorithmic rule) and A-Change Rule (changing an algorithmic rule) were the dominant activities in the study. These algorithmic representations could be a preferred medium for progressing design in a parametric environment, being akin to producing a sketch, and then modifying it in a traditional design environment. The second most dominant activity is E-Geometry (visually evaluating the outcome of a rule) and E-Rule (evaluating the rule itself). A large amount of EGeometry appeared in the protocol of Au1, whereas E-Rule was dominant in the other protocols for the verification of their design algorithms. Because parametric design can produce solutions that are unexpected, the outcomes must be regularly evaluated in the 3D view as well as in the scripting view. There were also some other notable differences between the participants, including that the first three designers regularly created new rules (A-Rule) rather than changing existing ones (A-Change Rule), while the last designer (Au4) tended to change existing rules. The number of coded segments of the last protocol (Au4) was also the highest, and his problemfinding activities were also relatively lower. This participant tended to solve problems by introducing small variations. This is consistent with the trial-and-error procedure of a novice problem-solver who may lack analytical or scoping skills and strategies. Au4 was also the least experienced of the designers who took part. Au3, who was a more experienced designer, also used some trial-and-error or backward reasoning, although his generative synthesis was significantly higher than the other participants. Au1 and Au2 contributed relatively higher percentages of their time to the problem-finding activity. Both used a graphical algorithm editor, which led to more F-Initial Goal and F-Geometry Sub Goal activities for problem-finding. Au1 in particular, who was identified as a more experienced designer, applied strategic rules from the initial states of the problem, which is a feature of the working-forward search strategy. One finding of the study related to Au3’s performance, which demonstrated a high level of both generative and evaluation activities (algorithm level). His approach may have provided an advantage in generating creative design variations, because these activities are related to both the problem and solution spaces in the “co-evolution process” (Dorst and Cross 2001; Maher and Poon 1996).

2.3.3.2

Level of Creativity of Individual Design Solution

The results for CAT are reported in Tables 2.6 and 2.7. Table 2.6 shows the independent non-criteria-based assessment of creativity by the judges (J1–J7). In these

2.3 Study II

55

Table 2.6 Independent non-criteria-based assessment of creativity (J: Judge) Designer

J1

J2

J3

J4

J5

J6

J7

Mean

SD

Au1

5

5

2

5

3

5

2

3.86

1.46

Au2

5

5

1

4

1

6

5

3.86

2.04

Au3

6

7

5

7

5

6

4

5.71

1.11

Au4

4

2

2

4

1

3

5

3.00

1.41

Table 2.7 Criteria-based assessment of creativity (J: Judge) Designer

J1

J2

J3

J4

J5

J6

J7

Mean

SD

Au1

6

7

4

5

4

4

1

4.43

1.90

Au2

6

5

3

4

1

5

5

4.14

1.68

Au3

7

7

7

7

6

6

6

6.57

0.53

Au4

4

2

2

3

2

1

2

2.29

0.95

Au1

5

7

3

5

3

4

5

4.57

1.40

Au2

4

5

3

4

2

6

7

4.43

1.72

Au3

6

7

2

7

4

2

2

4.29

2.36

Au4

4

3

3

4

4

4

6

4.00

1.00

Au1

3

6

3

5

5

6

5

4.71

1.25

Au2

4

5

4

3

4

4

3

3.86

0.69

Au3

5

7

6

7

7

7

6

6.43

0.79

Au4

3

2

2

4

2

1

3

2.43

0.98

Au1

5

7

1

5

3

3

1

3.57

2.23

Au2

2

2

1

2

1

4

3

2.14

1.07

Au3

3

2

5

6

5

7

4

4.57

1.72

Au4

6

1

4

4

4

1

5

3.57

1.90

Novelty

Usefulness

Complexity

Aesthetics

results, Au3’s design receives the highest mean score for creativity (5.71), while Au4 receives the lowest (3.00). Six judges identified Au1’s design as the most creative relative to the criteria and most judges assessed Au2’s and Au4’s designs as the least creative. Table 2.7 shows the results of the criteria-based assessment of creativity using novelty, usefulness, complexity and aesthetics. For the novelty criteria, there is a wide range of results, and the rank order from highest to lowest is Au3 (6.57), Au1 (4.43), Au2 (4.14) and Au4 (2.29). For the usefulness criterion, the range of results is relatively narrow, perhaps reflecting the difficulty of assessing the functional properties of a concept or schematic design. The rank order from most to least is

56

2 Design Strategies and Creativity

Au1 (4.57), Au2 (4.43), Au3 (4.29) and Au4 (4.00). For the complexity criteria, the rank order matches that of novelty, being Au3 (6.43), Au1 (4.71), Au2 (3.86) and Au4 (2.43). Finally, the rank order for aesthetics is Au3 (4.57), Au1 and Au4 (equal, 3.57) and Au2 (2.14). The CAT data identify the most creative designs as those of Au3 and Au1, who were also the most experienced of the participants. The question then arises, which cognitive activities and strategies did they use?

2.3.4 Parametric Design Strategies As noted previously, the problem-finding and solution-generating codes that might be regarded as signs of an experienced and creative designer are not strongly present in two of the protocols: Au2 and Au4. The solution-generating codes only appear towards the end of Au2’s protocol, while the problem-finding codes rarely occur in the protocol of the last designer Au4. Instead, Au4’s protocol highlights physical level activities like A-Change Rule and algorithmic solution-evaluating codes like E-Rule (Table 2.5). These more novice behaviours do not conform to any of the predicted indicators of creativity, and the results were not assessed as being in the top two in terms of this criterion. Focussing on the design strategies of the experienced designers, who were also ranked as the most creative, reveals several patterns. Figure 2.7 illustrates the five Au1 Problem-finding Physical level SoluƟon-generaƟng SoluƟon-evaluaƟng (geometry) SoluƟon-evaluaƟng Start (algorithm)

Time

End

Time

End

Au3 Problem-finding Physical level SoluƟon-generaƟng SoluƟon-evaluaƟng (geometry) SoluƟon-evaluaƟng Start (algorithm)

Fig. 2.7 Patterns of different categories of parametric design thinking over time by Au1 and Au3

2.3 Study II

57

categories of cognitive activities in the parametric design experiments of Au1 and Au3. These are presented in sequence, in order to compare the changes over time. The primary similarities are that in both protocols the physical level and solutionevaluating (geometry) activities were dominant (in volume) and consistent (over time). Codes for problem-finding and solution-generating were, however, less consistent. For example, Au1 uses solution-generating behaviours only rarely, and mostly near the end of the time. Au3, however, uses them consistently from start to finish. The problem-finding codes also appear regularly in the protocols of Au1 and Au3. The data show that Au3 tends to use a design strategy highlighting both the problem and the solution spaces, a strategy that may have enhanced his creativity and high scores in the CAT assessment. Au1’s protocol shows the regular use of problem-finding with a small number of activities encoded as solution-generating (for example, G-Generation). These features imply that Au1 adopted a problem-driven strategy rather than a solution-driven one to produce the design. Figure 2.8 shows the three cognitive activities of problem-finding over time. It highlights problem decomposition strategies in detail. F-Initial Goal refers to the explicit introduction of the main problem, while the other two codes introducing sub-goals deal with sub-problems. A closer examination of Fig. 2.8 reveals that Au1 produced F-Initial Goal activities at the beginning, middle and end of the protocol. Au1 also sequentially decomposed the problem into geometric subproblems and then algorithmic sub-problems. This is consistent with “the explicit problem-decomposing strategy” identified in past research (Ho 2001). This strategy has similarities to Kruger and Cross’ (Kruger and Cross 2006) problem-driven strategy that produces results which may be more highly rated in terms of solution quality and creativity. Significantly, the level of usefulness of Au1’s design is higher than that of Au3’s design. This analysis of the data suggests that both the problem-decomposing strategy and the problem-driven strategy may facilitate the generation of creative solutions. Au1 F-IniƟal Goal F-Geometry SubGoal F-Algorithm SubGoal Start

Time

End

Time

End

Au3 F-IniƟal Goal F-Geometry SubGoal F-Algorithm SubGoal Start

Fig. 2.8 Three cognitive problem-finding activities over time by Au1 and Au3

58

2 Design Strategies and Creativity

The results of the study also indicate that Au1 and Au3 explicitly decomposed the initial problem into both geometric and algorithmic sub-problems (Fig. 2.8). Furthermore, Au3 regularly used the solution-generating activity (Fig. 2.7). He adopted a design strategy considering both the problem and the solution, creating solutions using sub-problems (Fig. 2.7, see also sequential sub-problems in Fig. 2.8). As such, the data for Au3 show a solution-driven strategy coupled with a reflective process, possibly reminiscent of Schön’s theory (1984). In contrast, Au2 and Au4 tended to use an implicit problem-decomposing strategy which is more common in novice designers. For example, Au2’s protocol demonstrates F-Initial Goal at the beginning, which does not sequentially relate to geometric and algorithmic sub-problems. Instead, it shows a “stop-start” pattern where each problem is solved as it emerges, rather than following a more creative working-forward search strategy. Au4 produced the code F-Initial Goal at the beginning and end of the protocol but rarely dealt with sub-problems. Overall, Au4 worked unsystematically using a backward-search strategy. From the coding results (Table 2.5) and the graphical analysis, two creative parametric design strategies can be identified: problem-forwarding and solution-reflecting generative strategies (Fig. 2.9). The problem-forwarding generative strategy, as a problem-driven strategy, explicitly decomposes the initial problem into both geometric and algorithmic sub-problems. Au1’s problem-decomposing strategy, combined with a working-forward search strategy, resulted in a sequential analyticsynthesis procedure to achieving a final design solution. The strategy shows the “inventiveness” (Lindström 2006) typical in an expert design process. It is also followed sequentially by a number of activities encoded as making generation (or variation). The solution-reflecting generative strategy, as a solution-driven strategy, highlights the generation of variations. Au3’s use of a solution-reflecting strategy received the highest score from the judges in terms of creativity. This is consistent with the results of Kruger and Cross’ (2006) research that suggests a solution-driven strategy produces higher creativity scores. In order to achieve a comprehensive solution, the solution-reflecting strategy often evolves into making variations and reflecting variations activities recursively (Fig. 2.9). In practice, however, designers probably use both of these parametric design strategies—problem-forwarding and solution-reflecting—to produce creative solutions, but each strategy may be more effective in achieving different qualities in the outcomes.

2.4 Design Strategies This chapter has identified individual design strategies that can support creativity in two different design environments (traditional and parametric design). Study I exploring sketch-based design activities reveals three forwarding strategies. • The drawing-reflection forwarding strategy involves representing an initial goal directly into a draft solution (drawing) and then continuing refinements through

2.4 Design Strategies

59

Goal

Goal

Goal

Sub-Problems

Sub-Problems

Sub-Problems

Synthesis

Synthesis

Synthesis

Generation

Generation

Generation

forwarding SoluƟon

+

forwarding

SoluƟon

SoluƟon

Problem-forwarding generaƟve strategy

Goal

Sub-Problems

Sub-Problems

Sub-Problems

GeneraƟon

GeneraƟon

GeneraƟon

SoluƟon

reflecting

SoluƟon

reflecting

SoluƟon

SoluƟon-reflecƟng generaƟve strategy Fig. 2.9 Two generative strategies for creative parametric design

iterative graphic reflection. This generates sequential design solutions over the initial design solution or it evolves the solution. • The graphical-goal forwarding strategy starts by exhibiting graphical goal setting based on design requirements and then decomposing the initial goal into a set of sub-problems to deliver a series of design solutions. • The textual-goal forwarding strategy is similar to the graphical-goal forwarding strategy, but it expresses initial ideas in text. The textual-goal is often followed by graphical goals or draft solutions (drawings).

60

2 Design Strategies and Creativity

In contrast, study II investigating parametric design activities reveals two generative strategies: • The problem-forwarding generative strategy combines a problem-decomposing strategy with a working-forward search strategy, dividing the initial goals (ideas) into both geometric and algorithmic goals and then synthesising them using stepby-step generations to reach an overarching solution. • The solution-reflecting generative strategy starts by generating a quick solution from an initial goal and then develops new sub-problems reflecting the previous solution until a solution meets the initial goal or given design requirements.

2.5 Conclusion This chapter has examined the relationship between cognitive and problem-solving strategies and creativity. The first study in this chapter identifies three effective design strategies in a sketch-based environment: drawing-reflection, graphical-goal forwarding and textual-goal forwarding. In the second study, two parametric design strategies, the problem-forwarding and solution-reflecting generative strategies, are directly mapped to theories about, and assessments of, creativity. The results of these two studies provide insights into both traditional and parametric design processes that help to differentiate novice from expert practices and competent from creative outputs. The two coding schemes used for these studies successfully capture design strategies that supported problem-solving processes in sketch-based and parametric environments, but also support the co-evolution of problem and solution spaces. However, as is common in protocol analysis, the sample sizes for both studies are limited, even if large bodies of data are generated and used in the analysis. In addition, the raw scores of the intercoder reliability test for the first study were only in the range of “tentative conclusions”. However, such measures were developed largely for content analysis not cognitive analysis, and different benchmarks would be expected. Nonetheless, in this chapter at least two coding processes and an arbitration process between them are used to ensure the reliability of results. Acknowledging these limitations, the results in this chapter reveal important design strategies for the generation or production of creative designs. These strategies can assist both professional designers and design students alike to better understand and reflect on their practices.

References

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Park, Sang Min, Mahjoub Elnimeiri, David C. Sharpe, and Robert J. Krawczyk. 2004. Tall building form generation by parametric design process. Paper presented at the CTBUH 2004 Seoul Conference, Roberto, Barrios Hernandez Carlos. 2006. Thinking parametric design: Introducing parametric Gaudi. Design Studies 27 (3): 309–324. Rosenman, M.A., and J.S. Gero. 1993. Creativity in design using a design prototype approach. In Modeling Creativity and Knowledge-Based Creative Design, ed. J.S. Gero and M.L. Maher, 119–148. Hillsdale, New Jersey: Lawrence Erlbaum. Salim, Flora, and Jane Burry. 2010. Software openness: Evaluating parameters of parametric modeling tools to support creativity and multidisciplinary design integration. In Computational Science and Its Applications—ICCSA 2010, ed. David Taniar, Osvaldo Gervasi, Beniamino Murgante, Eric Pardede, and Bernady Apduhan, 483–497. Lecture Notes in Computer Science. Berlin/Heidelberg: Springer. Schön, Donald A. 1984. The architectural studio as an exemplar of education for reflection-in-action. Journal of Architectural Education 38 (1): 2–9. Simon, H.A. 1973. The structure of ill structured problems. Artificial Intelligence 4 (3): 181–201. https://doi.org/10.1016/0004-3702(73)90011-8. Simon, H.A. 1978. Information-processing theory of human problem solving. In Handbook of Learning and Cognitive Processes, ed. W.K. Estes, 271–295. Hillsdale, NJ: Lawrence Erlbaum Associates. Simon, H.A. 1981. The Sciences of the Artificial. Cambridge, MA: MIT Press. So, Chaehan, and Jaewoo Joo. 2017. Does a persona improve creativity? The Design Journal 20 (4): 459–475. https://doi.org/10.1080/14606925.2017.1319672. Stevens, Mark, Ward Lyles, and Philip Berke. 2014. Measuring and reporting intercoder reliability in plan quality evaluation research. Journal of Planning Education and Research 34 (1): 77–93. https://doi.org/10.1177/0739456X13513614. Sutherland, Ivan E. 1963. Sketch pad: a man-machine graphical communication system. In The AFIPS Spring Joint Computer Conference. ACM. Suwa, Masaki, and Barbara Tversky. 1997. What do architects and students perceive in their design sketches? A protocol analysis. Design Studies 18 (4): 385–403. https://doi.org/10.1016/S0142694X(97)00008-2. Suwa, M., T. Purcell, and J.S. Gero. 1998. Macroscopic analysis of design processes based on a scheme for coding designers’ cognitive actions. Design Studies 19 (4): 455–483. Tovey, Michael. 1989. Drawing and CAD in industrial design. Design Studies 10 (1): 24–39. https:// doi.org/10.1016/0142-694X(89)90022-7. von der Weth, Rudiger. 1999. Design instinct? The development of individual strategies. Design Studies 20 (5): 453–463. Wang, Yingxu, and Vincent Chiew. 2010. On the cognitive process of human problem solving. Cognitive Systems Research 11 (1): 81–92. https://doi.org/10.1016/j.cogsys.2008.08.003.

Chapter 3

Creative Micro-processes in Parametric Design

Abstract This chapter presents a detailed analysis of the actual, rather than theorised, relationship between cognitive activities and creativity in design. Focussing on parametric design, the chapter uses protocol data developed from four cognitive activities—changing parameters, perceiving geometries, introducing algorithmic ideas and evaluating geometries—to investigate creative problem-solving processes. This analysis of parametric design activities and their sequential patterns not only informs a better understanding of process-based creativity, it also identifies two important creative micro-processes in parametric design. Through this analysis, this chapter contributes to the evolution of new knowledge about the relationship between design and creativity.

3.1 Introduction The rapid rise of interest in parametric design that occurred over the past two decades arguably reached its popular apogee in “parametricism”, a biomorphic design style or movement (Schumacher 2009). At that time, influential architects, like Zaha Hadid, NOX and UNstudio, used dynamic factors for design generation and fabrication, employing methods that were indebted to parametric design to propose creative or novel works. Such works naturally encouraged speculation about the capacity of parametric design environments to support creativity, although the evidence for this has only gradually been tested (Lee et al. 2014b, 2015). For example, some researchers (Blosiu 1999; Iordanova 2007; Iordanova et al. 2009) argue that parametric design supports creativity through its capacity to enhance design exploration during the conceptual design stage. Such propositions, however, tend to be supported by theories, personal observations or reflections, not empirical evidence. In contrast, this chapter combines protocol analysis from design experiments with the Consensual Assessment Technique (CAT) to develop a deeper understanding of parametric design’s actual potential to support creativity. As discussed previously in Chap. 2, the act of designing, regardless of whether it relies on traditional or emerging digital tools, is understood as both a type of problem-solving activity and a series of cognitive processes (Bilda and Demirkan © Springer Nature Switzerland AG 2020 J. H. Lee et al., Design Thinking: Creativity, Collaboration and Culture, https://doi.org/10.1007/978-3-030-56558-9_3

65

66

3 Creative Micro-processes in Parametric Design

2003; Casakin and Kreitler 2011). In addition to these two—problem-solving and cognition—designing has always been associated with creativity, but the relationship between them remains highly contested. Further complicating our understanding of this tripartite relationship is the environment or toolset used to support designing. For example, previous studies have examined designers’ sketches to identify creative activities during the conceptual design stage (Akin and Moustapha 2004; Benami and Jin 2002; Hasirci and Demirkan 2007; Kim et al. 2010). The flexible and intuitive nature of the sketch has been repeatedly linked to creativity, and it would appear to offer clear advantages over CAD tools in this respect (Bilda and Demirkan 2003; Verstijnen et al. 1998). In contrast, both sketching and the use of conventional CAD tools have limitations as problem-solving devices (Ibrahim and Pour Rahimian 2010). For example, for constructing, a complex design sketching has serious deficiencies. Conversely, conventional CAD tools lack flexibility and a capacity for intuitive exploration. To overcome these limitations, Bilda and Demirkan (2003) suggest employing more adaptive and fluid digital design media. Interactive imagery may also be used to better visualise design features and organisational relations when generating alternative solutions. Furthermore, the careful management of partwhole relationships, design hierarchies and topology-geometry relationships, along with alternative approaches to handling ill-structured problems and problem-related parameters, are all strategies that may overcome the limitations of specific design toolsets (Akin and Moustapha (2004). These properties, which are thought to mitigate the misalignment between the need for creativity and the capacity of CAD tools, are noticeably present in recent parametric design environments (Madkour et al. 2009; Schumacher 2009; Aish and Woodbury 2005). Unlike previous CAD and geometric modelling tools, parametric design appears to support both intuitive and flexible design explorations (Lee et al. 2014a). Advanced parametric design tools also provide an increased capacity to first encode and understand the generative process and second, to evaluate the generated product. Through its use of scripting, parametric design environments appear to provide designers with instinctual and adaptable tools, thereby allowing them to potentially engage in higher levels of complexity in design (Aish and Woodbury 2005; Madkour et al. 2009). Somewhat ironically, it has also been argued that because parametric design environments rely on constrained relationships between objects to generate solutions, they are more flexible and responsive (Sakamoto and Ferr´e 2008). This position maintains that such constraints encapsulate complex structures in design logic, freeing the designer to invest their energies in creative thinking (Coorey and Jupp 2011). Furthermore, scripting or coding activities in parametric design can be understood as a channel for creativity and a means of representing design ideas (Salim and Burry 2010). These theorised parametric properties are well documented in the literature (Kolarevic 2003; Javier 2000; Sakamoto and Ferré 2008; Schnabel 2007), although there is a much more rudimentary understanding of what is actually happening in a design process in respect to creativity. Past research does identify that the generative modelling processes in parametric design can contribute to creativity (Iordanova et al.

3.1 Introduction

67

2009; Lee et al. 2014a; Lee et al. 2013). Such generative, parametric design activities are relatively novel and differ from traditional design activities (for example, sketches, drawings and models). The type of “divergent and convergent thinking processes” that can occur in parallel with creativity is also observed in parametric design (Cross 2008; Pugh 1991; Guilford 1967; Lawson 1980; Lee et al. 2015). Obviously, enabled by the computer, the generative capacity of parametric design supports the production of seemingly unlimited divergent options, while parametric knowledge and constraints clearly support convergent thinking. From this point of view, parametric design enhances not only the generation of ideas, but also their evolution, thereby possibly supporting creativity. Nonetheless, there is a general lack of empirical evidence linking specific parametric design activities to creativity. Fundamentally, we do not know if parametric environments enhance or hinder creativity. At the very least, some parametric design activities have flexibly generated divergent design possibilities and modified design alternatives, which should support creativity. To begin to clarify this situation, this chapter examines four cognitive activities that have been theorised as promoting creativity in parametric design. The four parametric design activities discussed in this chapter are (i) changing existing parameters, (ii) attending to existing geometry, (iii) introducing new algorithmic ideas from a previous idea and (iv) evaluating primitives or existing geometries. Using a coding scheme (Table 2.4), they are identified as A-Change Parameter, P-Geometry, F-Algorithm Sub Goal and E-Geometry, respectively. The next section reviews cognitive activities in the protocol data (developed in Study II in Chap. 2) and then presents an in-depth analysis of the four activities. Through a consideration of iterative cognitive patterns, this chapter concludes with a presentation of two important creative micro-processes in parametric design.

3.2 Four Parametric Design Activities The basis for the analysis conducted in this chapter is protocol data developed from a design experiment (see Chap. 2 for details). This chapter uses the data to examine the relationship between four parametric design activities and creativity. As discussed previously, the “making generation or variation” activity (G-Generation) is critical for developing the type of generative design strategies in parametric design that produce creative outcomes. In addition to analysing the generative activity, the cognitive activities of the design process (Chap. 2. Table 2.5) are also correlated to expert’s rating of the creativity implicit in the design product using CAT (Chap. 2. Table 2.7). To effectively deal with different types of data and values, the normalised coverage values of cognitive activities (subclasses)—A = (A – mean)/standard deviation—are calculated (Bilda and Gero 2007; Lee et al. 2014a, 2015). The average values of the criteria-based assessment scores are also normalised in order to compare each designer’s cognitive activities (see Table 3.1). Possible types of patterns emerging from these comparisons are listed in the lefthand-side column of Table 3.1. For example, “Au1, 3, 4 < Au2” means that three

68

3 Creative Micro-processes in Parametric Design

Table 3.1 Main types of patterns identified in the comparison between the four designers (normalised values of the coding coverages of each subclass and the average values of rating scores) Type Process

Au1, 3, 4 < Au2

Subclass

Au1

Au3

Au4

G-Geometry

−0.17

1.44

−0.86

−0.41

G-Change

−0.63

1.43

−0.75

−0.05

0.58

1.11

−0.81

−0.88

A-Change Rule −0.62

−0.78

−0.01

1.42

A-Reference

−0.55

−0.55

−0.40

1.50

P-Algorithm

−0.56

−0.67

−0.25

1.48

A-Parameter

1.16

−0.01

−1.28

0.14

F-Initial Goal

1.42

−0.33

−0.92

−0.17

−1.45

0.18

0.79

0.48

F-Geometry Sub Goal Au1, 2, 3 < Au4

Au1 > Au4 > Au2 > Au3

Product

Au2

Au1 > Au2 > Au4 > Au3

E-Rule

Au1 > Au2 > Au3 > Au4

E-Geometry

0.67

0.55

0.25

−1.48

Au1, 3 > Au2, 4

A-Change Parameter

1.38

−0.65

0.09

−0.82

P-Geometry

1.22

−0.27

0.22

−1.17

F-Algorithm Sub Goal

0.46

0.39

0.64

−1.49

G-Generation

−0.30

−0.69

1.48

−0.49

Average of criteria-based assessment scores

0.19

−0.47

1.30

−1.02

Au1, 3 > Au2, 4

designers (Au1, 3 and 4) have lower normalised coverage values than Au2. “Au 1 > Au2 > Au3 > Au4” means that designer Au1’s coding coverage value is higher than Au2’s, and Au2’s is higher than Au3’s, and Au3’s is higher than Au4’s. “Au1, 3 > Au2, 4” means that Au1’s and Au3’s values are similarly higher than Au2’s and Au4’s. This mapping convention allows for the comparison of characteristics of the design process with rating results of the design product. Because of this, the last type (“Au1, 3 > Au2, 4”) in process is consistent with the rating results of creativity in product. That is, four activities, A-Change Parameter, P-Geometry, F-Algorithm Sub Goal and G-Generation can be identified as creative design activities in parametric design. In conjunction with the four creative activities, E-Geometry (evaluating geometries) in a three-dimensional (3D) view is also linked to parametric design activities that support creativity (Lee et al. 2014a, 2015). In Table 3.1, E-Geometry conforms to a “Au1 > Au2 > Au3 > Au4” type. Designers Au1 and Au2, who used a graphical algorithm editor, tended to produce more geometry-based activities, which may explain why Au2 produced relatively higher amounts of E-Geometry. In parametric

3.2 Four Parametric Design Activities

A-Change Parameter F-Algorithm Sub goal

69

P-Geometry E-Geometry

RaƟng Score G-GeneraƟon

Normalised value

1.5 1 0.5 0 -0.5 -1 -1.5

Au1

Au2

Au3

Au4

Fig. 3.1 Normalised values of the coding coverages of cognitive activities (process) over the rating scores on creativity (product)

design environments, the design products should be repeatedly examined in the 3Dview mode, a visualisation process that fundamentally supports conceptual design. Thus, this chapter regards E-Geometry as a further important creative activity in parametric design. Visualised patterns in Fig. 3.1 not only inform the mapping process (Table 3.1), but also identify broadly similar allocations of five cognitive activities between the designers in the experiment. In summary, excluding G-Generation (already explored in Chap. 2) this chapter investigates the four cognitive activities, A-Change Parameter, P-Geometry, F-Algorithm Sub Goal and E-Geometry identified as being significant, so as to better understand each activity in relation to creative problem-solving.

3.2.1 Changing Parameters In parametric design processes, design variations are generated using constraints such as topological relationships, design rules and parameter controls. In contrast, parameters facilitate a range of activities, enabling divergence in design. For example, parameters support restructuring and regulating processes, which are commonly found in sketching activities. They are typically related to functions defining a range of variations (Cardenas 2008). Parametric design enables the production of large numbers of design that all comply with the given constraints. Thus, creativity in parametric design is likely to be related to generative activities such as G-Generation and AChange Parameter, an argument that is clearly supported by the correlation with the creativity scores shown in Fig. 3.1. This finding is further evident in the summary of generative activities (see Fig. 3.2). As discussed in Chap. 2, the expert designers, Au1 and Au3, generally had significantly higher frequency coverage of G-Generation and

3 Creative Micro-processes in Parametric Design

Coding coverage (%)

70

A-Change Parameter

G-GeneraƟon

20 18 16 14 12 10 8 6 4 2 0

Au1

Au2

Au3

Au4

Fig. 3.2 The coding coverage of two generative activities

A-Change Parameter than the novice designers. These results suggest that experts have a clear advantage when engaged in the generative aspects of parametric design. All four designers generated their design schemata and solutions and then modified them by creating and changing parameters. To generate different design solutions, they produced and edited different classes of objects by changing parameters. Au1 and Au2, using the same graphical algorithm editor (Grasshopper), explicitly defined and then changed parameters, whereas for the other two designers the coding process wasn’t able to distinguish between their use of parameters or rules. This is because Au3 and Au4 used a script editor and wrote using the system’s programming language to progress the design. Nonetheless, Table 3.2 shows that the designers tended to change parameters to generate different variations of the original solution, while Au3 changed sparameters to fix problematic outcomes (“troubleshooting”) or unintended consequences. All of the protocols indicate that parametric modelling is a process in which the generation of new design variations from existing ones is relatively effortless (Roberto 2006). Furthermore, while the designers had limited programming expertise, by utilising design constraints and parameters, they were able to explore a large variety of ideas without being restricted by their own drawing skills (Lawson 2002). Making variations is therefore the key to pursuing creativity as well as extending the boundaries of the designers’ existing knowledge and the state space of possible solutions (Gero 1996; Liu and Lim 2006). From this perspective, “changing parameters” is closely related to embodying “divergent thinking”, which has been identified as one of the most important factors in creative models (Cross 2008; Guilford 1967; Pugh 1991). The twin activities of creating and changing parameters also combine convergent and divergent thinking. For example, activities encoded by A-Change Parameter in Au1 and Au3 in Table 3.2 not only generate design alternatives, but also signal the exploration and identification of more appropriate solutions. In addition, Table 3.2 shows how variations were generated using the “changing parameters” activity and then evaluated and selected by each designer in the 3D view (E-Geometry) to

3.2 Four Parametric Design Activities

71

Table 3.2 Segments and subclasses related to “changing parameters” Segment No Au1

Au3

Transcription

Subclass

14

I am just going to create one by one. What I am F-Geometry Sub Goal going to do is I am going to work on structure solution first. And, what I want to do is to put a grid around the outside

15

(V: dragging and copying some parameters and rules) so I am going to create a series of grids

A-Parameter

16

On the outside, (V: changing parameters on the copied sliders)

A-Change Parameter

17

I am just creating a border of the grid, I copy that again. … (V: inserting and connecting rules)

A-Rule

18

Fifty, too much on them, 40 (V: changing parameters on the inserted rules)

A-Change Parameter

19

Okay (V: Examining forms in the 3D view mode)

E-Geometry

255 (V: Examining forms in the 3D view mode) why is this huge, isn’t it?

E-Geometry

256 What agents do I want to use, 50? (V: Examining rules in the script mode). I am going to rescale…

E-Rule

257 Tower by factors of 10…-3, … 140, 10, 40…

A-Change Parameter

258 problems solved… (V: Examining rules in the script E-Rule mode)

Au4

259 (V: Executing the agents…input numbers one by one)

A-Change Parameter

260 Frame skins to one, two…a kind of guarantee… (V: Examining forms in the 3D view mode)

E-Geometry

25

(V: modifying rules)

A-Change Rule

26

Just trying to fix errors, I gained (V: Examining forms in the 3D view mode) objects are in a wrong plane

E-Geometry

27

(V: changing parameters)

A-Change Parameter

28

(V: Examining forms in the 3D view mode) Okay

E-Geometry

(V:…): Visual cue in the video recording

arrive at a meaningful and feasible outcome. Considering Hanna and Turner’s (2006) criticism that parametric design variations may be too abstract, only making sense in a virtual design environment, “changing parameters” is clearly an important design abstraction and realisation activity.

72

3 Creative Micro-processes in Parametric Design

3.2.2 Perceiving Geometries The perception level of cognitive activities is related to visual imagery in the design process as well as the “incubation” stage in Wallas’s (1926) creative stage model. Thus, it is theorised that perception facilitates creativity, and it also involves seeing relationships between elements or components (Flowers and Garbin 1989). Although there are two different perception activities, P-Geometry and P-Algorithm, the coding coverage of P-Geometry (perceiving or attending to existing geometries) is the one consistent with the results of the product-based evaluation of creativity in Table 3.1. That is, perceiving geometries in parametric design may play a more significant role in creativity than reviewing or contemplating existing algorithms. In traditional “pen-and-paper” design environments (see Study I in Chap. 2), the encoding of perception refers to the act of concentrating on the visuospatial features of depicted elements (Suwa et al. 1998). Sensing visual cues is an essential part of the creative problem-solving processes associated with the formulation of mental imagery. In contrast, cognitive activities around P-Geometry and P-Algorithm in parametric design frequently signal a significant problem-solving event or situation in the design process. Importantly, the moment when designers switch to the 3D view to attend to existing geometries (thereby evoking the P-Geometry activity) often coincided with, or heralded, a mental insight. For example, designer Au1’s protocol highlights a correspondence between P-Geometry and a number of sudden realisations (so-called “A-ha” moments) related to particular problem-solving activities. Table 3.3 shows examples of Au1’s and Au2’s “A-ha” responses. Further design activities occurring in parallel with the P-Algorithm activity were also related to the occurrence of unexpected discoveries. In two instances, for example, Au4 was troubleshooting an algorithmic problem in the script editor when the design solution was re-framed and discovered. These sudden insights or discoveries were made during consideration of visual compositions and using knowledge of the design domain (Akin and Akin 1996). A further insight into how problems are solved can be seen in connection to the P-Geometry subclass, when designers were waiting for design variations to be generated by the computer. For example, perceiving geometries for designer Au3, as shown in Table 3.3, occurred as the designer was waiting for the generation of design outcomes. This visualisation process from a generative activity may enable the designer to critically reflect on their own design ideas and to construct mental imagery of outcomes. Akin and Akin (1996) examine the discovery of a creative solution that corresponds to the sudden insight in the sketching process for a design problem, structured with several restricting frames of reference. Akin and Akin (1996) describe the “Aha” response as a reference to the moment when a creative flash occurs. In parametric design, a number of sudden mental insights and unexpected discoveries were identified in the protocols as occurring in parallel with P-Geometry and P-Algorithm activities. These “A-ha” responses also relate to switching behaviours between different representations of the design model. This finding highlights the role that multiple

3.2 Four Parametric Design Activities

73

Table 3.3 Segments and subclasses related to “perceiving geometries” Segment no. Au1

Au2

Transcription

Subclass

95

What I will do is to settle with all of that

A-Change Parameter

96

I will just (V: just attending to the monitor screen)… E-Geometry

97

… A-ha … This is what happened

P-Geometry

98

I am still moving original points, I will move new points. (V: Scrolling the rule view)

E-Rule

99

Excuse me, hold things at that (V: Examining forms in the 3D view mode)

E-Geometry

205 Okay, That’s rotating a few objects (V: Examining forms in the 3D view mode)

E-Geometry

206 Change that from one to lists (V: changing parameters)

A-Change Parameter

207 (V: Examining rules in the 3D view mode)

E-Geometry

208 (V: Examining forms in the script mode)

E-Rule

209 What I’ve done, I wasn’t intended to, I am E-Geometry wondering, it all works. (V: examining forms in the 3D view mode)

Au3

210 (V: Attending to forms) … it’s a happy accident (A-ha)

P-Geometry

314 (V: Executing the agent to generate a cloud of points…input numbers one by one)

R-Generation

315 (V: Examining forms in the 3D view mode)

E-Geometry

316 (V: just attending to the monitor screen)

P-Geometry

317 (V: just waiting for completing the generation)

(No code)

318 (V: Examining forms in the 3D view mode) Oh, what the hell…

E-Geometry

(V:…): Visual cue in the video recording

representations and interfaces play in design processes and their potential significance to creative activities and outcomes. From this perspective, parametric design environments may open up more and greater possibilities for sudden mental insights and unexpected discoveries than traditional “pen-and-paper” or CAD environments. This may be due to the ability of designers to use the different interfaces (geometric and algorithmic modes) as “triggers” for breaking out of their frames of reference.

74

3 Creative Micro-processes in Parametric Design

3.2.3 Introducing Algorithmic Ideas

Coding coverage (%)

As discussed in Chap. 2, three cognitive “problem-finding” activities, F-Initial Goal, F-Geometry Sub Goal and F-Algorithm Sub Goal, play an important role in developing generative design strategies that can enhance creativity in parametric design. Figure 3.3 illustrates the coding coverages of the three “problem-finding” activities. Au4 produced the most frequent coverage in the physical level but the lowest coverage of “problem-finding” activities. His design solution also received the lowest scores from the judges. Conversely, Au2, the other novice designer, had the highest frequency coverage of “problem-finding” (18.5%) suggesting that these activities alone may not necessarily have a positive impact on creativity. Conversely, generative activities including G-Generation and A-Change Parameter may be more important for supporting creativity in parametric design. In addition, the two designers (Au1 and Au2) who used graphical algorithm editors produced more F-Initial Goal and FGeometry Sub Goal activities for problem-finding compared to the text-based editors. They also tended to produce more geometric activities in the physical representation level than the text-based editors. This suggests that the graphical editor is more likely to lead designers to conduct geometry-based activities, regardless of the application of a creative, generative design strategy. Thus, among the three activities, introducing algorithmic ideas or sub-goals, encoded as F-Algorithm Sub Goal, is explored as an important creative activity in parametric design. “Introducing algorithmic ideas” is related to the mathematical representation of design goals and the collection of schemata. Table 3.4 presents protocols related to the F-Algorithm Sub Goal, showing a series of representation and evaluation activities (e.g. A-Change Rule, E-Rule, E-Geometry) leading to the introduction of algorithmic ideas. Furthermore, “introducing algorithmic ideas” often occurs in conjunction with the F-Geometry Sub Goal. That is, new ideas at the geometric level are extensions F-Algorithm Sub goal F-Geometry Sub Goal F-IniƟal Goal

20 18 16 14 12 10 8 6 4 2 0

Au1

Au2

Au3

Fig. 3.3 The coding coverages of “problem-finding” activities

Au4

3.2 Four Parametric Design Activities

75

Table 3.4 Segments and subclasses related to “introducing algorithmic sub-goals” Segment No Au2

Au3

Transcription

Subclass

69

(V: Examining forms in the 3D view mode)

SE-Geometry

70

we’ll say give a core for this

F-Geometry Sub Goal

71

an arbitrary value of one quarter (V: drawing a box G-Geometry in the 3D view mode)

72

and so (V: dragging and changing a box in the 3D view mode)

73

I don’t know if its accurate or not, this is a basic E-Geometry track available to put the hotel rooms and the office space Maybe that core can be reconfigured later

74

We need a random point

F-Algorithm Sub Goal

75

(V: inserting a component in the script mode)

A-Rule

76

checking the component

E-Rule

77

(V: linking components in the script mode)

A-Rule

G-Change

264 (V: Examining forms in the 3D view mode)

E-Geometry

265 (V: Deleting all previous model traces)

(no code)

266 Okay. What time am I going to look?

(no code)

267 I am just lofting things

F-Geometry Sub Goal

268 I am … the actual rotation of the agent wants to set F-Algorithm Sub Goa aside to go… 269 Bake Agents a…. (V: Examining rules) cool…

E-Rule

270 Points…don’t need it (V: inserting rules) I am …range…Bake Agent…in range…

A-Rule

(V:…): Visual cue in the video recording

of previous ideas at the algorithmic level and vice versa. The introduction of algorithmic sub-goals can be regarded as an editing process of previous geometric or algorithmic design ideas. Thus, “introducing algorithmic ideas” can be seen to support the evolution of design ideas.

3.2.4 Evaluating Geometries Designers in digital environments typically rely on the 3D view to support perception and exploration of design ideas (Aish and Woodbury 2005). “Evaluating geometries” in designers’ transcripts (Tables 3.2, 3.3 and 3.4) indicates that there are potential cognitive sequences in parametric design and that this activity is closely related to several others in parametric design. For example, designers tend to change parameters and rules (A-Change Parameter and A-Change Rule) both before and after evaluating geometries (E-Geometry).

76

3 Creative Micro-processes in Parametric Design

E-Geometry, that is, visually evaluating the outcome of a rule, is the second most dominant activity in the coding results (Table 2.5), and E-Rule (evaluating the rule itself) is the third. A large number of E-Geometry activities, with a relatively small number of E-Rule, appear in Au1’s protocol, whereas E-Rule occurs more frequently in the other protocols for the verification of design algorithms. The logic behind this sequence is that, because parametric design often produces solutions that are unexpected, the outcomes must be regularly evaluated in the 3D view as well as in the scripting view mode (Lee et al. 2015). These evaluation activities can be seen to further refine and potentially restructure the design solution, which could in some cases lead to a re-conception of the design problem. Multiple interactions between the script editor interface and the 3D view also highlight the iterative patterns of evaluation activities consisting of E-Rule code followed by E-Geometry (see Tables 3.2 and 3.3). These two evaluation activities are frequently linked to each other which reinforces the importance of utilising multiple representations and interfaces. In essence, in a parametric environment, designers are able to model at both geometric and algorithmic levels and also make evaluations across them. These multi-level interactions enable multi-level evaluations, which may be a catalyst for a designer to break out of an existing mindset and explore new ideas. Thus, like “perceiving geometries”, E-Geometry can create opportunities for “A-ha” moments associated with unexpected discoveries. Au3 and Au4, who employed scripting as the main method to generate their designs, produced a relatively high number of instances of E-Rule in the verification of their algorithms. Au3’s and Au4’s design processes also produced more unexpected outcomes than the others using the graphical algorithm editor. Bilda and Demirkan (2003) claim that it is time-consuming to switch between the different representations in digital design tools. In parametric design, however, the switching between geometric and algorithmic modes is necessary to assess and explore both the visuospatial features and algorithmic ones of generated design outcomes. These iterative cognitive activities may be considered akin to Salim and Burry’s (2010) complementary creative method, which supports creativity in parametric design.

3.3 Creative Micro-processes in Parametric Design 3.3.1 Patterns of Parametric Design Activities Thus far in this chapter, it has been observed that the four parametric design activities develop iterative, sequential patterns consisting of two or more activities. Through correlation analysis, it would appear that these sequences of mental activities are also closely related to creativity and therefore, they could be treated as a creative thinking pattern. Chusllp and Jin (2004) propose a cognitive activity model consisting of three iterations to conceptualise creative thinkin6g patterns. The three iterations in their loop model are problem redefinition, idea stimulation and concept reuse. Generative

3.3 Creative Micro-processes in Parametric Design

77

and parametric design solutions conform to this pattern, as they evolve with extensive iterations by modifying parameters and rules. The “solution-reflecting generative strategy” (identified in Chap. 2) also features a series of iterative activities, wherein form generation is followed by the development or refinement of sub-problems (including evaluation activities). The iterative series of activities in parametric design are also reminiscent of Stempfle and Badke-Schaub’s (2002) “process 1”, which is a sequence of solution ideas followed by immediate evaluation. The sequence of cognitive activities surrounding these four activities facilitates identifying creative design processes in the context of parametric design and the co-evolution process of problem and solution spaces (Maher and Poon 1996; Dorst and Cross 2001). Thus, exploring the relationship between the four cognitive activities, their occurrence and sequence could reveal how they actually support creativity in a design process. To investigate the significance of sequences of activities, two segments before and after each segment encoded as F-Algorithm Sub Goal in a design protocol were examined to identify recurring sequential relationships. Because “evaluating geometries” occurred relatively frequently (on average, 16.1%) and “perceiving geometry” only rarely occurred (1.5%) in Table 2.5, the five-activity sequences include one of two algorithmic activities (A-Change Parameter and F-Algorithm Sub Goal) and thereby provide a considerable set of data for exploration. In addition, “introducing algorithmic ideas” as a “problem-finding” activity plays an identical role in refining and restructuring both design problem and solution in parametric design. This is why this section selects F-Algorithm Sub Goal as the centre code of a series of cognitive activities. Table 3.5 presents the different types of iterations of design activities identified by the sequential patterns of the three activities, A-Change Parameter (A), P-Geometry (P) and E-Geometry (E), surrounding F-Algorithm Sub Goal (F). An E–F repetition is a dominant pattern across all four designers’ protocols (14 times as a pair, and a further 11 times as part of sets of three activities). That is, the evaluation of existing geometries (E-Geometry) was frequently followed by the introduction of algorithmic ideas (F-Algorithm Sub Goal). This implies that the design problem and solution are being refined or restructured in a continuous manner in parametric design, which conforms to Chusllp and Jin’s (2004) “problem redefinition loop”. Table 3.5 Sequential patterns of the four activities Protocol

Au1

Pattern (Frequency)

E–F-A F-A-E E–F E–F-E E–F-P P-F-E

Au2 (3) (3) (2) (1) (1) (1)

E–F E–F-E F-F F-A-E P-F F-E

Au3 (6) (2) (2) (1) (1) (1)

E–F E–F-F F-F F-A-E A-E–F

Au4 (4) (2) (2) (2) (1)

E–F E–F-E F-E

(A: A-Change Parameter, P: P-Geometry, F: F-Algorithm Sub Goal, E: E-Geometry)

(2) (1) (1)

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An additional significant pattern is E–F-A in designer Au1’s protocol and its inverse, the F-A-E pattern in the first three designers’ protocols (Au1, Au2 and Au3) (Table 3.5). These patterns indicate that the F-Algorithm Sub Goal often results in AChange Parameter. That is, the introduction of a new algorithmic idea is the catalyst for changing an existing parameter. Obviously, making design variations is a relatively effortless process in parametric design due to the ease of changing parameters. Furthermore, the occurrence of both the E–F-A and F-A-E patterns can be seen as part of a cyclic pattern (i.e. E–F-A-E–F-A-…) of evaluating geometries, refining algorithmic (or sometimes geometric) goals, updating parameters (or sometimes rules) and then returning to evaluating geometries again. An A-E pattern also occurs in the protocol data. For example, 15 out of 34 AChange Parameter codes in Au1’s protocol preceded E-Geometry. In Au2’s and Au3’s protocols, many A-Change Parameter codes were followed by the evaluation of generated geometries, 75.0% (6/8) and 59.1% (13/22), respectively. Furthermore, the incidence of both E-A or A-E patterns is notable, occurring 18 times in Au3’s protocol. Au3 adopted an agent-based approach to resolving the design problem which typically generated programmatic relationships and an architectonic response to the design problem. Since Au3’s design variations were generated from emergent relationships between program and form, the pattern of E-A or A-E appears to be a crucial sequence of activities in his creative problem-solving process. Figure 3.4 presents the patterns of cognitive activities in the work of the two designers (Au1 and Au3) who generated the most creative design solutions. The activity of perceiving geometries (G-Geometry) was disregarded in this analysis due to its rare occurrence. G-Geometry may also be conflated with E-Geometry activities that supersede its role at the geometric level of design cognition. While E Au1 E F A Start

Time

End

Time

End

Au3 E F A Start

Fig. 3.4 Patterns of the three cognitive activities over time (A: A-Change Parameter, F: F-Algorithm Sub Goal, E: E-Geometry)

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is a dominant activity, occurrences of both F and A differ significantly between the two designers. F in Au3’s protocol occurs at more regular intervals throughout his design session, and many A activities appear late in the design session. Au3’s design activities focused on the evolution of ideas at the algorithmic level which steadily increased over time. In contrast, Au1’s protocol rarely develops F and A codes later in his design session. These features are also related to individual generative design strategies (see Study II of Chap. 2): Au1’s “problem-forwarding generative strategy” and Au3’s “solution-reflecting generative strategy”. Interestingly, in Au3’s protocol F activities (or E–F repetition) happen in the early stage of his session, with A activities in the later stage. Thus, he not only adopted the solution-reflecting generative strategy, but also employed a “problem redefinition loop” at the beginning of his design session and an “idea simulation loop” at the end. From this analysis, it appears that these micro-iterative patterns of cognitive activities may support creativity in a parametric design process.

3.3.2 Creative Micro-processes Design process models are typically based on theorised sets of cognitive patterns that occur during the act of designing. For example, Jones’ (1963) Analysis–Synthesis– Evaluation (ASE) framework, Oxman’s (2008) “representation–generation–evaluation–performance” model and Stempfle and Badke-Schaub’s (2002) four cognitive operations (generation, exploration, comparison, selection) all offer a macro-level description of designing. Specifically, Oxman (2006) identifies five paradigmatic classes of digital design models: CAD, formation, generation, performance and integrated models (Table 3.6). In order to explain the design processes embodied in each digital design model, she proposes a schematic framework consisting of four components: representation, generation, evaluation and performance. Because these Table 3.6 Oxman’s five types of digital design models (Oxman 2006) Type

Digital design model

CAD model

• CAD descriptive model • Generation-evaluation CAD model • CAD descriptive model and its evolution to dual-directional digital processes

Formation model

• Topological formation model • Associative design formation model • Motion-based formation model

Generative model

• Grammatical transformative design model • Evolutionary design model

Performance model

• Performance-based formation model • Performance-based generation model

Integrated compound model • Integrated compound model

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four are basically developed for traditional design activities, she argues that digital design thinking (DDT) equivalents are (i) representational digital media, (ii) generation and interaction with digital form, (iii) analytical and judgmental processes, and (iv) programmatic and contextual considerations, respectively. In this way, DDT supports the adoption of new design approaches such as morphogenesis and parametric formation. From these alternatives, Oxman (2006) singles out the “integrated compound model” as the most ideal for supporting DDT (see Table 3.6). Compared to these macro-level models, Botella et al. (2018) suggest that the micro-level is more important for identifying the mechanisms underlying the creative process. This chapter identifies two cyclic mechanisms (patterns) of parametric design activities, which enable the development of creative micro-processes. The first micro-process is a cyclic pattern of E–F-A-E activities, which is similar to the conventional ASE model. However, the introduction of new algorithmic ideas is a critical feature of this process, and it is important to note that the cyclic pattern crosses both the algorithmic and geometric levels. The second micro-process is an E-A-E cycle which appears as a solution-reflecting activity. It is reminiscent of the restructuring of components (Verstijnen et al. 1998) and the regulation of elements into a comprehensive solution (Akin and Moustapha 2004). However, the iterative E-A-E pattern in parametric design often occurred when algorithmic rules were being documented and “chunked” into scripts, such that the cyclic process is understood as a recurring generative activity. Based on these cyclic patterns of creative parametric design activities, this chapter identifies two creative micro-processes (see Fig. 3.5). The first creative micro-process is effectively a combined process of E–F-AE and E-A-E cycles. In this creative micro-process, the major loop consists of EGeometry, F-Algorithm Sub Goal and A-Change Parameter, and it also includes a minor loop comprising E-Geometry and A-Change Parameter. The second creative micro-process is as expansion or variation of the first. In it, the E-Geometry activity links (or swaps) to P-Geometry, while the A-Change Parameter is similar to GGeneration in terms of its generative role in parametric design. Many A-Rule and

F

E

F

A

CreaƟve micro-process 1

P

E

A

G

CreaƟve micro-process 2

Fig. 3.5 A creative micro-process model for parametric design (A: A-Change Parameter, F: FAlgorithm Sub Goal, E: E-Geometry, P: P-Geometry, G: G-Generation)

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F-Geometry Sub Goal activities are present in the protocol before and after the EGeometry activity. The evaluation activity can also be extended to the inclusion of E-Rule. Thus, this model can be used to explore general “parametric micro-process” as well as creative micro-processes. These two creative micro-processes highlight the significance of revisiting generative components in parametric design. In traditional design environments, visual representations, such as a sketch or drawing, can enhance idea-generation, structuring thinking and augmenting problem-solving abilities (Bresciani 2019). Visual elements (both textual and graphic) immediately organise information in preparation for communication or to provide clarity. In this context, designers continuously draw on their imagination and cognitive skills to develop new visualisations during the conceptual design stage that are often based on previous representations. Collectively, in the traditional design process (see sketch-based design strategies in Chap. 2), problem analysis and decomposition are important problem-solving activities and a starting point of creative design processes and strategies. However, designers in parametric environments frequently revisit a set of scripts or algorithmic codes, revising them until they fulfil expectations (the generative solution depicted in the 3D-view mode). Furthermore, designers often examine familiar scripts and even reuse previous algorithms to solve new design problems. However, even experts are not always prepared for the results (visualisations) produced when algorithms are modified. There is, therefore, a cognitive gap between a designer’s information processing through scripting and their capacity to visualise the results. Thus, the creative micro-processes in design start with “changing parameters” or “making generation”, activities are behind many “A-ha” moments. In addition, the major loop consisting of E, F and A is also similar to the “problem redefinition loop” which captures another important creative problem-solving process, “co-evolution”, where “problem leads to solution” and “solution refocusses the problem” (Maher and Poon 1996).

3.4 Conclusion This chapter has presented an in-depth analysis of four parametric design activities that have been identified as potentially supporting creativity. In particular, two algorithmic activities (A-Change Parameter and F-Algorithm Sub Goal) appear to provide a significant channel for creativity. Both can also be correlated to divergent and convergent thinking in a parametric design process. Two geometric activities (P-Geometry and E-Geometry) are associated with the exploration of design ideas in 3D modelling views, which support digital and visual synthetic geometric modelling. This is significant because it emphasises that even though parametric design highlights algorithmic approaches to creativity, geometric activities provide close support for this.

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The chapter further identifies two sequential patterns of cognitive activities that constitute distinct creative micro-processes. The creative micro-processes are E–FA-E and E-A-E, as well as the crossover of algorithmic and geometric levels of cognitive activities. The micro-processes are easily extended to explain other parametric design patterns as well. However, only the cyclic operations of both the geometric and algorithmic activities can support “unexpected discoveries” (Akin and Akin 1996) and thereby this type of creativity. Thus, while parametric design environments may still have limitations in supporting intuitive design activities, their generative activities effortlessly support flexible design visualisation and through this heightened creativity. The results of these empirical studies in laboratory settings certainly won’t perfectly reflect every designers’ quotidian activities or individual idiosyncrasies. Nonetheless, this in-depth analysis reveals how designers actually work in parametric design. Furthermore, the identification of the creative micro-process model for parametric design offers a plausible and valuable explanation for past theorised models of creativity, contributing to a better understanding of process-based creativity in design.

References Aish, Robert, and Robert Woodbury. 2005. Multi-level interaction in parametric design. In Smart Graphics, ed. Andreas Butz, Brian Fisher, Antonio Krüger, and Patrick Olivier, 924-924. Lecture Notes in Computer Science. Berlin/Heidelberg: Springer. Akin, Ömer, and Cem Akin. 1996. Frames of reference in architectural design: Analysing the hyperacclamation (A-h-a-!). Design Studies 17 (4): 341–361. Akin, Ömer, and Hoda Moustapha. 2004. Strategic use of representation in architectural massing. Design Studies 25 (1): 31–50. Benami, O., and Y. Jin. 2002. Creative stimulation in conceptual design. In Proceedings of the ASME Design Engineering Technical Conference. Bilda, Z., and H. Demirkan. 2003. An insight on designers’ sketching activities in traditional versus digital media. Design Studies 24 (1): 27–50. Bilda, Z., and J.S. Gero. 2007. The impact of working memory limitations on the design process during conceptualization. Design Studies 28 (4): 343–367. Blosiu, J. O. 1999. Use of synectics as an idea seeding technique to enhance design creativity. In1999 IEEE International Conference on Systems, Man, and Cybernetics, 1999. IEEE SMC ‘99 Conference Proceedings. Tokyo. Botella, Marion, Franck Zenasni, and Todd Lubart. 2018. What are the stages of the creative process? What visual art students are saying. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg. 2018.02266. Bresciani, Sabrina. 2019. Visual design thinking: a collaborative dimensions framework to profile visualisations. Design Studies 63: 92–124. https://doi.org/10.1016/j.destud.2019.04.001. Cardenas, Carlos Andres. 2008. Modeling strategies: Parametric design for fabrication in architectural practice. Harvard University. Casakin, Hernan, and Shulamith Kreitler. 2011. The cognitive profile of creativity in design. Thinking Skills and Creativity 6 (3): 159–168. Chusllp, P., and V. Jin. 2004. Cognitive modeling of iteration in conceptual design. In Proceedings of the ASME Design Engineering Technical Conference, 473–485.

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Chapter 4

Measuring Cognitive Complexity

Abstract Past research has theorised that high levels of individual cognitive complexity may result in heightened design thinking and creativity. The precise relationship, however, between cognitive complexity and creativity in design remains largely unexplored. This chapter develops two measures of cognitive complexity in design: content complexity and structural complexity. Using a combination of protocol analysis and linkography, it demonstrates how these two can be measured and studied. The demonstration uses two sets of protocol data developed from experiments in parametric design. The results indicate that (i) content complexity can be used to explain individual differences of cognitive complexity and (ii) structural complexity using decile growth plots of linkographs can reveal cognitive patterns over time. This method for measuring cognitive complexity contributes to advancing fundamental knowledge about design cognition and thinking.

4.1 Introduction Cognitive complexity is a measure of an individual’s capacity to conceptualise, organise, perceive or communicate. The greater the number of Mental Models (MMs) or conceptual structures available to an individual, along with a more nuanced or sensitive their capacity to apply them, the higher their cognitive complexity. A person with a low level of cognitive complexity might respond to a given situation with only limited, rigid, naïve or inappropriate actions or behaviours. They might also be unable to communicate alternative perspectives or understand personal and cultural differences. Conversely, a person with a high level of cognitive complexity will have the capacity to employ intricate abstraction and communication skills, while using and testing alternative theories or constructs. Design thinking has clear synergies with cognitive complexity as both rely on conceptual thinking, perception, abstraction and communication. Design has historically been described as both an art and a science, requiring a substantial understanding of human needs and a capacity to respond to them in creative ways. Even the oldest architectural treatises call for the designer to have a rich and deep awareness of multiple fields of knowledge. In the first century BC, for example, Vitruvius © Springer Nature Switzerland AG 2020 J. H. Lee et al., Design Thinking: Creativity, Collaboration and Culture, https://doi.org/10.1007/978-3-030-56558-9_4

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argued that architectural education must not only include geometry and drawing, but also history, philosophy, law and music. His core message is that an effective designer must be equally well versed in theory as in practice. He dismisses mere copyists and technicians, and those who work without an adequate grasp of optics, physics, strategy, symbolism and mathematics. In his Ten Books of Architecture, Vitruvius effectively argues that a successful designer must possess a high level of cognitive complexity. In recent years, the need for advanced design skills and increased cognitive capacity has become more pronounced. With the rise of data-driven approaches, mathematical models, algorithmic thinking and interactive simulation, design has become both multi-dimensional and multi-disciplinary (Weinstock 2010; Aish and Woodbury 2005). For example, the application of parameters and rules in parametric design may support advanced problem-solving, but it also potentially increases the cognitive complexity required of a designer. In addition, generative design requires the use of abstract constructs to program and assess outputs (Chien and Flemming 2002). In parametric and generative environments, designers must work with unconventional algorithm editors and employ intricate design thinking and functions, which collectively increase the complexity of the process. Past research has confirmed that design is not only a complex process (Almeida et al. 2005; Earl et al. 2005; Khan and Angeles 2007), it relies on emergence, selforganisation and hologrammaticity (Almeida et al. 2005). In other words, design is a system wherein constituent elements interact nonlinearly and interdependently, requiring increased cognitive capacity to manage effectively. Given this background, it is not surprising that measuring and understanding cognitive complexity is potentially significant in design. Throughout history, it has been theorised that a heightened capacity to use conceptual thinking, abstraction and communication is linked to creativity. As such, developing insights into cognitive complexity may provide a pathway to more innovative, original and visionary designs. This chapter explores cognitive complexity in design using two indicators or measures: content complexity and structural complexity. The theoretical and mathematical origins of both measures are derived from Information Theory (Shannon 1948; Krus 2013; Khan and Angeles 2007). Like Kolmogorov’s complexity theory, Shannon’s information theory seeks to measure the “information” in an “object”. While there are differences between the two theories, conceptually they both compare quality of information with volume of information. This chapter uses entropy¸ a quantitative measure for “disorder” or “order compared with disorder” (Baranger 2001), for quantifying individual cognitive complexity. To explain how cognitive complexity may be captured, measured and compared, this chapter revisits the protocol data presented in Chap. 2. This chapter is divided into three main parts. This first section introduces cognitive complexity and its content and structure in design. Following this, the entropy measure is described along with the use of a coding scheme and linkography to develop two measures of cognitive complexity. In the third section, protocol data is used to investigate cognitive processes and measure the content and structural complexity of parametric design.

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4.2 Background The concept of cognitive complexity was first proposed in the 1950s in the fields of psychology and communication, and multiple definitions and assessment techniques have since been established (Bieri 1955; Kelly 1955). Despite continuing developments in this field, one of the earliest theories to explain it, Kelly’s “personal construct psychology” (PCP), remains in wide use today. PCP describes a person’s capacity to construct, construe or interpret the world. It argues that each person anticipates events by interpreting their properties, categorising them and then determining a course of behaviour (Kelly 1955; Pervin 1975). The extent to which a person has the capacity to construe widely and strategically is a measure of their cognitive simplicity or complexity (Bieri 1955; Pervin 1975). PCP is effectively an MM of cognitive construction. Such “models play a central and unifying role in representing objects, states of affairs, sequences of events, the way the world is, and the social and psychological actions of daily life” (Johnson-Laird 1983, p. 397). MMs of this type are representations of reality that people use to understand specific phenomena (Norman 1983). The more conceptually intricate, abstract and nuanced the representational model, the higher the level of cognitive complexity. O’Keefe and Sypher (1981) define cognitive complexity as a variable that describes the limits of a person’s socio-cognitive system. The degree of differentiation, articulation and abstraction within the socio-cognitive system is measured using “individual difference variables” (Burleson and Caplan 1998). For example, Benet-Martínez et al. (2006) examine cultural representations finding that a higher level of cognitive complexity is related to increased information clustering (more differentiation and integration) and abstraction (less tangible and structured descriptions). Collectively, these psychological models of cognitive complexity are reliant on the process of categorising socio-cognitive systems and communicative behaviours using complexity measures (O’Keefe and Sypher 1981). This last point is significant as psychological, social and semiotic researchers have repeatedly noted that ideas are intrinsically tied to the language in which they are both constructed and expressed (Bonvillain 2010; Lewis 2012; Lopez 2003). The reciprocal relationship between ideation and conceptualisation on one side, and language and communication on the other, has been repeatedly noted in past research. A key example of this is the construction and communication of spatial and formal relationships and reasoning (Herskovits 1986; Tenbrink and Ragni 2012; van der Zee and Slack 2003). Linguistic studies, like “naming tasks” (Munnich et al. 2001) and “descriptive tests” (Tenbrink and Ragni 2012), also confirm this relationship. The close connection between linguistic complexity and cognitive complexity is significant because the former can be measured with relative ease. For this reason, linguistic analysis is often used to reveal cognitive complexity and illuminate a range of other characteristics (Bowerman 1996; Levinson 1996). For example, Tenbrink’s (2015) cognitive discourse analysis technique uses language protocols to assess complex cognitive processes such as problem-solving, cognitive strategies and heuristics.

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Linguistic or syntactic complexity can be defined and measured in several ways. Syntactic complexity is an indicator of the range of forms uncovered in language production and the degree of sophistication of these forms (Ortega (2003). It is used to describe linguistic maturity or competency, and many measures of it have been developed, including length of sentence, length of “T-unit” (a unit of “thought” consisting of one main clause with all subordinate clausal and non-clausal elements embedded in it), length of clause, clauses per T-unit (C/T ) and dependant clauses per clause (Hunt 1970). “T-unit complexity ratio” allows for linguistic comparisons of relative complexity in grammatical constructions (Lu 2010). This linguistic measure is useful in the present context because designers’ spoken words are so variable in length in the design process that a comparable value is required to measure individual differences. Furthermore, Kelly’s (1955) PCP theory and Bieri’s (1955) cognitive complexity theory both propose possible mathematical grounds for comparisons between design and linguistics. While such studies are largely associated with social cognition and communication, they allow researchers to conceptualise individual difference in terms of complexity. Moreover, they draw on advances in science, mathematics and social science which have emphasised the importance of measuring and understanding the complexity in systems (Baranger 2001). In contrast to the psychological, psychosocial, communication and linguistic approaches to cognitive complexity, in the design domain the terms cognition and complexity are rarely used together (Lee and Ostwald 2019). Design process and cognition, for example, are recurring themes in design research (Chai and Xiao 2012), whereas complexity tends to be used in the design domain to interrogate notions of novelty or as a means of imbuing a design with unique or distinct properties (Almeida et al. 2005). Complexity has also been used as a criterion for assessing “levels of creativity” (Amabile 1983). As such, complexity is used to describe or assess design as a product, not as a process, and there is no established use of the concept of cognitive complexity to describe the actions and behaviours of designers. One possible reason why cognitive complexity is not commonly used or discussed in design is that there are not any methods to measure it. Protocol analysis, for example, has been limited in the past to describing and categorising design activities through various cognitive methods (Chan 2015). However, design problem-solving behaviour is a core and measurable cognitive indicator in the design process. Moreover, several coding schemes used in protocol analysis do have the potential to be adapted to measure cognitive complexity. Such coding schemes identify cognitive micro-activities and associated behaviours that occur during design experiments and categorise them in such a way as to match measures of cognitive complexity in psychosocial research. As discussed in previous chapters, researchers in the design domain have developed multiple coding schemes corresponding to various research agendas (Gero and Neill 1998; Kim et al. 2010; Kim and Maher 2008; Stempfle and Badke-Schaub 2002; Suwa et al. 1998; Akin 1986; Eastman 1969). For example, Gero’s Function–Behaviour–Structure (FBS) framework has frequently been applied to measure cognitive properties in design, and Gero and McNeill (1998) identify five categories for understanding models of engineering design. These coding schemes capture

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various cognitive events and associated behaviours in design, and thereby allow for the exploration of individual designer’s cognitive systems. This approach has much in common with Burleson’s and Caplan’s (1998) individual difference variable, which is used in psychosocial experiments to measure cognitive complexity. Suwa et al.’s (1998) coding scheme enables the examination of physical, perceptual, functional and conceptual processes of human cognition. Such schemes support the collection of diverse information that can be used for measuring, among other things, cognitive complexity.

4.3 Measuring Cognitive Complexity One of the earliest methods used in psychosocial research for measuring cognitive complexity was based on Kelly’s repertory grid (1955). This method requires the capturing of data using transcribed interviews, questionaries or written descriptions. Participants’ responses are then rated on a tailored complexity scale to quantitatively identify individual cognitive traits. This method can be used to capture and compare cognitive activities that occur during complex multi-variable problemsolving processes. Such psychological processes are strongly reminiscent of the cognitive activities that occur while designing. In psychological research, two dimensions of cognitive complexity have been measured and analysed. Benet-Martínez et al. (2006) describe these two dimensions as pertaining to the content (properties and features) of a cognitive process and its underlying structure (relationships and dynamics). The design equivalents that are measured in this chapter are degrees of differentiation of individual cognitive content (categorisations of cognitive activities) and cognitive structure (relationships between cognitive activities). Collectively, these two contribute to a determination of levels of cognitive complexity (Fig. 4.1). This two-part definition has parallels to those used in psychology, but with the focus here being solely on design as a series of cognitive activities and associated behaviours.

Cognitive complexity

Content complexity Coding scheme  CategorisaƟons of cogniƟve acƟviƟes  Weighted coverage value

Structural complexity Linkography  RelaƟonships between cogniƟve acƟviƟes  Complexity over Ɵme

Fig. 4.1 Content and structure of cognitive complexity

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4 Measuring Cognitive Complexity

Content complexity relates to the individual categories of cognitive activities evident in a design process. These categories can be understood as the set of all possible states of the design cognition system, and therefore the presence or absence of various categories can be used to measure the uncertainty of cognitive activities in a system. When considered in the context of classical information theory (Shannon 1948), this means that cognitive categorisations are the “variables” or “events” that generate the dynamical system. These events are equivalent to “letters” in a typical message, or “bits” in a stream of data. In this chapter, the data for analysing content complexity in design is developed using a coding scheme to capture the cognitive content of the process (that is, its cognitive properties). Because different design sessions have different lengths, the frequency of each code is weighted by its time duration, or weighted coverage value. Structural complexity is concerned with the relations between cognitive activities in a design process. Whereas content complexity is about the presence, absence or frequency of cognitive categories, structural complexity is a measure of the connections between these categories. To capture structural complexity in protocol data, this chapter uses a variation of linkography (Goldschmidt 1990, 1995, 2014). Combining linkography with distance graphs supports the exploration of the effect of inspiration in design (Cai et al. 2010). Measuring the degree of development through a distance graph can also reveal the individual level of abstraction present. Because the variables in a dynamical system can vary with time (Baranger 2001), the structural complexity measure considers changing connectivity and dynamics over time. In summary, if there is cognitive disorder in a design process, individual categorisations of cognitive activities and their structures can be used as critical indicators of subjective operations governing cognitive complexity.

4.4 Method 4.4.1 Entropy Measure In Shannon’s (1948) mathematical theory of communication, entropy is used as a measure of complexity, describing the average amount of information in a message or the degree of randomness in the system. Conceptually, the greater the entropy, the greater the uncertainty and the higher the complexity. This chapter uses “information entropy” to quantify complexity in the design process. In theoretical physics and mathematics, complexity is explained in terms of entropy, which refers to a state that lacks order or predictability. While “disordered” or “unpredictable” states may not appear desirable, in a mathematical sense these properties simply denote a system with a higher level of information or a higher rate of connectivity. A higher level of entropy indicates increased complexity in a system, or in the case of the design process, higher cognitive complexity. Thus, in the discussion and explanation that

4.4 Method

91

follows, references to the mathematical property “uncertainty” can be generally read as indicators of potential complexity in a system. There have been multiple uses of entropy in design in the past. For example, entropy has been used as a measure of the “design space” in the early stages of the design process, quantifying the information content of a design (Frey and Jahangir 1999; Krus 2013; LaFleur 1996; Suh 1999). LaFleur (1996) employs entropy as a measure of multi-functional problems, wherein the complexity of the design problem is a product of the number of functions it encompasses. Suh (1999) also defines complexity as a measure of uncertainty in achieving a set of specific functions or functional requirements. Frey and Jahangir (1999) suggest the use of differential entropy as a measure of information content in axiomatic design, transforming design parameters into functional characteristics. Khan and Angeles (2007) propose using entropy to define diversity in design solution alternatives. Krus (2013) also suggests using design information entropy as a state—reflecting both complexity and refinement—to quantitatively describe various aspects in the design process. The logic being that through concept generation, concept selection and parameter optimisation, complexity and uncertainty are reduced, while information is gained. The mathematical formula for calculating entropy (H) follows Shannon’s (1948) equation combining information, choice and uncertainty: H =−

n 

pi log pi

(4.1)

i=1

where H is named after Boltzmann’s H-theorem and pi is the probability of a complex system being in state i. The choice of a logarithmic base determines the unit used to measure information. When the base is 2, the unit is called binary digits (bits). While sometimes adopting base e can be useful (resulting in natural units), decimal or Briggs logarithms are also used to measure H (Khan and Angeles 2007). As base 2 is widely used in applications in information theory (Bansiya et al. 1999; Frey and Jahangir 1999; Kan and Gero 2008; Khan and Angeles 2007; Krus 2013; LaFleur 1996), this chapter focuses on bits of information content. Shannon describes this Eq. (4.1) as measuring the entropy of the set of probabilities pi , …, pn . Thus, in the case of two possibilities with probabilities p and q = 1–p, the entropy is H = −( p log p + q log q)

(4.2)

This can also be plotted (Fig. 4.2). When calculated in this way, H is a measure of choice or information (Shannon 1948). In a design protocol, each segment can be encoded using the initials of the five codes of Gero’s FBS framework (Gero 1990; Gero and Kannengiesser 2014)— function (F), behaviour (B), structure (S), requirement (R) and design description (D)—and two behaviours (Be and Bs). However, designers’ cognitive choices are, of course, independent in the model. Thus, R precedes F and F is often followed by B and then S. S often returns to F and leads to D. Thus, an example of a typical

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1 0.9 0.8 0.7

Entropy (H)

0.6 0.5 0.4 0.3 0.2 0.1 0

0

0.5 Probability(p)

1

Fig. 4.2 Entropy in the case of two probabilities (p and q = 1 – p)

sequence of FBS codes in a design process might be R F B S F B S F B S F B S F B S F B S D. In this example, both R (requirement) and D (design description) occur once in the process over 20 segments (design activities). Thus, there is a possibility of 1/20 of the designer undertaking R or D activities. The possibilities of F, B and S are 6/20, 6/20 and 6/20, respectively. Collectively, the total information content of the given design process is H = −H R −HF −H B −HS −H D 1 6 6 6 1 1 6 6 6 1 H = − log2 − log2 − log2 − log2 − log2 20 20 20 20 20 20 20 20 20 20 H = − (−0.2161)− (−0.5211)− (−0.5211)− (−0.5211)− (−0.2161) H = 1.9955 bits Because the FBS framework is a process-oriented coding scheme, it is not entirely appropriate for measuring cognitive complexity. There are, for example, both content and process components of a designer’s action that also need to be considered (Dorst and Dijkhuis 1995; Lee and Ostwald 2019; Lee et al. 2014a). Thus, a content-oriented coding scheme is required to identify the information content of a design process.

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93

4.4.2 A Coding System for Content Complexity Measuring content complexity requires a content-oriented coding scheme that captures both the content of cognitive activities and the uncertainty of information processing in human cognition. As in previous chapters, Suwa et al.’s (1998) coding scheme is used as the basis, because it provides a general taxonomy for the content of designers’ cognitive processes as well as information processing. In this scheme, physical actions correspond to sensory level, perceptual actions to the perceptual level, and conceptual actions to semantic. Its cognitive categories are also derived from information categories (Suwa and Tversky 1997). This coding system is used to reveal the sequence of cognitive activities in a design protocol. This variation uses three categories—physical, perceptual and conceptual—but with revised activity classifications that are more suitable for geometric and algorithmic activities in parametric design (Table 4.1). As discussed previously, the geometric activities are regarded as the modelling activities for creating digital geometries, while the algorithmic activities represent the programming processes using “parameters” and “rules” as well as the “reference” code. The physical level in the coding scheme represents descriptive activities. The perception level represents the activities related to visual imagery in the design process (P-Geometry and P-Algorithm), while the conceptual level highlights problem-solving processes, such as problem-finding, solution-generating and solution-evaluating evaluating. This chapter aims to measure cognitive complexity through the coding results of subclasses in Table 4.1, because the micro-activities can be regarded as equivalent to the events in a complex system. The different frequencies of cognitive activities observed in an experiment can be used to indicate the individual uncertainty of information processing, which is the set of all possible states of the cognitive system. Thus, the frequencies of microcognitive categories (subclasses) are the key cognitive complexity variables because they can be thought of as degrees of uncertainty or complexity. Design protocols are initially divided into smaller segments (episodes or events), which are encoded as at least one of the subclasses in Table 4.1. An example of this type of coded protocol data is shown in Table 4.2. A typical information entropy measure uses the frequencies of sub-codes to calculate the probabilities of events. However, as described in Table 4.2, segments (cognitive events) can have different time spans, which must be taken into account to Table 4.1 Subclasses (codes) of the coding scheme for parametric design Level

Subclass

Physical

G-Geometry, G-Change, A-Parameter, A-Change Parameter, A-Rule, A-Change Rule, A-Reference

Perceptual

P-Geometry, P-Algorithm

Conceptual

F-Initial Goal, F-Geometry Sub Goal, F-Algorithm Sub Goal, G-Generation, E-Geometry, E-Parameter, E-Rule, E-Reference

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Table 4.2 Example of an encoded protocol (V: Visual cue in the video recording) Segment No Time span (s) Transcription of Design Experiment

Level

Subclass

14

13.1

What I am going to do is Conceptual F-Geometry Sub Goal work on a structural (or F-GSG) solution first. What I want to do is to put a grid around the outside …

15

10.0

(V: dragging and copying some rules) so I am creating a series of grids …

Physical

A-Rule

16

15.2

… on the outside, (V: changing parameters on the copied sliders)

Physical

A-Change Parameter (or A-CP)

17

32.8

I am just creating a border Physical of the grid, I copy that again. … (V: inserting and connecting rules)

A-Rule

18

13.1

Fifty, too many of them, Physical forty (V: changing parameters on the inserted rules)

A-Change Parameter (or A-CP)

19

1.4

Okay (V: Examining forms in the 3D view mode)

Conceptual E-Geometry

calculate the given probabilities of cognitive activities. The weighted frequency by time duration of each subclass and the probability of the subclass of cognition (P) is P(a) = 

T (a) x∈S T (x)

(4.3)

In this formula, T(a) is the time duration of the code in the protocol and S is the set of subclasses. That is, T(a) is the summary of time spans of cognitive events being the subclass. For example, consider a sample protocol consisting of six segments (Table 4.2). The probability of the F-Geometry Sub Goal (shortly, F-GSG) is T (F − G SG) T (F − G SG) + T (A − Rule) + T (A − C P) + T (E − Geometr y) 13.1 = 13.1 + (10.0 + 32.8) + (15.2 + 13.1) + 1.4 = 0.1530

P(F − G SG) =

4.4 Method

95

Since the lengths of design protocols are often different, probabilities based on weighted frequencies must be determined to compare individual cognitive categories (subclasses) in the set of protocols. Thus, the percentage of the weighted coverage value of each subclass in a design protocol can also be regarded as the corresponding probability for measuring the content complexity of the protocol. The content complexity (H C ) that is the summary of H values of all subclasses is then calculated (Eq. 4.1). In addition to these complexity values, this chapter calculates the normalised coverage values of cognitive levels (macro-thinking categories), where normalised A = (A – mean)/standard deviation (Bilda and Gero 2007; Lee et al. 2014a, 2015). Specifically, the normalised data over time can be used to refer to individual patterns of cognitive complexity in parametric design, because they show individual changes and differences of the content of cognitive activities as well as the levels of information processing in parametric design. In summary, the coding scheme for content complexity is a fundamental component for measuring cognitive complexity.

4.4.3 Linkography Measures for Structural Complexity Linkography is a graphical and topographic analysis technique that is used to investigate the way one idea, action or event interlinks with another (Goldschmidt 1990, 2014, 1995; Kan and Gero 2008; Lee and Ostwald 2019; Goldschmidt and Tatsa 2005). Linkography allows for recording, visualising and analysing the structure of connections between things. For the purpose of measuring cognitive complexity, the structural relationship of components in a linkograph is used. Although linkography has been used to map idea-generation process in design research, the task of objectively constructing a linkograph with “moves” and “links” remains a significant challenge. The primary means by which a link can be derived from protocol data is through the application of a coding scheme that identifies explicit connections. To develop a linkograph from protocol data, Kan and Gero (2008) identify three critical procedures: segmentation, linking and analysis. Only a few studies (Lee et al. 2016; Lee and Ostwald 2019), however, have identified moves and links in parametric design. The segment or “move”, is the step, act or operation that transforms a design situation, while the “link” is the connection between the segments (Goldschmidt 1995). Goldschmidt also suggests that rational links should be based on “common sense” reasoning in the analysis of a protocol. In this chapter, algorithmic scripts connecting design moves are used for understanding the parametric design process. Scripting activities are regarded as a channel for creativity and a means of representing design ideas in parametric design (Salim and Burry 2010). Furthermore, different types of moves and links develop in the design process. For example, van der Lugt (2000) categorises links into three types: supplementary, modification and tangential, and Perttula and Sipilä (2007) identify an alternative set of three types: parts sharing, same principle and modification. “Parts sharing” deals with primary parts of examples used in a follow-up concept,

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4 Measuring Cognitive Complexity

and “same principle” is regarded as a follow-up concept that is based on the same overall principle as an example. “Modification” is a follow-up concept that comprises a minor modification of an example at the embodiment level. Figure 4.3 shows examples of algorithmic components (units) and their connections (links in the graphic-based scripts and adjacencies in the text-based scripts). Scripts in parametric design, like sketches in traditional design environments, act

a. Graphic-based scripts (Grasshopper)

b. Text-based scripts (Python) Fig. 4.3 Algorithmic components/units and their connections

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97

as blueprints for the identification of algorithmic components or units and their sequence. Designers create and change parameters and rules on both the graphical and text-based scripting editors. For example, Fig. 4.3a shows the sequence of algorithmic components (or units) with numbering and clear graphical links between components. In the case of a text-based scripting editor (Fig. 4.3b), a group of related scripts that were created or modified together are defined as a unit (or a block of codes). Each numbered unit can then be linked to adjacent unit(s) within a programming component (see units 11 and 15 in Fig. 4.3b). This scripted structure allows us to identify algorithmic components and their sequence in parametric design, when designers create and change parameters or rules. From these algorithmic scripts, a linkograph can be constructed using four significant types of moves: (i) introducing geometric/algorithmic ideas, (ii) creating algorithmic components (as a unit), (iii) modification activities and (iv) evaluation activities. Once the moves and links are defined, then the next stage analyses the four significant moves. These moves correlate to Goldschmidt’s (1995) “link-intensive” or “critical moves” (CMs). CMs also serve as an effective indicator of productivity. Figure 4.4 illustrates part of a linkograph including the example of coded protocol data (Table 4.2). As shown in this graph, introducing geometric ideas (F-Geometry Sub Goal) and evaluation activities (E-Geometry) develop the CMs. After developing the linkograph, statistical and other measures, including entropy, can be calculated (Kan and Gero 2008). Individuals with high cognitive complexity have the capacity to analyse a situation to discern constituent elements and explore connections and possible relationships between elements (Lee and Ostwald 2019). The linkograph is especially useful in this context because it captures the structural complexity of parts and connections (the dynamic complexity of behaviour), allowing for information entropy in the design process to be measured. Kan and Gero (2008) use entropy as a means of interpreting linkographic results, focusing on acquiring abstracted information from the graphs. In contrast, information entropy relating to relationships between cognitive Fig. 4.4 A part of a linkograph of cognitive categories and connections

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activities in a linkograph is a structural aspect of cognitive complexity. The structural complexity enables capturing cognitive information processing in parametric design. Furthermore, this measurement considers the complexity of connectivity and dynamics over time because the set of levels of cognition can vary during the design process. Entropy can also be used to measure idea development opportunity (Kan and Gero 2008). Since the probability of a link being present complies with an On/Off rule, the entropy of each row in a graph is H = − p(O N ) log2 p(O N ) − p(O F F) log2 p(O F F)

(4.4)

The “row” in this context refers to Goldschmidt’s (1995) two types of links: “backlinks” and “forelinks” (see Fig. 4.5). Both forelink entropy and backlink entropy can be determined using an Eq. (4.4). Forelink entropy measures reflect idea-generation opportunities, while backlink measures relate to opportunities associated with enhancements or responses. Horizonlink (horizontal-link) entropy can also be measured as the distance between the linked moves, and it measures the opportunities relating to cohesiveness and incubation (Kan and Gero 2008). Thus, individual values derived from these three types of entropy can be used to identify a variety of cognitive features in a set of design protocols. The total cumulative entropy in the system indicates the volume of idea development opportunities (Kan and Gero (2008), which is similar to the link index calculated by dividing the number of links by the number of moves (Goldschmidt and Tatsa 2005). In this chapter, cumulative value is regarded as the degree of individual structural complexity. Because the links of a linkograph indicate the relationships between cognitive activities they conform to the second characteristic of cognitive complexity in design (structural complexity). In order to compare different lengths of design sessions, the structural complexity (H S ) of each protocol is defined as the cumulative total per move. A final consideration when looking at these three forms of links is that, conceptually at least, entropy as part of a design process increases as a system evolves and time passes (Baranger 2001). While it might be possible to imagine a backward evolution process in design, the more common model sees entropy increase over time (Eddington’s so-called “arrow of time” phenomenon). Using statistical methods, additional measures of complexity can be derived using this assumption. The measuring of changes in coded activities or links over time is often shown in

a. Forelinks

b. Backlinks

Fig. 4.5 Forelinks, backlinks and horizonlinks

c. Horizonlinks

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99

protocol analysis (Gero and Neill 1998; Lee et al. 2015, 2014b; Suwa et al. 1998). Earl et al. (2005) indicate that complex systems are also dynamic, changing and evolving over time, like design processes. In order to deal with these complexities of connectivity and dynamics over time, this chapter introduces time-based entropies through decile growth plots of a linkograph that allows for a measure of the variation of entropies in a design protocol to be produced.

4.5 Application This chapter uses the protocol data developed from experiments in Chap. 2 to examine the content and structural complexity in individual designer’s cognitive systems and associated design strategies.

4.5.1 Content Complexity Table 4.3 presents the coding results for the experiments of two designers (from Study II in Chap. 2) divided into subclasses and expressed as the percentage of the frequency weighted by time span (calculated by time duration of each code) as well as its entropy value. If the percentage is 0, its entropy cannot be calculated because log 0 is undefined. Individual cognitive complexity (content) uses the summary of all entropy values in a protocol. Thus, the content complexity (H C ) of Au1’s protocol is 3.3339 and Au3’s is 2.9897. The data record that Au1 adopts a clear problemdecomposing strategy with a working-forward search strategy, while Au3 uses trialand-error sequences or backward reasoning. These different design processes may explain the different content complexity results. In addition, the content complexities of the novice designers, Au2 and Au4, are 3.2879 and 3.0611, respectively. These results illuminate the theorised connection between cognitive complexity and creativity in an unexpected way. For example, while Au3’s design solution received the highest score for creativity from the judges, it was the lowest in terms of content complexity. One possible explanation for this anomaly is that both Au1 and Au2 contributed relatively higher percentages of their time to the “problem-finding” activity (Au1 is the second highest and Au2 is the highest percentage). Au3 and Au4, however, struggled with trial and error in their design sessions and produced relatively higher percentages of A-Change Rule and P-Algorithm in Table 2.5 (Chap. 2.). Thus, content complexity may be a better indicator of the probability of problem-finding or problem-decomposing in a design session, than of creativity. This finding is also reflected in the six “pen-and-paper” design protocols presented previously (from Study I in Chap. 2). The two designers (Ko3 and Ko5) who used a clear problem-forwarding strategy produced relatively higher complexity values (2.3733, 3.2241, respectively) than the others. Although Ko1 developed a productive design strategy, the complexity value of her protocol (2.1994) is the second lowest.

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Table 4.3 The percentage of the coverage of each subclass and its entropy value by Au1 and Au3 Category

Subclass

Percentage Au1

Physical

Conceptual

Au3 –

2.0

0.0

0.1129

G-Change

0.5

0.0

0.0382

–

A-Parameter

4.9

0.1

0.2132

0.0100 0.2073

A-Change Parameter

10.0

4.7

0.3322

A-Rule

24.7

20.0

0.4983

0.4644

6.0

12.2

0.2435

0.3703

A-Reference

0.0

0.2

–

0.0179

P-Geometry

2.8

1.8

0.1444

0.1043

P-Algorithm

3.2

4.0

0.1589

0.1858

F-Initial Goal

3.0

0.2

0.1518

0.0179

F-Geometry Sub Goal

7.0

2.5

0.2686

0.1330

F-Algorithm Sub goal

4.1

4.4

0.1889

0.1983

G-Generation

Summary (H C )

Au1

G-Geometry

A-Change Rule Perceptual

Entropy Au3

4.0

13.0

0.1858

0.3826

E-Geometry

19.4

17.3

0.4590

0.4379

E-Parameter

0.8

0.0

0.0557

–

E-Rule

7.6

19.5

0.2826

0.4599

E-Reference

0.0

0.1

100.0

100.0

–

–

3.3339

2.9897

This result may be because her design process is based on “drawing-reflection”, a type of novice behaviour. Specifically, Ko2’s design process, which produced only one rough plan layout and was the shortest design session, resulted in the lowest complexity (1.9806). A further interpretation of the content complexity result might be that it is dependent on a coding scheme itself. In other words, the relatively broad subclasses—such as R-drawing in Table 2.3 and A-Rule in Table 2.4—may produce a lower result. These results do, however, show that the content complexity measure not only provides a meaningful basis for analysis, it also reveals individual differences and cognitive properties. The other way of examining the protocol data in terms of information content is to consider changes in cognitive levels over time. The summed coverage data in each decile interval can be visualised using the normalised coverage value to explore sequential patterns. Figure 4.6 shows the normalised coverage of three levels of cognition over time. It enables the representation of physical, perceptual and conceptual activities in sequence and comparison of their evolution. A close examination reveals different patterns in levels of cognition within each protocol. For example, both designers produced multiple physical activities in the early stages of their protocols, while Au1’s protocol has a further peak at the sixth decile of the timeframe, whereas the normalised coverage of physical activities in Au3’s protocol decreases after the

4.5 Application

101

2.5

Normalised coverage

2.0

Au1

1.5 1.0 0.5 0.0 -0.5

1

2

3

4

5

6

7

8

9

10

Physical Perceptual Conceptual

-1.0

Decile Time

-1.5 -2.0

Normalised coverage

2.5 2.0

Au3

1.5 1.0 0.5 0.0 -0.5

1

2

3

4

5

6

7

8

9

10

Physical Perceptual Conceptual

-1.0 -1.5

Decile Time

-2.0

Fig. 4.6 Normalised changes of three cognitive levels (physical, perceptual and conceptual) over time by Au1 and Au3

fourth decile. The value of conceptual activities is relatively high in the middle of the timeframe of Au3’s protocol, while the same activities in Au1’s protocol often occurred later in the design process. Thus, the set of these normalised coverage values over time in a protocol can be understood as an individual pattern of complex cognitive content. The coding results identify the design activities undertaken by each participant. They also indicate different usages of cognitive categories (levels and subclasses) that potentially allow for the two to be differentiated in terms of content complexity. However, the frequency coverage of categories of design cognition has its limits, as categorising cognitive levels in this way is both subjective and sensitive to the research perspective or researcher. However, the segmentation of design protocols and their structures may better support the quantitative measurement of cognitive complexity in terms of moves and links in a linkograph (structural complexity).

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4.5.2 Structural Complexity The linkography analysis commences by addressing the four distinct move types identified previously in this chapter. Because these move types tend to generate a high number of forward or backward links (over five links), they can be said to constitute Goldschmidt’s (1995) “link-intensive” or “critical moves” (CMs). In this test, the two designers (Au1 and Au3) generally started by examining the design brief before considering ideas that respond to the brief at both the geometry and the algorithm thinking levels, coded as introducing geometric/algorithmic ideas (the first move type). Thus, 84.4% (Au1) and 68.3% (Au3) of three codes in the “Finding” category (F-Initial Goal, F-Geometry Sub Goal and F-Algorithm Sub Goal) in Table 4.4 link to more than five moves, which result in CMs (forelinks) that earn their designation. The third type of moves, modification activities, occurs when designers revisit and change algorithmic units. When a designer modifies an algorithmic unit, it is linked to the original move that created the unit. This process also tends to generate more than five links, being a CM (forelink). Many algorithmic units in parametric design are related to such modification activities (e.g. A-Change Parameter and A-Change Rule). The final type of significant move in parametric design is the “evaluation activity” that can form “backlinks”, recording the path of generating the move. For example, a move wherein designers evaluate geometries in the 3D view is often linked to all previous “physical” moves before “evaluating geometries (E-Geometry)”. For this chapter, Excel and Linkoder (Pourmohamadi and Gero 2011) are used to produce the linkographs. Figure 4.7 illustrates the linkographs of the two design protocols. Au1 presents a design strategy that commences with problem-finding activities (F-Geometry Sub Goal and F-Algorithm Sub Goal) in both the geometry and algorithm activities before creating rules and then ending with evaluation (EGeometry) in the geometry category. Conversely, Au3 starts using physical synthesis (e.g. A-Rule) and generation (G-Generation) activities in the algorithm category, even at the start of the design session. Au3 then evaluates and revisits the algorithmic units. Au3’s approach may be related to either a “troubleshooting” approach or a “solution-driven” strategy of design. Table 4.4 The frequency of move and CM of three subclasses in the “Finding” category by Au1 and Au3

Subclass Au1

Au3

F-Initial Goal

Move

CM (%)

4

2 (50.0%)

F-Geometry Sub Goal

17

15 (88.2%)

F-Algorithm Sub Goal

11

10 (90.9%)

Sum

32

27 (84.4%)

2

1 (50.0%)

F-Geometry Sub Goal

16

12 (75.0%)

F-Algorithm Sub Goal

23

15 (65.2%)

Sum

41

28 (68.3%)

F-Initial Goal

E-Geometry E-Rule A-Change Rule

G-Generation

E-Geometry

A-Change Rule

E-Geometry

Pa3

A-Change Rule G-Generation

G-Generation

Pa1

E-Geometry E-Rule

103

F-Geometry Sub Goal F-Algorithm Sub goal A-Rule A-Parameter A-Change Parameter A-Change Rule

4.5 Application

Fig. 4.7 Linkographs of the two design protocols (and details of a selected section of each as circled)

The total number of design moves undertaken by Au1 and Au3 were 220 and 363, respectively. However, the former’s link index (2.75) is marginally higher than the latter’s (2.68). The percentages of all three types of critical moves—according to Goldschmidt (1995), CM5 , CM6 and CM7 which each have more than five, six and seven links, respectively—in Au1’s protocol are also higher than in Au3’s. According to the rationale of Goldschmidt (1995) and Kan and Gero (2008), these figures suggest that Au1’s design protocol is more productive than Au3’s. However, to better understand and compare the structural characteristics of the two cognitive processes, this test measures forelink and backlink entropies which are indicators of structural complexity. Table 4.5 shows the results of entropy calculations derived from the two design protocols. In both protocols, backlink entropy is higher than forelink entropy. This Table 4.5 Entropy of the two design protocols Forelink total H

Backlink total H

Horizonlink total H

Cumulative total

Au1

35.23

52.56

14.17

101.96

Au3

36.18

62.07

16.18

114.43

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Table 4.6 Entropy per move and structural complexity (H S ) of the two design protocols Forelink H

Backlink H

Horizonlink H

HS

Au1

0.161

0.239

0.064

0.464

Au3

0.100

0.171

0.045

0.315

suggests that design activities in parametric environments are focused on responding to or restructuring existing components. Backlink entropy measures the opportunities arising from enhancements or responses (Kan and Gero 2008). Table 4.6 records the structural entropy per move of the two design protocols. The total cumulative entropy per move agrees with the results of each individual link index as well as the results of the content complexity in Table 4.3. Consequently, for comparing design protocols with different durations, the structural-entropy-per-move measure can be effective at identifying individual structural complexity. Thus, the cumulative total per move would be regarded as the structural complexity (H S ) of each design process (Au1: 0.464 and Au3: 0.315). Decile growth plots of linkographs developed in this chapter further illuminate the variation of entropies that arise from growing complexity over time. Table 4.7 contains the data for entropies per move over decile time in the two design protocols. The decile entropy value is also normalised to effectively show its variation. Figure 4.8 represents the normalised value of the three types of entropies (i.e. forelinks, backlinks and horizonlinks) on the decile timeline and the sequential changes that also describe their complexity patterns over time. Although the values of the first two entropy types would increase, corresponding to the ten growths of each graph, “horizonlink entropy” illustrates similar patterns (a single peak over the session) between the two protocols. Because Kan and Gero (2008) propose that horizonlink entropy could reveal opportunities relating to cohesiveness and incubation in design, it may be conceptually related to the perceptual level of cognition in Fig. 4.6. However, the relation is weak considering the relatively small amount of the frequency coverage of perceptual activities in Table 4.3. The results of the entropy calculations (Table 4.7 and Fig. 4.8) are derived from the structures of linkographs in Fig. 4.7. For example, the peaks of the normalised coverages of both horizonlink entropies in Fig. 4.8 are closely mapped to the largest “chunks” of densely interlinked segmentations in both protocols. In addition, somewhat smaller “chunks” of link concentrations at the later stage of Au1’s protocol in Fig. 4.8 may cause forelink entropy coverage to be higher than backlink entropy. This may imply that the backlinks relate to dense, larger “chunks” in linkographs. In addition, both protocols show that the coverage of forelink entropy in Fig. 4.8 is often higher than the backlink entropy, when the coverage of conceptual activities in Fig. 4.6 is higher than in physical activities. Forelink entropy measures idea-generation opportunities (Kan and Gero 2008). One of the dominant activities in the conceptual level of cognition is E-Geometry (visually evaluating the outcome of a rule), and it usually contributes to the construction of backlinks. In contrast, problem-finding activities at the conceptual level of cognition relate to the

Forelink

0.0209

0.0593

0.0873

0.1094

0.1190

0.1236

0.1371

0.1551

0.1658

0.1601

Decile time

1

2

3

4

5

6

7

8

9

10

Au1

0.2389

0.2338

0.2271

0.2175

0.2042

0.1918

0.1609

0.1224

0.0826

0.0284

Backlink

0.0644

0.0675

0.0699

0.0752

0.0798

0.0898

0.0667

0.0579

0.0427

0.0186

Horizonlink

0.4634

0.4671

0.4521

0.4298

0.4076

0.4006

0.3370

0.2676

0.1846

0.0679

Cumulative

Table 4.7 Entropies per move over decile time in the two design protocols Au3

0.0997

0.0983

0.095

0.0882

0.0779

0.0807

0.0679

0.0599

0.0426

0.0317

Forelink

0.171

0.1669

0.1605

0.1539

0.1404

0.1291

0.1215

0.1053

0.0760

0.0512

Backlink

0.0446

0.046

0.0474

0.0490

0.0488

0.0484

0.0505

0.0535

0.0397

0.0263

Horizonlink

0.3153

0.3112

0.3029

0.2911

0.2671

0.2582

0.2399

0.2187

0.1583

0.1092

Cumulative

4.5 Application 105

106

4 Measuring Cognitive Complexity 1.5

Au1

Normalised coverage

1.0 0.5 0.0 1

2

3

4

5

6

7

8

9

10

Forelink Backlink

-0.5 -1.0

Horizonlink entropy

Decile Time

-1.5 -2.0 -2.5

Normalised coverage

1.5

Au3

1.0 0.5 0.0 -0.5

1

2

3

4

5

6

7

9

10

Forelink Backlink

-1.0 -1.5 -2.0

8

Decile Time

Horizonlink entropy

-2.5 -3.0

Fig. 4.8 Changes of normalised values of three types of entropies over time by Au1 and Au3

coverage of forelink entropy, supporting Kan and Gero’s hypothesis. Ultimately, these results, while limited in scope, identify that the entropy measures are able to capture complex combinations of cognitive activities and reveal various sequential patterns of cognition and their structural complexities in the design process.

4.6 Conclusion This chapter presents an entropy measure to quantify cognitive complexity in the design process. Cognitive complexity has two characteristics, cognitive properties and relationships, that can be measured using the content and structure of a design process. This chapter introduces these concepts and a way of understanding the design process in terms of complexity. It also offers a combined approach of protocol analysis and linkography for its quantitative measurement. Two complexity measures, content complexity and structural complexity (both based on information entropy), are examined in this chapter. Both content and structural complexity values developed from our data are consistent with the individual

4.6 Conclusion

107

design strategies and design thinking processes exhibited by participants in the design experiments. Furthermore, the normalised coverage values of cognitive activities and entropies over time allow for capturing patterns of cognitive content and structure. The complexity values developed in this way can also be easily compared to other findings (for example, the results of product evaluation). In this way, the proposed measures presented here are applicable to broader design research and practice. Finally, although this chapter presents and examines a method for quantitatively measuring cognitive complexity, these measures are limited to indicating the degree of individual cognitive difference. Design thinking is dynamic and individualistic, and it is difficult to interpret the measures of complexity developed here without a clear scale and standard to compare them with. Categorisation of cognitive activities is also a challenge when measuring information entropy. Thus, further research is required to verify variables and categorisations. For this reason, the complexity measures developed in this chapter are only suitable for comparative purposes. While the method is consistent and repeatable, further studies are required to develop meaningful scaled content and structural complexity.

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Part II

Collaboration

Chapter 5

Collaborative Design: Team Cognition and Communication

Abstract This chapter examines two cognitive issues in collaborative design: team cognition and communication. It commences with a detailed review of past research, before developing a new framework for design team cognition. This framework is built around an understanding of individual, team and distributed mental models and cognitive behaviours, including a consideration of different modes of communication and working. This framework is elaborated, and its implications for team cognition and communication are examined, through a discussion of the results of two experiments. The first uses the results of a protocol study of cognitive traits to reveal the importance of distributed mental models. The second uses protocol data from a study of cross-national teams to investigate design communication. This chapter contributes to the development of new methodologies for examining design teams and to advanced knowledge about design cognition and representation. These advances are potentially significant for supporting creativity and cross-cultural collaboration in teams.

5.1 Introduction A team can be defined as “a distinguishable set of two or more people who interact, dynamically, interdependently, and adaptively toward a common and valued goal, [and] who have each been assigned specific roles or functions to perform” (Salas et al. 1992, p. 4). Design practice is typically a team process wherein a collaborative environment is the setting for complex problem-solving. A design team is a designated group who are operating collaboratively and synergistically to achieve a singular vision or outcome. The team typically comprises two or more professionals with different areas of expertise, levels of experience, innate and developed design strategies and working practices. In the design industries, the team typically work together on a project-by-project basis to produce solutions for clients. These solutions are achieved through the managed application of individual and collective strengths and abilities. A related definition describes collaborative design as the process wherein team members “from different disciplines share their knowledge about both the design process and the design content” (Kleinsmann 2006, p. 30). By © Springer Nature Switzerland AG 2020 J. H. Lee et al., Design Thinking: Creativity, Collaboration and Culture, https://doi.org/10.1007/978-3-030-56558-9_5

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definition, the creation of a “shared understanding” in a design team is an essential component for collaborative design and as such, developing a deeper understanding of knowledge creation, integration and communication between team members, is essential for improving collaborative design (Kleinsmann 2006). Design thinking in collaborative environments encompasses multiple, simultaneous cognitive activities and processes. As discussed in previous chapters, cognitive design processes are not always easy to identify or study. They are largely intuitive, contingent and subliminal, because design itself is a conceptual, creative and strategic process that evolves out of an internalised cognitive system. That is, individual cognition is not always clear, not even to the designer. A further complicating factor occurs in teamwork, where cognitive processes and their results must be communicated to, or shared with, team members. To be effective, the team needs to engage collectively in this process of cognitive transference and processing, which may require a change in individual or collective Mental Models (MMs). As such, improving team performance and effectiveness in design practice requires a dynamic, collective and situated understanding of design cognition (Marshall 2007). It also necessitates a consideration of the construction and communication of socially held representations (Dong 2005). This is why past research in this field emphasises the importance of understanding team cognitive and communication processes (Badke-Schaub et al. 2007; Bierhals et al. 2007; Dong et al. 2013; Du et al. 2012). Indeed, these two issues, team cognition and design communication, are so closely connected in past research that it is difficult to completely separate them. Thus, this chapter explores both concepts in the operations of the design team. It must also be acknowledged that there is a tension implicit in the relationship between cognition and communication in design teams. In most team-based circumstances, it might be assumed that transparent communication and achieving perfect shared understanding are core to effective team processes, in part because they support the management of tasks and resources (Du et al. 2012). Such assumptions, not unreasonably, are the catalyst for Valkenburg and Dorst (1998) to argue that a lack of shared understanding in a team causes unnecessary iterative loops and delays. Consequently, “team performance improves if team members have a shared understanding of the task that is to be performed and of the involved team work” (Jonker et al. 2011, p. 132). While this might appear to be a reasonable proposition, a counter argument is that possessing an overly shared understanding can actually hinder creativity. Creative problem-solving techniques typically encourage and reward diverse thought processes. This is because collective cognitive structures, such as shared cognitive maps, belief structures and MMs in a team, may cause “individuals to ignore discrepant information and may inhibit creative problem solving” (Klimoski and Mohammed 1994, p. 405). In order to explain the tension between communication and cognition in teams, Langan-Fox et al. (2001) contrast the notions of a “Team Mental Model” (TMM) and a “Shared Mental Model” (SMM). The former refers to a diverse, rich, collaborative cognitive process, whereas the second describes a more homogenous cognitive model, like “group think”, which they call a “collectivity”. Whereas TMMs contribute to social creativity, SMMs can stifle expression and

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115

novelty (Fischer et al. 2005). Such arguments lead to the view that diverse, collaborative cognitive processes can be more creative than shared or collective ones. As a counterpoint to this position, Banks and Millward (2009) call for the development of a “Distributed Mental Model” (DMM), which negates the effects of collectivity through effective communication. The DMM is also an effective reminder that the situation is not a binary (either/or) one. A theme in many of these models is that in order to support team performance and creativity, design teams should possess a balanced mixture of shared and distributed team cognitions. But beyond this general consensus, there remains debate about how we can develop a deeper understanding of what this means. The development of new knowledge to support improved design teamwork skills and abilities has been the subject of several empirical studies (Chiu 2002; Cross and Cross 1995; Mulet et al. 2016; Stempfle and Badke-Schaub 2002; Eppler and Kernbach 2016). Despite this, there remain substantial gaps in our knowledge of design team cognitive processes. Furthermore, there are two fundamental challenges that research into design teamwork faces. First, the environment where the team undertakes a given design task has an impact on both the way a team works and on its capacity to be creative (Bilda and Demirkan 2003; Ibrahim and Rahimian 2010; Mulet et al. 2016). Second, communication barriers—not only visual and verbal but also cognitive and linguistic—complicate any empirical approach to the topic (Dong 2005; Lee et al. 2016; Valkenburg and Dorst 1998). For this reason, research into team cognition should not only consider the impacts of emerging design environments, but also individual differences in communication skills. These factors have become important in recent years as digital design environments have become the common interface for large multi-national design teams. The design environment comprises the set of creative and communicative tools, platforms, media, systems and locations that support the operations of individuals and teams. While historically, the design team was almost always co-located, physically sharing the same space, tools and systems, over the last few decades advances in Information and Communication Technology (ICT) have broadened the definitions of collaboration and teamwork, to include a range of Computer Supported Cooperative Work (CSCW) tools. Such CSCW tools tend to be optimised for efficient teamwork, and advances in this field have tended to be focussed on creating appropriate environments for collaboration and supporting asynchronous communication (Chiu 2002; Cross and Cross 1995; Grudin 1994; Ibrahim and Rahimian 2010). As a result of the success of CSCW applications, they have become core to the cognitive and communicative operations of design teams around the world. Against this backdrop, the present chapter undertakes a review of literature on cognition and representation, before proposing a mixed Design Team Cognition (DTC) model to conceptualise both mental and communicative processes in design. Thereafter protocol data (from Study II in Chap. 2) is revisited to explore individual cognitive patterns in different design spaces and modes and then expanded to consider their impact on DTC. The spaces are the problem and solution spaces of design strategy and cognition, and the modes or environments are geometric and algorithmic modelling in parametric design. The chapter then employs data from two additional

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design protocols, one from an Australian design session and another from a Swedish session, to investigate individual communicative differences in design information and spatial language. The studies in this chapter are not, however, its core contribution, they serve as a counterpoint and as a catalyst for discussion, rather than for drawing conclusions. These are studies of individuals, working in environments that are optimised for team processes and to enable the development of CSCW applications for sharing and managing design information, coding and visualisation (Holzer et al. 2007; Kolarevic 2003). The results of these studies are used to highlight cognition and communication in design teams, and their implications are analysed using the DTC model.

5.2 Cognition and Representation for Teamwork 5.2.1 Team Cognition The phrase “team cognition”, not unexpectedly, refers to the set of team members’ cognitive operations, perceptions, reasoning, conscious thought and MM, among other things. The relationship between an individual’s cognitive operations and that of a team they are a member of, is a rich one, which has been linked to creativity (Fischer et al. 2005). The emergence and sharing of creative activities and cognitions in a social environment is also known as “co-creation” (Giaccardi 2004, 2005). The topic of team cognition has been extensively researched in the fields of psychology, sociology and education. From this past research DeChurch and Mesmer-Magnus (2010) identify two major cognitive constructs of team cognition: the Mental Model (MM) and Transactive Memory (TM). The former refers to the knowledge commonly held by team members, while the latter describes the knowledge distributed among team members. A MM is a “mechanism whereby humans generate descriptions of system purpose and form, explanations of system functioning and observed system states, and predictions of future system states” (Rouse and Morris 1986, p. 360). MMs contribute to “representing objects, states of affairs, sequences of events, the way the world is, and the social and psychological actions of daily life”. They support people to forecast results, draw inferences, “to understand phenomena, to decide what action to take and to control its execution” (Johnson-Land 1983, p. 397). An effective MM can be used by team members to describe, explain and predict events and situations (Mathieu et al. 2000). Importantly, MMs are not universal: “there can be (and probably would be) multiple MMs co-existing among team members at a given point in time”. (Klimoski and Mohammed 1994, p. 432). A MM must be shared among team members before it can be tested, adopted or applied, and complex tasks will often require the application of multiple MMs (Cannon-Bowers et al. 1993).

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The second cognitive construct, TM, refers to the collective capacity of each member of a team to store and share information. It encapsulates an awareness of different areas of expertise that can be accessed as required, and as such it has been described as being analogous to memory in a computer (Wegner et al. 1991). It is also closely related to notions of “cognitive diversity” (Sauer et al. 2006) and DMMs (Banks and Millward 2009) in a complex task environment. Austin (2003), for example, reveals that TM impacts on group performance in terms of both volume of knowledge available (both stored and specialisation) and capacity for consensus and accuracy (transactive processes). TM also operates in support of diversity, because access to specialised knowledge and a capacity to share it with team members are often prerequisites for complex problem-solving. As these descriptions of MM and TM suggest, they can, and probably should, occur simultaneously in effective teams. MM provides a basis for convergent knowledge and the management of tasks of processes. TM supports access to its divergent counterpart, specialisation and diversity, which potentially leverages team productivity and creativity. This realisation, that MM and TM are both operative in a team process, also highlights that they are co-dependent. Teams consisting of interdependent, diverse individuals face significant challenges in terms of shared responses to management and performance (Santandreu Calonge and Safiullin 2015). Managing or developing a team’s MMs is even “more important whenever diverse knowledge has to be coordinated” (Badke-Schaub et al. 2007, p. 14). Thus, in a balanced combination, MM and TM enhance team cognition in collaborative environments and especially those involving multidisciplinary and multifunctional teams and complex design tasks. In cognitive science, SMMs and TMMs have been employed to examine the influence of MM and TM on teamwork processes and performance (DeChurch and Mesmer-Magnus 2010; Holyoak 1984; Klimoski and Mohammed 1994; Langan-Fox et al. 2001; Mathieu et al. 2000; Johnson-Land 1983). Problematically though, in past research, the difference between SMMs and TMMs is not always clear and they are often used interchangeably. As such, for the purposes of the present chapter, precise definitions of these are less important than understanding the themes they articulate. The TMM, for example, can be used to refer to an agreed MM or TM, akin to a type of “collectivity”, whereas the SMM can capture “shared cognition among dyads of individuals” (Langan-Fox et al. 2001, p. 99). This means that the SMM can be used to describe the way multiple individuals can possess similar cognitive representations of a situation or phenomenon, without a formal agreement between them. Unlike a pure “collectivity” or “group mind”, SMMs function to “allow team members to draw on their own well-structured knowledge as a basis for selecting actions that are consistent and coordinated with those of their teammates” (Mathieu et al. 2000, p. 274). SMMs allow team members to predict what their teammates are going to do or require, allowing them to effectively conduct a task without the need for formal communication (Cannon-Bowers et al. 1993). They may not represent all individual MMs, but SMMs can at least partially describe the individual information processes conceptually involved in “group information processing” (Hinsz et al. 1997; Tindale 1989). Whereas TMMs may be employed to describe agreed cognitive states, like a collective MM, SMMs describe differences, transferences and negotiations, akin to

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TM. Such distinctions are not always agreed in other fields, and in design research, as the next few sections reveal, they are often not considered at all. In the design domain, the notion of “team cognition” has been used to explain the relationship between TMMs and collective processes and performances (BadkeSchaub et al. 2007; Bierhals et al. 2007; Boos 2007; Casakin and Badke-Schaub 2015; Dong et al. 2013; Marshall 2007). Past research argues that teams are “more suitable for complex tasks because they allow members to share the workload, monitor the work behaviours of other members, and develop and contribute expertise on subtasks” (Mathieu et al. 2000, p. 273). Early definitions of team cognition in design tended to emphasise properties and behaviours of teams akin to the “group mind” or “collectivity” models of psychology and sociology. In a narrow sense, a collective approach of this type, supported by transactional memory and socio-cognition, is effective because it has a common purpose and shared frames of reference (Klimoski and Mohammed 1994). However, if the team’s purpose is to undertake complex and creative, multi-dimensional problem-solving, say for a design, then such variations of TMM are not enough. Effective collaborative design naturally requires both diverse participants and the close coordination of design information and tasks, because its purpose is to share expertise, ideas, resources and responsibilities (Chiu 2002). Team organisation also has an impact on communication and performance, and Bierhals et al. (2007) reveal that design team performance is enabled by SMMs. Marshall’s (2007) practice-based approach further highlights social, dynamic and emergent characteristics of shared knowledge in design teams. These factors and many others have made TMMs and MMs in design more difficult to understand and model. In a pioneering study on TMMs in design, Badke-Schaub et al. (2007) identify five types of MMs: team, process, task, context and competence. 1. Team-MMs deal with coordination and communication and are most closely associated with operational efficiency. They are important to ensure that resources and expertise are appropriately deployed and managed to achieve a particular outcome in a given timeframe or within defined performance parameters. 2. Process-MMs could be regarded as a subset of team-MMs, but rather than emphasising holistic structures and systems, they help manage distributed tasks in the design process. For example, they might serve to share individual designers’ steps and strategies with the team. Conceptually, process-MMs highlight individual cognitive operations in design in a manner reminiscent of a DMM (Banks and Millward 2009). 3. Task-MMs share specific problems or challenges across the team with the goal of producing a common solution. Although using diverse knowledge to solve a given design problem can lead to divergent thinking and creativity, task-MMs are shared within the scope of the information process in a team, making them more akin to a convergent thought process. These first three MM types have been the subject of further research, whereas of the last two identified by Badke-Schaub et al. (2007), context and competence have tended to be undervalued or ignored.

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4. Context-MMs assist in managing and sharing knowledge about an organisational setting or condition. They are used to communicate, store or retrieve a deep understanding of, for example, clients, users, availability of materials, approval processes, legal time-lines or market forces. 5. Competence-MMs relate to a capacity to understand the relative levels of experience or types of expertise in a team. A shared sense of the capacity and skills available to support a design process can be essential for motivating and coordinating a team. These five MM types provide a foundation for the DTC model proposed later in the chapter. Past research has used multiple team cognition attributes to understand problemsolving behaviours (Stempfle and Badke-Schaub 2002) and collaborative design attributes (Ostergaard and Summers 2003) (Table 5.1). The emergence of cognition has also been identified as an important indicator of effective teamwork. “Team cognition is a bottom–up emergent construct, originating in the cognition of individuals” (DeChurch and Mesmer-Magnus 2010, p. 35). Cognition is said to emerge in a team process when either “composition” or “compilation” occurs. The first of these refers to the accuracy or congruity of a SMM. The second, “compilation” arises when a SMM encapsulates relevant specialisation and task coordination in the team. Across the multiple models of collective cognition, there are also three recurring MM types: perceptual, structural and interpretive. Perceptual-MMs encompass team members’ values, beliefs, attitudes and expectations. Structural-MMs highlight existing and evolving knowledge patterns within a team. Interpretive-MMs support the collective development of SMMs or DMMs. If we compare the three main team cognition models—from Stempfle and BadkeSchaub (2002), Ostergaard and Summers (2003) and DeChurch and Mesmer-Magnus (2010)—there are several common themes (Table 5.1). For example, both the problem-solving process in design (Stempfle and Badke-Schaub 2002) and collaborative processes (Kleinsmann 2006) are recurring themes. Task-related and teamrelated cognitive processes also appear in Badke-Schaub et al.’s (2007) and DeChurch and Mesmer-Magnus’ (2010) models. Further commonalities are present in the lists of attributes for the content and process of team cognition. For example, Stempfle and Badke-Schaub’s (2002) “content” includes goal clarification, solution generation, analysis, evaluation, decision and control, while Ostergaard and Summers’s (2003) “nature of the problem” addresses design sub-tasks, abstraction, scope and complexity. The overlaps between the multiple models also reflect the concept of the “compatibility in cognitions”, which refers in part to those factors which are not only common between models, but are complementary (DeChurch and Mesmer-Magnus 2010). There are also some differences between the three models. For example, the first three categories of collaborative design attributes identified in Ostergaard and Summers’s (2003) research are associated with effective interactions between team members. Team composition deals with team member relations and leadership, while the distribution category covers personnel and information in collaborative design.

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Table 5.1 Team cognition attributes identified in the literature Study

Stempfle and Badke-Schaub (2002)

Ostergaard and Summers (2003)

DeChurch and Mesmer-Magnus (2010)

Focus

Design team problem-solving behaviour

Collaborative design attributes

Cognitive underpinnings

Categories and attributes

Content Goal clarification, Solution generation, Analysis, Evaluation, Decision, Control Process Planning, Analysis Evaluation, Decision Control Residual Residual

Team composition Group, Individual, Team member relations, Leadership Communication Mode, Quantity, Syntax Proficiency of team, Dependability of resources, Intent Distribution Personnel, Information Design approach Design tools, Evaluation of progress, Degree of structure, Process approach, Stage Information Form, Management, Level of criticality, Dependability Nature of the problem Type of design, Coupling of sub-tasks, Level of abstraction, Scope, Complexity

Nature of emergence of cognition Composition (congruence or accuracy), Compilation (specialisation or global of complementarity/transactive memory) Form of cognition Perceptual, Structured, Interpretive Content of cognition Task-related cognition, Team-related cognition

Importantly, the communication category recognises the intent, mode and quantity of the communication, the proficiency of the team, and the reliability or predictability of resources. Different types of communication “may facilitate or hinder” the design or team process. “It follows that some communication forms (verbal, written, graphic, or gestures) may be better suited for use in particular tasks or phases of the design process” (Ostergaard and Summers 2003, p. 759). Both task-related and team-related

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cognitions, or content and process of MMs, are fundamental to understanding how team cognition for collaborative design is developed and shared.

5.2.2 Cognitive Representation A MM is a representation of the way a system, process or event operates or occurs. They are necessary abstractions or simplifications and tend to be based more on experience and intuition than facts. They do, however, allow individuals to understand complex phenomena, draw inferences, and “experience events by proxy” (JohnsonLand 1983). A MM can be regarded as a “psychological representation of the environment and its expected behaviour” (Holyoak 1984, p. 193). It can be understood both as a type of knowledge and as “a general class of cognitive constructs that have been invoked to explain how knowledge and information are represented in the mind” (Klimoski and Mohammed 1994, p. 405). MMs support team members to describe, explain, predict and communicate future system states (Rouse and Morris 1986). TMMs capture representations that support collaborative (both collective and distributed) cognitive processes (Klimoski and Mohammed 1994). TMMs are fundamentally involved in, or instrumental to, communication. Paivio (1971), for example, extends the concept of the TMM’s “imagery and verbal processes” of representation to encapsulate “dual coding theory”, which describes the situation that occurs when an encounter with an image and a word develops both non-verbal and verbal representations and associations. He classifies the “non-verbal (symbolic) subsystem” as the site of imagery and representation, “because its critical functions include the analysis of scenes and the generation of mental images” (Paivio 1983, pp. 53–54). In contrast, spoken linguistic systems, or “verbal systems” transmit representations and can be used to achieve confirmation of understanding. Collectively the non-verbal subsystem and the verbal system (Paivio 1991), support an in-depth understanding of individuals’ MMs and their cognitive representations. These cognitive representations, which are expressed by visual and verbal language, are the focus of the next two sections.

5.2.2.1

Visual Representation

After individual MMs are established, they must be exchanged and synchronised with TMMs for effective collaboration (Badke-Schaub et al. 2007; Boos 2007). Sonnenwald (1996) indicates that the processes of exchange and synchronisation facilitate “boundary sharing”, which plays a vital role in knowledge exchange, exploration and task coordination. While language clearly supports design communication in teamwork, visual representations, such as diagrams and models, make a significant contribution to the development of SMMs or DMMs. Visual representations are also intrinsic to “designerly ways of knowing” (Cross 1982). As such, it is

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not unexpected that the role of sketching in visual design thinking has been extensively explored (Goldschmidt 1991, 1994; van der Lugt 2005). Furthermore, there has been a sustained interest in design thinking in comparisons between sketching (traditional design environment) and various computer-aided design environments (Bilda and Demirkan 2003; Ibrahim and Rahimian 2010; Won 2001). The findings of these studies often emphasise the differences between visual thinking in the two environments (Won 2001). In addition to a consideration of MMs in collaborative design, social processes, technical tools and environments also have an impact on the design team (Cross and Cross (1995). For example, Oxman (2004) highlights that an “explicit shared representational schema” can be employed “to represent the conceptual content in design precedents” in such a way that “helps to organise knowledge and to provide structure” (p. 71). In addition, Gonçalves et al. (2014) identify fourteen approaches— brainstorming, function analysis, scenarios, mind map, checklists, analogies, howto’s, storyboards, metaphors, collages, context mapping, morphological charts, role playing and synectics—to help designer’s idea-generation tasks. The first three techniques are common ones used by professional designers. Brainstorming enables idea generation, while scenarios facilitate an overall understanding of design options. “Function analysis represents a systematic analysis of the relationship between the functions and the different parts of the future product” (Gonçalves et al. 2014, p. 43). These visual thinking techniques potentially support both divergent and convergent collaborative tasks (Eppler and Kernbach 2016). That is, design thinking in teams relies on visual representations for idea generation as well as collaborative analysis and decision-making. These processes also reflect the team cognition attributes identified in the previous discussion of TMMs, SMMs and DMMs (Table 5.1). In summary, visual representation has a significant influence on the content and process of collaborative design, as well as the development and application of TMMs. The role of visual representation in cognition has been studied in multiple projects, four of which are compared in this section (Table 5.2). The first examines the cognitive dimensions of notations that are typically communicated using visuals. Green and Petre (1996) suggest that seven dimensions are required to capture the cognitive aspects of non-textual reasoning in Visual Programming (language) Environments (VPEs). The first of these, closeness of mapping, deals with the mapping of program entities to problem entities, to ensure that usable languages for end-users reflect task-specific entities. The second, viscosity or resistance to local change, highlights the need for VPEs to be readily revised. The third, hidden dependencies in programs may result in unexpected outcomes or flaws. VPEs (for example, the box-and-wire representation of program and data flow in graphic-based scripts in Fig. 4.3) can communicate the consequences of changes in textual programming environments, and also increase cognitive load, leading to the fourth dimension, hard mental operations, through their use of complex visual notations (“brain-twisters”). Although VPEs provide more flexibility in programming than standard linear textbased languages, they still require guess-ahead strategies, the next dimension, to avoid creating “visual spaghetti”, a common flaw in “box-and-wire” VPEs. The sixth dimension, secondary notation, describes visual systems that communicate

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Table 5.2 Visual representation attributes identified in the literature Study

Green and Petre (1996)

Blackwell et al. (2001)

Eppler (2004)

Bresciani (2019)

Focus

Cognitive dimensions of notations

Cognitive dimensions of notations

Knowledge communication

Visualisation characteristics

Attribute

Closeness of mapping Viscosity Hidden dependencies Hard mental operations Imposed guess-ahead Secondary notation Visibility

Creative ambiguity Specificity Detail in context Indexing Synopsis Free rides Useful awkwardness Unevenness Lability Permissiveness

Focus Coordination Documentation Consistency Accountability Traceability

Structural restrictiveness Content modifiability Directed focus Perceived finishedness Outcome clarity Visual appeal Collaboration support

supplementary information. The final, visibility dimension, supports mapping of effective search strategies to improve cognitive representations in VPEs. Blackwell et al. (2001) extend Green and Petre’s (1996) cognitive dimensions to describe the usability of notational systems and information artefacts, suggesting ten dimensions which have some overlap, but also include additional nuanced readings of factors like awkwardness and unevenness, which can support or prohibit transmission of knowledge (Table 5.2). The third visual representation model, from Eppler (2004), examines “knowledge communication” in visualisation and the fourth, from Bresciani (2019), explores its characteristics. Knowledge visualisation systems facilitate team communication and reasoning processes. Such systems are used to shape knowledge—to abstract, diverge, converge, structure, elaborate, and evaluate—as part of the process of building a TMM (Briggs et al. 2001). In this context, Eppler (2004) examines software-based, collaborative visual communication tools that serve cognitive purposes through systematic interactive visualisation. Three visual communication tools—the OnTrack visual protocol tool, the Parameter Ruler application and the Synergy Map—are presented which support knowledge focussing, coordination, documentation, accountability and tracking. Bresciani (2019) presents an alternative set of seven knowledge visualisation attributes of collaborative visualisation systems—structural restrictiveness, content modifiability, directed focus, perceived finishedness, outcome clarity, visual appeal and support for collaboration—that control and enable specific cognitive and collaborative actions. Collectively these attributes support the sequential tracking, progressive evaluation, revision and simultaneous modification of knowledge visualisation. Bresciani’s (2019) last dimension, collaboration support describes “the ability of a visual representation’s format and environment to facilitate the group interaction in the co-creation process” (Bresciani 2019, p. 106).

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These four models—Green and Petre (1996); Blackwell et al. (2001); Eppler (2004); Bresciani (2019)—can be used to explain how visual representation works in design communication as well as the externalisation of an individual MM. Thus, they support knowledge sharing between teammates and facilitate team design cognitive processes in collaborative design.

5.2.2.2

Verbal Representation

Past research on design communication has largely focused on visual representation, with only a limited body of research concerned with verbal representation. However, visualisation and verbalisation are both fundamental forms of design communication and external representation of design thinking. Other, non-verbal representations, like hand gestures, can also impact on design collaboration, but the visual and the verbal systems are the primary means of expressing shared or reasoned information. The structure of language-based communication is founded on socially shared representations of understanding (Dong 2005). The degree of psychological correlation between a “designer’s own cognitive representation and the socially held representation of the designed artefact is reflected in the semantic coherence between words in the way they co-occur in dialog and other language-based communication” (Dong 2005, p. 447). This correlation of collective mental models and modes of work, “facilitates the coordinated action that is required for successful team-based design” (Dong 2005, p. 448). Language, in this sense, provides the “mental space” for the creation or evocation of knowledge domains. Furthermore, language “builds up mental spaces, relations between them, and relations between elements within them” (Fauconnier 1985, p. 2). Communication is, therefore, dependent on the extent to which people may “build up similar space configurations from the same linguistic and pragmatic data” (Fauconnier 1985, p. 2). That is, people represent objects and their relations within mental spaces and language expresses and articulate these representations (Bloom 1993). Thus, in cognitive science, these representations constitute a type of “working memory” or “consciousness”. As noted previously, Casakin and Badke-Schaub (2015) use three categories of MMs to investigate different types of verbal activities in design team meetings: Task-MMs, Process-MMs and Team-MMs. Interestingly, their research reveals that a repeated verbal communication loop of task-MM and Team-MM, can reflect a behavioural structural pattern in a design team. That is, effective design team problem-solving processes require structured communication between members. Such a structure accommodates both verbal and visual communication simultaneously. “Moreover, the language system is peculiar in that it deals directly with linguistic input and output … while at the same time serving a symbolic function with respect to nonverbal objects, events, and behaviours” (Paivio 1983, p. 53). Therefore, understanding the relationship between spatial language and cognition is a crucial process for design teams, and especially if there are differences in languages spoken in the team (Lee et al. 2017).

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In summary, visual and verbal representations play a vital role in both design communication and cognition. As visual and verbal representations also support and sustain individual MMs, they must be clearly expressed between members for effective design team performance. One aspect of this verbal and visual communication that is especially important in the construction and transmission of TMMs is spatial-relation reasoning. Communicating spatial relations requires sufficient and determinate information to define a relationship qualitatively (Tenbrink and Ragni 2012). Thus, the following sections of this chapter introduce a new approach to exploring visual and verbal representations in the design process.

5.2.3 Design Team Cognition (DTC) Model In past research, TMMs have been used to understand performance differences between teams and also to describe knowledge shared by team members (Mohammed and Dumville 2001). The research suggests that some specific TMMs, like “group minds” and “knowledge structures”, can enhance “interpretive processes by enabling individuals to screen out information in order to prevent information overload and intolerable levels of uncertainty” (Klimoski and Mohammed 1994, p. 405). In parallel, individual belief structures also enable the development of socially shared cognitive processes (Resnick 1991). As previously noted, individual processes are core to group processes (Hinsz et al. 1997). Notwithstanding distinctions between individual, collective and distributed MMs, communication and information processing across a group have a substantial impact on the development and application of TMMs. The theory of DMMs, in particular, accommodates different roles or expertise that cannot be easily shared. A DMM provides a means of understanding the capacity of a team to accommodate different perspectives (Banks and Millward 2009). In this context, a DMM presents a “basis for understanding highlevel cognition in distributed systems, specifically cognition which relies on mental models including reasoning, naturalistic decision making [and] the cognitive basis of teamwork” (Banks and Millward 2009, p. 259). Thus, the combination of DMMs and SMMs would appear to provide a foundation for exploring complex cognitions in diverse design teams. Past research has considered this combination and how it sustains team activities. Dong et al. (2013) present a conceptual TMM for design teamwork that consists of two levels: the cognitive realm and the realm of action. The first of these is concerned with TMM quality, which is a factor of two, secondary concepts: (i) the emergence of “sharedness” or “accordance” arising from the TMM; and (ii) the accuracy of the TMM in respect to the real-world phenomenon, occurrence or event which it seeks to model. The second realm, the realm of action, is concerned with the way shared cognition is implemented or actioned. It addresses the goal-directed behaviour of the TMM. The cognitive realm can be investigated using “latent semantic analysis” (Dong 2005) to assess the quality of a TMM in terms of accuracy and the emergence of sharedness. The realm of action can be examined using “reflective practice analysis”

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(Valkenburg and Dorst 1998) to reveal the relationships between frames of reference and a team’s design process. Consequently, the combined method of latent semantic analysis and reflective practice analysis can be effective for investigating design team cognition and allow for the assessment of design TMMs. It also enables the analysis of both the content and the process of team cognition and communication. As the last few sections in this chapter have demonstrated, the foundations for a model of design team cognition were laid in previous research on SMMs and TMMs. While there are many subtle distinctions in past research into team cognition, for the purposes of the present chapter it is clear that a TMM in design is a combined model involving a SMM, for transactive memory, and a DMM, to encapsulate the emergent phenomenon and collective, “bottom-up” development that arises from individual MMs. Such a model has not previously been articulated in this way or presented. Thus, this chapter proposes a new DTC model, for representing mixed cognitive, collaborative and creative processes (Fig. 5.1). At the centre of the DTC model is transactive memory, the common, shared or distributed knowledge in the design team. “Transactive memory systems are a form of cognitive architecture that encompasses both the knowledge uniquely held by particular group members with a collective awareness of who knows what” (DeChurch and Mesmer-Magnus 2010, p. 33). The transactive memory is a “shared system for encoding, storing, and retrieving information” (Wegner et al. 1991, p. 923). Like an individual memory, a team transactive memory evolves through three processes: encoding, storing, and retrieving. Thus, both the shared and distributed knowledge of design team members can be considered transactive. Furthermore, like a human memory system, the transactive memory has both Long-Term Memory (LTM) and Short-Term Memory (STM). For example, team-related cognition is regarded as a component of LTM because it is sustained or exists over an extended ProducƟvity, Design informaƟon, Design representaƟon Performance

Emergence and Sharedness

CommunicaƟon Visual & verbal representaƟons

TransacƟve Memory

Task

SoluƟon CogniƟon

CreaƟvity

Distributed Knowledge

Content & process

Design strategies, ComplexiƟes, Design spaces, Design modes Fig. 5.1 A Design Team Cognition (DTC) model for the mixed cognitive, collaborative and creative design processes

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period. Task-related cognition is part of STM because it is limited to the given situation. The DTC model (Fig. 5.1) has two knowledge stores: (i) emergence and sharedness and (ii) distributed knowledge. According to the given design task, the transactive memory continuously retrieves appropriate information from the two stores until the team produces the final solution. Thus, the two knowledge stores also grow and change during the team design process. The first knowledge store, emergence and sharedness, is closely related to the emergent construct of the collection of individual cognitions and also to the shared knowledge of SMMs. TMMs are considered “emergent characteristics” of a “group which reflect organised knowledge and the tendency of individuals to categorise what they know” (Klimoski and Mohammed 1994, p. 417). The nature of emergence is well described in DeChurch and Mesmer-Magnus’ study (2010) in terms of either composition or compilation (Table 5.1). SMMs also provide guidance for team members about how to progress, coordinate and adapt actions required by the task or team (Cannon-Bowers et al. 1993). “As the design progresses, team members interact with each other to exchange opinions and ideas, while their mental models are modified, adapted, and eventually shared within the team”. (Casakin and BadkeSchaub 2015, p. 158). SMMs of this type also shape the future responses of design teams to new problems and situations. The second store in the DCT, distributed knowledge, encompasses “cognitive artefacts, the environment, culture, social factors and other elements which might be adopted into the unit of analysis” (Banks and Millward 2009, p. 259). Whereas the first store is closely associated with team performance, the second deals with information that is dispersed throughout the team, and relationships between individual cognitions, both of which are connected to creativity. Performance-related aspects of the emergence and sharedness store are captured by measuring productivity, design information and design representation. In contrast, the creativity of the distributed knowledge store may be explored using individual design strategies, complexities, design spaces and design modes. These categories in both knowledge stores reflect the dominant themes in design thinking and cognition research set out in the present chapter and in previous ones. The DCT, through its communication and cognition components, also begins to accommodate the types of individual thinking and reasoning styles that shape each person’s capacity to communicate with, and understand, teammates (Mulet et al. 2016). According to Herrmann (1991, 1989), people who have different thinking styles communicate and collaborate in different ways. Herrmann’s famous model of “thinking dominances” identifies four different modes: analytical, imaginative, sequential and interpersonal. Analytical people have logical, cogent reasoning styles, while imaginative people have abstract, metaphorical or conceptual styles. A common way of differentiating analytical and imaginative modes identifies the former with a mathematical or engineering approach, and the latter with an artistic or aesthetic predilection. Despite this simplification (an example of an artificial, although useful MM), neither category is exclusive, and mathematicians can be creative and artists can be logical. Herrmann’s third mode, sequential thinking,

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governs structured cognitive processes, while his fourth, interpersonal thinking, captures processes that require dynamic interaction and expression. These thinking styles have an impact on creativity or novelty arising from design, with rational thinking being potentially more effective in contemporary virtual environments, and the interpersonal style better suited to face-to-face environments (Mulet et al. 2016). This is because some collaborative design environments, and especially computational and parametric ones, may reward people who possess a higher level of systematic preparedness, whereas, in traditional design environments, more spontaneous interpersonal styles are not only possible, but may even be beneficial. Despite Herrmann’s model, and the findings of some past studies, cognition in design is not so easily categorised, and it is not likely to perfectly conform to either the TMM or DMM paradigms. For example, Chap. 2 explored individual design strategies and processes in both traditional and parametric design environments. An individual design strategy defines sub-goals which limit or enable certain cognitive operations, which are also linked to personal preferences and habits and contribute to defining designers’ thought processes (Lee et al. 2014; von der Weth 1999). Diverse team membership (for example, expert and novice designers in a team) can increase complexity due to heterogeneous design strategies and thought processes (Eppler and Kernbach 2016; Ho 2001). Integrating different individual cognitive strategies into a team design process is a significant challenge for teamwork (Lee et al. 2017). Thus, the five individual design strategies identified in Chap. 2 can be understood as part of the distributed store of knowledge, offering complimentary, but not collective, approaches to problem-solving in the design team. A further complicating factor is that, as previously noted, productivity and “economy of thought” can be associated with increased levels of creativity (Perkins 1981). Efficiency in a design team has been linked with both creativity and expertise and, as Chap. 4 demonstrates, can reveal “design productivity” (Goldschmidt 1995). This productivity is associated with the effectiveness, efficiency and creativity of different cognitive approaches to design, whether individual or collaborative (van der Lugt 2000). Indeed, generating ideas and defining problems at a system level results in the highest positive correlation with increased productivity in a team (Costa and Sobek 2004). Individual content and structural complexities (see Chap. 4) can also be used to understand individual cognitive differences as well as potential measures of team performance. In addition to the individual cognitive patterns and strategies discussed previously in Part I of this book, the next section of this chapter addresses two components of distributed knowledge, design spaces (problem and solution spaces) and design modes (geometric and algorithmic designing). The following section investigates design representation, in terms of design information and spatial language. These representations are indicators of emergence and sharedness in design teamwork. Although this chapter is the first in Part II (Collaboration), its design cognition section is closely related to Part I (Creativity), and design representation is also connected to Part III (Culture). That is, this chapter is an intermediate stage in developing an understanding of the relationship between collaboration, creativity and culture in design thinking.

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129

5.3 Design Cognition In this section, protocols of individual designers, derived from experiments in a parametric design environment (Study II in Chap. 2), are explored from a different perspective to understand two components of distributed knowledge in the mixed DTC model (Fig. 5.1). The first is associated with the argument that design is an iterative process of transition between two design spaces—problem and solution spaces—that evolve over time (Maher and Poon 1996). Creativity in design is normally accepted as arising from the process of cyclically developing and refining both the formulation of a problem and ideas for a solution (Dorst and Cross 2001). The co-evolutionary design process is the first component investigated in this section to explain individual cognition. By understanding individual cognition, inferences and lessons can be drawn about team cognition. The second component is associated with the different design environments that shape DMMs. As discussed in Chaps. 2 and 3, one of the more advanced collaborative environments, parametric design, enables two types of design activities: algorithmic and geometric. One specific operation in parametric design, switching behaviour between operations and three-dimensional (3D) views of the design model, can be understood as the transition between geometric and algorithmic spaces, which is a type of co-evolutionary activity, albeit between the two different representation modes (Bilda and Demirkan 2003). The switching is significant for several reasons. It supports self-reflection, which is key to successful designing for both individuals and teams (Schön 1988; Valkenburg and Dorst 1998). It signals a change from analytical (algorithmic) to imaginative (geometric) MMs, from mathematical to aesthetic dimensions of individual and team design processes. In combination, the investigation into co-evolutionary design spaces and modes in this section draws on the theory developed and described in Part I of this book while highlighting the connection to distributed knowledge for effective design teamwork in the DTC model.

5.3.1 Problem and Solution Spaces Problem-solving is a core and measurable cognitive indicator within the design process (Coley et al. 2007). Multiple cognitive operations in design team communication can serve to widen (divergent thinking) or narrow (convergent thinking) problemsolving potential. Significantly, the transitions between these cognitive operations support individual reflection that contributes to social inquiry and creativity (Fischer et al. 2005). A similar idea is implicit in the theory of a co-evolutionary process for explorative design which, arguably, results in creative products or outcomes. This

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5 Collaborative Design: Team Cognition and Communication

Table 5.3 The percentage of the coverage of each category in the conceptual level Category

Au1

Au2

Au3

Au4

Mean

SD

Problem-finding

14.1

13.6

7.1

4.4

9.8

4.8

Solution-generating

4.0

2.0

13.0

3.0

5.5

5.1

Solution-evaluating

27.8

35.1

36.9

26.7

31.6

5.1

Table 5.4 Mean ratings for creativity, novelty, usefulness, complexity and aesthetics Category

Au1

Au2

Au3

Au4

Mean

SD

Creativity

3.86

3.86

5.71

3.00

4.11

1.14

Novelty

4.43

4.14

6.57

2.29

4.36

1.75

Usefulness

4.57

4.43

4.29

4.00

4.32

0.24

Complexity

4.71

3.86

6.43

2.43

4.36

1.67

Aesthetics

3.57

2.14

4.57

3.57

3.46

1.00

Mean

4.23

3.69

5.51

3.06

4.12

1.04

cooperative co-evolutionary activity in between problem and solution spaces facilitates mutual interactions and explicit relationships between designers. The conceptual level of the coding scheme (Table 2.4) enables the investigation of both problem and solution spaces in the parametric design process. Table 5.3 shows coding results for the experiments of four designers divided into problem- or solution-related categories and expressed as the percentage of the frequency weighted by time span (calculated by time duration of each category). As a point of comparison, Table 5.4 summarises expert ratings for each designer’s works, in terms of creativity, novelty, usefulness, complexity and aesthetics. The problem-finding category identifies cognitive activities in the problem space, while the solution space can be explored by the solution-generating and solution-evaluating categories. The frequency coverage of problem-finding shows that designers using the graphical algorithm editor adopted a problem-driven strategy (Au1: 14.8% and Au2- : 13.6%) more than those using the text-based algorithm editor (Au3: 7.1% and Au4: 4.4%). It has previously been argued that a problem-driven design strategy (see Chap. 2) can result in the best balance between quality and innovation (Kruger and Cross 2006). In contrast, the expert panel assessment identifies Au1’s and Au2’s designs as among the most useful designs whereas Au2’s design received relatively lower scores for other criteria. Au3 demonstrates a solution-driven process for design, as his protocol had the highest frequency coverage of solution-generating and solution-evaluating activities (combination of 49.9%). The frequency coverage of G-Generation for Au2 was the highest and his protocol had the second highest frequency coverage of AChange Parameter. Complementing the generative activities in the solution space, G-Generation and A-Change Parameter may have enabled Au2 to generate more creative solutions. Consequently, his model had the highest creativity score as

5.3 Design Cognition

131

Normalised coverage

assessed by the expert panel, indicating that a solution-driven process can lead to outcomes with higher creativity scores (Kruger and Cross 2006). In the design experiments, participants typically switch backwards and forwards between problem and solution spaces until they deliver a final design outcome. In order to explore these switching activities, the summed coverage data in each 20segment interval is visualised using the normalised coverage value (normalised A = (A − mean)/SD) to facilitate the exploration of cognitive activities in problem and solution spaces over time. Figure 5.2 shows the normalised coverage of Au1 and Au3’s conceptual activities in the two spaces over time. Au1 tended to produce design activities in the problem space at the start of the session, and then solutionrelated activities at the end. In contrast, A3 switched repeatedly between problem and solution spaces, displaying the iterative co-evolution of the two. The solution-generating activities in the solution space are the most important for facilitating creative cognition. Furthermore, teamwork should have an advantage over individual work in terms of a number and variety of concepts generated (Cross and Cross 1995; Visser 1993). The solution-evaluating activities are also aligned to Schön’s “reflection-in-action” (Schön 1984). Consequently, it is suggested that both conceptual activities (generating and evaluating) in the solution space are key Problem

Au1

2

SoluƟon

1.5 1 0.5 0 -0.5 -1

Start

Time

End

Time

End

Au3

3

Normalised coverage

2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2

Start

Fig. 5.2 The normalised coverage of Au1’s and Au3’s conceptual activities in problem and solution spaces over time

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5 Collaborative Design: Team Cognition and Communication

to support creativity in both an individual and a team creative process in parametric design. Individual cognitive activities in the problem and solution spaces collectively develop a “task-MM”. The problem-finding activities in the problem space arise from the problem definition of the task-MM, while solution-generating and solutionevaluating activities can be associated with new ideas, solution evaluation, explanation and decision-making. In particular, the last two activities (explanations and decisions) are not strongly present in the coding schemes for individual design processes, whereas both should be considered when exploring design team processes. The cognitive patterns shown in Fig. 5.2 also broadly support the results of Study II in Chap. 2, confirming two parametric design strategies for enhancing creativity: the problem-forwarding generative strategy (Au1) and the solution-reflecting generative strategy (Au3). That is, the different co-evolutionary patterns of problem and solution spaces are closely related to individual design strategies and designers may use both problem- and solution-driven strategies to produce creative outcomes. However, focusing on one or the other strategy could be more effective in achieving different qualities in a design, and combining different cognitive and learning styles in a team. In order to achieve a comprehensive solution, the solution-reflecting generative strategy often leads to the production of variations and to reflecting on variations recursively. These reflective and self-evaluative processes are difficult to instil in young designers. Especially given, as Stempfle and Badke-Schaub (2002) observe, criticism of a team’s approach to design mostly produces unfavourable reactions, which can undermine useful self-reflection. Collaborative design, therefore, needs to emphasise the importance of continuous self-reflection, or formative guidance and assessment, rather than its more summative variant.

5.3.2 Geometric and Algorithmic Modes Parametric modelling in design employs two simultaneous “modes”—geometric and algorithmic modelling—to propose and test decisions (Holzer et al. 2007). The first of these is akin to the standard CAD interface, where outcomes are created by changing geometry, but the second is more abstract and mathematical, as it comprises the scripted rules used to generate options. The design process in a parametric environment requires switching between these two modes. Figure 5.3 illustrates the coding coverage (%) of geometric and algorithmic codes in three cognitive levels (physical, perceptual and conceptual). The two designers (Au1 and Au2) using graphical algorithm editors, produce more geometric activities in the conceptual level than the text-based editors, while the others (Au3 and Au4) using text-based algorithm editors produce more algorithmic activities across three levels. This confirms that the text-based algorithm editor is likely to lead designers to conduct more algorithmic activities. In particular, P-Algorithm activities dominate the experiments of designers using algorithm editors and both Au3 and Au4 invested time in trouble-shooting their scripts (A-Change Rule).

5.3 Design Cognition Geometry

133 Algorithm

60 50

Coding coverage (%)

60

10

50

8

40

40 6

30

30 4

20

20 2

10

10 0

0

0

Au1

Au2

Au3

Physical

Au4

Au1

Au2

Au3

Perceptual

Au4

Au1

Au2

Au3

Au4

Conceptual

Fig. 5.3 The coding coverage (%) of geometric and algorithmic codes in three cognitive levels (physical, perceptual and conceptual)

Parametric design processes vary when adopting the problem- versus solutiondriven strategies as well as geometric versus algorithmic approaches. Participant Au1 produced a relatively large amount of F-Geometry Sub Goal and E-Geometry activities in the conceptual level of design cognition, which appear to be the core activities in the geometric approach. Au1’s algorithmic approach in the physical level was also dominated by A-Change Parameter which is one of the significant activities required to produce design variations. As discussed in Chap. 3, the use of A-Change Parameter tends to be related to design generation. In contrast, participant Au3’s protocol produced a relatively large amount of FAlgorithm Sub Goal and E-Rule activities in the conceptual level, which appear to be the core activities in the algorithmic approach. Au3’s geometric approach was dominated by E-Geometry (evaluating geometries) which has also been linked to supporting creativity (see Chap. 3). It has been argued that this type of switching between geometric and algorithmic modes in parametric design has the potential to support creativity (Salim and Burry 2010), and even constitutes a type of co-evolution of modes. Compared to conventional design environments, which highlight the coevolution of problem and solution spaces, the co-evolution of the two different design modes is a critical factor for creative cognition in parametric design. This realisation also draws attention to the way a team of designers comprising people with different backgrounds and expertise, can support the type of co-creative situated experience, that is usually evolved by a spiral process of socialisation, externalisation, combination and internalisation (Nonaka and Konno 1998). Thus, it would be essential to bridge the gaps of distributed knowledge between the conceptually different team members to enhance creative cognition in parametric design as well as teamwork performance. Aish and Woodbury (2005) identify that a key design feature of parametric systems is the requirement for multiple views of the design model and simultaneous interaction across these views. Moments of insight and discovery arising from this capacity

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5 Collaborative Design: Team Cognition and Communication

may be triggered by the complexity of both the representation and the interface. At the representation level, a designer must understand the graph, node compilation, intentionality and a set of mathematical ideas related to descriptive geometry and linear algebra (Aish and Woodbury 2005). The interface level requires the designer to understand hierarchical operations, navigate menus and linked file structures. Collectively, the complexity of multiple representations and interfaces not only provide a foundation for insight and discovery, they may offer opportunities for generating creative solutions. Perceiving and evaluating geometries in the 3D modelling view may, therefore, not only enable designers to identify new visuo-spatial features but also algorithmic ones, and vice versa, thus discovering relationships between multiple representations of the model and generating creative outcomes from unexpected consequences. While current parametric tools may highlight creative thinking at the algorithmic level, perceiving and evaluating geometries—an activity traditionally reported as significant to design cognition—continue to be critical for creativity in parametric design. Furthermore, in the creative micro-processes discussed in Chap. 3, “changing parameters” naturally lead to “evaluating geometries”. That is, generating alternative design solutions is frequently linked to the evaluation or perception of visuo-spatial features. Switching between different design modes (interfaces) and utilising multiple representations, appear to be critical activities for creative problem-solving processes in parametric design. Csikszentmihalyi (1997, 1994) proposes a creativity model, consisting of person, field and domain. He argues that creativity is the result of the dynamic operation of a system comprising three components: “a culture that contains symbolic rules, a person who brings novelty into the domain, and a field of experts who recognise and validate the innovation” (Csikszentmihalyi 1997, p. 6). An individual designer’s cognition is a unitary system that resembles the team (Goldschmidt 1995). Switching between design spaces and/or design modes is a co-evolutionary process and at the same time related to widening and narrowing (or divergent and convergent) cognitive processes for collaborative design. Thus, it contributes to advances in creative cognition for a design team.

5.4 Design Communication Design teamwork is a complex process made up of multiple designers’ cognitive representations and communications. Design expertise is typically spread across project teams and locations and uses collaborative design management processes (London and Singh 2013), where design communication is supported by additional media such as sketches and drawings, and/or digital teamwork tools. Burleson and Caplan (1998) define a communication process as entailing four components: (i) perceiving others and defining the social context, (ii) message production, (ii) message reception, interpretation and response, and (iv) coordinating interaction with others. Cognitive and linguistic abstraction in design communication is also, as

5.4 Design Communication

135

these past models imply, related to individual linguistic and cultural experiences and preferences (Lee et al. 2019). That is, for designers, language is not just a spoken or written system, it also shapes how they use and understand design representation and communication. The language of design is explored in detail in Part III of this book, Culture, while this section highlights the way designers develop ideas and solve problems and communicate solutions. Regardless of the content of philosophical debates about the relationship between visuals and verbals—including the “picture theory of language” (Wittgenstein 1922) and “picture theory” (Mitchell 1994)—design cognition is a product of the interplay between two distinct subsystems, visual imagery and verbal systems (Paivio 1971). There are two aspects of representations, cognitive externalisation and communication, which compositely emerge in teamwork. Specifically, emergence and sharedness in the DTC model (Fig. 5.1) can be regarded as “shared knowledge”, which is common information developed through design communication. In this context, this section introduces two coding schemes to capture “design information” and “spatial language” in the design process. Both components of emergence and sharedness are then explored through cross-linguistic (one Australian and one Swedish) protocol data. The following “design information” section seeks to develop a better understanding of designers’ imagery in MMs, while “spatial language” deals with their verbal representations.

5.4.1 Design Information Suwa and Tversky (1997) argue that external representations—diagrams, sketches, charts, graphs and hand-written memos—“not only serve as memory aids, but also facilitate and constrain inference, problem solving and understanding” (p. 385). Beyond the communicative role of visual representation, Goldschmidt (1991) claims that sketching supports design reasoning through a special kind of visual imagery. She further emphasises the optimisation of intuitive visuality, because visual reasoning plays a significant role in problem-solving (Goldschmidt 1994). Interactive imagery produced through sketching can be understood as a systematic exchange between cognitive and visual representations. van der Lugt (2005) for example, proposes that sketching can serve as a visible graphic memory and thereby affect idea generation in design groups. Thus, an investigation of image-based-information in design can reveal how designers externalise and share their ideas and design solutions. Suwa and Tversky (1997) use four information categories—emergent properties, spatial relations, functional relations and background knowledge—to investigate how external representation delivers meanings and concepts. With the exception of their last category, background knowledge, information categories and their subclasses in designers’ external representation are described in Table 5.5. Emergent properties are associated with depicted elements, such as shapes and sizes, visual properties and descriptions or names given to them. Spatial relations capture two spatial arrangements in design, local and global relations, which also relate to spatial

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5 Collaborative Design: Team Cognition and Communication

Table 5.5 Information categories and subclasses (Suwa and Tversky 1997, p. 388)

Category

Subclasses

Emergent properties

Spaces, Things, Shapes/angles, Sizes

Spatial relations

Local relation, Global relation

Functional relations

Practical roles, Abstract features/reactions, Views, Lights, Circulation of people/cars

relational reasoning in linguistic representation (Tenbrink and Ragni 2012). Functional relations, as non-visual representations, identify “interactions among spaces, things, people visiting or using them, and/or environments” (Suwa and Tversky 1997, p. 389). In order to determine if individual designers from different backgrounds deal with design information differently, two cross-national designers (one Australian expert and one Swedish novice) were asked to conduct a conceptual design task that was the same as Study II of Chap. 2, both “thought aloud” in English. The Swedish designer was not an English-native speaker, but fluent in English. The design protocols involved analysing 30-min periods (from the designers’ verbal introduction to the design brief) of the protocol data to get the same time-duration protocols from each test subject, which varied in length. Table 5.6 reports the coding results of two design protocols using the “information categories” coding scheme. Considering the different volumes of verbalisation between the two designers, the frequencies are also normalised using the number of “T-units” in each protocol. “T-units” or “thought units” (Hunt 1970) and often used for linguistic comparisons (Cheung Table 5.6 The coding results of two design protocols about “information categories” (F: Frequency) Category Emergent properties

Subclass

Australian designer

Swedish designer

F

F/T-unit

F

F/T-unit

Spaces

26

0.123

5

0.037

Things

–

–

–

–

Shapes/angles

22

0.104

10

0.074

Sizes

24

0.114

27

0.199

Spatial relations

Local relation

27

0.128

7

0.051

Global relation

–

–

–

–

Functional relations

Practical roles

2

0.009

–

–

Abstract features/reactions

19

0.090

2

0.015

Views

–

–

1

0.007

Lights

–

–

–

–

Circulation of people/cars

–

–

–

–

5.4 Design Communication

137

and Kemper 1992). The number of clauses per T-unit (or per utterance) is also related to level of language proficiency (Nippold et al. 2008, 2014). The number of T-units was 211 and 136, respectively, and the Australian designer’s linguistic complexity (C/T ) is slightly higher than the Swedish designer’s: 1.81 and 1.73, respectively. This result indicates that both designers have no difficulty in concurrent verbalisation in English. However, Table 5.5 reveals there are differences between the two in the production of information categories when they are designing. The Australian designer considered spaces and shapes/angles more than the Swedish designer. The expert also produced more terms describing spatial relations (local) and functional relations (abstract features/reactions) than the novice. That is, the experienced designer attended to visual elements and spatial and functional relations more frequently than the student designer. The results of this analysis are similar to Suwa and Tversky’s (1997) comparison between architects and students. In our experiment, however, the student dealt with sizes more than the expert, frequently thinking about the visual attribute of their 3D models. This disparity might indicate that both had different design concepts and/or strategies, because the given design task was a conceptual design for a high-rise building with a lack of design constraints (see Box 2.2). In summary, there are clear differences in external representation between the two designers. These differences may come from their linguistic and cultural backgrounds, but their different levels of design expertise and experience may provide a simpler explanation. As Suwa and Tversky (1997) note, experienced designers are more capable of considering design information in the design process than novice designers. In a design team, “team dynamics” (Eppler and Kernbach 2016) is both a natural phenomenon and a challenge for effective collaborative design. Multiple techniques are available to overcome this challenge in design teamwork, including concept maps (Yin et al. 2005), visual stimuli (Goldschmidt and Smolkov 2006), nonhierarchical mind maps (Kokotovich 2008), think-maps (Oxman 2004) and dynagrams (Eppler and Kernbach 2016). These visual devices provide structured representation of concepts as well as constraints, which facilitate shared understanding of task-based MMs for collaborative design.

5.4.2 Spatial Language Like a visual representation, a verbal representation plays a significant role in sharing and constructing MMs and in the ability to use language. Thus, the construction of elaborate representations is the process of cognitive development (Bloom 1993). Spatial MMs are an integrated representation of premise information and analogical representation of space (Knauff and Johnson-Laird 2002). Spatial language includes qualitative, function-based distinctions (Talmy 2000). Importantly, Tenbrink and Ragni (2012) identify four linguistic principles – (i) spatial representation, (ii) spatial directness, (iii) trajectory, (iv) syntactic format and information structure—for spatial relational reasoning in a verbal representation. “Spatial representation” refers to the

138

5 Collaborative Design: Team Cognition and Communication

general mapping of one entity (locatum) against another (relatum), distinguishing between 1-object, 2-object, and multi-object descriptions. “Spatial directness” deals with compass-based or cardinal terms (north of, south of), comparative terms and projective terms (left, right, front, behind, above, below). “Trajectory” describes the sequential order of successive spatial descriptions using motion verbs as, for example, the speaker guides the listener through a sequence of spaces (Linde and Labov 1975). The last principle, “syntactic format and information structure”, is related to the allocation of the roles of locatum and relatum in the verbal representation. Such spatial descriptors and prepositions are very influential in spatial cognition (Coventry and Garrod 2004; Herskovits 1986). Using these linguistic categories, this chapter presents spatial language categories that identify spatial relationships in English (Table 5.7). The first six categories are related to general spatial relationships. The locative prepositions are used to express the location of one entity in relation to one or two others (so-called “relatum”). The directional prepositions are further categorised into three types: (i) expresses a direction (e.g. move the house forward); (ii) direction in which an object is located (e.g. the house is to the north); (iii) and a change in position (e.g. drive across the bridge to the house). The spatial adverbs involve two terms, here and there, while the spatial deixis captures two types of spatial terms, near (this, these) and far (that, those). The motion verbs identify those incorporating spatial/motion relationships (path encoding motion verbs), such as enter, insert, exit, leave and ascend or descend. The contact category examines if an entity is in contact with a reference object, such as support or adhesion (on) and non-support (above). In contrast, the last six categories deal with the syntactic structures of spatial relationships, which are similar to Tenbrink and Ragni’s (2012) “syntactic format and information structure”. Table 5.8 presents the coding results of the two designers’ spatial language protocols (Australian and Swedish designers). Except for the spatial adverb category, the Australian designer used more spatial language terms to describe spatial relationships than the Swedish designer. In particular, the expert developed more spatial representations using locative prepositions than the novice, which is related to the Table 5.7 Spatial language categories (Pr: Preposition, Adv: Adverb, Adj: Adjective, N: Noun, D: Demonstrative)

Category

Example

Category

Example

Locative Pr.

the door is in the wall

Pr N

in Sydney

Directional Pr.

the house is to Pr D N the north

in the box

Spatial Adv.

here, there

Pr Adj N

above large openings

Spatial deixis

this, these, that, those

Pr D Adj N

above the big window

Motion verbs

enter, extract, insert, exit

Pr Adv

in here

Contact

on, above

Pr D

in that

5.4 Design Communication

139

Table 5.8 The coding results of two design protocols in terms of the spatial language categories (Pr: Preposition, Adv: Adverb, Adj: Adjective, N: Noun, D: Demonstrative, F: Frequency) Category

Australian designer F

Swedish designer F/T-unit

F

F/T-unit

Locative Pr.

31

0.147

5

0.037

Directional Pr.

9

0.043

5

0.037

Spatial Adv.

17

0.081

22

0.162

Deixis

–

–

–

–

Motion verbs

39

0.185

16

0.118

Contact (on)

–

–

1

0.007

Pr N

6

0.028

3

0.022

Pr D N

20

0.095

4

0.029

Pr Adj N

2

0.009

0

0.000

Pr D Adj N

10

0.047

4

0.029

Pr Adv

6

0.028

3

0.022

Pr D

1

0.005

1

0.007

Australian’s frequent production of “spatial relations” in the “information categories” (Table 5.4). This result, importantly, indicates the relationship between verbal and visual representations. The frequent use of motion verbs in the Australian protocol is also interesting, because it may have an impact on both “kinaesthetic imagery” (Rabahi et al. 2013) and “spatial semantics” (Sen and Janowicz 2005). Although this protocol study deals with design information and spatial language, the results differentiate patterns in usages of visual and spatial terms which can be used as an indicator of “productivity” and of individual cognitive differences. The advantage the team has over the individual, which is often stressed in descriptions of the DMM, is the capacity to combining members’ abilities and MMs (Fischer et al. 2005; Prather and Middleton 2002; Safoutin and Thurston 1993). In parallel, there is a process loss in the group because of its reliance on imperfect visual and verbal communication (Hackman and Morris 1975; Steiner 1972). As such, developing a “communication-based technique” (Safoutin and Thurston 1993) for interdisciplinary design teams may be a core factor for improving both productivity and performance in design teams.

5.5 Conclusion This chapter has developed a Design Team Cognition (DTC) model that builds on important messages in the literature about team TMs, MMs and cognitive representations. The DTC model is centred on transactive memory dealing with emergence and sharedness and distributed knowledge or SMMs and DMMs, respectively, promoting

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5 Collaborative Design: Team Cognition and Communication

both performance and creativity in collaborative design. Significantly, four components of the model—design spaces, design modes, design information and design representation—are explored using two sets of protocol data relating to communication and cognition in the design process. Furthermore, this chapter introduced two coding schemes to identify information categories and spatial language in the design process. The coding schemes are further developed in Part III of this book to investigate the cultural aspect of design thinking. Design cognition is not fixed, it evolves as it moves from problem space to solution space, as well as between different modes of representation. Both past research, and the simple protocol study discussed in this chapter, suggest that the switching between geometric and algorithmic modes in parametric design could support cocreation between team members to enhance creativity. If this is true, it would constitute a new type of co-evolution of cognition in design. In contrast, design communication is a process of combined visual and verbal representation. Combining past research and theories with data from a pilot protocol study shows the importance of different modes of linguistic expression and spatial relational reasoning. Considering that communication is the most important factor for design teams to increase both creativity and performance, the approaches developed in this chapter can be extended to examine further collaborative issues, such as social cognition, coordination and design management. This approach can be used to provide a new understanding of multi-cultural differences in spatial design thinking and communication. Without an appreciation of the differences in visual and verbal design thinking, supporting effective design teamwork may be impossible. This topic is further explored by Part III of this book addressing the relationships between language and design. Even though this chapter describes some significant cognitive contributors to a creative teamwork process, the data presented herein is only used as a catalyst for reviewing theories, not for providing “proof”. The cognitive issues examined in this chapter should be the subject of future research, using an appropriate sampling method and design, and a variety of team members. In particular, while task-related MMs can be investigated using individual design protocols, team-related MMs must naturally be explored through team design experiments. Nevertheless, a precursor to conducting such a series of studies of design thinking in teams is developing a model and a method that can be effectively used for this purpose. These are the goals of the present chapter.

References Aish, Robert, and Robert Woodbury. 2005. Multi-level interaction in parametric design. In Smart Graphics, ed. Andreas Butz, Brian Fisher, Antonio Krüger, and Patrick Olivier, 924-924. Lecture Notes in Computer Science. Berlin/Heidelberg: Springer. Austin, J.R. 2003. Transactive memory in organizational groups: The effects of content, consensus, specialization, and accuracy on group performance. Journal of Applied Psychology 88 (5): 866– 878. https://doi.org/10.1037/0021-9010.88.5.866.

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Chapter 6

Design Thinking and Building Information Modelling

Abstract Building Information Modelling (BIM) is an approach to computerenabled multi-disciplinary collaboration, communication and coordination. A BIM model is a consolidated digital data repository of a design, which facilitates seamless information exchange between stakeholders during a project lifecycle. BIM is the industry standard in the design and construction sector. It has strong synergies with the emerging concept of a “Digital Twin”, and BIM is pivotal to implementing innovations arising from this digital ecosystem. This chapter presents a new BIM knowledge framework which is founded on a collaborative design thinking approach. This new framework adopts emerging ontologies and technologies that enable collaborative design thinking and decision-making through better support for design representation and communication among users.

6.1 Introduction In the last two decades, Building Information Modelling (BIM) has played a significant role in the transformation of the design, collaboration and management processes across the Architecture, Engineering and Construction (AEC) sector (Daniotti et al. 2020). BIM is defined as “a modelling technology and associated set of processes to produce, communicate and analyse building models” (Eastman et al. 2011, p. 16). In essence, BIM creates a combined digital representation of a building (Kiviniemi et al. 2008; Rinella 2008; Simeone et al. 2019) and an information management system for its components (Alizadehsalehi et al. 2020; Jang and Collinge 2020). BIM is the most pervasive example of a design platform that enables both “interaction” (individual interface with technology) and “collectivity” (collective use of technology) (Verstegen et al. 2019). The former function, interaction, encapsulates the ways individuals use technology, while the latter addresses the ways they organise its use. Within the digital ecology, these platforms are the services, systems and architectures that provide the infrastructure for businesses to operate. They also address the need for a collaborative, real-time, co-present system in a multi-user digital design ecology (Martinez-Maldonado et al. 2017). Given that design thinking for business strategy and transformation addresses “the visualisation of concepts and the actual © Springer Nature Switzerland AG 2020 J. H. Lee et al., Design Thinking: Creativity, Collaboration and Culture, https://doi.org/10.1007/978-3-030-56558-9_6

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delivery of new products and services” (Cooper et al. 2009, p. 48), this chapter introduces BIM as an approach to facilitating a particular type of collaborative digital design thinking process. A BIM model comprises a single, consolidated digital data repository of a design, which allows integrated digital representation of all information about that design throughout the entire project lifecycle (Gu and London 2010). In a practical sense, BIM is an object-oriented Computer-Aided Design (CAD) package with a heightened capacity to handle complexity and automation in contemporary building projects. It is, importantly, responsible for fostering the uptake and exchange of 3D data during the collaborative design process in the AEC sector (Singh et al. 2011). That is, BIM is a “digital design medium” (Bilda and Demirkan 2003; Ibrahim and Rahimian 2010; Won 2001), which enables the type of visual representation (or visualisation) discussed previously in this book. Furthermore, BIM platforms use parametric modelling to support the types of divergent and convergent thinking described in Part I of this book, along with enhanced design analysis and decision-making (Ning et al. 2018; Tang et al. 2020; Utkucu and Sözer 2020). Through its production of a singular, consistent and coordinated digital model, BIM assists users to break down the communication barriers between different team members and stakeholders, supporting new ways of generating, managing and sharing building data. The advanced visualisation and communication capabilities of BIM have also facilitated collaboration in building design. Moreover, successful BIM implementation is core to the digital ecosystem in design, where related products, processes and people can co-evolve (Gu et al. 2015). Thus, exploring BIM applications and technologies not only facilitates wider adoption in the AEC industry but provides a better understanding of collective and interactive design and management processes in dynamic design practices (Verstegen et al. 2019). This chapter positions BIM as a domain-specific example of design collaboration in the digital ecosystem. It starts by presenting a background to BIM research and practice, focussing in part on collaboration. Thereafter, the chapter develops an advanced BIM knowledge framework. This framework adopts emerging ontologies and technologies that allow “collaborative design thinking” to better support heterogeneous design representations and communications among different stakeholders. Importantly, the framework complies with the philosophy of the “BIM ecosystem”, which considers BIM-related products, processes and people in parallel (Gu et al. 2015). The framework also takes into account both new (“as-planned”) designs and existing (“as-built”) buildings.

6.2 Digital Design Collaboration and BIM As described in Chap. 5, research in Computer-Supported Cooperative Work (CSCW) provides support for design teams, by creating appropriate digital environments for collaboration and communication (Chiu 2002; Grudin 1994; Ibrahim and Rahimian 2010). Chiu (2002) further suggests that CSCW can support three

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levels of communication: the individual, group and project. BIM is a type of CSCW that is specifically optimised for AEC projects across these three levels. The implementation and reinforcement of BIM through commercial modelling software (like Autodesk Revit) has made it one of the most widely adopted and professionally aligned CSCW applications in the sector.

6.2.1 Design Collaboration Through Digital Platforms The catalysts for BIM can be traced to multiple simultaneous pressures in the AEC industry. Design collaboration in the AEC industry has long involved the development and exchange of 2D drawings and documents, featuring both the visual and descriptive specifications that make up a design proposition. It is also common for designers to use scaled “mock-ups”, analogue models and renderings from 3D models to supplement these 2D representations. 2D documentation, despite its many limitations, still plays an important role in conventional collaboration practices across the AEC sector. Furthermore, uncertainty surrounding the readiness of digital technologies means that parts of the sector continue to rely on 2D documentation for completeness, accuracy and legalities (Gu and London 2010). One solution to the problem of collaboration and its reliance on 2D drawings and documents is the Document Management System (DMS), which stores and catalogues 2D data for controlled access. In addition to DMS, a range of strategies and enablers—such as agreed protocols, version-control nomenclature, standardised quality-assurance procedures and tools—have been introduced to improve the quality and accountability of representations for design collaboration. Despite such developments, over the last few decades there has been a growing need for more responsive and immediate design tools. In addition, with the increased degree of complexity and collaboration required in a contemporary building project, an integrated digital platform is required that is capable of representing design and building data in different forms and multiple disciplinary formats. In parallel with this growing pressure to improve collaboration and document exchange, the AEC industry has also begun to take responsibility for the creation and exchange of information across a project lifecycle. BIM is the AEC industry’s solution to these challenges. As a generic interactive, collaborative system, BIM provides a new design thinking and management platform for creating and sharing 3D digital models and their embedded knowledge. This does, however, have implications for modelling, worksharing and coordinating. A client–server model (cloud collaboration and data management) may be required for adapting to this new context, where multiple parties seamlessly contribute to a centralised model at any time and from anywhere. In addition, this type of emerging digital collaborative platform is typically the catalyst for new roles and evolving relationships within a project team. For key stakeholders, in addition to technical competencies, cultural issues and disciplinary differences require careful attention in order to establish shared understandings within a team. For example, design team members with CAD backgrounds, like architects, often

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prioritise features that support design visualisation and model navigation and which are comparable with other familiar CAD applications. In contrast, team members with DMS backgrounds, such as contractors and project managers, may only expect these visualisation and navigation features as add-ons (Singh et al. 2011). Thus, a mature CSCW platform like BIM integrates CAD, DMS and multiple other components and functionalities into a single product. It offers an effective computational approach to design collaboration which meets the requirements of diverse stakeholders and also provides support features to assist users in assessing, planning and implementing the BIM approach in their projects.

6.2.2 BIM Collaboration Essentials BIM, as a model-centric platform, supports “information modelling” in a building project. BIM also refers to a digital design and management process that visualises the project lifecycle of a building in an integrated digital information environment. Thus, it represents the built environment (both a new proposition and an existing one) virtually as a consistent data model enriched with added knowledge and intelligence (Biagini et al. 2016; Eastman et al. 2011; Tang et al. 2010). ISO standard 29481–1 defines BIM as an approach that creates and maintains “a shared digital representation of a built object (including buildings, bridges, roads, process plants, etc.) to facilitate design, construction and operation processes to form a reliable basis for decisions” (ISO 2016, np). The phrase “shared digital representation” refers to both the physical and functional characteristics of a built object (ISO 2016). BIM manages the diverse sets of data and provides a platform for describing and displaying information required for the planning, design, construction and operation of built works. In the AEC industry, BIM is typically described as an object-oriented CAD system enabling parametric representation of building components (Cerovsek 2011; Volk et al. 2014). Objects can have geometric or non-geometric attributes including functional, semantic or topological information (Eastman et al. 2011). Functional attributes may include construction duration, staging or cost. Semantic attributes are concerned with the connectivity, aggregation, containment or intersection of building data. Topological attributes store information regarding objects’ relative locations in a system (Biagini et al. 2016). As such, responding to these three attributes, BIM platforms have features that support data management and integration, building component libraries and general functionalities related to visualisation and analysis (Eastman et al. 2011). Furthermore, because BIM can be used across an entire project lifecycle, a much larger range of stakeholders need to interact with the data model (Gu et al. 2015). Despite such definitions, attitudes to BIM and expectations of its performance vary across AEC disciplines (Jiao et al. 2013a; Shirowzhan et al. 2020). For example, in the design disciplines, BIM is sometimes described as an extension of CAD. In contrast, the non-design disciplines tend to regard BIM as an intelligent DMS that can extract data from CAD packages. Because of these conflicting attitudes, BIM

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developers and ICT service providers often integrate and balance these two requirements. Furthermore, because there is a need for multiple parties to simultaneously contribute to a single coordinated BIM model, a “concurrent engineering approach” is often used for BIM model development (Eppinger 1991; Evbuomwan and Anumba 1996). Recently, the cloud approach to unified lifecycle data has become an industry standard for design collaboration (Chen et al. 2020; Jiao et al. 2013a, b; Liu et al. 2017). Customised business strategy and transformation have also been considered to suit varying implementation needs. For example, a BIM model can be maintained in-house or outsourced to a service provider, each having different capacities that address diverse organisational and financial needs and limitations. Effective information management and integration in BIM requires the close cooperation of the entire project team. Quality communication and knowledge exchange between different stakeholders (both within and beyond the stand-alone BIM model) is also essential for successful BIM implementation. It is also critical for collaborative or collective participation from different team members (and sometimes different teams) in a building project lifecycle. In practice, however, many BIM models are developed in local repositories and are typically accessible by individual team members through proprietary stand-alone software. This means that the scope for collaboration is confined to a single isolated BIM model and to ad hoc decentralised and traditional forms of communication such as email, analogue outputs or other traditional channels of information exchange (El-Diraby et al. 2017). Furthermore, it can be costly to implement a full BIM model, and often the budget is not available to support its use as a multi-disciplinary lifecycle model. Thus, despite the aspirations of the AEC sector, and the growing capacity of BIM platforms, in practice a proportion of BIM models have only partial functionality. Research and development in BIM is ongoing, and advances in technology and data interoperability are essential for creating an enhanced understanding of BIM’s potential and for exploring emerging roles and team dynamics within this platform. A critical examination of existing project workflows and resourcing capabilities can also help to define and support these roles and dynamics as well as determine whether they would be internally or externally resourced. Research also identifies that there is a need to develop BIM adoption strategies tailored for specific stakeholder needs, contingent upon the capabilities of their collaborators (Singh et al. 2011). Such strategies must take into account diverse readiness levels and the challenges and costs of training for BIM applications and upgrades (Gu and London 2010). Past research emphasises that the complexities of achieving successful collaboration in BIM are not entirely techno-centric. An integrated socio-technological approach to managing the complex interdependencies across key BIM players is perhaps of even greater importance for collaboration. The Design Team Cognition (DTC) model in Chap. 5 highlights this issue and helps us to understand the dynamic cognitive, collaborative and creative design processes that occur in the BIM ecosystem.

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6.3 An Advanced BIM Knowledge Framework The need for an integrated socio-technological approach to digital design collaboration in the AEC industry has been identified in past research (Gu et al. 2015). In the BIM ecosystem, traditional 2D and 3D CAD exists alongside and is transforming into multi-dimensional modelling platforms (Bortolini et al. 2019; Charef et al. 2018; Lu et al. 2016; Rinella 2008; Sheikhkhoshkar et al. 2019). The multidimensional platform functionalities, as the previous section notes, include planning and scheduling (4D), cost estimation (5D), sustainability (6D) and FM (7D) (Charef et al. 2018; Rinella 2008). The “level of development” (LOD) (sometimes called “level of detail”) of the BIM ecosystem can also be mapped to reflect its evolutionary state (Table 6.1) (Eastman et al. 2011; Leite et al. 2011; Volk et al. 2014). This is significant in the present context, because this chapter seeks to assist design teams in the implementation of BIM in an evolved ecosystem. In the following sections, this chapter develops an advanced BIM framework that considers BIM-related products, processes and people in the co-evolutionary collaboration phases of a design process. These correspond to the range from pre-design (LOD-100) to maintenance (LOD-500) in Table 6.1. The new framework developed in this chapter is focussed on two dimensions: diverse project application and advanced knowledge capture. The first requires a Table 6.1 LOD definitions and collaboration phases (Source BIMForum 2019) LOD

Definition

Related data

Collaboration phase

100

The element is represented in the model with a symbol or other generic representation

Non-geometric representations

Pre-design

200

The element is represented within the model as a generic system, object or assembly

Approximate quantities, size, shape, location and orientation

Schematic design

300

The element is represented within the model as a specific system, object or assembly

Quantity, size, shape, location and orientation

Design development

Quantity, size, shape, location, orientation and interfaces with other building systems

Construction documentation

Quantity, size, shape, location and orientation with detailing, fabrication, assembly and installation information

Construction

350

400

500

The Model Element is a Quantity, size, shape, field verified representation location and orientation

As-built or Maintenance

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consideration of a diversity of BIM applications, from new designs to existing buildings. While BIM research and practice have largely addressed new building design, its application to existing buildings is equally important (Volk et al. 2014). Furthermore, “Historic BIM” has been proposed to address the integration of historical, cultural and social parameters into object-oriented models (Biagini et al. 2016; Lee and Gu 2019; Quattrini et al. 2017). “As-built BIM” accommodates additional collaboration phases such as building maintenance, refurbishment and deconstruction, which are often overlooked in “as-planned” or “as-designed” BIM development. The integration of as-built BIM alongside as-planned reflects the importance of the whole lifecycle in an advanced BIM framework. The second dimension of the knowledge framework is that it adopts emerging ontologies and technologies for enhancing systems to enable collaborative design thinking and decision-making.

6.3.1 A BIM Knowledge Framework The BIM knowledge framework (Fig. 6.1) supports digital design collaboration in the evolving BIM ecosystem. Initially, such collaboration commences by the way of asplanned BIM, facilitated through the design of a single coordinated building model. Collaboration is core to BIM, as it serves multiple purposes throughout the design and construction phases. From community interaction and client engagement to design modelling, analysis and review, BIM supports inter and multi-disciplinary collaboration. After the schematic design stage, BIM supports subcontractor tendering, construction management and site management. FM operation and maintenance then uses as-built BIM. In contrast, traditional as-built or historic BIM is often limited to the maintenance stage of existing buildings and only occasionally includes a consideration of retrofit potential or logistics (Logothetis et al. 2015; Volk et al. 2014). That is, both asplanned and as-built BIM do not always consider the full lifecycle, while the advanced BIM knowledge framework deals with the project lifecycle spanning either over an extended period or even on a continuing basis. Thus, the information flow of the framework is both as-planned and as-built. BIM operation and collaboration can also be enhanced by adopting new and improved communication media and technologies (Shafiq et al. 2013). A wide range of emerging ontologies and technologies have potential for advancing BIM platforms. Some of these include geomatics, Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) technologies, mobile and smartphone applications, groupware, server technologies and cloud computing (Alizadehsalehi et al. 2020; Lee and Gu 2019). In comparison with as-planned BIM, the support for communication is even more critical in as-built BIM. This is because support must not only be provided for building and construction professionals but also for other users including the general public. The new ontologies and technologies mentioned above are especially useful when considering social and cultural information in BIM. For example, VR and AR applications have already been utilised for heritage conservation (Acierno et al. 2017;

Social Network Analysis

Fig. 6.1 Advanced BIM knowledge framework

BIM CommunicaƟon

Data integraƟon

Public Engagement

BIM Plaƞorm

Design

Design workflow

Data Management

DocumentaƟon/simulaƟon/analysis

EsƟmaƟon/ recycling

Quality control/ monitoring

3D modeling/ pre-processing

Refurbishment opƟons

Planning

InformaƟon maintenance

DeconstrucƟon

Data collecƟon

Refurbishment

Maintenance

InformaƟon modeling

InformaƟon Exchange

4D/5D scheduling

Procurement/ fabricaƟon

ConstrucƟon

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155

Napolitano et al. 2017). Murphy et al. (2017) propose a semantically enriched framework for historic BIM using a web-based game engine for coordinating visualisation, communication and participation. These emerging technologies are key to accessing, engaging and supporting diverse stakeholder groups as well as their communication and collaboration in the advanced BIM knowledge framework (Fig. 6.1). The framework is divided into six main phases: information modelling, maintenance, refurbishment, deconstruction, design and construction. While many BIM frameworks start with a design phase, this framework spans from information modelling to construction. However, this doesn’t mean the framework necessarily commences with information modelling. BIM creation can start from any phase, and the information modelling phase would be the conventional starting point for as-built BIM. The six phases synthesise the cyclical information flows abstracted from BIM creation for both new and existing building projects. This defines a formal structure for creating, maintaining and sharing information of both new and existing buildings. These phases are supported by the BIM platform (Fig. 6.1) which contains five independent but interchangeable modules: (i) BIM-enabled communication, (ii) social network analysis, (iii) public engagement, (iv) data management and (v) information exchange. These modules, which are informed by advances in ontology and technology, are closely connected with the key processes in BIM creation. The following two sections describe the phases and modules in more detail.

6.3.2 Six Phases of the BIM Knowledge Framework In the information modelling phase, the goal is to create a BIM model from an existing building, typically using survey data. Key tasks in this phase can be optimised through a range of computational techniques such as terrestrial laser scanning and photogrammetry. The raw survey data produced can be further processed to create 3D digital models from which BIM models can be developed (Dore and Murphy 2017). An alternative approach to as-built models is to use automated modelling techniques. This requires that data are structured in a machine-readable format, such as “Resource Data Framework” or “Ontology Web Language”. Modelling the domain ontology is essential here, and it should carefully consider how end users may read and query the data set (Quattrini et al. 2017). Once the 3D digital model is complete, non-geometric information including current building usage and performance, or heritage and historical records can then be structured and integrated to form the complete BIM model. In summary, this first phase of the framework involves three collaborative tasks: data collection, 3D modelling and/or pre-processing, and data integration. The information modelling phase is typically the starting point for as-built BIM projects, while new BIM projects typically start with the design phase. The second phase, maintenance, is important for both new and existing buildings. It encapsulates operations, FM, retrofitting and monitoring processes along with multi-disciplinary activities and information requirements (Becerik-Gerber et al. 2012). As such, the first priority of this phase is to address the management of data

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that is continuously accumulating and changing during the operation of a building. Singh et al. (2011) define three operational requirements for a BIM server to manage this data: (i) BIM model management-related requirements, (ii) design review-related requirements and (iii) data security-related requirements. The first of these, the BIM model management-related requirements, is related to the storage, operation and preservation of a model which contains 3D geometries, 2D documents and nongeometric building information. The second requirement, design review-related, includes the functions needed for visualisation and evaluation, as well as for team communication and interaction during the review process. Finally, data securityrelated requirements are important for maintaining the veracity of the overall BIM model. To connect the maintenance and construction phases in the framework, an overarching third task group—documentation, simulation and analysis—is used to aid decision-making. Thus, the maintenance phase of the BIM framework highlights information maintenance, quality control or monitoring along with documentation, simulation and analysis. The refurbishment, deconstruction and design phases start with another overarching task—planning—that connects these three together. Planning considers the diverse needs and perspectives of the data stakeholders. Through the planning process, the whole of the building is considered and evaluated, through consultation and participation of interdisciplinary experts with communities and end users. In addition, building-specific factors relating to preservation, refurbishment, adaptive reuse and deconstruction or demolition are considered. After the planning task is complete, the team responsible for a refurbishment or reuse task can select various solutions. Conventional, as-planned BIM, already supports advanced design and drafting standards for refurbishment or reuse using hierarchical data in the BIM model (Giuda et al. 2015). To develop an optimal refurbishment or reuse solution, BIM tools also need to address properties related to time and costs (Khaddaj and Srour 2016). The BIM framework supports decisions of this type for both as-planned and as-built BIM. This phase ends with the overarching documentation, simulation and analysis task. The deconstruction phase in BIM supports the visualisation and estimation of demolition options as well as the related costs and environmental impacts. The latter dimension includes identification and implementation of recycling and waste management solutions, material removal deployment, as well as simulation and management of building end-of-life alternatives (Akinade et al. 2017). From a sustainability perspective, efficient building and construction waste management strategies should be incorporated into this phase of BIM (Jaillon and Poon 2014; Kibert 2012). Akinade et al. (2017) suggest the use of a “closed material-loop” process that can eliminate the linear pattern of material movement in demolition, thereby minimising building and construction waste. In the future, this phase could also collaborate more effectively with the following design phase to effectively “designing out waste”—designing and optimising for deconstruction. The fourth phase, design, consists of three key collaborative works: (i) planning, (ii) design workflow and (iii) documentation, simulation and analysis. The focus of this phase is assisting design development, review and evaluation. In contrast,

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the final construction phase comprises different collaborative tasks concerned with procurement and fabrication. BIM in this phase adopts 4D/5D scheduling, as well as documentation, simulation and analysis for monitoring, evaluating and ensuring on-time delivery.

6.3.3 Five Modules of the BIM Platform The BIM knowledge framework contains five independent but interchangeable modules (Fig. 6.1). These five are based on El-Diraby et al.’s (2017) modules for BIM-based collaborative design and socio-technical analytics. For the new BIM framework, however, they are revised to accommodate both as-planned and as-built models. The first module, BIM-enabled communication, provides support for online communication and collaboration. Users commonly employ 3D visualisation tools to explore, identify and reference specific elements in a digital building model (ElDiraby et al. 2017). Sharing a multi-disciplinary BIM model typically requires the use of open-standard Industry Foundation Classes (IFC). IFC is a transferrable and, notionally, universal data format. Most communication features of BIM are derived from DMS. In practice, standard DMS provides multiple modes of communication from SMS and email to voice recording and transcription. DMS in BIM supports both synchronous and asynchronous communication during the project lifecycle. This communication may include direct knowledge transmission and documentation, as well as automated notifications and reminders. The social network analysis module supports social interactions between users during collaboration, and it also gathers valuable information for subsequent analysis (Newman 2001). This module accommodates large-scale multi-user scenarios and is able to address complex relations during mass participation. Many existing Social Network Service (SNS) applications support high levels of interactivity and information sharing and collaboration, as well as providing personalisation through individual queries and the delivery of user-specific content. The subsequent analysis is used to enhance user experience and support decision-making. In BIM projects, this module is responsible for the visualisation of project networks and interfaces (El-Diraby et al. 2017). It is especially significant for as-built BIM projects, because of the increase of user types and participant numbers. Public engagement is a special module in the BIM platform for improving engagement with the non-AEC community. These people include experts from other disciplines as well as the general public. The latter may be especially interested in cultural and social information in as-built BIM data. To facilitate this level of engagement, a range of multi-modal media and technologies can be considered and incorporated to suit different user needs and behavioural patterns. For example, Keil et al.’s ARbased architectural history model (2011) and web-based game-engine BIM platform (Murphy et al. 2017) and Napolitano et al.’s virtual tours of cultural sites (2017) all demonstrate the effectiveness of media-rich technologies for supporting heritage conservation. As discussed previously in this chapter, Geographic Information Systems (GIS), Global Positioning Systems (GPS), Location-Based Services

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(LBS), VR, AR, and mobile or cloud computing are associated with this module. In contrast, the data management module is largely used by building and construction professionals. It supports the monitoring, exploration, optimisation and evaluation of a new design, or of different intervention strategies and their implementation processes. The information exchange module serves information sharing and exchange functions across different building data sets and different disciplines. Ontologies play an important role in this module because they enable the creation of formal structures, principles and rules for information exchange. They also provide the capacity to identify and overcome constraints in BIM tools and systems (Li et al. 2017). In other words, sound ontologies in BIM provide the foundation for effective communication between stakeholders and enhanced interoperability between systems and data sets (Succar 2009). Collectively, these five modules in a BIM platform are not only essential for digital design collaboration but applicable to collaborative design thinking in a broader digital ecosystem. Thus, they contribute to the development of technology-enabled collaboration as well as increased understanding of design thinking arising from digital technologies. The following section discusses the use of the BIM framework and the conceptual transformation of BIM into a Digital Twin.

6.4 BIM Futures 1. The BIM framework introduced in this chapter has several potential applications. For example, it can be used to understand and structure a project team in order to promote improved design thinking and collaboration. It also has the capacity to support an integrated socio-technological approach to BIM, which enables the adoption and implementation of advances relating to products, processes and people. Such advances include cloud computing, machine learning, artificial intelligence and cognitive computing. In addition, advances in “extended reality” (Alizadehsalehi et al. 2020), “nD BIM” (Ghaffarian Hoseini et al. 2017), “Digital Twins” (Grieves 2014; Qi and Tao 2018; Tao et al. 2018) and mobile computing (Cook and Das 2007) can be conceptualised using this framework. The most important of these developments is the Digital Twin. 2. The concept of a Digital Twin was first proposed by Michael Grieves in the early 2000s for product design. In essence, a Digital Twin is a digital or virtual replica of a physical product or process. Since being proposed as a product lifecycle management concept (Grieves 2014), the Digital Twin has been adopted by technology developers such as IBM and Dassault Systèmes as well as major organisations like NASA. Recently, the application of Digital Twins has extended beyond product design to other domains for simulating a wide range of physical, social and economic systems (Batty 2018). From buildings, precincts and cities to commercial organisations and communities, the concept of a Digital Twin has grown to describe a category of data model of which BIM could be regarded as a growing subset.

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Digital Twins can be distinguished from common simulation models through their capacity for real-time input, which relies on the close and rapid interaction between digital and physical counterparts. As such, through real-time data integration, analytics and machine learning, Digital Twins are moving “closer and closer to the real thing” (Batty 2018, p. 819). The benefit of this, unlike BIM which either lags behind real-time or is only used for simulation, is that the Digital Twin can better inform our decision-making processes. This poses a challenge for BIM, to be able to accommodate real-time data and adaptive or responsive modelling, which extend beyond design and construction. The BIM framework presented in this chapter provides a starting point for considering a Digital Twin–BIM hybrid, for both new designs and existing buildings. Looking into the future, the enduring value of BIM may be associated with its capacity to support design collaboration. The following are just three thoughts about new research directions that could enhance BIM to better support design and collaboration. • Critical knowledge about design cognition and representation—the cognitive, visual and verbal—for design teams (as discussed in Chap. 5) should be carefully integrated into BIM systems. In combination with Herrmann’s model (1989,1991) of “thinking dominances”, it may provide a better design environment for different designers and design team dynamics. • In terms of communication, current BIM systems are largely concerned with Chiu’s (2002) project level of communication. Current BIM collaboration is also mostly limited to professional teams. With the design context becoming more diverse and the broadening of the project team and end-user group, communication on the individual and group levels should be more adequately considered and integrated into the overall ecosystem. This is important for promoting “collaborative design thinking”. • This book identifies several strategies and processes for supporting “creative design thinking” in parametric design (Chaps. 2 and 3) and essential factors for supporting cognitive complexity (Chap. 4). There are significant opportunities for enhancing the BIM ecosystem to more explicitly support and encourage such strategies and activities, and to effectively execute them in both individual and collaborative modes.

6.5 Conclusion This chapter has presented a new integrated BIM knowledge framework for facilitating digital design collaboration in the BIM ecosystem. Acknowledging that digital technologies and our social systems are continuously evolving, emerging BIMrelated technologies are unified in this framework. In particular, the five modules— BIM-enabled communication, social network analysis, public engagement, data management and information exchange—can be extended to accommodate multidimensional BIM creation, ranging from 4D/5D to nD BIM. Moreover, the Digital

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Twin is a clear future direction of BIM as well as of the wider digital ecosystem, and modules could be developed to begin to understand its implications. The new framework proposed in this chapter contributes to addressing the entire BIM lifecycle, specifically focusing on sharing, preserving and reusing building data beyond the design, construction and maintenance phases. However, in proposing this framework, it highlights that further research on interoperability and data exchange across multiple collaboration phases within a BIM platform is needed. Finally, the future of BIM, and its hybrid Digital Twin-BIM, is as a multidisciplinary collaboration platform that allows interactive creative processes with an increased number and range of participants. Through the involvement of a more diverse group of participants, and the subsequent knowledge sharing this entails, the integrated collaboration platform can support new commercial, cultural and social values beyond the intelligent model or the building project. These properties of interactive and collective digital collaboration platforms are explored in the next chapter.

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Chapter 7

Design Thinking and the Digital Ecosystem

Abstract Because design thinking is contextual, insomuch as it varies depending on the tools being used and the environments that support it, there is a need to understand the cognitive impacts of any new platforms that are developed. The focus of this chapter is design thinking in digital, interactive and collective platforms. Drawing on past research, models and theories, it proposes two conceptual frameworks for facilitating interactive and collective design thinking in a digital ecosystem. The new frameworks of Digital Design Thinking (DDT) processes and functionalities are then used to examine six examples of collective and interactive platforms. This chapter contributes to a better understanding of creative, collaborative design thinking using digital tools and within digital environments.

7.1 Introduction The rapid evolution of communication and networked technologies is changing our world, redefining the ways we create, access and trade digital content. This digitalisation of everyday life also challenges traditional approaches to design creativity, collaboration and culture. This chapter introduces and explores the concept of Digital Design Thinking (DDT), which extends conventional concepts of design thinking to encompass both digital environments and tools as well as collaborative approaches to innovation and creativity. The specific environments and tools considered in this chapter are interactive and collective platforms. The relationship between these platforms and DDT also encompasses a consideration of Collective Intelligence (CI). These three themes—DDT, CI and design platforms that operate in the digital design ecology—are briefly introduced in this first section. A technical definition of DDT is that it is a “non-typological and nondeterministic” approach, which emphasises the use of “discrete and differentiated” components to develop, explore and test design propositions (Oxman 2006b, p. 262). This definition contains traces of both computational design’s heuristic and algorithmic modes and classical design’s cognitive and behavioural approaches. More recently, however, the definition of DDT has been expanded to accommodate the types of social creativity that are enabled by digital networks. In conventional © Springer Nature Switzerland AG 2020 J. H. Lee et al., Design Thinking: Creativity, Collaboration and Culture, https://doi.org/10.1007/978-3-030-56558-9_7

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design thinking some well-known strategies for social creativity include participatory design (Luck 2018; Smith and Iversen 2018), co-creation (Nonaka and Konno 1998; Prahalad and Ramaswamy 2004) and co-design (Huybrechts et al. 2017; Mitchell et al. 2016). In the contemporary world, however, social design interactions are increasingly occurring online using specialised digital design platforms. This is why a more recent definition of DDT describes it as a cognitive process of “collective creativity” that is supported by digital platforms (Nakakoji et al. 2000). The present chapter adopts this expanded definition of DDT and investigates it in the context of the world’s growing digital ecology. The concept of a digital ecology is central to some of the most influential business models of the last two decades. Its core components include the commodification of digital processes, support for collective and interactive platforms (Markus and Loebbecke 2013) and the use of distributed supply-chains to create a “sharing economy” (Richter et al. 2017). The technologies that enable these business models— mobile computing, social media, “internet of things” and big data—are now so ubiquitous that their collective agency is almost taken for granted. The concept of a digital ecosystem reflects this omnipresence, describing a connected network where digital actors (customers, partners and providers), their activities and online organisations, evolve to sustain production (Elia et al. 2020). The technologies that enable the digital ecosystem, and which also support DDT, encompass artefacts, infrastructures and platforms (Nambisan et al. 2017). The artefacts are the products, both physical and virtual, produced or traded within the ecology. Some of the key characteristics of these artefacts are that they are malleable, flexible or adaptable. The infrastructure is the set of digital systems that allow communication and collaboration. These can range from cloud computing to physical, networked makerspaces. The platforms are the services and architectures that support or merge with the infrastructure and are the foundations for the DDT environment. In combination, the artefacts, infrastructures and platforms sustain the digital economy and are core to its capacity for innovation (Nambisan et al. 2017; Tiwana et al. 2010; Verstegen et al. 2019). The focus of this chapter is interactive and collective platforms, which are the parts of the digital ecology that support collaboration. Collaboration in the digital ecology is significant because it has been scaled up and linked to CI and innovation (Alag 2008; Halpin 2008; Heylighen 1999; Lévy 2010). CI is core to “distributed participatory design” and its more common counterpart, “crowdsourcing” (Farshchian and Divitini 1999). The design industry is an important part of the digital ecology and it is not surprising that design research should be interested in CI as a component of DDT. Pierre Lévy defines CI as “the capacity of human collectives to engage in intellectual cooperation in order to create, innovate and invent” (Lévy 2010, p. 71). More than just a simple aggregation of individuals, in the digital ecology CI combines the social network with the design environment itself (Halpin 2008). CI often uses dynamic, user-created and responsive systems (e.g. “Web 2.0”) to improve social network services (Lopez Flores et al. 2015b) and support collaborative innovation networks (Gloor 2006). Like artificial intelligence and open-platform architecture,

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CI is frequently associated with innovation (Hüsig and Kohn 2011; Leon 2009; Lopez Flores et al. 2015a, b; Wang et al. 2002). This introduction to DDT, CI and collaborative and interactive platforms, sets the scene for the present chapter. As discussed previously in Part I of this book, design thinking in “digital” (scripting) environments differs from design thinking in “traditional” (pen-and-paper) environments (Oxman 2006a, b; Lee et al. 2013, 2014a; Reffat 2006). Design thinking in immersive, collaborative, shared and responsive environments (the digital ecosystem) is also dissimilar to its more traditional colocated, face-to-face version. This is why the present chapter commences by adapting an analytical framework for understanding DDT and creative, collaborative design. The framework encompasses key DDT characteristics identified in past research on digital collaboration and CI. A second framework is then developed to investigate DDT functionalities in interactive and collective platforms. Finally, both frameworks are used to compare six examples of interactive art and media platforms. The chapter concludes with a discussion of the implications of these examples for DDT and further applications of the frameworks.

7.2 A Digital Design Thinking (DDT) Framework This section presents two conceptual frameworks for understanding and analysing DDT in the digital ecosystem. The first—adapted and expanded versions of digital design models in the literature—is used for identifying DDT processes. The second identifies DDT functionalities in interactive and collective platforms.

7.2.1 Types of DDT Processes As a precursor to proposing a framework for DDT processes, it is useful to consider past research that theorises relationships between digital environments, design and CI. Five past theories are reviewed in this section, all with different intentions, but which cover dimensions of DDT. The five theories are from Oxman (2006b), Burdick and Willis (2011), Fischer et al. (2005), Balestrini et al. (2017) and Alag (2008). Firstly, Oxman’s (2006b) “integrated compound” model combines DDT processes of formation, generation, evaluation and performance, describing interactions and information flows in an integrated digital system. For the purpose of improving form generation, she identifies the performance-based process as the best (Oxman 2007). However, despite setting up these parameters, Oxman’s five types of DDT models (Table 3.7) are closer in spirit to those of computational designers, than the current group of collaborative DDT models which are more concerned with shared design processes in distributed, ubiquitous and mobile environments. Thus, Oxman’s framework is valuable, but it does not accommodate the CI processes or the digital ecosystem that are so important today.

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Burdick and Willis (2011) propose a framework that, while not explicitly concerned with DDT, offers a epistemic understanding of its strategies and processes. They identify three design thinking models to support new modes of learning: (i) interpretive (performative and rhetorical), (ii) situated (networked and contingent) and (iii) user-oriented. Their framework, despite being intended for new-media education and digital-humanities research, accommodates the idea that CI—by way of the Internet and social networking—can emerge through the open contribution and participation of a large group (Alag 2008; Elia et al. 2020; Lévy 1997; Malone et al. 2010). While emergent CI is not the same as the “wisdom of crowds” (Surowiecki 2005), both operate through decentralised user engagement. The difference is that CI produces aggregative knowledge through increased interactivity, whereas the “wisdom of crowds” is often used to describe a reductive group problem-solving process (Lee and Chang 2010). In order to differentiate between types of social creativity Fischer et al. (2005) propose five models: (i) the “fish-scale” model of overlapping flexible collaboration; (ii) the structural model based on communities (“of practice” or “of interest”); (iii) the defined objective model, which is supported by distributed cognition; (iv) the seeding, evolutionary growth and reseeding (SER) process model; and (v) the meta-design model. Across these collective cognitive models, CI is one of the main mechanisms that supports interactions between users Fischer et al. (2005). Also highlight the distributed nature of interactions and the need for open and transparent access to systems. Balestrini et al. (2017) identify six cyclic steps (identification, framing, design, deployment, orchestration and outcome) for exploring public engagement on community issues. This model is of interest because Satnam Alag (2008) highlights a similar cyclic CI framework, where one user influences others through the production of reviews, ratings, recommendations or blogs. Alag (2008) also proposes three ways of using CI: (i) allowing interaction; (ii) aggregating contributions; and (iii) leveraging models to develop personalised content. Using Alag’s (2008) work as a foundation, and informed by the other models described previously in this section, it is possible to adapt his interactive and collective components to propose a DDT process model (Fig. 7.1). • The interactive side of the DDT model supports learning about each user’s connections, transferences and contributions in CI platforms. Its detailed content encompasses concepts of individual creativity discussed in Part I of this book, and also has overlap with Oxman’s digital design models. • The collective side of the DDT model is aligned to the Design Team Cognition (DTC) model for collaborative design (introduced in Chap. 5). It also embodies the main theories used for social creativity and creative interactions between an individual and society (Fischer et al. 2005). A core sequence in this DDT and CI process framework is interacting, collecting and leveraging (I-C-L) (Fig. 7.1).

7.2 A Digital Design Thinking (DDT) Framework (iii) Leveraging Personalised content

169

CollecƟve Intelligence

(i) InteracƟng

User

(ii) CollecƟng

InteracƟve DDT model Contribute and interact

ColleccƟve DDT model

Content

User Contribute and interact

Fig. 7.1 A cyclic I-C-L (Interacting, Collecting and Leveraging) process in digital platforms, adapted from “three components to harnessing collective intelligence” (Alag 2008, p. 11)

Table 7.1 Digital Design Thinking (DDT) processes identified in past research Study

Elia et al. (2020)

Type

Digital collaboration Problem-solving

DDT process • • • • • • • • •

Conceptualising Creating Deciding Inspiring Networking Recommending Sharing Suggesting Transferring

Elia and Margherita (2018) • Problem identification and conceptualisation • Problem analysis and study • Problem synthesis and modelling • Solutions proposition and definition • Solutions prototyping and Test • Solution implementation • Solution maintenance

Stelzle et al. (2017) Collective design • • • • • • • •

Project initiation Co-briefing Co-designing Professional design Ranking voting Integration Approving Formal assessment

In addition to the CI processes that support DDT, three further processes are significant in the present context: digital collaboration, problem-solving and collective design (CD) (Table 7.1). For the first of these, Elia et al. (2020) identify the importance of digital collaboration in their CI model of the digital entrepreneurship ecosystem. They divide the process of digital collaboration into ten steps, flows or actions—conceptualising, creating, deciding, inspiring, networking, recommending, sharing, suggesting and transferring. For the second of the three processes, the role of CI in problem-solving, Elia and Margherita (2018) identify seven steps in a CI system to support collective problem analysis and solution generation. These steps are problem identification, analysis, synthesis, solution proposition, prototyping, implementation and maintenance. They are similar to those in the famous five-stage design thinking model: empathise, define, ideate, prototype and test. Each step requires an appropriate application and leads to the production of solutions to complex or nonlinear problems. For example, a “sematic aggregator” for problem formulation and conceptualisation leads to a shared problem definition. Then a “parameter graph generator” develops the taxonomy of factors and variables for problem analysis. In this way, a problem visualiser, solution matrix, solution builder, application monitor and performance assessment are all components of the CI system. For the third

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Asynchronous global Asynchronous local Synchronous global Synchronous local

Content

hy rc ra

d ow cr hy ic rc ns ra tri ie h Ex ic d ns ow tri cr In ic ns tri In

User

Fig. 7.2 Three functionalities of interactive and collective platforms

process, CD, Stelzle et al. (2017) develop a platform identifying co-design and codecision processes. They focus on the importance of convergent decision-making after divergent ideation, along with key types of decisions in CD. The decision types involve prioritising/ranking, solution selection, criteria/value setting and stakeholder setting. These three DDT processes, supported and promoted by digital collaboration and CI platforms, facilitate individual and social creativity. Collectively, these processes are all dependent on the functionalities of digital platforms. Which is why four DDT processes—(i) I-C-L (Fig. 7.2), (ii) digital collaboration, (iii) problem-solving and (iv) collective design—must be accommodated in the framework. The following section defines the key functionalities of interactive and collective platforms which make up the second framework.

7.2.2 Key Functionalities of Interactive and Collective Platforms The platforms that currently support DDT in design have often arisen from advances in Computer-Aided Design (CAD) that have enabled collaborative and synchronous designing in real-time virtual environments (Reffat 2006). There are, however, other

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types of platforms which are also significant for understanding DDT. For example, Morschheuser et al. (2017) identify “gamification” and “crowdsourcing” as drivers for particular interactive, collective platforms. Both are related to crowd-rating (by way of collective homogeneous inputs) and crowd-creating (by way of collective heterogeneous inputs). The former is more concerned with immersive or contextual interaction (collective interaction), while the latter emphasises emergent, collective activities (interactive collectivity). An alternative example of a design platform is Lee and Chang’s (2010) genetic algorithm for CI that uses both (i) interactive behaviours between designers and a design environment and (ii) collective, effective responses from clients and users. Interactive and collective platforms developed to support CI have three comparable abilities: (i) increased intelligence, (ii) increased sense and (iii) collective evaluation. • “Increased intelligence”, like the wisdom of crowds, refers to the emergence of CI through the volume of participants making contributions to a synergistic solution, like user-generated content (for example, Wikipedia or YouTube). That is, CI can be regarded as socially extended knowledge. • “Increased sense” refers to the capacity to expand data collection using digital platforms and devices. For example, a smartphone may have embedded sensors such as an accelerometer, electronic compass, gyroscope and image sensor. Many Location-Based Services (LBSs) and Social Networking Services (SNSs) on smartphones interact with unrestricted information created by unlimited numbers of users using a diverse range of sensors. • “Collective evaluation” captures the decision-support functions provided by crowd reviews, ratings and recommendations (for example, Tripadvisor and Amazon). CI in these examples supports the generation of a collective decisionmaking process (Maher et al. 2011), while collective problem-solving requires suitable platforms to coordinate the actions of collective users (Heylighen 1999). The characteristics of CI-supported platforms have been researched in the past using the “5W1H” framework, which asks “who, what, where, when, why and how” (Dey 2000; Ha et al. 2006; Lee et al. 2014b; Schilit et al. 1994). Malone et al. (2010), for example, develop a gene-table of CI systems categorised using the overarching design questions: what, who, why and how. The who question traces two genes, crowd and hierarchy (distributed and structured organisations), to identify the nature of the collective undertaking. The why relates to motivations or incentives that drive the activity. These could include financial gain, social capital or emotional satisfaction. The what interrogates two basic genes that determine actors conduct: create and decide. Finally, how has two genes arising from the answers to what. How–Create identifies processes of collection, contestation and collaboration. How– Decide deals with group decision processes (voting and consensus) and individual decisions (social network). Huang et al. (2017, p. 2) extend the genome framework to include two additional categories—when and where—for analysing “mobile and situated crowdsourcing systems”. The combined properties of interactive and collective digital platforms for design thinking can be explored using three types of functionalities, each of which reflect

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aspects of the “5W1H” genome framework. The first is user-based functionality, associated with the who and why categories. The next is content-based functionality, which is developed from the what and how questions. Finally, location and time-based functionality responds to where and when themes. These CI platform attributes can be mapped into a comprehensive set of DDT functionalities (Table 7.2). Each of the three categories and their sub attributes are described in more detail hereafter. The first category, user-based functionality, has four attributes: extrinsic hierarchy, extrinsic crowd, intrinsic hierarchy and intrinsic crowd. These attributes are concerned with the people who contribute to content creation in an interactive and collective platform and why they do it. Other than normal users, the people behind user-generated content are commonly classified into four types: power users, content providers, developers and service providers (Belimpasakis et al. 2010). The relations between the different users can be conceptualised as interactions between Csikszentmihalyi’s three systems: person, field and domain (Csikszentmihalyi 1988, 1994, Table 7.2 Key functionalities of interactive and collective platforms developed from CI attributes Malone et al. (2010)

Huang et al. (2017)

Who

• Crowd • Hierarchy (or management)

Who

Why

• Money • Love • Glory

Why

What

• Create • Decide

What

• Environment-centric • People-centric • Service-centric

How-create How-decide

• • • •

How

• Participatory • Opportunistic

Where

• Local • Anywhere

When

• Time-bound • Anytime

Collection Collaboration Group decision Individual decision

* Malone et al. identify only four gene categories above

DDT functionality and its attributes

• Crowd User-based • Requester/Contributor • Extrinsic • Practitioner/Crowd hierarchy • Extrinsic crowd • Extrinsic • Intrinsic • Intrinsic hierarchy • Intrinsic crowd Content-based • Collaborative creation • Collaborative decision • Collective creation • Collective decision • Opportunistic creation • Opportunistic decision Location and time-based • Synchronous local • Synchronous global • Asynchronous local • Asynchronous global

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1997). Setting aside these types of users and their dynamic operations, interactive and collective platforms deal with two user structures: crowd and hierarchy. A crowd is a collection of people who are widely distributed, with roles that are not known in advance, but have an implicit purpose or function (Malone et al. 2010). In contrast, a hierarchy is a collection of users who are organised by explicit roles or functions. In addition to these user structures, user-based functionality deals with the motivations of participations (why). Huang et al. (2017) suggest differentiating extrinsic and intrinsic motivations although combinations of the two are potentially more common. Extrinsic motivations, for example, relate to external rewards such as wealth, power or fame. Intrinsic ones are independent of other consequences and arguably, self-motivated or voluntary participants may be the most important for a platform. Furthermore, growth in interactive and collective activities occurs in the shift from extrinsic hierarchy to intrinsic crowd, leading to potentially unlimited relationships between thousands of users. The second category of DDT functionality, content-based functionality has six attributes: collaborative creation, collaborative decision, collective creation, collective decision, opportunistic creation and opportunistic decision. These attributes are used to understand what is occurring and how. Interactive and collective platforms allow and respond to each user’s personal interactivity, sharing, collaboration, usergenerated content and artistic content. For example, users participating in an SNS blogging platform produce a variety of content that is shared with others. Furthermore, a Mixed Reality (MR) platform is usually developed as an open-source Application Programming Interface (API) for power users to easily generate and share content. These activities are associated with creation, and the response from other users is the need for evaluation and decision. When the processes and products of creation are expressed or externalised, social evaluation and social appreciation (rewards and acknowledgements) may result, being the catalyst for further social creativity (Fischer et al. 2005). Examples of the creation and decision attributes are “crowd-creating” and “crowd-rating”, respectively (Morschheuser et al. 2017). Both creation and decision attributes can be employed as part of three different approaches: collaborative, collective and opportunistic. Collaborative approaches are used in hierarchies and collective in crowds. These first two attributes are related to “participatory” contributions by users, whereas non-participatory or opportunistic contributions are typically generated by sensors or settings in mobile devices (Huang et al. 2017). For example, LBSs or health-monitoring applications of mobile devices generate unforeseen, pervasive content, using location tracking and wearable sensors. The last category of DDT functionality is location and time-based. A capacity to work anywhere (local verses global) and anytime (asynchronous or synchronous) is a defining feature of interactive and collective platforms for design. Thus, this category has four attributes: synchronous local, synchronous global, asynchronous local and asynchronous global. Collectively they explain where and when a function or activity is occurring. For example, considering location-based functionality, the primary division is between local and global, although Huang et al. (2017) suggest an alternative, “local” and “anywhere”. The former is defined as “place-specific”, whereas the latter is not. Location-aware technologies—Global Positioning Systems

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(GPS) or Wi-fi Positioning Systems (WPS)—allow for the creation, management and sharing of data from the local to the global. Interactive and collective platforms usually support this type of mobility-enabling functionality. The second part of this category differentiates asynchronous from synchronous activities. Although an alternative framing of these attributes could consider “time-limited” and “non-timelimited” functionalities, from a design perspective the primary issues are communication and co-working which is why the former pairing is more appropriate. In a DDT platform “co-creation” may require synchronisation (Nonaka and Konno 1998) or it could occur in serial or parallel (Fischer et al. 2005). For this reason, most digital design platforms accommodate both asynchronous and synchronous interaction. In summary, location and time-based functionality increase along a continuum from synchronous local to asynchronous global. Figure 7.2 illustrates the three categories of DDT functionalities of interactive and collective platforms. It also identifies their attribute values and maps them into a 3D (x, y, z dimensions) model for quantitative and qualitative analysis and assessment. The DDT functionalities framework (Fig. 7.2) also identifies three optimal or increasingly functional directions for interactive and collective experience: the zaxis increases to intrinsic crowd (user); x-axis to opportunistic decision (content); and y-axis to asynchronisation (location and time). The functionality (F) of a digital platform can, therefore, be quantified as follows: F = aU + bC + cL

(7.1)

In this formula, U is the user-based functionality, C is the content-based functionality and T is the location and time-based functionality. The second functionality (content) ranges from 1 to 6, with the other two, from 1 to 4. In order to normalise or weight each functionality, the formula has three constants (a, b, c) as correction factors, which can be measured using case studies and evaluations. Nonetheless, for a simple assessment of a balanced-use platform, “1” can be used as a default constant. As an example of how this would work (Fig. 7.2), consider a hypothetical “balanced” interactive and collective platform that can accommodate collective decisionmaking (C = 4), asynchronous local interaction (L = 3) and an extrinsic crowd (U = 2). The combined DDT functionality (F = 9) reflects the way these attributes shape the platform’s cognitive impacts. Using this method, the functionality of digital platforms can be quantitatively compared. The following section presents a series of examples of mobile and situated platforms, which are examined using the DDT process and functionality frameworks. The primary examples are of interactive art and media platforms, although some of these also support design processes.

7.3 Detailed Examples and Analysis

175

7.3 Detailed Examples and Analysis Interactive, collective platforms, from BIM to social-media blogging sites, provide the foundations or the architecture for groups to produce innovative or creative outcomes. The two DDT frameworks presented in this chapter propose ways of understanding platform processes and comparing functionalities. While these frameworks might appear to be optimised for specific design platforms, the rise of pervasive and ubiquitous computing means they are also useful for investigating many other types of collective, interactive systems. Today, microprocessors and sensors are increasingly embedded in everyday objects, buildings and environments, engaging users in a collective, interactive process. This capacity for interaction, conscious or not, is central to many new creative platforms developed for the arts. Before considering specific media art platforms, it is important to differentiate between several aspects of Tangible User Interfaces (TUIs) and draw some distinctions between CI in experiential art and in the design process. Using Hornecker and Buur’s (2006) classification of social and spatial interaction, it is possible to differentiate between four types of platform interfaces: tangible manipulation, spatial interaction, embodied facilitation and expressive representation. The first of these refers to physical or tactile interfaces, the second to those that are site-specific and the third to collective interfaces in a physical space. The last encompasses interfaces that provide a clear and responsive connection between an action and an outcome. These categories are not conventional ones in design research. The closest example of a collective tangible interaction in design might be the creation of a physical model by a team, something that is becoming rare in design practice today. In the arts, however, collective interaction between the general public, artists and a platform can constitute the entirety of a creative output. Or to put this another way, in interactive media arts, the process is the product (Ascott 1969). Being engaged in this collective, dynamic and creative process can influence the cognitive behaviours of individuals, groups and even whole communities. Thus, interactive artworks provide some informative examples of interactive, collective platforms that engage with CI. The following sections describe and analyse two types of interactive platforms— mobile and situated—and their processes and functionalities. The mobile platforms engage participants in an on-going creative process that exists in the continuum between real and digital worlds. Mobile platforms are often used by artists to engage audiences with their work using a Mobile Augment Reality (MAR) platform, typically supported by smartphones, Personal Digital Assistants (PDAs) or tablet PCs. The participants are not a passive audience in these examples, they are actors, composers and performers, self-organising their behaviours in response to the environment. The work Differential Life Integral City by Tesoc Hah is an example of interactive art using a mobile platform for its collective interface (Fig. 7.3). In contrast, situated platforms rely on two-way simultaneous communication supported by ubiquitous sensor networks. They extract real-time data from the “thick air” of information in the world (Velikov et al. 2012) and provide new, collective opportunities to interact with, and create in public space (Yoon 2008). Open Columns by

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a. DiīerenƟal life Integral city

b. Open Columns

Fig. 7.3 Differential Life Integral City (left) by Tesoc Hah and Open Columns (right) by Omar Khan. Two examples of interactive and collective platforms. Incheon International Digital Art Festival, South Korea, 2010

Omar Khan is an interactive artwork that uses a situated platform to gather data from participants and respond to their presence (Fig. 7.3). These two examples, along with four more, are investigated and assessed in the following sections. Throughout the following discussion of mobile and situated platforms, reference is made to the DDT framework’s four types of processes (I-C-L, digital collaboration, problem-solving and collective design) and three categories of functionalities (user-based, content-based and location and time-based). Using these frameworks, the chapter characterises and compares the emergent creative and collaborative characteristics of six interactive digital platforms.

7.3.1 Mobile Platforms The smartphone is arguably the most powerful platform for ubiquitous computing available today (Henrysson and Ollila 2004; Abowd et al. 2005). Smartphones support a myriad of interactive and collective behaviours, creating a pervasive Mixed Reality (MR) (Stapleton and Rolland 2010). In combination, the mobility and ubiquity of the smartphone’s MR environment support basic DDT functionality, providing a platform for creative interactive processes. The smartphone is also a TUI, which gives “physical form to digital information” and to social interaction (Ullmer and Ishii 2000). For all of these reasons, it is ideal for MAR (Lee and Kim 2011; Lee et al. 2013, 2019). In media arts, the smartphone is regularly used to blur the boundaries between art and computer science, artist and viewer. As an example, Tesoc Hah’s 2010 Differential Life Integral City presents a collective virtual environment that supports a creative

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177

on-going process (Fig. 7.3a). Visitors to the work input their lifestyle information into the platform using a smartphone, and the platform simultaneously generates and continuously transforms virtual residences in response to each participant’s needs. This process not only shapes individual housing units, but collectively develops a dynamic virtual city in response to the crowd’s requirements. While the audience can participate in the platform anytime and from anywhere, the exhibition itself remains spatially situated. Furthermore, while people can actively shape its content, they cannot completely determine its outcome. Differential Life Integral City can be analysed using the frameworks presented previously in this chapter. In terms of its functionalities, its user category is intrinsic crowd, its content is collective creation and its location and time are asynchronous global. This leads to an unweighed functionality value of 11 (4 + 3 + 4). The DDT process supported by this platform is a simple three step I-C-L (Interacting, Collecting and Leveraging). The audience interacts with the digital platform through input of their information and the platform collects and simulates their data in virtual space. Platforms of this type make the link between tangible and intangible culture more interactive and playful (Marques and Borba 2017). Simulated visualisation based on participation can also be understood as “embodied design thinking” which is situated and generated through socio-cultural experience (Diethelm 2019; Jerald 2015). Digital platforms using MAR technologies like this, support direct access to digital media, projecting a collective experience.

7.3.1.1

Mobile Augmented Reality (MAR) Platforms

Mixed Reality (MR) refers to real-time interaction that spans the boundaries between the real and the virtual. Rather than having fixed properties, MR can be conceptualised as a continuum ranging from Augmented Reality (AR) to Augmented Virtuality (Milgram and Kishino 1994). MR platforms use mobile ubiquitous computing to create interactive relations between people, objects and locations, and then overlay computer-generated and real visualisations on the physical world. Such platforms visually connect the physical environment with digital information, as well as with people, through the social network. An example of an MR project is SLARiPS (Second Life Augmented Reality in Physical Space), which set out to extract Second Life avatars from the virtual world and make them visible and responsive in the real world (Stadon 2009). MAR, which combines ubiquitous computing and AR (Liberati 2016), is often supported by cloud-based servers (Chatzopoulos et al. 2017). An example of a smartphone-based MAR is EYEPLY (Hurwitz and Jeffs 2009), which provides personalised marketing and promotion of products and services to individual users. EYEPLY operates in real time at stadiums, parks, conventions or shopping malls. Because it shares controllable contents already developed from existing third-party data, its content-based functionality is limited. EYEPLY ’s functionalities can be categorised as follows. For the user category, extrinsic crowd; for the content category, collaborative creation; and for location and time, synchronous local. Therefore,

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its functionality value is 4 (2 + 1 + 1), which is low for an interactive, collaborative platform. In addition to its functionality, its DDT process consists of three steps, interacting, collecting and leveraging, meaning it uses a basic I-C-L process (Fig. 7.1). Santana et al. (2017) developed a MAR platform using an LBS enabling visual analysis of temporal data series and 3D building models. This type of MAR platform uses AR to visualise and simulate virtual models at a particular location in the real world. Like most location-based MAR platforms, it has limited functionalities: extrinsic crowd, collaborative creation and synchronous local (F = 4). Despite this, AR combining physical and virtual interaction can be a powerful mechanism for engagement and decision-making (Allen et al. 2011; Gordon and Manosevitch 2010). An example of a more recent MAR platform is Urban CoBuilder, which uses a multiplayer, outdoor urban simulation to support rule-based emergent planning (Imottesjo and Kain 2018). Using the collective results of individual design and planning decisions, Urban CoBuilder enables process simulation through multistakeholder inclusion as well as rule-based simulation using a gaming engine. Its capacity for interactive and collective visualisation seeks to support increased social participation and communication between experts and the community in the DDT process. Thus, the Urban CoBuilder platform serves collaborative design and collective decision-making. The functional characteristics of Urban CoBuilder are as follows: intrinsic crowd, collective decision and asynchronous local. Its functionality value is 11 (4 + 4 + 3). Although this location-based MAR platform involves both hierarchy and crowd users, the creation of digital content is only undertaken by the extrinsic hierarchy with the collaborative work occurring separately. Furthermore, collective decision by the crowd does not directly support “co-creation” but is limited to suggesting ideas for designers (hierarchy). Thus, the DDT process is closer to digital collaboration—focusing on deciding, inspiring, networking, recommending and sharing—and only partly supports problem-solving limited to solution prototyping and testing.

7.3.2 Situated Platforms As the previous section notes, while computers in the contemporary world have almost “disappeared”, computation is everywhere (Weiser 1991). The concept of a “tangible bit” encapsulates integration of different types of interactive surfaces and ambient media into objects, rendering it both invisible and ever-present (Ishii and Ullmer 1997). Situated platforms use ubiquitous computing (Weiser 1991) to support spatial interaction and embodied facilitation (Hornecker and Buur 2006). These platforms empower collective interactions and context-awareness in physical space, sustaining opportunistic creation and decision functionalities. They recognise users’ behaviours and needs and then respond with suitable, personalised services (Hara et al. 2002; Lee et al. 2014b; MacDorman et al. 2004; Nakauchi et al. 2003). In this

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way, “embodied interaction” in a situated platform leads to “embodied cognition”, arising through the crowd’s unconscious or intuitive interactions in space (Dourish 2004). This phenomenon, the leveraging of cognition through interaction, is central to technologically mediated social participation (Fortin et al. 2014b). As a simple example of a situated platform, the interactive artwork Open Columns by Omar Khan uses carbon dioxide (CO2 ) sensors in an exhibition space to record the physical outputs of participants and reflect this in the installation (Fig. 7.3b). The installation, made up of a mesh of columns, changes in response to the number of people in the space and their interactions. The functionalities of this platform can be categorised as intrinsic crowd, opportunistic creation and synchronous local. The combined functionality value is 10 (4 + 5 + 1). In comparison with the mobile platform supporting Differential Life Integral City, this situated platform provides opportunistic creation, pervasively generated by sensors and a self-organising system (columns) within the space of the exhibition. Because Open Columns lacks a decision point, its DDT process conforms to the most common model for interactive artworks, I-C-L.

7.3.2.1

Interactive Media (IM) Platforms

The location of an IM platform in a public space is an example of a situated platform that can blur and merge physical and digital spaces. This type of platform has, in the recent past, largely relied on the use of ubiquitous computing, sensor networks, robotics, middleware and agent-based software (Cook and Das 2007). In an IM installation, spatial interactions are embedded and situated in a physical space, and consequently users need to physically interact with the platform. Moreover, interactions are not restricted to touching or moving objects, but can require physically moving around an area (Hornecker and Buur 2006; O’Neill 2008). As such, IM displays in urban space often reflect collective experience or movement. To sustain social interactions in an IM platform, Hespanhol and Tomitsch (2012) suggest that three factors are important. First, active participation, second, welllocated sensor locations and finally, close alignment of medium and message. These points are especially relevant when considering the performative aspects of IM platforms. As participants become aware of their collective presence they may begin to perform to an audience of their peers. Hespanhol and Tomitsch (2012) warn that performative settings “may pose cognitive challenges for the audience but also allow for more open expressive co-experiences to emerge” (p. 40). The most common approach to IM platforms is to employ large “situated displays” in public spaces to facilitate in-situ and real-time responses. Some examples of these include Opinionizer (Brignull and Rogers 2003), CityWall (Morrison et al. 2008), Discussions in Space (Schroeter and Foth 2009), Sapporo World Window (Choi and Seeburger 2011), SMSlingshot (Fischer and Hornecker 2012), Smart Citizen Sentiment Dashboard (Behrens et al. 2014) and City-Share (Ludwig et al. 2017). Ludvigsen’s (2005) interactive media installation iFloor is an IM platform that explores social interaction in a public building. Ludvigsen argues that an effective IM platform must have a shared representation, support collective responses and possess

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social interfaces. Ludvigsen’s iFloor is an interactive floor surface that provides library users with a new way of finding materials. This situated platform allows users to post questions to the digital floor using text messages or email, and then review questions using a shared cursor. The cursor is operated pervasively by the physical position of users who also receive visual feedback of their movements. The combined physical activity and shared interface facilitate situated engagement. This game-like platform provides previously unforeseen opportunities for collaborative exploration. Collectively, the functionalities of iFloor are intrinsic crowd, opportunistic creation and synchronous local, leading to a functionality value of 10 (4 + 5 + 1). Its DDT process is digital collaboration including creating, deciding, inspiring, networking, recommending and sharing. It is, however, limited to face-to-face communication in a fixed location. Fortin et al.’s (2014a, b) paired media installation, Mégaphone and Speakers’ Corner, is focussed on the act of public speaking and its reception. In Speakers’ Corner a person’s voice is captured by a microphone and sent to a voice recognition system. The data collected and processed in this way is then projected on the Mégaphone platform’s interfaces: loudspeakers, a set of responsive stage lights and media façades. In this work, the public not only contribute to creating the content (through “public” speaking) but also receive other’s content, visually and aurally, stimulating social interaction. The functionalities of this platform are intrinsic crowd, collective creation and synchronous local, and its functionality value is 8 (4 + 3 + 1). Compared to iFloor’s embodied interaction, the Speakers’ Corner and Mégaphone situated platform involves direct creation (the initial voice input), although the platform automatically transfers this into various formats. Collectively, its DDT process is not a simple I-C-L, but rather digital collaboration addressing creation, inspiration, networking, sharing and transferring. However, without the deciding and recommending steps, its collaborative potential is relatively limited.

7.4 Discussion The relatively recent convergence of wireless technologies and sensor networks has enabled new types of everyday experience in the digital ecosystem. In this ecosystem local interactions and self-organising behaviours enable individuals to seamlessly interact with the environment and each other. Despite this, our understanding of the design thinking process and its products in this ecosystem is still in its infancy. Thus, this chapter adapts and develops two frameworks to systematically investigate the properties and functionalities of digital platforms. Using these frameworks, the previous sections in this chapter analysed two types of interactive and collective platforms, mobile and situated. The results of this analysis are summarised in Table 7.3. The DDT processes in the six cases are limited to just two types: I-C-L and digital collaboration. The mobile and situated platforms examined in this chapter do not use “co-decision”, which is one of the most important CI processes. In most of the

7.4 Discussion

181

Table 7.3 DDT process and functionality of interactive and collective platforms analysed in the case study (F: functionality value) Platform

Case

Process

Functionality

F

Mobile

Differential life Integral city

I-C-L

Intrinsic crowd, Collective creation, Asynchronous global

11

EYEPLY

I-C-L

Extrinsic crowd, Collaborative creation, Synchronous local

Urban CoBuilder

Digital collaboration

Intrinsic crowd, Collective decision, Asynchronous local

11

Open Columns

I-C-L

Intrinsic crowd, Opportunistic creation, Synchronous local

10

IFloor

Digital collaboration

Intrinsic crowd, Opportunistic creation, Synchronous local

10

Mégaphone and Speakers’ Corner

Digital collaboration

Intrinsic crowd, Collective creation, Synchronous local

Situated

4

8

platforms, a hierarchy is the foundation for the operations of the crowd or collective, limiting the capacity for true creative and collective emergence. Without capacity for collective, convergent thought processes, users’ engagement and immersion are constrained. DDT is an interactive and collective process like I-C-L, but it evolves through collaborative and collective decision-making. By incorporating the third or fourth types of DDT processes (Table 7.1) in a platform, more interactive and collective activities will arise in the ecosystem. The Collective Design (CD) process, in particular, could offer an optimal model of DDT in the future. CD, by way of the cloud or online sharing platforms, challenges traditional design thinking (Özkil 2017), accommodating participatory design (Barcellini et al. 2015; Smith and Iversen 2018), collaborative product development (Lin et al. 2010; Wang and Zhang 2010) and computer-supported cooperation (Papangelis et al. 2019; Schrott and Glückler 2004). CD, however, has different knowledge production and communication processes, through loose or informal design networks (Özkil 2017). Whereas collaborative design is a controlled formal process, CD proposes an informal, flexible collaborative process (Huang et al. 2010). CD’s benefits are that it is intrinsically collaborative, part of on-going process of developing and sharing design knowledge across a body of physically distributed, but similarly motivated people. This feature of CD processes has parallels with the properties of SMMs and DMMs (Chap. 5). This also signals that the DDT framework can accommodate the DTC model in Chap. 5, highlighting the communication and information processing of individual, collective and distributed MMs.

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In terms of functionality, the mobile platforms examined in this chapter tend to address asynchronous interaction (Table 7.3). In contrast, the situated platforms naturally support synchronous local functionality. Acknowledging the spatial and temporal limitations of IM installations, future media platforms should consider three additional functionalities: synchronous global, asynchronous local and asynchronous global. With the expansion of social networking, future platforms should have access to these functions. In addition, socially extended cognition is mediated by interaction with the digital platforms examined in this chapter, which are in turn updated to reflect users’ collective authorising actions (Bruns 2008). Consequently, this heterogeneous, collective interaction often emerges through the use of hand-held devices and tangible interaction. In this way, embodied interaction in situated platforms can be extended to supporting asynchronous global activities. Indeed, as larger numbers of users engage in CI, user experience may be further amplified through inclusion of three additional functionalities: (i) intrinsic crowd, (ii) opportunistic decision and (iii) asynchronous global. The functionality (F) values in Table 7.3 enable the quantitative comparison of DDT platforms, although the measures adopt a simple default position, using “1” for all constants. Furthermore, the results indicate that three functionalities are common in interactive and collective platforms. For example, the functionality values of Differential Life Integral City and Urban CoBuilder are identical, but their content and location and time-based functionalities are different. In addition, the use of opportunistic creation in the two situated platforms results in a similar functionality value (F = 10), which is the highest of the two mobile platforms. Therefore, this measure offers a promising quantitative mechanism for reviewing platform DDT functionality. A further factor to consider when assessing mobile or situated platforms is there are now a growing number of examples of mobile-situated platforms. For example, SMSlingshot (Fischer and Hornecker 2012), Smart Citizen Sentiment Dashboard (Behrens et al. 2014), mood.cloud (Scolere et al. 2016) and City-Share (Ludwig et al. 2017) are all integrated mobile-situated platforms. These combined platforms may be catalysts for further opportunities in the digital ecosystem.

7.5 Conclusion With the advent of mobile and ubiquitous computing, our everyday experience is getting closer to that illustrated in Langton’s (1995) Artificial Life. For example, using embodied interaction, seamless responsive behaviours are exhibited in interactive art and media platforms. In most cases, the goal of the art is to develop performative and pervasive interactions, based on direct or indirect communication between people and the environment. These participatory behaviours can be enhanced, and their creative outcomes improved, through incorporation of advances in three types of functionality: (i) user, (ii) content and (iii) location and time. A heightened understanding of DDT process types is also a precursor to improved outcomes. The four DDT processes identified in this chapter can be regarded as stages in the evolution of

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DDT and CI, ranging from the more primitive I-C-L to the more advanced collective design process. The analytic approach proposed in this chapter facilitates the systematic exploration of both DDT processes and functionalities, as can be seen in the six example platforms. The functionality measure (F) provides a way of comparing digital platforms and evaluating their potential for supporting CI, DMM and TMM. The two DDT frameworks in the chapter do, however, require further development and testing in different contexts to understand their limits and opportunities, for example, to be adopted in and advance professional or commercial applications. This chapter, the last of Part II Collaboration, addresses interaction collaboration and collectivity in design thinking. It also identifies the way social interaction can support social creativity and CI. In Part III, these themes are expanded to consider cultural aspects of design thinking, which necessarily include those relating to communication and language in design.

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Part III

Culture

Chapter 8

Design Thinking Across Borders

Abstract This chapter investigates cross-national aspects of design thinking, with a focus on three themes: design cognition, complexity and spatial language. The chapter uses a new method, a dual-coding system (design cognition and language) for protocol data combined with linkography. This system formally captures cognitive and linguistic characteristics of the design process. This method is applied to analyse parametric design processes undertaken by a set of Australian and Swedish architects. Through this analysis, and the discussion of its results, the chapter contributes to the in-depth exploration of multiple aspects of cross-national design and cultural design thinking.

8.1 Introduction Globalisation is a process where organisations and systems operate across national boundaries to support productivity or to achieve power, success or influence. Globalisation typically entails integration of resources, flows of production and transportation networks (James 2009). Trade of raw materials, technology or expertise, has historically been a driver of globalisation. The exchange or sale of design expertise is today recognised as being central to the global “creative economy”. Florida (2002) famously predicted that the creative economy, and the “creative class” behind it, could eventually rise to challenge traditional economic and social capital models, built on resources or manufacturing. While it is arguable whether this has occurred, it is clear that design practice has become increasingly global in its aspirations and operations. Contemporary design teams typically include members from many countries, with different linguistic and cultural backgrounds. This situation has become so prevalent that the challenges of culturally and geographically distributed teams are now recognised as a core issue in design practice and thinking (Man 2014). Regardless of whether these teams are working in the same building, or in different cities, multicultural and multi-lingual teams pose both a challenge and an opportunity for the global creative economy. These challenges and opportunities are at the heart of the present chapter, which is about linguistic and cultural dimensions in design cognition. © Springer Nature Switzerland AG 2020 J. H. Lee et al., Design Thinking: Creativity, Collaboration and Culture, https://doi.org/10.1007/978-3-030-56558-9_8

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Language, be it verbal, visual or gestural, is central to the development of Shared, Team and Distributed Mental Models (SMMs, TMMs and DMMs, respectively). Language is also culturally and socially coded, which has implications for both knowledge transmission and reception. As such, the native language (the spoken or written word) is just one of many factors affecting communication in teams. Individual religious beliefs, cultural practices, social attitudes and learnt behaviours all potentially complicate the capacity to share mental constructs. In a simple sense, homogeneity in teams might be the best starting point if the focus is solely on productivity and a linear process, because it limits complicating factors associated with communication (Horwitz and Horwitz 2007). Conversely, and more importantly for design, heterogeneity in teams is often linked to innovation (Chen 2018) and multi-cultural diversity is a factor in team creativity (Cox and Stacy 1991; Jackson 1991; Santandreu Calonge and Safiullin 2015; Shalley et al. 2004). On balance, the evidence in favour of the benefits of a diverse design team outweighs any detriments. Despite understanding the benefits of diversity in teams, just including designers with different backgrounds does not guarantee creative thinking. The Mental Models (MMs) of team members must be properly shared to be effective (Rosenman et al. 2007). Chen (2018), for example, argues that bicultural communication, which combines two cultures’ original features into an integrated solution, is a necessary prerequisite for innovation in heterogenous teams. Whether this is true or not, such arguments about diversity and creativity, and the need for TMMs, SMMs or DMMs, must all eventually consider the relationship between language and cognition. Language, as a system, is both a reflection of the way we think and of our sociocultural differences and values (Boroditsky 2001; Gleitman and Papafragou 2005). Linguists, philosophers, anthropologists and psychologists have long been interested in the relationship between language and thought (Lee et al. 2016). Structuralist thinkers, for example, argue that language is fundamentally universal in its cognitive intent and logic (Chomsky 1965), whereas post-structuralists maintain that cognition must precede language development (Pinker 1994). There is also disagreement about the nature of cognitive in communication, with some researchers emphasising the importance of visual imagery and models (Arnheim 1997; Paivio 1971, 1983; Ware 2008), while a counterargument maintains that word order and information structure are more important (Hörnig et al. (2006). In both cases, evidence confirms that language influences conceptual development, which in turn shapes the capacity to develop and communicate MMs (Bowerman 1996; Levinson 1996, 2003). In design teams, this capacity turns out to be especially reliant on spatial reasoning. Tenbrink and Ragni (2012) identify three stages of spatial thinking in the communication of MMs: comprehension, description and validation. In the comprehension stage, the given conditions or controls are integrated into a unified MM using people’s knowledge of the semantics of spatial expression. In the description stage, the MM is defined and examined to reveal implicit relations. Finally, in the validation stage, if there is an alternative model of the original conditions or controls the designer returns to the start to review it. Alternatively, the team can arrive at an agreed MM and apply it.

8.1 Introduction

193

Because MMs are “abstractions and represent only the essential parts” of a process or system, they tend to be “more abstract than visual models” and thereby prioritise “qualitative relations instead of metric information” (Tenbrink and Ragni 2012, p. 281). Thus, the tripartite comprehension-description-validation model is itself an example of a MM that is reliant on spatial reasoning. It has both a linear progression and an in-built loop returning to the start if needed. There is also an assumption in this model that MMs use spatial reasoning for communication and this improves their effectiveness, because spatial reasoning is relatively universal. The evidence for this perspective is not, however, so compelling. There are indicators that the MMs which some teams presume are shared, are not. Misunderstandings of this type are often linked to linguistic terms or concepts and they may have general consequences for problem-solving (Munnich et al. 2001). But is this also true of design? Are the MMs of designers linguistically constrained? To address these two questions, this chapter conducts a parallel investigation of both cognitive and linguistic characteristics in the design process. To undertake this investigation a method must be available to record and analyse cognitive and linguistic characteristics in design. As a useful point of reference for this, Paivio’s dual coding theory identifies three types of cognitive processing: representational, referential and associative (Clark and Paivio 1987; Paivio 1983, 1991). Representational processing refers to the direct activation of verbal or nonverbal characterisations. Referential processing deals with the activation of the verbal system by the non-verbal system or vice versa. Associative processing is the activation of representations within the same verbal or non-verbal system. As discussed previously (Chap. 5), both visual and verbal representations not only support the development of individuals’ design cognition and distributed MMs, but also facilitate design communication for SMMs. In addition, several components of the Design Team Cognition (DTC) model (Fig. 5.1) directly support design teams. Spatial language is crucial to cognitive representation and communication in design teams. Furthermore, individual fluency in verbal representation has an impact on both the practical operations of the team, and the transformation of MMs to SMMs. As such, “verbal literacy” is important in design because language facilitates bridging between individual knowledge and experience and that of the team (Dong 2005; Jara 2014). Past research into communication (Burleson and Caplan 1998) also identifies the role played by an individual’s social information-processing capacity in the development of “team-related cognition” (DeChurch and Mesmer-Magnus 2010). This brief overview sets the context for the present chapter which examines multiple approaches to understanding cultural design thinking in terms of (i) design cognition, (ii) cognitive and syntactical complexities and (iii) spatial language. The multi-aspect methodology developed in this chapter is used to ensure that the investigation of design cognition considers language, and the investigation of language takes into account cognitive factors. Thus, a dual-coding system, which captures both design cognition and spatial language, is used to analyse protocol data derived from design experiments. It allows for comparisons to be constructed between the actions and thought processes of designers and the ways they describe these actions. The protocol data is derived from two Australian design protocols and two Swedish

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design protocols developed under the same experimental conditions. The protocol data is quantitatively measured, mathematically analysed and then visualised using linkography (see Chap. 4).

8.2 Research Procedure By observing groups of designers working separately on the same task and in the same environment, a direct comparison can be made between the ways different designers or groups think and work. For this chapter two Australian designers (Au1 and Au2 in Chap. 2) and two Swedish (Sw1 and Sw2) undertook the same task (a concept design for a high-rise building), using the same tool, Grasshopper, and while “thinking aloud” during their design sessions. All participants had experience in parametric design including successfully completing at least one major architectural design project using the graphical algorithm editor. All designers were asked to undertake the task in approximately one hour. However, taking into account varying needs of processing time and debugging, they were allowed to finish earlier or continue overtime. The research procedure is the same as Study II in Chap. 2. All think-aloud vocalisations for this study were in English regardless of the native language of the participant. Thus, the interpretation of the actions has an innate English bias, but the common language supports a more consistent coding and analysis process. This also responds to the limitations identified by Boroditsky (2001) when using and comparing native languages. Importantly, both English and Swedish languages share some similar characteristics, but they also have differences. For example, both languages use the standard word order—Subject-Verb-Object (SVO),—while Swedish also follows the Verb-second (V2) order, as does the German language (Lee et al. 2016). The composition, “there + verb”, is common in Swedish, whereas “there + to be” is more common in English. In the case of this study, there might be further differences in the use of spatial language between two groups of designers. However, this study doesn’t aim to capture this level of cultural or linguistic differentiation, but rather to highlight whether or not the spatial language reasoning of each group can be identified through the spatial language coding scheme. The analysis of the results of the cross-national protocols is reported in three stages: design cognition, complexity and spatial language.

8.3 Design Cognition 8.3.1 Coding Results Table 8.1 reports the general characteristics of the four design protocols. Three participants in the experiment completed the design within one hour, while Au2 took longer.

8.3 Design Cognition

195

Table 8.1 General results of the four design protocols Designer

Design time

Num. of segments

Coded segments

Average time of segments (s)

Au1

47 min 54.7 s

220

208 (94.5%)

13.0

Au2

1 h 27 min 8 s

319

277 (86.8%)

16.5

Sw1

1 h 4 min 22 s

360

347 (96.4%)

10.7

Sw2

1 h 3 min 12 s

303

278 (91.7%)

12.5

Mean

1 h 5 min 42 s

300.5

277.5 (92.3%)

13.2

SD

16 min 9.9 s

58.8

56.7

2.4

The average duration of each coded segment was 13.2 s and the average value of the number of segments was 300.5. Sw1’s protocol has the highest number of encoded segments (360), and the shortest average time of segments (10.7 s). This result indicates that Sw1 produced clearer cognitive activities and developed them more quickly than the others. In contrast, Au2 has the smallest percentage of the encoded segments (86.8%) and therefore took more time to complete a cognitive activity. Au2 also produced more unnecessary (non-encoded) and trouble-shooting activities, including finding algorithms (rules) and arranging rules. On average, 92.3% of segments were encoded using the design cognition coding scheme. Table 8.2 reports the coding coverage (%) of each cognitive subclass. The most dominant activity coded in this study is A-Rule (writing an algorithm), the second most is E-Geometry (visually evaluating the outcome of a rule) and the third is E-Rule (evaluating the rule itself). On average, these three activities account for over 50% of the codes, a result that is similar to the percentage of coding results for Study II in Chap. 2. The algorithmic representation is the preferred medium in the physical level of parametric design, where it is regularly evaluated in the 3D view as well as in the scripting view. This switching pattern closely corresponds to the creative micro-processes (see Chap. 3) and co-evolutionary processes between design spaces and modes (see Chap. 5). Despite some similarities, there are clear differences between the results for the participants’ cognitive activities. For example, Au2 and Sw2 each produce a larger number of geometric activities, in essence, because they both drew shapes in the 3D view and then imported them into the algorithm editor. Both also constantly reevaluated their algorithmic rules (E-Rule), which accounts for over 16% of their time, and were more limited in generating behaviours (G-Generation). Of interest is the fact that the Australian designers addressed rule-creation activities more frequently, whereas the Swedish designers developed more parameters than the comparative group. Au1 and Au2 often undertook perceptual activities, while Sw1 and Sw2 tended to evaluate existing parameters (E-Parameter). The sample is too small to draw conclusions about these trends, but the coding scheme identifies these clear differences. In addition, each national group followed a similar pattern of algorithmic representations, which might be shaped by their past experience and skills. That is, while this protocol study is limited to identifying cross-national differences in

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Table 8.2 The percentage of coding results Level

Category

Subclass

Au1

Au2

Sw1

Sw2

Mean

SD

Physical

Geometry

G-Geometry

2.0

6.6

0.0

4.2

3.2

2.8

G-Change

0.5

9.3

0.7

0.4

2.7

4.4

Algorithm

A-Parameter

4.9

2.6

6.0

6.1

4.9

1.6

A-Change Parameter

10.0

1.7

10.2

8.0

7.5

4.0

A-Rule

24.7

20.5

17.4

16.9

19.9

3.6

A-Change Rule

6.0

4.3

8.9

5.2

6.1

2.0

A-Reference

0.0

0.0

1.4

0.0

0.4

0.7

Geometry

P-Geometry

2.8

1.3

0.1

0.4

1.1

1.2

Algorithm

P-Algorithm

3.2

2.9

1.0

2.2

2.3

1.0

Problem-finding

F-Initial Goal

3.0

0.9

0.6

4.2

2.2

1.7

F-Geometry Sub Goal

7.0

8.7

5.9

6.0

6.9

1.3

F-Algorithm Sub goal

4.1

4.0

1.8

7.1

4.3

2.2

Solution-generating

G-Generation

4.0

2.0

3.9

2.5

3.1

1.0

Solution-evaluating

E-Geometry

19.4

18.8

22.2

11.2

18.0

4.7

E-Parameter

0.8

0.0

9.6

9.0

4.9

5.2

E-Rule

7.6

16.3

10.2

16.5

12.7

4.5

E-Reference

0.0

0.0

0.0

0.0

0.0

0.0

100

100

100

100

100

100

Perceptual Conceptual

Sum

parametric design, it also captures cognitive similarity and disparity between divisible groups.

8.3.2 Linkographic Analysis As discussed previously in Chaps. 4 and 5, linkography is typically used for measuring “productivity” in teamwork (Costa and Sobek 2004; Goldschmidt 1990, 1995). After developing linkographs of the four design sessions, their link indexes are calculated. The link indexes of Au1 (2.75) and Sw2 (2.76) are slightly higher than the others (Au2, 2.39 and Sw1, 2.62) in their language groups. That is, the volume of linking activities in Au1’s and Sw2’s design protocols indicate that the interconnectedness of their thoughts is greater than the others. Goldschmidt and Tatsa (2005) argue that there is a correlation between such high link-index values and productivity. Furthermore, moves that are particularly rich in links also serve as a sound, and arguably the best, indicator of productivity. In particular, “link-intensive”

8.3 Design Cognition

197

or “Critical Move” (CM) links are clearly related to four distinct moves in parametric design: introducing geometric/algorithmic ideas, creating algorithms (as a unit), modification activities and evaluation activities (see Chap. 4). These moves result in CM (forelinks) that earn their designation. Table 8.3 shows the CMs which have more than five (CM5 ), six (CM6 ) and seven links (CM7 ). Au1’s protocol develops the highest proportions of CMs over the total number of moves, while Sw1 produced the lowest. For example, the CM5 percentage of the total number of moves is 28.6% for Au1 and 19.7% for Sw1. If we accept the rationale of Goldschmidt (1995), Kan and Gero (2008), the link indexes and the figures in Table 8.3 (the percentages of CMs) confirm that both Au1 and Sw2 are more productive than the others in their language groups. Interestingly, Sw1’s design activities (Table 8.2), which featured the highest number of moves, didn’t contribute to productivity in terms of idea generation. These linkographic indexes are useful for identifying individual “productivity” in design, while less so for revealing similarity and disparity between the two groups of designers. A critical aspect of parametric design is the algorithmic component or set of components in an algorithm unit. Thus, the number of algorithmic units in a protocol is a possible indicator of productivity in parametric design. Figure 8.1 illustrates algorithmic scripts showing clear graphical links between algorithmic units (or components). Unexpectedly, the two less productive designers (Au2 and Sw1) developed more algorithmic units than the more productive designers. That is, the number of algorithmic units generated in a parametric design session may be not related to the generation of ideas or to productive design thinking. Alternatively, a better way to analyse graphical algorithms may be topological analysis using graph theory. Table 8.3 Critical moves with more than five (CM5 ), six (CM6 ) and seven links (CM7 ) Designer

Link

CM5 (CM5 %)

CM6 (CM6 %)

CM7 (CM7 %)

Au1

Forelinks

44 (20.0%)

40 (18.2%)

35 (15.9%)

Backlinks

19 (8.6%)

10 (4.6%)

9 (4.1%)

Au2

Sw1

Sw2

Total

63 (28.6%)

50 (22.7%)

44 (20.0%)

Forelinks

51 (16.0%)

39 (12.2%)

34 (10.7%)

Backlinks

24 (7.5%)

15 (4.7%)

9 (2.8%)

Total

75 (23.5%)

54 (16.9%)

43 (13.5%)

Forelinks

51 (14.2%)

44 (12.2%)

41 (11.4%)

Backlinks

20 (5.6%)

7 (1.9%)

5 (1.4%)

Total

71 (19.7%)

51 (14.1%)

46 (12.8%)

Forelinks

48 (15.8%)

43 (14.2%)

36 (11.9%)

Backlinks

31 (10.2%)

20 (6.6%)

13 (4.3%)

Total

79 (26.0%)

63 (20.8%)

49 (16.2%)

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8 Design Thinking Across Borders

Au1

Au2

Sw1

Sw2

Fig. 8.1 Four designers’ algorithmic scripts consisting of algorithmic units and links

8.4 Cognitive and Syntactical Complexities

199

8.4 Cognitive and Syntactical Complexities 8.4.1 Cognitive Complexity 8.4.1.1

Content Complexity

Table 8.4 presents the entropy and content complexity (H C ) values of the four design protocols. The H C —being the summary of entropy values of all subclasses—of Sw2’s protocol is the highest (3.5048) and Au2’s is the lowest (3.2540). Interestingly, the ranking of H C values of the four protocols is identical to the order of their link indexes. That is, H C may provide an alternative index for design productivity (idea generation), which does not always result in a constructive outcome. One reason for this is that a designer may be distracted by too many ideas in a limited timeframe. For example, Sw2 generated many ideas but barely completed his design in the one-hour session. Thus, Sw2’s H C value indicates that his design process is very productive, even though it isn’t necessarily very timely or efficient. In addition, H C is dependent on the development of categories (subclasses) of a coding scheme as discussed in Chap. 4. Nonetheless, H C provides a simpler quantification of individual design cognition than structural complexity, which requires the more time-consuming Table 8.4 Entropy and content complexity (H C ) values of the four design protocols Subclass

Au1

Au2

Sw1

Sw2

Mean

SD

G-Geometry

0.1129

0.2588

–

0.1921

0.1879

0.0730

G-Change

0.0382

0.3187

0.0501

0.0319

0.1097

0.1395

A-Parameter

0.2132

0.1369

0.2435

0.2461

0.2099

0.0509

A-Change Parameter

0.3322

0.0999

0.3359

0.2915

0.2649

0.1118

A-Rule

0.4983

0.4687

0.4390

0.4335

0.4599

0.0299

A-Change Rule

0.2435

0.1952

0.3106

0.2218

0.2428

0.0493

A-Reference

–

–

0.0862

–

0.0862

–

P-Geometry

0.1444

0.0814

0.0100

0.0319

0.0669

0.0597

P-Algorithm

0.1589

0.1481

0.0664

0.1211

0.1236

0.0413

F-Initial Goal

0.1518

0.0612

0.0443

0.1921

0.1124

0.0711

F-Geometry Sub Goal

0.2686

0.3065

0.2409

0.2435

0.2649

0.0304

F-Algorithm Sub goal

0.1889

0.1858

0.1043

0.2709

0.1875

0.0680

G-Generation

0.1858

0.1129

0.1825

0.1330

0.1536

0.0363

E-Geometry

0.459

0.4533

0.4820

0.3537

0.4370

0.0569

E-Parameter

0.0557

–

0.3246

0.3127

0.2310

0.1519

E-Rule

0.2826

0.4266

0.3359

0.4289

0.3685

0.0718

E-Reference

–

–

–

–

–

–

HC

3.3339

3.2540

3.2564

3.5048

3.3373

0.1177

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8 Design Thinking Across Borders

development of a linkograph. Furthermore, since the entropy values of subclasses are based on the cognitive coding results, they may be able to capture cognitive similarity and disparity between design groups.

8.4.1.2

Structural Complexity

Entropy values developed from a linkograph can used to measure structural complexity (H S ). Table 8.5 shows the results of entropy calculations derived from the design protocols. In all protocols, backlink entropy, which measures the opportunities arising from enhancements or responses (Kan and Gero 2008), is higher than forelink entropy. To normalise the entropy values developed from different lengths of design protocols, Table 8.6 records the entropy per move of the four design protocols. The total cumulative entropy per move is regarded as the H S of each protocol. Interestingly, the H S values parallel those of the percentages of CMs (Table 8.3). The entropies per move in Au1’s protocol is higher than the others (Table 8.6). This implies that Au1 has a higher level of H S (0.4635) and the resulting linkograph has relatively greater information clustering and abstraction. Conversely, the lowest value of H S in Sw1’s protocol (0.3053) suggests a relative reduction in cognitive complexity. Sw1’s protocol has the shortest average duration of moves with the highest number of segments that may be related to higher productivity. However, the lowest value of entropy reveals that Sw1’s transition between design activities in the process is fast but doesn’t produce a meaningful CM in terms of information clustering and abstraction. Both Sw1’s and Au2’s protocols are relatively weaker in cognitive terms, as a result of lower opportunities relating to idea generation (forelink), enhancements or responses (backlink) and cohesiveness or incubation (horizonlink). Table 8.5 Entropy of the four design protocols Forelink total H

Backlink total H

Horizonlink total H

Cumulative total

Au1

35.23

52.56

14.17

101.96

Au2

32.70

50.67

15.93

99.30

Sw1

35.22

57.64

17.05

109.91

Sw2

33.50

55.14

17.95

106.59

Table 8.6 Entropy per move and structural complexity (H S ) of the four design protocols Forelink H

Backlink H

Horizonlink H

HS

Au1

0.1601

0.2389

0.0644

0.4635

Au2

0.1025

0.1588

0.0499

0.3113

Sw1

0.0978

0.1601

0.0474

0.3053

Sw2

0.1106

0.1820

0.0592

0.3518

8.4 Cognitive and Syntactical Complexities

201

This data reveals that H S is also potentially a good indicator of productivity. In addition, while H C corresponds with the link index of each protocol, H S corresponds to the result of CMs. That is, both complexities can be used to explore “productivity”, but the latter (H S ) is more related to the support for creativity, because quality outcomes and creativity rely on ideas in CMs (Goldschmidt and Tatsa 2005). Thus, H S might be more useful for comparing individual creative behaviours in a set of design protocols.

Fig. 8.2 Decile growths of four designers’ linkographs

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8 Design Thinking Across Borders Au1 0.2 0.18

Sw1

Sw2 0.30

Forelink Entropy per total move

0.16 0.2

Entropy per total move

Au2

0.1 0.14 0.1 0.12 0.10 0.1 0.08 0.1 0.1 0.06 0.04

Backlink

0.25 0.20 0.15 0.10 0.05

0.02 0.00

0.00 1

2

3

4

5

6

7

8

9

10

1

2

3

4

Decile time 0.10

0.45 0.5

Entropy per total move

Entropy per total move

0.5 0.50

Horizontalink

0.09

5

6

7

8

9

10

7

8

9

10

Decile time

0.08 0.07 0.06 0.05 0.04 0.03 0.02

CumulaƟve

0.4 0.40 0.35 0.4 0.30 0.3

0.3 0.25 0.2 0.20 0.2 0.15 0.10 0.1

0.1 0.05

0.01

0.00 0.0

0.00 1

2

3

4

5

6

7

8

9

10

Decile time

1

2

3

4

5

6

Decile time

Fig. 8.3 Changes of entropy per total moves

Decile growth plots of the linkograph for each protocol (Fig. 8.2) can be used to describe the changes of entropy per total moves over time (Fig. 8.3), which shows individual differences in the set of protocols and their unique patterns. For example, in Fig. 8.3 Au1’s data dominates the results for forelink, backlink and horizonlink entropies, and Sw2’s results are second. This is reflected in the final cumulative value per move (H S ) column in Table 8.6, but the figures of the forelink and horizonlink entropy capture some additional features of individual cognitive complexity. Au1’s results for forelink entropy increase until the ninth decile time, while Au2’s and Sw2’s entropies show only a small amount of change after the sixth and the seventh deciles, respectively (Fig. 8.3). In the horizonlink entropy results, Au1’s protocol develops a pyramidal peak at the fifth decile, while the others are almost flat after the fourth. The horizonlink entropy result is related to the shape of each linkograph, and in particular to any large “chunks” (the dense pyramidal zones) in the graph. For example, adopting the problem-forwarding generative strategy that develops designs in a step-by-step manner, Au1’s initial idea is divided into two design goals, and the resultant linkographs develop into two large chunks (Fig. 8.2). Thus, Au1’s horizonlink entropy has its highest value at the fifth decile where the linkograph of the first chunk is completed (see Fig. 8.3). In contrast, Sw1’s entropy values at the third or fourth decile are higher than the others, but they are almost flat or even reduce thereafter. This unique pattern may be caused by too many small chucks, meaning a lower level of information clustering in Sw1’s linkograph. While the interpretation of the results of this small sample size is necessarily limited, the

8.4 Cognitive and Syntactical Complexities

203

measurement of entropies over time is useful for revealing features of individual design processes as well as for visualising cognitive complexity in design protocols.

8.4.2 Syntactical Complexity In the fields of psychology, education, communication and linguistics, cognitive research uses verbal and/or written data derived from experiments to rate the relative complexity of participants (Benet-Martínez et al. 2006; O’Keefe and Sypher 1981). To quantitatively calculate the linguistic complexity embodied in transcriptions of design protocols, this chapter uses syntactic complexity. Ortega (2003) defines syntactic complexity as the range of forms uncovered in language production and the degree of sophistication of these forms. Syntactic complexity is often used to describe linguistic maturity or competency and many measures of it have been developed in linguistics, including length of sentence, length of T-unit (thought-unit), length of clause, clauses per T-unit (C/T ) and dependant clauses per clause. A T-unit (or minimal terminable unit) is a unit of “thought”, consisting of one main clause with all subordinate clausal and non-clausal elements embedded in it (Hunt 1970). The number of clauses per T-unit (or per utterance) is accepted as an index of linguistic complexity (Cheung and Kemper 1992). As an example, Table 8.7 presents part of a design protocol transcript that indicates the number of clauses and T-units. Based on these values, the T-unit complexity ratio (C/T ) for this fragment of the transcript can be calculated as 2.33 (7/3). The higher the value of this ratio, the higher the syntactic complexity of the description. Lu (2010) categorises fourteen measures of syntactic complexity into five types: length of production unit, sentence complexity, subordination, coordination and specific structures. Among the complexity measures, T-unit complexity ratio allows for linguistic comparisons of relative complexity in grammatical constructions. This measure is especially useful in the present context because designers’ utterances are so variable in length in the design process, that a comparable value is required to measure differences. Table 8.7 Example of a design protocol showing the number of clauses and T-units Transcript

Clauses T-units

“So the brief is for the design of a high-rise building … its a conceptual design 2 of a high-rise building that will have two main areas, hotel and office. …

1

The first thing I’ll do is I’ll put the site constraints in, which is a maximum floor area of fifty by fifty metres. …

4

1

So I’ll start by putting in a box.”

1

1

Total

7

3

C/T = 2.33

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8 Design Thinking Across Borders

Table 8.8 Syntactic complexity of each protocol Num of segments

Num of clauses (C)

Num of T-units (T )

Syntactic complexity (C/T )

Au1

152.0

382.0 (2.51)

211.0 (1.39)

1.81

Au2

105.0

141.0 (1.34)

112.0 (1.07)

1.26

Sw1

192.0

116.0 (0.60)

73.0 (0.38)

1.59

Sw2

144.0

235.0 (1.63)

136.0 (0.94)

1.73

Mean

148.3

218.5 (1.47)

133.0 (0.90)

1.60

35.7

120.4 (0.80)

58.1 (0.40)

0.20

SD

(…) = value per segment

The linguistic analysis of cross-national protocol data in this chapter commences by examining the syntactic complexity (C/T ) of each protocol (Table 8.8). The syntactic analysis involves 30-min protocols developed from the four design protocols. Au1 produced the most complex protocol syntactically (1.81), while Au2 had the lowest (1.26). In the cognitive coding, Au1 followed a higher proportion of “expert” design processes, resulting in the problem-forwarding generative strategy and a higher level of cognitive complexity. In contrast, Au2 presented a more “novice” approach, often stopping to solve a problem as it emerged. The two Swedish language designers also demonstrated quite different patterns of results. Sw1 produced the smallest number of clause (C) and T-unit (T ) codes, while Sw2 produced slightly higher figures. In addition, there are more trouble-shooting activities in Sw2’s protocols, while Sw1 developed the shortest value in the average time of moves. That is, Sw1 changed design activities relatively quickly but used only a limited volume of “thinking aloud”. This might be a limitation of the concurrent verbalisation technique adopted in the study, but the shorter moves resulted in a lower level of cognitive complexity in the previous section. Conversely, Sw1 produced an average value of the syntactic complexity (C/T ). Collectively, the figures of C/T in Table 8.8 are similar to the levels of H S in Table 8.6. That is, a designer’s syntactic complexity is related to their cognitive complexity, despite being developed from completely different coding schemes.

8.5 Spatial Language As earlier sections in this book and chapter reveal, “spatial language” is central to the development of design cognition, MMs and their shared, team or distributed counterparts. In a transcript, explanations of objects, and their absolute or relative relationships can be used to analyse the depth and intricacy of the spatial language being used. Four types of spatial language—spatial relation, spatial object, spatial

8.5 Spatial Language

205

Table 8.9 Spatial language coding scheme Category

Subclass

Description

Spatial relation

L-relation G-relation D-pronoun

Local relation (e.g. the usage of projective terms (left, right, front, behind, above)) Global relation (e.g. the usage of compass-based terms such as north and south) Demonstrative pronouns (e.g. the use of here, there)

Spatial object

1-object 2-object M-object

Describe one object Describe two objects Describe multiple objects

Spatial direction

Direct Indirect

Express a direction, in which an object is located e.g. the usage of “beside”

Syntactic format

L-st-R st-R-L st-L

Locatum—Spatial term—Relatum Spatial term—Relatum—Locatum Spatial term—Locatum

direction and syntactic format—are captured by the spatial language coding scheme in Table 8.9. The first category, spatial relation, characterises the spatial terms in each protocol used to describe the connection between locatum (a position of something) and relatum(s) (which it is relative too). This category accommodates the axial structure of the reference object and its contact or support with respect to a surface (Munnich et al. 2001). It also includes functional relationships between entities based on spatial descriptions (Coventry and Garrod 2004) and the tendency to use spatial relational terms that are sufficient and determinate enough to qualitatively define the spatial relationship (Tenbrink and Ragni 2012). The spatial relation category also classifies spatial terms into three subclasses: L-relation (local relation), G-relation (global relation) and D-pronouns (demonstrative pronouns). L-relation and G-relation highlight the use of prepositional phrases to describe location, while D-pronouns (“there” and “here”) replace the longer or more detailed descriptions of place or time. The spatial object category in the coding identifies the number of objects involved in each description. The description of an object and its relationship to other objects is an indicator of the linguistic purpose, complexity and expertise. Past linguistic research identifies that the third category, spatial direction, is significant because projective terms (left, right, front, behind, above, below) can have very different meanings in different languages (Tenbrink and Ragni 2012). Spatiotemporal configurations and spatial distinctions are both examples where linguistic differences are exhibited (van der Zee and Slack 2003). The final category, syntactic format, focuses on the way speakers frame their spatial descriptions syntactically. Hörnig et al. (2006) argue that word order and information structure are an important part of the mental reasoning process. Speakers typically adopt syntactic formats that are appropriate for both the information structure chosen and for their descriptions (Tenbrink and Ragni 2012). For Tenbrink and Ragni (2012) a locatum is necessarily described in terms of both a relatum and the

206

8 Design Thinking Across Borders

Table 8.10 Spatial language coding results Spatial relation

Spatial object

Spatial direction

Syntactic format

L-relation

D-pronoun

1-object

2-object

Direct

L-st-R

Au1

27 (0.24)

10 (0.05)

Au2

7 (0.03)

3 (0.03)

3 (0.01)

3 (0.01)

1 (0.00)

24 (0.11)

0

0

1 (0.01)

3 (0.03)

Sw1

7 (0.10)

6 (0.08)

0

0

0 (0.00)

1 (0.01)

Sw2

7 (0.05)

18 (0.13)

0

1 (0.01)

1 (0.01)

16 (0.12)

Mean

12 (0.08)

9.25 (0.07)

1.50 (0.01)

1.41 (0.01)

0.75 (0.01) 11.00 (0.07)

SD

10 (0.03)

6.50 (0.05)

0.75 (0.00)

1.00 (0.01)

0.50 (0.00) 10.92 (0.06)

(…) = normalised value calculated by the frequency per T-unit

particular spatial term that defines the nature of the relationship. For example, the sentence, “a bedroom is to the right of the entrance”, is encoded as “L-sr-R” because it consists of a locatum (bedroom), a spatial term (to the right of) and a relatum (entrance). Such a coding reveals the linguistic structure of a decision or proposal, as well as its general grammatical format. The coding results for spatial language are presented in Table 8.10. In the spatial format category, the designers only used “L-st-R”. Furthermore, no designers used compass-based terms describing global relations (G-relation) and indirect directness. For the spatial object category, the linguistic analysis identifies only a small number of objects that are in spatial relationships. The two native Swedish speakers used higher frequencies of demonstrative pronouns, 6 and 18, respectively, than the two native English speakers. Au1, the more “expert” designer, used spatial relation, spatial object and syntactic format terms and clauses more often than Au2, the more “novice”. While these outcomes generally support the syntactic complexity values of the four protocols, these results may also reflect linguistic or cognitive differences. In order to examine this aspect of concurrent verbalisations and designers’ different think-aloud skills, the Frequency (F) divided by T-unit normalises the data. These normalised values (F/T ) confirm the possibility that the cognitive design process of a designer may be related to the linguistic features of his or her cognitive representations.

8.6 Conclusion This chapter has examined multiple approaches to exploring cultural design thinking in cross-national design protocols. Its purpose was to explore the relationship between language and cognition in design, a relationship which is significant for the formation and communication of MMs, SMMs, TMMs and DMMs.

8.6 Conclusion

207

The first major finding of the chapter is methodological. This chapter demonstrates that a dual-coding system (design cognition and spatial language) enables the capturing of (i) design cognition, (ii) cognitive and syntactical complexities and (iii) spatial language in a design environment. With a small sample size, the study is not able to compare the design thinking processes of the two sets of designers in a generalisable way, but the results confirm that the mixed analytic approach can be effective for exploring diverse aspects of the design process and design cognition. A potentially more important outcome of the chapter is that its analysis suggests that cognitive and linguistic indexes—including link index, the percentage of CM, Hc, Hs and syntactical complexity—are co-related with each other. These indexes are potential indicators of creative, collaborative and cultural aspects of design thinking. In particular, cognitive analysis using Hc may offer a valuable method for understanding similarities and differences in the cognitive characteristics of different groups of designers. Linguistic analysis using the spatial language coding scheme can capture cross-national differences in spatial representation. However, a conceptual design-form generation task would not be appropriate for future research, because it may only contain limited spatial relationship content. That’s why linguistic research often emphasises the importance of spatial descriptive tasks, like “3-term series tasks”. Finally, this chapter demonstrates that the CMs and structural complexity measures developed through linkography can be employed to capture the individual differences of design productivity as well as creativity in a set of protocols. The development of linkography, however, is still a significant challenge and a timeconsuming process. As an alternative, this chapter shows that Hc might be used for measuring both individual and group characteristics in the design process. The next chapter of Part III Culture in this book, continues the exploration of multi-cultural design thinking in terms of the language of design.

References Arnheim, R. 1997. Visual Thinking. Los Angeles: University of California Press. Benet-Martínez, Verónica, Fiona Lee, and Janxin Leu. 2006. Biculturalism and cognitive complexity: expertise in cultural representations. Journal of Cross-Cultural Psychology 37 (4): 386–407. https://doi.org/10.1177/0022022106288476. Boroditsky, Lera. 2001. Does language shape thought?: Mandarin and English speakers’ conceptions of time. Cognitive Psychology 43 (1): 1–22. https://doi.org/10.1006/cogp.2001.0748. Bowerman, M. 1996. Which way to the present? Cross-linguistic differences in thinking about time. In Rethinking Linguistic Relativity, ed. J. Gumperz and S. Levinson, 145–176. Cambridge, MA: Cambridge University Press. Burleson, B.R., and S.E. Caplan. 1998. Cognitive complexity. In Communication and personality: Trait perspectives, eds. J.C. McCroskey, J.A. Daly, M.M. Martin, and M.J. Beatty, 233–286. Cresskill, NJ: Hampton Press. Chen, Chong-Wen. 2018. New product styles and concepts in the bicultural context. The Design Journal 21 (6): 771–787. https://doi.org/10.1080/14606925.2018.1516496.

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Cheung, Hintat, and Susan Kemper. 1992. Competing complexity metrics and adults’ production of complex sentences. Applied Psycholinguistics 13 (01): 53–76. https://doi.org/10.1017/S01427 16400005427. Chomsky, N. 1965. Aspects of the Theory of Syntax. Cambridge, MA: MIT Press. Clark, James M., and Allan Paivio. 1987. A Dual Coding Perspective on Encoding Processes. In Imagery and Related Mnemonic Processes: Theories, Individual Differences, and Applications, eds. Mark A. McDaniel, and Michael Pressley, 5–33. New York, NY: Springer New York. Costa, Ramon, and Durward K Sobek. 2004. How process affects performance: an analysis of student design productivity. In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Salt Lake City, Utah, USA: American Society of Mechanical Engineers. Coventry, K.R., and S.C. Garrod. 2004. Saying, Seeing And Acting: The Psychological Semantics of Spatial Prepositions. Hove and New YorK: Psychology Press. Cox, Taylor H., and Blake Stacy. 1991. Managing cultural diversity: Implications for organizational competitiveness. The Executive 5 (3): 45–56. DeChurch, Leslie A., and Jessica R. Mesmer-Magnus. 2010. The cognitive underpinnings of effective teamwork: A meta-analysis. Journal of Applied Psychology 95 (1): 32–53. https://doi.org/ 10.1037/a0017328. Dong, Andy. 2005. The latent semantic approach to studying design team communication. Design Studies 26 (5): 445–461. Florida, R. 2002. The Rise of the Creative Class: And How It’s Transforming Work, Leisure, Community and Everyday Life. New York: Basic Books. Gleitman, Lila, and Anna Papafragou. 2005. Language and thought. In Cambridge Handbook of Thinking and Reasoning, ed. Keith J. Holyoak and Robert G. Morrison, 633–661. New York: Cambridge University Press. Goldschmidt, Gabriela. 1990. Linkography: Assessing design productivity. In Cyberbetics and System ‘90, World Scientific, ed. R. Trappl, 291–298. Singapore: World Scientific. Goldschmidt, Gabriela. 1995. The designer as a team of one. Design Studies 16 (2): 189–209. https://doi.org/10.1016/0142-694X(94)00009-3. Goldschmidt, Gabriela, and Dan Tatsa. 2005. How good are good ideas? Correlates of design creativity. Design Studies 26 (6): 593–611. https://doi.org/10.1016/j.destud.2005.02.004. Hörnig, Robin, Klaus Oberauer, and Andrea Weidenfeld. 2006. Between reasoning. Quarterly Journal of Experimental Psychology 59 (10): 1805–1825. https://doi.org/10.1080/174702105 00416151. Horwitz, Sujin K., and Irwin B. Horwitz. 2007. The effects of team diversity on team outcomes: A meta-analytic review of team demography. Journal of Management 33 (6): 987–1015. https:// doi.org/10.1177/0149206307308587. Hunt, Kellogg W. 1970. Syntactic maturity in schoolchildren and adults. Monographs of the Society for Research in Child Development 35 (1): iii–67. https://doi.org/10.2307/1165818. Jackson, Susan E. 1991. Team composition in organizational settings: Issues in managing an increasingly diverse work force. In Group Process and Productivity, 138–173. Thousand Oaks, CA, US: Sage Publications, Inc. James, Harold. 2009. The Creation and Destruction of Value: The Globalization Cycle. Cambridge, MA: Harvard University Press. Jara, Cynthia. 2014. Verbal literacy in the design process: Enthusiasm and reservation. In ARCC/EAAE 2014 International Conference on Architectural Research Conference. Honolulu, USA: University of Hawai‘i at Manoa. Kan, Jeff W.T., and John S. Gero. 2008. Acquiring information from linkography in protocol studies of designing. Design Studies 29 (4): 315–337. https://doi.org/10.1016/j.destud.2008.03.001. Lee, Ju Hyun, Michael J. Ostwald, and Ning Gu. 2016. The language of design: Spatial cognition and spatial language in parametric design. International Journal of Architectural Computing 14 (3): 277–288. https://doi.org/10.1177/1478077116663350.

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Chapter 9

The Language of Design Thinking

Abstract This chapter investigates the relationship between language and cognition in design. This is a critical topic for supporting effective multi-national design teams, and it also illuminates assumptions about design’s capacity to function as a type of universal language. The chapter reports on the results of a design experiment where 23 participants from three culturally and linguistically different groups completed the same design task. The protocol analysis reveals the characteristics of “design thinking” and “design language” across the groups. The comparative analysis confirms that cognitive allocation is related to the production of information categories and spatial linguistic principles. It also indicates that there are observable differences in design cognition, design information and spatial language between Australian and Asian designers. This research develops fundamental knowledge about how different cultural and linguistic groups understand, communicate and undertake design.

9.1 Introduction Throughout history, a major driver of globalisation was trade in resources, from one area with a surfeit (of materials, expertise or capacity) to another with a relative lack. In effect, trade thrives where there is an inequity of access to resources. As a result of this, the value proposition of globalisation is predicated on the existence of diversity at a local level. Indeed, contrary to concerns about potential mass homogenisation of consumer habits or values, a common side effect of globalisation is a revival of interest in local produce, practices and experiences (Luna and Forquer Gupta 2001). Even the obvious signs of a global economy, including the proliferation of brand names or restaurant chains across international borders, often mask the fact that the products or menus they offer vary from place to place to accommodate local conditions. Values, social structures and behavioural characteristics differ across nations and cultures, and globalisation does not necessarily erase these. People from different countries have different value orientations and the capacity to merge global and local conditions is arguably a necessity for survival in the modern world (Man 2014).

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9 The Language of Design Thinking

In terms of cultural behaviours in a globalised world, it has been proposed that western and eastern countries have much greater potential to work collaboratively than they are currently (Molina et al. 2005). This is especially true in the case of multinational design teams, where a mixture of local and global knowledge is regularly linked to enhanced productivity and creativity. There is, however, on-going debate about the relationship between language and cognition in general, and specifically in design teams. Although linguistic determinism (the view that language shapes thought) has often been criticised, research into the use of language to describe space and time has grown in significance, developing important insights about communication (Boroditsky et al. 2011; Corballis 2018; Tenbrink 2011). Nonetheless, thus far relatively little is known about the relationship between design language and design thinking. Linguistic studies on spatial reasoning suggest that language, along with its intrinsic cultural and value structures, has a clear impact on how knowledge about space and form is developed, assessed and transmitted (Herskovits 1986; Tenbrink and Ragni 2012; van der Zee and Slack 2003). Despite such findings, equivalent information about design thinking and design language remains scarce. Our own research on these topics (Lee et al. 2016), for example, draws connections between spatial language use and productive and entropic cognitive processes. In order to expand our knowledge base about this topic, this chapter compares cognitive design activities across three culturally and linguistically diverse groups. Its goal is to develop an understanding of the way the characteristics of visual and verbal representations correlate to design cognition. This research necessarily considers the impact of language on design communication by way of information categories (Suwa and Tversky 1997) and spatio-linguistic principles (Tenbrink and Ragni 2012). This chapter uses protocol analysis to investigate recordings of a design process and then encode and classify various activities using a specific system (Chai and Xiao 2012; Cross et al. 1996; Gero and Neill 1998; Lee et al. 2015). This system is a triple-coding scheme of design cognition, design information and spatial language. It captures design thinking activities (design cognition) and design language properties (“design information” and “spatial language”) in the design process. Twentythree protocols are examined in this chapter, selected from each of the three different cultural and linguistic groups. This volume of participants and protocols is statistically significant for this method, providing results that are reliable and generalisable. The chapter commences by presenting a conceptual model for analysing the relationship between design thinking and design language. After describing the model, the chapter briefly outlines its methodological procedures. Thereafter the chapter reports the results of the protocol analysis of the design experiment, including a series of descriptive analyses, correlations and ANOVA results. It concludes with a discussion about the implications of the results.

9.2 Design Thinking and Language Model

213

9.2 Design Thinking and Language Model This chapter uses data derived from an empirical study to explore the relationship between design thinking and design language during design communication. In order to do this, a conceptual model is developed to study the language of design across two parallel domains, thereby providing a new way of investigating how they are related (Fig. 9.1). The core of the method is a triple-perspective-coding scheme for protocol analysis. The first perspective analyses design activities to measure the cognitive allocations of designers. The next two perspectives examine the words used by designers while they are engaged in the design process. The second considers instances of “design information” which are central to the way design is represented (design representation). The third addresses the use of “spatial language” (spatial representation). Consequently, the design thinking and language model (Fig. 9.1) is suggested from the coding categories that were developed from past research (Suwa et al. 1998; Suwa and Tversky 1997; Tenbrink and Ragni 2012) and are described previously in this book (Chaps. 2, 5 and 8, respectively). The first component of the model, design cognition, captures five categories: representation, perception, function, evaluation and goal setting. These categories are selectively adapted from Suwa et al.’s coding scheme (1998). Representation activities consist of three subclasses: R-drawing, R-writing and R-label. R-drawing is used to capture designer’s drawing activities, while R-writing and R-label encapsulate writing texts and making labels, respectively. The perception category comprises focussing on, or paying close attention to, something. It has three subclasses, Pdrawing, P-brief , and P-writing, which relate to a designer paying attention to drawings, a design brief or a given site (functional or performative needs), and its written content, respectively. The function, evaluation and goal-setting categories also have subclasses to code each cognitive allocation in the design process. Design cogniƟon   

RepresentaƟon PercepƟon FuncƟon

 

EvaluaƟon Goal seƫng

Design Thinking

CogniƟve acƟviƟes

The Language of Design Design informaƟon  

Property FuncƟonality

SpaƟal language   

Fig. 9.1 A design thinking and language model

Object LocalisaƟon Directness

Design Language CogniƟve representaƟons

214

9 The Language of Design Thinking

Design language is explored in the second component of the model, which considers both “design information” and “spatial language” in visual and verbal (cognitive) representations. The “design information” code highlights two categories, property and functionality, which are based on Suwa and Tversky’s coding scheme (1997) (Table 5.4). The property category consists of five subclasses: P-space, Pobject, P-shape, P-size and P-enclosure. The first four subclasses are adopted from the emergent properties in Suwa and Tversky’s development, while the last captures the depiction of an enclosure (e.g. walls, doors, windows, etc.). The functionality category also consists of five subclasses, F-role, F-features, F-view, F-light and F-circulation, which relate to designers’ consideration of practical roles, abstract features and their interactions, views, lights and circulation, respectively. The last component of the model, “spatial language” consists of three categories— object, localisation and directness—which address spatial representations characterised by the tendency to employ spatio-relational terms. The spatial language coding scheme (see Table 8.9) draws on the work of Tenbrink and Ragni (2012). The object category identifies the number of objects in each segment. Since the objects refer to representations of spatial relations by way of position, the first category captures the fundamental property of spatial representation. The localisation category has three subclasses: L-projective, L-absolute and L-demonstrative. L-projective relates to projective expressions often used in spatial-reasoning tasks, while L-absolute identifies the absolute reference-reasoning system. Directions and spatial distinctions can be used to identify the different linguistic expressions used when referring to spatio-temporal configurations (van der Zee and Slack 2003). Thus, the directness coding category consists of two subclasses, D-direct and D-indirect, to investigate both direct and indirect spatial relationships.

9.3 Methodological Procedure 9.3.1 Design Experiment The 23 participants in the design experiment were masters-level architecture students studying in Australia, but drawn from multiple countries of origin. The participants were divided into three culturally and linguistically different groups: Group A, native English speakers; Group B from Asian countries or regions for which English is an official or common language; Group C, from Asian countries or regions for which English is not an official language. All participants had proficient language skills to conduct general verbal and written communication in English. The students were recruited from the University of Newcastle, University of South Australia, Deakin University and RMIT University. The experimental procedures and encoding processes are identical to the ones for Study I in Chap. 2. All of the participants were provided with a design brief and a 45-min timeframe and asked to produce a number of design sketches, diagrams and drawings. They

9.3 Methodological Procedure

215

were also asked to verbalise their thoughts in English as they carried out design activities. The think-aloud verbalisation included a request to describe what they were looking at, thinking, doing and feeling. Participants were allowed to cease the experiment at any time. Each design experiment was video recorded for analysis. The design brief was for a house on a vacant suburban block, to accommodate the needs of a mixed-generational family (see Box 2.1). Designers were asked to deliver sketches of design stages/components of the mixed-generational housing design, including initial conceptual design, site layout(s) of the mixed-generational house, plan(s) and elevation(s) and/or section(s). Design drawings produced during the experiment were analysed in conjunction with the transcript to reveal the design thinking processes.

9.3.2 Analysis Procedure For the initial analysis, a descriptive review of the coding results was conducted to identify and describe the basic features of the protocol data. Then bivariate (Pearson) correlations were run using IBM SPSS Statistics (vers. 25) to provide a structure for defining the relationships between the categories of the three schemes. Finally, oneway analysis of variance (ANOVA) with Scheffé post hoc tests were conducted to examine if there are any statistically significant differences in terms of design cognition, design information and spatial language among the three cultural groups. The Scheffe tests allow comprehensive comparisons involving contrasts of more than two means at a time (Stevens 1999). Before the ANOVA, normality tests are used to determine if each variable in a set of protocol data has a normalised distribution. In summary, the Pearson correlations are used to investigate the relationships between the three sets of results encoded with the triple-coding systems for design cognition, design information and spatial language. The ANOVA is used to compare the cognitive and linguistic characteristics among the three cultural groups (as one control and two experiment groups).

9.4 Results 9.4.1 Coding Results The 23 design protocols collected for analysis consist of eight from Group A (control group), eight from Group B (experiment group) and seven from Group C (experiment group). The complete group comprises 12 females and 11 males. Table 9.1 shows the properties of the design protocols. The average time per segment is 17.73 s, while the average time duration is 42 min 19 s to complete the conceptual design task. Because some participants took slightly longer or shorter times to complete the task,

216

9 The Language of Design Thinking

Table 9.1 Design protocols analysed across the three groups Group

Designer code

Gender

Time duration

Num. of Segments

Time per segment (s)

A

A1

Female

44 min 5 s

216

12.24

A2

Male

42 min 56 s

134

19.22

A11

Female

44 min 45 s

170

15.80

A15

Male

41 min 21 s

120

20.68

A23

Female

44 min 33 s

173

15.45

A24

Male

38 min 18 s

121

18.99

A28

Female

43 min 4 s

143

18.07

B

C

A29

Male

44 min 16 s

145

18.32

B3

Male

43 min 20 s

182

14.29

B5

Male

44 min 41 s

140

19.15

B6

Female

46 min 27 s

153

18.22

B13

Female

46 min 52 s

174

16.16

B20

Female

44 min 2 s

135

19.57

B21

Female

46 min 8 s

149

18.58

B26

Male

37 min 35 s

128

17.61

B38

Female

41 min 1 s

123

20.01

C8

Female

46 min 20 s

172

16.16

C19

Male

44 min 4 s

151

17.51

C31

Female

32 min 5 s

120

16.04

C36

Male

43 min 0 s

143

18.04

C37

Male

42 min 20 s

131

19.39

C39

Male

31 min 34 s

93

20.36

C40

Female

40 min 33 s

135

18.03

Mean

42 min 19 s

145.70

17.73

SD

4 min 6 s

26.41

2.06

the results are normalised for comparison. The coding results of “design cognition” are then weighted by time duration of each code, while the coding results of “design information” and “spatial language” use the average frequencies per five minutes of each protocol.

9.4.1.1

Design Cognition

Table 9.2 presents the percentage of the frequency weighted by time duration of each code of Group A’s protocols. The table also presents the content complexity (H C ) value of each protocol. On average, representation activities account for 50.4% (R-drawing: 43.6%, R-writing: 3.6%, R-label: 3.2%); perception activities account

7.5

4.3

3.3

R-writing

R-label

P-drawing

0.0

0.0

2.7

3.2

1.0

E-writing

E-label

G-initial

G-sub

G-repeat

HC

2.8322

100.0

6.9

Sum

3.2

E-drawing

27.4

0.5

F-reaction

F-interaction

P-writing

10.0

30.0

R-drawing

P-brief

A1

Subclass

2.1269

100.0

2.1

0.8

2.4

0.0

0.0

3.3

0.0

5.7

0.0

6.8

14.2

4.0

2.3

58.5

A2

2.5280

100.0

0.0

0.2

2.7

0.0

0.7

11.0

1.2

6.8

0.0

3.0

14.4

13.2

3.4

43.3

A11

2.4189

100.0

1.4

1.5

0.6

0.0

0.0

8.4

0.0

3.8

1.7

10.8

12.5

2.9

6.2

50.1

A15

2.5513

100.0

1.7

1.3

2.1

0.0

0.0

9.1

1.4

9.3

0.2

14.9

15.8

0.2

2.7

41.3

A23

2.2630

100.0

0.4

0.4

2.4

0.0

0.0

12.7

0.0

11.1

0.0

11.5

17.6

0.0

0.2

43.6

A24

2.5770

100.0

0.9

1.5

0.2

0.0

0.0

3.0

0.0

16.9

7.6

10.2

12.9

0.4

6.7

39.8

A28

2.2866

100.0

0.9

0.6

2.1

0.0

0.0

8.3

0.0

9.0

0.0

15.3

21.3

0.5

0.0

42.0

A29

2.4480

100.0

1.1

1.2

1.9

0.0

0.1

7.8

0.7

11.3

1.3

10.3

14.0

3.2

3.6

43.6

Mean

0.2222

–

0.68

0.95

0.96

0.00

0.25

3.39

1.16

7.63

2.63

4.03

5.18

4.41

2.89

8.21

SD

Table 9.2 The coding results (the percentage of time duration) of Group A’s protocols (R: Representation, P: Perception, F: Function, E: Evaluation, G: Goal setting)

9.4 Results 217

218

9 The Language of Design Thinking

for 25.6% (P-drawing: 14.0%, P-brief : 10.3%, P-writing: 1.3%); function activities account for 12.0% (F-interaction: 11.3%, F-reaction: 0.7%); evaluation activities account for 7.9% (E-drawing: 7.8%, E-writing: 0.1%); and goal-setting activities account for 4.1% (G-initial: 1.9%, G-sub: 1.2%, G-repeat: 1.1%). The average H C value is 2.4480. The coding results for each Group also identify the different design strategies used by participants. For example, A1 documented formal and configurational ideas in writing at an early stage of her design session and often revisited the brief during the session to set up new goals. She used graphic devices (arrow symbols) to capture interactions between people and design elements. In this way, her protocol produces the highest percentages of R-writing (7.5%), F-interaction (27.4%) and F-reaction (3.2%) in Group A. Conversely, A1 also generated the lowest percentage of Rdrawing (30.0%) and her design session developed the highset H C value (2.8322) in Group A. In contrast, A2, despite being a productive designer, offered only limited verbalisations while drawing. He repeatedly examined massing options and then focused on the production of a detailed plan during the session. Thus, his protocol produces the highest percentage of R-drawing (58.5%) and lower percentages of F-interaction (5.7%) and E-drawing (3.3%). Collectively his protocol results in the lowest H C value (2.4480) in Group A. These are just a few examples of distinct cognitive design strategies in the results. As identified in Chap. 2, three strategies are common in sketch-based design cognitive activities. Evidence of the first of these, the textual-goal forwarding strategy, is seen in three protocols in Group A (A1, A2 and A23). This strategy involves developing a clear, written summary of goals at the beginning of a design session, and then continuing to refer to these throughout for assessing the designs against the textualgoals. The second strategy, graphical-goal forwarding is found in the work of A28 and A29. They initially developed a set of conceptual sketches closely relating to their goal-setting activities and then subsequently generated designs based on these graphical-goals. The third strategy, drawing reflection, is seen in the work of A11 and A24 who each developed their ideas through graphic exploration and subsequent contemplation of these sketches. Both designers drew over existing drawings after attending to (P-drawing) or evaluating (E-drawing) them. These approaches explain the relatively larger proportions of E-drawing in the two protocols (11.0%, 12.7%, respectively). There is also evidence in the work of A23 of drawing reflection behaviours, where she combines both textual-goal forwarding and drawing reflection. Table 9.3 presents the results of Group B’s protocols and associated H C values. On average, representation activities account for 56.2%; perception for 23.6%; function for 10.74%; evaluation for 4.3%; and goal setting for 5.1% of the overall activities. Compared to Group A’s results, Group B tended to produce more representation and goal-setting activities, but less perception, function and evaluation activities. Although design activities in the problem space, like goal setting, have a positive influence on cognitive complexity (see Chaps. 4 and 8), the average H C value of Group B (2.3974) is lower than Group A. This may indicate that Group B’s design processes are less complex or productive than Group A’s.

0.0

0.0

1.3

6.4

0.0

E-writing

E-label

G-initial

G-sub

G-repeat

HC

2.6885

100.0

3.4

E-drawing

Sum

0.9

F-reaction

10.6

2.4

F-interaction

3.4

P-writing

12.1

P-drawing

P-brief

6.1

10.9

R-label

42.5

R-drawing

R-writing

B3

Subclass

2.4577

100.0

0.0

1.2

3.4

0.0

0.0

4.5

0.5

10.6

0.0

8.3

16.4

4.7

4.0

46.5

B5

1.9410

100.0

0.0

0.0

0.0

0.0

0.0

5.7

0.0

10.8

0.0

2.2

14.2

3.6

4.9

58.6

B6

2.4625

100.0

0.4

3.8

3.3

0.0

0.0

8.5

1.1

3.9

0.0

4.9

16.9

2.8

5.7

48.6

B13

2.0017

100.0

1.6

0.8

1.2

0.0

0.0

3.2

1.2

3.2

0.0

3.9

17.0

9.0

0.0

58.9

B20

2.6620

100.0

0.8

1.5

5.7

0.0

0.0

3.4

0.7

10.7

0.5

8.6

17.0

6.9

2.9

41.0

B21

2.5831

100.0

0.9

0.0

4.1

0.0

0.0

5.6

0.0

9.4

0.0

16.7

20.1

5.4

2.3

35.5

B26

2.3823

100.0

1.6

0.7

2.3

0.0

0.0

0.2

1.5

20.8

0.6

1.7

22.0

7.2

1.8

39.7

B38

2.3974

100.0

0.7

1.8

2.7

0.0

0.0

4.3

0.7

10.0

0.4

6.2

17.0

6.3

3.5

46.4

Mean

0.2834

–

0.68

2.21

1.83

0.00

0.00

2.42

0.55

5.38

0.83

4.95

3.09

2.74

2.10

8.60

SD

Table 9.3 The coding results (the percentage of time duration) of Group B’s protocols (R: Representation, P: Perception, F: Function, E: Evaluation, G: Goal setting)

9.4 Results 219

220

9 The Language of Design Thinking

Considering individual cognitive design strategies, B3’s and B5’s protocols demonstrate a textual-goal forwarding strategy, while B38’s uses a graphical-goalforwarding strategy. Two of the members in Group B (B13 and B21) employ both textual-goal and graphical-goal-forwarding strategies in their design sessions. The H C values of these five designers confirm that their design processes are relatively higher in complexity than the others in this group. In contrast, designers B6 and B20 develop their designs by responding to the given design brief or site, without developing a goal-setting activity in advance. This results in a relatively larger proportion of R-drawing (58.6% and 58.9%) and smaller amounts of goal-setting activities among Group B. Thus, data for both B6 and B20 shapes the lowest H C values in Group B, 1.9410 and 2.0017. In contrast, B26’s data shows a lack of a clear design strategy. Despite conducting a detailed site analysis, drawing site sections and formulating a written list of requirements he only produced a minimal plan layout. Thus, his protocol has the lowest amount of R-drawing (35.5%), the highest amount of P-brief (16.7%) and the second highest amount of P-drawing (20.1%) in Group B. One of the most significant observations about Group B is that several designers tried to remember their experiences growing up in native countries or regions, such as Hong Kong and Malaysia. While doing this they would often physically close their eyes or look upward, exhibiting the “memory recall or retrieval” behaviour. This behaviour is not apparent in Group A’s protocols and may be worthy of further investigation. The coding results for Group C’s protocols and H C values are in Table 9.4. On average, representation activities account for 52.5% (perception 33.9%; function for 8.0%; evaluation for 3.4%; and goal setting for 2.1%) of the overall time spent on activities. The results indicate that Group C’s designers tended to undertake more perception activities than the other groups, with only limited evaluation and goal setting. The average H C value of Group C (2.2311) is lower than that of the other two groups. Thus, based on these measures, Group C’s design processes are the least complex and productive of the three. Interestingly, when solving the design task, most of the designers in this group also exhibited overt memory and recall activities, evoking their own experience of multi-generational living in their native countries or regions. Group C also spent a significant amount of time calculating dimensions, sizes or volumes. That is, they tended to employ what might be called an “engineering” approach to design thinking. Because of this pattern across Group C, it could be speculated that their educational or cultural experiences (or potentially both) have led to this approach. Looking more closely at the cognitive patterns in Group C, C8 and C40 stand out from the rest because their protocols emphasise graphical-goal-setting activities at the start of their sessions. In contrast, the other designers in the Group C do not conduct explicit goal-setting activities. In addition to the graphical-goal-forwarding strategy, C8 and C40 also adopt a drawing reflection strategy and produce relatively higher proportions of P-drawing (29.1%, 31.3%, respectively) and relatively lower proportions of R-drawing (30.9%, 30.5%, respectively). Both designers’ protocols develop relatively high H C values in their group, further differentiating them. Conversely, C39 spent the majority of the session developing a detailed plan layout with only limited verbalisation. He then completed the session by evaluating and extending

0.0

8.5

0.0

0.0

0.7

0.0

0.0

F-reaction

E-drawing

E-writing

E-label

G-initial

G-sub

G-repeat

HC

2.4297

100.0

5.1

F-interaction

Sum

0.0

P-writing

16.6

6.7

R-label

P-brief

2.3

R-writing

29.1

30.9

R-drawing

P-drawing

C8

Subclass

1.864

100.0

0.0

0.0

0.0

0.0

0.0

2.2

0.6

3.1

0.0

3.8

21.3

6.1

4.2

58.8

C19

2.1035

100.0

0.0

1.9

2.6

0.0

0.0

2.5

0.0

8.6

0.0

5.7

20.7

2.4

1.8

53.8

C31

2.3454

100.0

0.6

0.4

1.5

0.0

0.0

0.6

0.8

7.3

0.0

10.5

26.8

12.3

0.7

38.4

C36

2.7229

100.0

0.0

2.3

0.7

0.0

0.0

5.3

0.6

11.2

0.0

17.6

15.6

5.3

9.1

32.2

C37

1.7253

100.0

1.5

0.0

2.1

0.0

0.0

1.4

0.0

3.0

0.0

14.0

15.8

0.0

0.4

61.7

C39

2.4267

100.0

0.0

0.0

0.6

0.0

0.0

3.6

0.0

15.4

0.3

8.2

31.3

6.8

3.3

30.5

C40

2.2311

100.0

0.3

0.7

1.2

0.0

0.0

3.4

0.3

7.7

0.0

10.9

22.9

5.7

3.1

43.8

Mean

0.3511

–

0.53

1.00

0.93

0.00

0.00

2.70

0.36

4.53

0.09

5.35

6.27

3.86

2.96

13.87

SD

Table 9.4 The coding results (the percentage of time duration) of Group C’s protocols (R: Representation, P: Perception, F: Function, E: Evaluation, G: Goal setting)

9.4 Results 221

222

9 The Language of Design Thinking

Coding coverage (%)

the design with a perspective and two sections. His design approach results in the highest percentage of R-drawing (61.7%) and the relatively lower percentages of Finteraction (3.0%) and E-drawing (1.4%) across Group C. Collectively, C39’s design session results in the lowest H C value (1.7253) in the experiments. Although each designer in Group C uses slightly different cognitive strategies (see Table 9.4) the data uncovers similar distributions and weights across the codes, reflecting some common tendencies. Most of Group C’s designers, for example, only developed “micro” goals, that do not direct the overall design in an integrated way. Such micro-goals tend to arise from specific requirements of the design brief, rather than holistic ones. By prioritising micro-goals, the primary drivers for the design are limited to local or isolated configurational solutions to practical issues (like separation, mobility, privacy and accessibility). Thus, these designers continuously developed secondary solutions by reflecting on the capacity of their proposals (in existing drawings) to meet specific aspects of the brief. This leads to multiple Pdrawing and P-brief activities as a precursor to finalising the design deliverables.

Group A

60.0

RepresentaƟon PercepƟon

40.0

FuncƟon EvaluaƟon

20.0

Goal seƫng 0.0

A1

A2

A11

A15

A23

A24

A28

A29

Average

Coding coverage (%)

Designer

Group B

60.0

RepresentaƟon 40.0

PercepƟon FuncƟon

20.0

EvaluaƟon Goal seƫng

0.0

B3

B5

B6

B13

B20

B21

B26

B38

Average

Coding coverage (%)

Designer

Group C

60.0

RepresentaƟon 40.0

PercepƟon FuncƟon

20.0

EvaluaƟon Goal seƫng

0.0

C8

C19

C31

C36

C37

C39

C40

Average

Designer

Fig. 9.2 The percentage of coding results of each cognitive category across the three groups

9.4 Results

223

Figure 9.2 presents graphs of the three groups’ coding results for “design cognition”. These graphs display the proportion (%) of the frequency, weighted by time duration, of each category. The most dominant category is representation, the next is perception and the last is function. While the graphs for each group suggest a similar order of cognitive allocation, they also demonstrate different patterns in the data. For example, the graphs show that the rise in representation activities often corresponds to a fall in perception activities. Group A’s designers tended to produce more function and evaluation activities than the other groups, while Group B’s designers tended to exhibit more representation and goal-setting activities than the other groups. The graphs also indicate that Group C’s designers tended to produce more perception activities and less evaluation and goal-setting activities, than the other two groups.

9.4.1.2

Design Information

Figure 9.3 graphs the three groups’ coding results for “design information”, the first component of “design language”. The graphs describe the average frequencies of each category for each five-minute interval. Across the three groups, most designers produce more terms and clauses associated with the property category than functionality. Although the graphs show individual differences, on average Group A’s designers produce more “design information” (average 20.1 for five minutes) than the other two groups. In contrast, Group C’s designers produce the least (average 9.3). Although Group C’s designers may be more limited in their capacity for verbal representation in English, Group B’s designers display a similarly low level of verbal representation. This might mean that English proficiency isn’t the only factor shaping the result. It is also possible that different design styles and strategies have an impact on cognitive allocations. For example, the two designers (A1 and A28) who are the most productive in terms of “design information”, adopted clear design strategies: textual-goal forwarding and graphical-goal forwarding, respectively. These classical or well-organised design strategies may be related to the advantageous use of design language. In contrast, designer C39 has the lowest frequency in the coding results of “design information”. He also developed the highest percentage of representation category (69.1%) in Fig. 9.2. This could be the reason for his low values for function and evaluation categories in “design cognition”, which also result in the lowest frequencies in both “design information” (Fig. 9.3) and “spatial language” (Fig. 9.4).

9.4.1.3

Spatial Language

Tables 9.5, 9.6 and 9.7 report the three groups’ coding results for “spatial language”, the second component of “design language”. These results describe the average frequencies of each subclass for each five-minute interval. On average, the designers tend to describe either one object or multiple objects, but only rarely two objects. This result is somewhat contrary to expectations, as the

224

9 The Language of Design Thinking FuncƟonality

Property

Group A

Frequency

30.0

20.0

10.0

0.0

A1

A2

A11

A15

A23 Designer

A24

A28

A29

Average

Group B

FuncƟonality

Property

Frequency

30.0

20.0

10.0

0.0

B3

B5

Property

B6

B13

B20 Designer

B21

B26

B38

Average

Group C

FuncƟonality

Frequency

30.0

20.0

10.0

0.0

C8

C19

C31

C36

C37 Designer

C39

C40

Average

Fig. 9.3 The frequencies of each category for five minutes (design information) across the three groups

A

B

C

Goal Seƫng

Percentage

Percentage

EvaluaƟon

Percentage

PercepƟon

A

B

C

A

B

C

Fig. 9.4 Box and whisker plots of the coding results for perception, evaluation and goal setting across Groups A, B and C

9.4 Results

225

Table 9.5 The coding results of Group A’s protocols for spatial language (average frequencies per 5 min) Category

Subclass

Object

O-one

2.2

0.0

0.8 0.1

0.3

0.3

2.0

0.1

0.7

O-two

0.6

0.0

0.1 0.0

0.0

0.3

0.0

0.0

0.1

0.20

O-multiple

1.5

0.0

0.7 0.1

0.2

0.1

1.2

0.2

0.5

0.55

3.3

3.3

4.2 2.2

6.8

2.4

4.4

3.6

3.8

1.47

2.6

0.6

0.4 0.0

2.4

1.3

3.8

0.9

1.5

1.31

6.4 12.4 10.7

6.72 2.26

Localisation L-projective L-absolute Directness

A1

A2

A11 A15 A23 A24 A28 A29 Mean SD

L-demonstrative 18.2

3.5 19.1 0.6

D-direct

3.9

2.1

2.7 1.2

7.3

2.1

6.7

2.6

3.6

D-indirect

2.0

1.7

0.3 1.0

1.7

1.3

1.5

1.9

1.4

0.56

33.2 18.9 26.0 21.8 22.4

10.27

Sum

34.2 11.2 28.4 5.2

14.5 11.2

0.87

Table 9.6 The coding results of Group B’s protocols for spatial language (average frequencies per 5 min) Category

Subclass

B3

B5 B6

Object

O-one

2.0

0.3

0.0 0.0

0.0

0.1

0.6

0.5

0.70

O-two

0.0

0.0

0.0

0.0 0.1

0.0

0.1

0.0

0.0

0.06

O-multiple

1.5

0.0

0.0

0.0 0.0

0.2

0.8

0.0

0.3

0.55

2.1

2.0

1.3

1.3 1.1

1.1

2.7

3.2

1.8

0.78

Localisation L-projective L-absolute Directness

B13 B20 B21 B26 B38 Mean SD

1.1

0.1

1.1

0.5

0.0 0.0

1.7

1.3

1.2

0.8

0.68

L-demonstrative 8.0

2.5

6.1 16.9 5.8

3.5

6.4

4.3

6.7

4.48

D-direct

0.9

2.0

1.3

0.6 0.8

2.4

1.6

3.2

1.6

0.88

D-indirect

1.3

1.1

0.5

0.6 0.3

0.4

2.4

1.2

1.0

0.67

15.8 9.1 10.9 19.4 8.2

9.3

15.4 13.7 12.7

3.99

Sum

Table 9.7 The coding results of Group C’s protocols for spatial language (average frequencies per 5 min) Category

Subclass

C19

C31

C36

C37

C39

C40

Mean

SD

Object

O-one

0.0

0.1

0.6

0.1

0.6

0.0

0.2

0.2

0.26

O-two

0.1

0.0

0.3

0.0

0.0

0.0

0.1

0.1

0.12

Localisation

O-multiple

0.2

0.2

0.8

0.5

0.4

0.0

0.1

0.3

0.26

L-projective

1.7

0.1

1.6

1.9

1.7

1.4

1.4

1.4

0.59

L-absolute

0.5

0.1

0.6

0.0

0.7

0.0

0.2

0.3

0.30

13.6

5.0

12.5

1.4

3.0

0.2

1.5

5.3

5.51

1.4

0.2

1.9

0.2

1.4

0.5

0.5

0.9

0.67

0.9

0.0

0.3

1.6

0.9

1.0

1.1

0.8

0.53

18.5

5.8

18.5

5.7

8.6

3.0

5.2

9.3

L-demonstrative Directness

D-direct D-indirect

Sum

C8

226

9 The Language of Design Thinking

design task deals with space, enclosure and function, which might be assumed to have singular, binary and multiple components. It is, however, a complex design task, and not a relational-reasoning task that specifically considers pairs (Tenbrink and Ragni 2012). In the data the most dominant code is L-demonstrative (using a demonstrative pronoun system), the second is L-projective (a projective expression using projective terms) and the third is D-direct (expressing a direct relationship between objects). While designers only rarely described two objects, the production of subclasses in the object category is limited across the three groups of designers. Consequently, the most dominant category is localisation, the second is directness and the third is object. In summary, Group A’s designers produced more “spatial language” (average 22.4 for five minutes) than the other two groups. Group A’s designers naturally used more projective terms and demonstrative pronouns and produce more direct relationships than the other groups. Table 9.5 shows the coding results of Group A’s protocols for “spatial language”, based on the average frequencies for five minutes. Two designers, A1 and A23, produced the most frequent spatial-language activities, 34.2 and 33.2, respectively. A23 only produced an average number of codes in “design information”, but she produced the highest number of terms and clauses associated with localisation (23.7) and directness (9.0) in the spatial-language coding. These results may reflect her practice of evaluating drawings and generating ideas by reflecting on her drawings. In contrast, A15, who produced the lowest frequency overall in “design information” coding, also developed the lowest frequency in the production of “spatial language” (in total, 5.2). The coding results of Group B’s protocols for “spatial language” are in Table 9.6. B13 produced the highest frequency of terms and clauses associated with “spatial language” (19.4), but this result may be traced to the frequent use of L-demonstrative (demonstrative pronoun system). Other than this specific code, her spatial language result is very limited. The two designers (B3 and B26), who produced the highest numbers of activities in “design information”, also generated relatively high numbers of terms and clauses in the spatial-language coding, 15.8 and 15.4, respectively. In contrast, B20 who had the least frequency overall in “design information” coding also had the least frequency in the use of “spatial language” (8.2). Table 9.7 shows the coding results of Group C’s protocols for “spatial language” based on the average frequencies for five-minute intervals. C8’s data contain the highest frequency overall for the use of terms and clauses associated with spatial language, although she develops only an average number of codes in the “design information” results (Fig. 9.3). Her design style focussed on graphical-goal settings at a micro-level for more than 17 min in the experiment. She also repeatedly reviewed the design brief and site information. The combination of these factors contributes to the overall result. C31, one of the most productive designers in the “design information” coding, and C8 produced an equally high level of “spatial language” (18.5). In contrast, C39, who was less productive in the “design information” coding, develops the least activities in spatial-language coding (3.0). An interesting observation arising from the data is that the designers in each group responsible for producing the lowest frequency of “design information” (A15,

9.4 Results

227

B20, and C39) also develop the lowest frequency in each group of “spatial language”. This implies that the production of “design information” is strongly related to the use of “spatial language”. Conversely, the highest frequency use of terms and clauses associated with “spatial language” tend to be associated with the frequent use of L-demonstrative. Thus, higher frequencies of “spatial language” are often unrelated to higher frequency of “design information”. That is, the production of Ldemonstrative alone does not develop a meaningful design language. The next section conducts a detailed investigation of the relationships between design cognition, design information and spatial language.

9.4.2 Correlation Correlation analyses (Table 9.8) using Pearson correlation coefficients (r) and significance values (p) for the number of cases (n = 23) provide an indicator of the strengths and directions of the relationships between variables (Brace et al. 2012). First, the representation category in “design cognition” has an inverse relationship with the functionality and directness categories (p < 0.05). This suggests that representation activities typically associated with the production of functionality and directness, are frequently negatively related and are moderately affected (r = −0.472 and −0.521, respectively). The function category in “design cognition” has a significant, positive relationship with all categories in “design information” and two categories (object and directness) in “spatial language”. This suggests that designers who produce more function-related activities tend to use more terms and clauses associated with “design information” and “spatial language”. The evaluation category in “design cognition” is also a predictor of the functionality and localisation categories in “design information” and “spatial language” (0.419 and 0.637, respectively). That is, the evaluation-based “design thinking” is closely related to both components of “design language”. Both the property and functionality categories in “design information” have significant, positive correlations with all categories in “spatial language”. The property category has a particularly strong, positive relationship with the object category (r = 0.859, p < 0.01) and the functionality category also has a strong, positive relationship with the directness category (r = 0.778, p < 0.01). In summary, the data suggest that cognitive activities are related to designers’ considerations of both design information and spatial language. Specifically, the more function and evaluation activities a designer employs, the higher the frequency of the use of terms and clauses related to design information and spatial language. In a multi-cultural and multi-linguistic context, this finding suggests that design thinking may be influenced by the different usages of design language shaped by cultural and linguistic backgrounds and differences (through their impact on “design information” and “spatial language”).

−0.371

−0.180

0.041

−0.308

−0.472*

−0.185

−0.358

−0.521*

Goal setting

Property

Functionality

Object

Localisation

Directness

< 0.05

−0.182

−0.067

Evaluation

< 0.01,

−0.229

−0.297

Function

*p

−0.396

−0.519*

Perception

** p

−0.208

−0.017

−0.616**

0.527** 0.211

0.248

0.269

0.637**

0.326

0.251

0.003

0.661**

0.379

0.231

0.419*

0.131

0.007

0.577**

0.604**

0.239

−0.077

Goal setting

0.664**

0.606**

0.859**

0.671**

0.778**

0.632**

0.544**

Functionality

Design information Evaluation

Property

Function

Representation

Perception

Design cognition

Table 9.8 Correlations between the coding results of design cognition, design information, and spatial language (n = 23) Spatial language

0.401

0.439*

Object

0.576**

Localisation

228 9 The Language of Design Thinking

9.4 Results

229

A

B

C

Directness

Frequency

LocalisaƟon

Frequency

Frequency

FuncƟonality

A

B

C

A

B

C

Fig. 9.5 Box and whisker plots of the coding results for functionality, localisation and directness across Groups A, B and C

9.4.3 Comparison The second important result from the experimental data highlights the differences across the three cultural groups in terms of design cognition, design information and spatial language. Significant differences are observed in the coding results of six out of nine categories. Figure 9.4 shows the “box and whisker” plots of the coding results of three “design cognition” categories (the percentage of the frequency weighted by time duration) across the three cultural groups, while Fig. 9.5 illustrates the coding results for functionality (from “design information”), localisation and directness categories (from “spatial language”), describing average frequencies for five-minute intervals. The box and whisker plots indicate that there are differences in the cognitive allocation as well as in the production of “design information” and “spatial language” in each cultural group. Table 9.9 shows the results of one-way ANOVA with post hoc Scheffé test for the eight selected subclasses where significant differences were identified among groups. Group C’s designers, for example, more frequently attended to drawings (P-drawing) than Group A’s designers, while Group A’s designers more frequently evaluated existing drawings (‘E-drawing) than Group C’s designers. Consequently, the group of Australian designers produced more terms relating to shape (e.g. place, room, space, area and site) than the other two groups of Asian designers. Group A’s designers also produced more descriptions of abstract features or concepts and interactions than Group C’s designers. The Australian designers also considered circulation more explicitly than the other two groups. In terms of spatial language, three subclasses show significant differences across the groups. The Australian designers describe spatial relations more frequently using projective terms than the other two groups, which is supported by the largest F ratio and the smallest P value (F = 11.632, P = 0.000). Group A data also shows a more frequent production of absolute reference-reasoning systems and expresses more direct relationships between objects than Group C. Thus, the results of ANOVA for the eight selected subclasses confirm there are significant differences in “design cognition” and “spatial language” between the three cultural groups.

230

9 The Language of Design Thinking

Table 9.9 The results of one-way ANOVA with Scheffé post hoc test for eight selected subclasses Category

Group

Mean

SD

F

Sig

Scheffé a, b

P-drawing

A

14.000

5.177

6.253**

0.008

AC

B

4.313

2.419

C

3.443

2.700

A

5.550

3.673

7.393**

0.004

A > B, C

B

3.938

2.605

C

2.986

2.706

A

3.613

1.596

6.484**

0.007

A>C

B

2.713

1.438

C

1.186

0.584

A

1.400

0.693

6.506**

0.007

A > B, C

B

0.638

0.421

C

0.529

0.359

A

3.775

1.443

11.632**

0.000

A > B, C

B

1.850

0.789

C

1.400

0.600

A

1.500

1.308

3.574*

0.047

A>C

B

0.738

0.670

C

0.300

0.294

A

3.575

2.251

6.832**

0.005

A>C

B

1.600

0.893

C

0.871

0.682

E-drawing

P-shape

F-features

F-circulation

L-projective

L-absolute

D-direct

** p

< 0.01, *p < 0.05 Harmonic Mean Sample Size = 7.636 b The group sizes are unequal and because of this the harmonic mean of the group sizes is used. Type I error levels are not guaranteed a Uses

9.5 Discussion Past research (Gerrig and Banaji 1994; Gleitman and Papafragou 2005) claims that language, as a system, is both a reflection of the way we think and of our sociocultural differences and values. Importantly, linguistic systems may shape our thought processes, and cognitive processes may be linguistically constrained. It could be assumed then, that this is a bidirectional effect between design cognition and spatial language (Lee et al. 2016; Lee et al. 2019). Thus, this chapter investigates the influence of “design language” on “design thinking” and vice versa. Our examination, furthermore, explores cultural and linguistic differences in the design process.

9.5 Discussion

231

The results of correlations and ANOVA in this chapter confirm that there is a clear relationship between design and language. There are also clear differences between culturally and linguistically different groups in terms of design cognition, design information and spatial language. However, Pinker (1994) provides empirical evidence that there are thought processes at work long before people develop any language skills. All individuals initially possess a “universal mentalese” (Pinker 1994). In parallel, Arnheim (1969) provides evidence of visual reasoning independent from language. Visual perception could, therefore, be an intelligent act with no relation to language. Thus, the arguments in this chapter may be open to criticisms of linguistic determinism and bias. Nonetheless, this chapter finds that “spatial language” is strongly correlated with “design information” in design language, operating complementarily in terms of cognitive representations. These also correlate with representation, function and evaluation categories in design cognition, which are critical to design thinking. That is, both spatial representation (or linguistic abstraction) and the use of design information relate to cognitive allocations. As such, surely design language is closely linked to design thinking. This finding is reflected in the results of previous linguistic studies (Munnich and Landau 2003; Munnich et al. 2001; Tenbrink and Ragni 2012). This chapter not only highlights the relationship between design and language, but it also addresses the impact of culture and language on design. There are, however, several limitations in our experimental procedures. For example, although there are some differences in the level of English proficiency across the groups of designers, all participants were asked to verbalise their thoughts in English. This was more difficult for Group C’s designers. Thus, the interpretation of the differences observed between the groups might have some limits. However, the results of our research have parallels with past results that suggest speakers of different native languages might approach spatial problem-solving quite differently (Munnich and Landau 2003). For example, Group A’s and B’s designers often developed textualgoals or graphical-goals at the beginning of their design sessions, while Group C’s designers rarely conducted obvious goal-setting activities, solving isolated microproblems in a more mechanistic way. The different design thinking styles evident across the three cultural groups may be related to the rational and the creative phases of the design process (Bashier 2014) or “rational-intuitive” and “intuitive-rational” design approaches (Donoso et al. 2018). In addition, Group A’s designers tended to develop “macro” design solutions while most of Group C’s designers were limited to producing “local” design configurations. Statistically, there are significant differences in perception, evaluation and goalsetting categories among the three cultural groups. Group C’s designers emphasise perception activities in design cognition (also subclass P-drawing), while Group A’s designers produce more evaluation activities (also subclass E-drawing) than Group C’s designers. Group B’s designers produce more goal-setting activities than Group C’s designers. This result implies that a group of designers of similar cultural background can have their own style of design thinking (cognitive process and preference),

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which may produce similar patterns of cognitive activities within the group when solving a design problem. Moreover, these may well differ from the design thinking strategies used by other cultural groups.

9.6 Conclusion This chapter has developed and investigated a rich set of design protocols from three different cultural and linguistic groups. Protocol analysis has been used to formally capture the characteristics of both design thinking and design language using a new triple-coding system. The results of correlations and ANOVA support our assumptions regarding the existence of certain relationships between design and language as well as revealing some cultural and linguistic differences in design. We argue that a bidirectional relationship between design thinking and design language is not just likely, but is perhaps a key element that needs to be understood for supporting a global creative economy. The different characteristics observed between Australian and Asian designers suggest that, even if design is a “universal language”, cognitive allocation and representation are influenced by individual designers’ cultural experiences and preferences. The cognitive (content) complexity (H C ) introduced in Chap. 4 of this book has been used in this chapter to quantify individual design cognitions. It facilitates capturing the different levels of complexity and productivity in a set of design protocols. Thus, this chapter confirms that cognitive complexity is a promising measure for analysis of this type. Finally, both chapters of Part III contribute to a deeper understanding of the relationship between design thinking and design language as well as design processes in the multi-cultural context. The findings are especially relevant to the growing numbers of international studios in both architectural practice and design education. Future research could also consider collaborative settings with participants across different cultural groups to enrich and deepen the investigation.

References Arnheim, R. 1969. Visual Thinking. Berkeley, Los Angeles, London: University of California Press. Bashier, Fathi. 2014. Reflections on architectural design education: The return of rationalism in the studio. Frontiers of Architectural Research 3 (4): 424–430. https://doi.org/10.1016/j.foar.2014. 08.004. Boroditsky, Lera, Orly Fuhrman, and Kelly McCormick. 2011. Do English and Mandarin speakers think about time differently? Cognition 118 (1): 123–129. https://doi.org/10.1016/j.cognition. 2010.09.010. Brace, Nicola, Richard Kemp, and Rosemary Snelgar. 2012. SPSS for Psychologists. Basingstoke, UK: Palgrave Macmillan. Chai, Kah-Hin., and Xin Xiao. 2012. Understanding design research: A bibliometric analysis of Design Studies (1996–2010). Design Studies 33 (1): 24–43.

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Corballis, Michael C. 2018. Space, time, and language. Cognitive Processing 19 (1): 89–92. https:// doi.org/10.1007/s10339-018-0878-1. Cross, Nigel, Henri Christiaans, and Kees Dorst. 1996. Analysing Design Activity. New York: Willy. Donoso, Sergio, Pedro Mirauda, and Rubén Jacob. 2018. Some ideological considerations in the Bauhaus for the development of didactic activities: The influence of the Montessori method, the modernism and the gothic. Thinking Skills and Creativity 27: 167–176. https://doi.org/10.1016/ j.tsc.2018.02.007. Gero, J.S., and Thomas Mc Neill. 1998. An approach to the analysis of design protocols. Design Studies 19 (1): 21–61. Gerrig, R. J., and M. R. Banaji. 1994. Language and thought. In Thinking and Problem Solving. Handbook of Perception and Cognition, ed. R.J. Sternberg, 233–261. London: Academic Press. Gleitman, Lila, and Anna Papafragou. 2005. Language and thought. In Cambridge Handbook of Thinking and Reasoning, 633–661. Herskovits, A. 1986. Language and Spatial Cognition: A Interdisciplinary Study of the Prepositions in English. Cambridge, England: Cambridge University Press. Lee, Ju Hyun, Ning Gu, and Michael J. Ostwald. 2015. Creativity and parametric design? Comparing designer’s cognitive approaches with assessed levels of creativity. International Journal of Design Creativity and Innovation 3 (2): 78–94. https://doi.org/10.1080/21650349.2014.931826. Lee, Ju Hyun, Ning Gu, and Michael J. Ostwald. 2019. Cognitive and linguistic differences in architectural design. Architectural Science Review 62 (3): 248–260. https://doi.org/10.1080/000 38628.2019.1606777. Lee, Ju Hyun, Michael J. Ostwald, and Ning Gu. 2016. The language of design: Spatial cognition and spatial language in parametric design. International Journal of Architectural Computing 14 (3): 277–288. https://doi.org/10.1177/1478077116663350. Luna, D., and S. Forquer Gupta. 2001. An integrative framework for cross-cultural consumer behavior. International Marketing Review 18 (1): 45–69. Man, Jinfan. 2014. Design Teamwork in Distributed Intercultural Teams: Competition, Collaboration, Cooperation. Eindhoven: Technische Universiteit Eindhoven. Molina, A., J. Aca, and P. Wright. 2005. Global collaborative engineering environment for integrated product development. International Journal of Computer Integrated Manufacturing 18 (8): 635– 651. https://doi.org/10.1080/09511920500324472. Munnich, Edward, and Barbara Landau. 2003. The effects of spatial language on spatial representation: Setting some boundaries. In Language in Mind: Advances in the Study of Language and Thought, ed. Dedre Getner, and Susan Goldin-Meadow, 113–155. Mit Press. Munnich, Edward, Barbara Landau, and Barbara Anne Dosher. 2001. Spatial language and spatial representation: A cross-linguistic comparison. Cognition 81 (3): 171–208. Pinker, Steven. 1994. The Language Instinct: How the Mind Creates Language. New York: William Morrow and Company. Stevens, J.P. 1999. Intermediate Statistics: A Modern Approach, 2nd ed. Mahwah, NJ: Lawrence Erlbaum Associates. Suwa, Masaki, Terry Purcell, and John S. Gero. 1998. Macroscopic analysis of design processes based on a scheme for coding designers’ cognitive actions. Design Studies 19 (4): 455–483. Suwa, Masaki, and Barbara Tversky. 1997. What do architects and students perceive in their design sketches? A protocol analysis. Design Studies 18 (4): 385–403. https://doi.org/10.1016/S0142694X(97)00008-2. Tenbrink, Thora. 2011. Reference frames of space and time in language. Journal of Pragmatics 43 (3): 704–722. https://doi.org/10.1016/j.pragma.2010.06.020. Tenbrink, Thora, and Marco Ragni. 2012. Linguistic principles for spatial relational reasoning. In Cyrill Stachniss, Kerstin Schill, and David Uttal, ed. V.I.I.I. Spatial Cognition, 279–298. Lecture Notes in Computer Science: Springer, Berlin Heidelberg. van der Zee, E., and J.M. Slack. 2003. Representing Direction in Language and Space. Oxford University Press.

Part IV

Conclusion

Chapter 10

Conclusion: Three C’s of Design Thinking

Abstract The final chapter in this book reflects on the findings, models and frameworks presented previously. The first part, “creative design thinking” revisits the results of two cognitive studies (Chap. 2) and the qualitative analysis and quantitative measures of complexity developed for design thinking (Chaps. 3 and 4). The second part, “collaborative design thinking”, considers the wider implications of the Design Team Cognition (DTC) model and the Digital Design Thinking (DDT) frameworks (Chaps. 5, 6 and 7). Finally, “cultural design thinking” returns to the language of design and its consequences for spatial reasoning and communication (Chaps. 8 and 9). This chapter summarises the book’s contribution to advances in design thinking in terms of creativity, collaboration and culture.

10.1 Introduction In the preface to this book, we employed a simple spatial analogy to introduce the topic of design thinking. This classic descriptive trope—the problem of navigating an unknown landscape—was used to develop an initial picture of the complexity of design cognition. It is also an example of a Mental Model (MM), which was shared with readers to foreshadow some key messages and themes in the book. For example, in the MM we used spatio-relational reasoning to hint at the importance of both problem spaces (“off-ramps”, “cul-de-sacs” and “dead-ends”) and solution spaces (“freeways”, “bridges” and “tunnels”) while travelling to a destination. The different types of paths taken by expert and novice explorers were also characterised in terms of way-finding strategies. Several of the design strategies discussed in this book— “forwarding”, “searching” and “reflecting”—encapsulate these paths and the sense of exploration they entail. We also warned the readers that experts were more likely to be open to the possibility that, not only isn’t the planned destination accessible, it may not even be desirable. Design is not just about finding a solution to a problem, it may require questioning the problem itself. Similarly, design is not necessarily about optimal functionality, a solution that balances function, inspiration and innovation is likely to be more effective.

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Like any MM, the information contained in our preface was abstract and simplistic. Readers of the complete book will be aware that investigating design thinking requires special methods and techniques. Using a detailed review of past theories, coupled with new frameworks, experimental results and mathematical analysis, this book constructs a deep and critical review of design thinking for the digital era. This context, the “digital era” (and its counterparts, the “digital ecosystem” and “digital Anthropocene”), is significant because design collaboration and production are increasingly digital. In the design fields of architecture and industrial design, BIM, CAD and parametric design are fast becoming the primary platforms. Furthermore, there are multiple digital design platforms in use in business, marketing and the arts. In the contemporary world, design thinking has become so intricately tied to digital contexts that there is an urgent need to develop an understanding of the relationship between the two. This is especially important as a growing body of research is now indicating that designers think and act differently when working in diverse environments. Within this general scope, this book identifies three themes—creativity, collaboration and culture—that are crucial for the future of design thinking. These “three C’s” are not just connected. A reciprocal relationship exists between them (Fig. 1.1). Creativity is one of the most desired by-products of design thinking and arguably the entire reason the design process has been identified as a unique and valuable resource. Designers, however, rarely work alone and creativity is often tied to a team’s capacity to develop and communicate MMs. Furthermore, team members from different cultural and linguistic backgrounds promote divergent thinking and collective intelligence, which recursively foster creativity. Thus, the “three C’s” cannot be isolated from each other and all are important in contemporary design. This book is the first to take a consistent approach to examining creativity, collaboration and culture in the context of the design process and product of the digital era. Its purpose, however, is not just to describe or propose methodological advances in cognitive research. Instead, this book introduces and then uses quantitative measures of cognitive complexity (H C and H S ), to study patterns in design thinking associated with the “three C’s”. Furthermore, within each of these themes, new design thinking models for the digital era are developed. The three main ones in this book are the “creative micro-process” model (Fig. 3.5), the DTC model (Fig. 5.1) and the “design thinking and language” model (Fig. 9.1). Furthermore, while many cognitive studies are only concerned with individual strategies and behaviours, the DTC model in this book deals with team cognition. This concluding chapter revisits the content of the book, including the results of empirical protocol studies and the application of advanced methods, and then reiterates their contributions and limitations.

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10.2 Creative Design Thinking Design thinking is valued for its capacity to generate innovative ideas and solutions that have the potential to transform society and industry (Brown 2009; Martin 2009; Neumeier 2009). While design thinking is sometimes conflated with creative thinking in cognitive research, there is a common alternative position which argues that design thinking pursues innovation rather than creativity. This position arises from the idea that the processes and products of design are measurable and manageable in terms of the levels of innovation they develop. In contrast, creativity has often been viewed as elusive and subjective, being a mysterious territory in the realm of complex human operations. As such, some people argue that creativity is not a product of design thinking, because strategic and deliberate thinking leads to innovation. This disagreement partially explains why much past research on design thinking has been limited to examining explicit cognitive processes and products. Despite this position, studies about creativity have been conducted using rigorous and scientific methods for many years (Sternberg and Lubart 1999). Multiple formal models of, and theories about creativity have been developed throughout the twentieth century. Some of the most important of these are Wallas’s (1926) four-stage model of the creative process, Gordon’s (1961) synectics theory, Guilford’s (1967) Structure of Intellect (SI) theory, Hayes’ (1989) cognitive processes in creativity and Gero’s (2000) computational model of innovative and creative design processes. This past research confirms that creativity in design thinking is not uncharted territory, it can be formally explored, mapped and measured. In this context, Part I of this book not only provides an in-depth investigation into creative design thinking but also introduces new measures of design cognition. Specifically, the results of a series of protocol studies are used to reveal implicit processes and hidden cognitive representations in the design process. The creativity implicit in a product is also examined using expert panel assessment and a set of evaluation criteria. Thus, this book contributes to the exploration of creative design thinking in both breadth and depth, neither of which are common in design thinking research. Chapter 2 uses the results of two protocol studies to identify individual design strategies that support creativity. The first of these studies explores sketch-based design activities and the second investigates parametric design activities. The individual strategies identified in these cognitive studies contribute to improving designers’ creative practices and are especially valuable for novice designers. Importantly, this chapter confirms that design thinking in a digital environment does differ from design thinking in a more traditional, pen-and-paper or sketching environment. While acknowledging limitations associated with the small sample sizes, and the difficulty of developing intercoder-reliability measures for cognitive studies, Chap. 2 develops three forwarding (sketch-based) strategies and two generative (parametric) strategies that are useful for understanding creative design thinking. To balance the problems of conducting design studies in controlled contexts, both studies adopt post-experimental interviews as retrospective protocols. While interview and observational (Cross 2011) studies may be superior for examining design practice for

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diverse “real-world” briefs, controlled experimental settings provide the best basis for comparison and generalisation of results. Nevertheless, the results developed in the first two studies are most informative for examining creative micro-processes in a short-term design task. Advantageously, these are similar to the short “bursts” of design activity, which occur in the quotidian work of a professional designer. Chapter 3 provides an in-depth investigation of parametric design protocols, capturing important micro-processes that support creativity. As the first stage of this, the combined method of protocol analysis and expert panel assessment identifies four parametric design activities—(i) changing parameters, (ii) perceiving geometries, (iii) introducing algorithmic ideas, and (iv) evaluating geometries—that support creative problem-solving processes. A close review of encoded segments in the protocols reveals iterative cognitive patterns related to these activities. From this data, the chapter develops a creative micro-process model (Fig. 3.5) for parametric design. The importance of micro design activities and patterns has been identified in a number of past studies of design cognition, while the evidence-based selection of key design activities (the mapping process) is valuable for a focussed study on cognitive activities. Furthermore, the new understanding of sequential and/or cyclic design activities (both algorithmic and geometric activities) in the creative micro-process model contributes to developing generative capacity in parametric design, as well as the design thinking in the digital ecosystem. While Chaps. 2 and 3 address cognitive activities that support creativity in design, Chap. 4 develops two measures to quantify design thinking in terms of cognitive complexity. In the design domain, complexity has been used as a criterion to assess both the design product and the design problem. There has, however, been no clear way to quantify complexity in the design process or in design cognition. Thus, Chap. 4 proposes two measures of cognitive complexity (H C and H S ), providing a significant methodological contribution to research in this field. Both can be used to index individual cognitive differences and facilitate comparisons between design processes. The content complexity (H C ) measure is particularly useful, as demonstrated in Part III, as it provides an alternative index for design productivity (idea generation). Measuring information entropy for H C is based on the micro-categorisation of design activities (i.e. subclasses), which is then correlated to other cognitive and linguistic indexes. However, the verification of variables and categorisations for H C requires further research. Nevertheless, Chap. 8 later indicates that H C corresponds with the link index of each protocol, while structural complexity (H S ) corresponds to the result of Critical Moves (CMs), which is more closely related to creativity. In summary, each chapter in Part I of this book contributes to advances in creative design thinking. The combined method of protocol analysis and expert panel assessment in Chap. 2 is also applied in multiple chapters in this book and can be further used for the mapping process of design process and product (Chap. 3) and the interpretation of complexity measures (Chap. 4). Importantly, the two generative strategies (Chap. 2) and the creative micro-process model (Chap. 3) are beneficial for developing creative design thinking in parametric design.

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10.3 Collaborative Design Thinking While the first part of this book examines individual design thinking in relation to creativity, Part II deals with design cognition and communication in teams. Teamwork protocols have been extensively explored in past design research (Goldschmidt 1995; Stempfle and Badke-Schaub 2002; Valkenburg and Dorst 1998). Rather than revisiting this work, this book highlights the way individual designer’s work, as a precursor to understanding the “collective” and “interactive” cognitive processes that underpin the DTC model. In Part II, collaborative design thinking in the digital ecosystem is examined using a series of technology-enabled collaborative platforms as cases (e.g. BIM in Chap. 6; mobile, situated and collective digital platforms in Chap. 7). In contrast to the other Parts of this book, which use data derived from protocol studies, Part II contains a series of detailed reviews of theories of “team cognition”, “cognitive representation”, “emerging technologies”, “Digital Design Thinking (DDT) processes” and “DDT platforms”. Through this process, it uncovers new or emerging trends in digital, networked design collaboration. Chapter 5 presents a review of past research on “team cognition” and “cognitive representation” and then distills key lessons from this about collaborative design. This review highlights the significance of Transactive Memories (TMs), shared and distributed MMs (SMMs and DMMs, respectively) and visual and verbal representations. These elements provide the foundations for an integrated team cognition model for design collaboration. This DTC model positions TM as the system supporting emergence and sharedness and distributed knowledge. While the model can be understood as merging aspects of SMMs and DMMs, its purpose is to accommodate the performative and creative aspects of collaborative design. To examine the model, two of the four creative components—“design spaces” and “design modes”—are explored using the protocols developed in Chap. 2. Two performative components (“design information” and “spatial language”) of designers’ image-based and verbal representations in MMs are also investigated through cross-national design protocols. Although the capacity to verify the DTC model using this data is limited, it demonstrates the importance of individuals’ creative design activities (distributed knowledge) and their design communication processes (emergence and sharedness). Thus, the DTC model provides a baseline for conducting future research on collaborative design thinking, social cognition, coordination and design management. The knowledge contained in the DTC model also contributes to future team cognition models and formal research approaches in the design domain. The “design thinking and language” model proposed in Part III of this book is also founded on the way the DTC model deals with different modes of design activities as well as linguistic expression and spatial relational reasoning in design teams. Chapter 6 introduces an advanced BIM knowledge framework for design collaboration. BIM is one of the most important platforms supporting design visualisation, communication and collaboration in the Architecture, Engineering and Construction (AEC) sector. Unlike the other theories and models discussed in this book, the BIM framework is a domain-specific knowledge construct. However, since BIM

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enables both interaction and collectivity (Verstegen et al. 2019), it is one of the most practical examples of Computer Supported Cooperative Work (CSCW) tools. The BIM framework in Chap. 6 provides a better understanding of the BIM microcosm of the digital ecosystem, in which products, processes and people co-evolve. This chapter describes a domain-specific example of digital design collaboration, while Chap. 7 uses DDT frameworks to understand the mobile, situated and collective digital platforms that surround us every day. Chapter 7 proposes two DDT frameworks that represent an important step towards improving our understanding of the relationship between design thinking, design creativity and collaboration. The first framework encapsulates processes (I-C-L, digital collaboration, problem-solving and collective design) and the second, functionality (user-based, content-based and location and time-based). Both frameworks are founded in the results of past research into Collective Intelligence (CI), digital collaboration and Collective Design (CD). The DDT processes and functionalities frameworks are also closely related to the DTC model’s emergence and sharedness (SMMs) and distributed knowledge (DMMs). In Chap. 7 the DDT frameworks are used for analysing and measuring the information processing and functional properties of examples of mobile, situated and collective platforms. For each platform, the functionalities are quantified and their capacities and characteristics compared. This enables the evaluation of these DDT platforms in terms of CI, CD, DMM and SMM. Importantly, the two DDT frameworks in Chap. 7 identify the growth or evolution trajectories of interactive and collective capacity in digital platforms. For example, DDT processes evolve from the I-C-L to collective design to accommodate more crowd-based activities, while DDT functionalities evolve in three possible dimensions: from extrinsic hierarchy to intrinsic crowd, from collaborative creation to opportunistic decision and from synchronous local to asynchronous global. Because of this capacity to model developmental trajectories, the DDT frameworks can be used to examine or shape the creation of a range of digital platforms including design thinking tools and collaboration software. The major frameworks presented in Part II—the DTC model (Chap. 5), BIM knowledge framework (Chap. 6) and two DDT frameworks (Chap. 7)—support improved collaborative design thinking through engaging social creativity in the digital ecology. While the exploratory research in each chapter is limited to providing models or frameworks, the reviews or past findings contribute to building a comprehensive knowledge base in the field of design collaboration. The topics covered in Part II are expanded in Part III to accommodate the cultural aspects of design thinking, in particular, “design language”.

10.4 Cultural Design Thinking The cultural aspects of design thinking have become increasingly important in contemporary design practice, with significant growth in geographically distributed, multi-national teams (Man 2014). Part III addresses the relationship between design

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and language, which is significant because language shapes, and is in turn shaped by, thought (Boroditsky 2001; Gleitman and Papafragou 2005). Moreover, all of the design thinking topics addressed in this book—from design strategies and creative processes to TMs, SMMs and DMMs—must consider linguistic factors. Given the importance of this topic, it is surprising how little research has been conducted. For this reason, the “design thinking and language” model proposed on Chap. 9 is one of the most valuable outcomes of this book. Acknowledging the criticisms of linguistic determinism (Chomsky 1965; Pinker 1994) and the parallel importance of visual imagery (Arnheim 1997; Paivio 1971; Ware 2008), language both shapes and shares our ideas in design environments. The two chapters in Part III examine the cultural aspects of design thinking, developing and applying formal methodological frameworks and using empirical data to support the process. Chapter 8 examines multiple aspects of cross-national design thinking using cognitive and linguistic indexes: link index, percentage of CM, H C , H S and syntactical complexity. Although there are methodological limitations associated with the sample size and experimental setting, this chapter presents multiple new measures and assesses them in a comprehensive way. The cognitive and linguistic indexes are valuable because they can be used to identify diverse aspects of creative, collaborative and cultural design thinking. Specifically, the chapter reveals the relationship between cognitive and linguistic characteristics in the design process, including the correlation of quantitative indicators. The dual-coding system (“design cognition” and “spatial language”), which enables in-depth investigation of the language of design, is further elaborated in the following chapter. Chapter 9 presents a research model for “design thinking and language”, which is used for investigating the relationship between design and language. It highlights three components of cultural design thinking (“design cognition”, “design information” and “spatial language”) which are closely related to the DTC model. That is, the “design thinking and language” model is not only used for investigating cultural design thinking, but also facilitates the understanding of creativity and collaboration in design thinking. The protocol study in this chapter (23 participants) rigorously assesses and confirms that “design thinking” (in terms of cognitive allocation) is related to “design language” (in terms of the use of information categories and spatial linguistic principles). It concludes that different cultural and linguistic groups use different cognitive methods or strategies to develop and represent design. In traditional design research, the concept of “culture” is used to describe a distinct value, belief and action (Strickfaden and Heylighen 2010). In contrast, in this book it is concerned with the linguistic dimension of design thinking. The linguistic and cognitive results presented in Part III are, however, explored in an individual experimental setting, and future research should consider different team settings (collaboration, cooperation and competition) as well as “co-creation” protocols in a multicultural team. Thus, the joint application of the DTC model and the “design thinking and language” model will not just be valuable for future research, but such a combination may well be inevitable as multi-cultural teams continue to play a major role in design. The “creativity” aspects of design thinking also need to be revisited to elaborate both models. That is, the triangular model of the “three C’s” presented in

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this book provides a significant step towards transforming the traditional concept of “design thinking” into an integrated notion of multiple correlative themes.

10.5 Conclusion This book conducts the first integrated study of the creative, collaborative and cultural aspects of design thinking. These three themes facilitate and promote diverse design thinking in the digital ecosystem. The models and frameworks presented in this book combine multiple aspects of design process and product, developing new insights while also asking new questions. They contribute to the structuring and content of future studies on individual and team design thinking, corresponding to their cognitive and communicative properties. Although this book highlights emerging design thinking arising from digital technologies and new networked platforms, traditional design thinking and its conventional approaches are also comprehensively discussed in the literature reviews, as the hybrid nature of the co-existence of the old and new will continue. The human operations are unchanged, but are supported by, or evolve with, the digital ecosystem. Thus, it is acknowledged that this book is really a starting point of a longer journey which cannot be accommodated in a single volume. Nonetheless, this book provides an important map or guide, for people navigating their way around design thinking and considering its applications in design practice, critical thinking and design research.

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Hayes, John R. 1989. Cognitive processes in Creativity. In Handbook of creativity. Perspectives on individual differences, ed. John A. Glover, Royce R. Ronning, and Cecil R. Reynolds, 135–145. New York: Plenum Press. Man, J. 2014. Design Teamwork in Distributed Intercultural Teams: Competition, Collaboration, Cooperation. Eindhoven: Technische Universiteit Eindhoven. Martin, Roger. 2009. The Design of Business: Why Design Thinking is the Next Competitive Advantage. Boston, MA: Harvard Business Press. Neumeier, Marty. 2009. The Designful Company: How to build a culture of nonstop innovation. Berkeley, CA: New Riders. Paivio, Allan. 1971. Imagery and Verbal Processes. Oxford: Holt, Rinehart & Winston. Pinker, S. 1994. The Language Instinct: The New Science of Language and Mind. New York: William Morrow and Company. Stempfle, Joachim, and Petra Badke-Schaub. 2002. Thinking in design teams—An analysis of team communication. Design Studies 23 (5): 473–496. https://doi.org/10.1016/S0142-694X(02)000 04-2. Sternberg, Robert J., and Todd I. Lubart. 1999. The Concept of Creativity: Prospects and Paradigms. In Handbook of Creativity, ed. Robert J. Sternberg, 3–15. Cambridge University Press. Strickfaden, Megan, and Ann Heylighen. 2010. Cultural Capital: A Thesaurus for Teaching Design. International Journal of Art & Design Education 29 (2): 121–133. https://doi.org/10.1111/j.14768070.2010.01653.x. Valkenburg, Rianne, and Kees Dorst. 1998. The reflective practice of design teams. Design Studies 19 (3): 249–271. https://doi.org/10.1016/S0142-694X(98)00011-8. Verstegen, Luuk, Wybo Houkes, and Isabelle Reymen. 2019. Configuring collective digitaltechnology usage in dynamic and complex design practices. Research Policy 48 (8): 103696. https://doi.org/10.1016/j.respol.2018.10.020. Wallas, Graham. 1926. The Art of Thought. New York: Harcourt Brace. Ware, C. 2008. Visual Thinking for Design. Burlington, MA: Morgan Kaufmann.