Handbook of Research Methods in Organizational Change 1800378513, 9781800378513

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Handbook of Research Methods in Organizational Change

David B. Szabla

HANDBOOK OF RESEARCH METHODS IN ORGANIZATIONAL CHANGE

Handbook of Research Methods in Organizational Change Edited by

David B. Szabla Associate Professor, Department of Educational Leadership, Research and Technology, Western Michigan University, USA

David Coghlan Professor Emeritus, Trinity Business School, Trinity College Dublin, Ireland

William Pasmore Professor of Practice, Teachers College, Columbia University, USA

Jennifer Y. Kim Assistant Professor, Tufts University School of Medicine, Center for the Study of Drug Development, USA

Cheltenham, UK • Northampton, MA, USA

© David B. Szabla, David Coghlan, William Pasmore and Jennifer Y. Kim 2023

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA A catalogue record for this book is available from the British Library Library of Congress Control Number: 2023939593 This book is available electronically in the Business subject collection http://dx.doi.org/10.4337/9781800378520

ISBN 978 1 80037 851 3 (cased) ISBN 978 1 80037 852 0 (eBook)

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Contents

List of figuresvii List of tablesix List of boxesxi List of contributorsxii Acknowledgementsxiv PART I 1

INTRODUCTION

An invitation to revitalize research into organizational change William Pasmore and David B. Szabla

PART II

2

METHODS

FOUNDATIONAL 2

Action research as the social science of change and changing David Coghlan

19

3

Conducting processual research on organisation change Deepak Saxena and Joe McDonagh

47

4

The grounded theory methodology: over fifty years of inquiry! John Loonam

69

5

Longitudinal research methods for studying processes of organizational change 88 Elaine Rabelo Neiva and Leonardo Fernandes Martins

CONTEMPORARY 6

Psychoanalytic and socioanalytic approaches to organizational change research 124 Susan Long

7

Qualimetric intervention-research as an approach to studying organizational change Henri Savall, Véronique Zardet, Marc Bonnet, and Anthony F. Buono

150

8

Collaborative management research: theoretical foundations, mechanisms and practices Abraham B. (Rami) Shani

172

9

Learning history: engaging multiple perspectives for learning Margaret Gearty v

194

vi  Handbook of research methods in organizational change 10

Principles for productive inquiry into ICT-enabled change in organisations Joe McDonagh

221

11

Using participatory mixed methods to study “grand challenges”: an illustrative case of diversity, equity, and inclusion change research in organizations242 Regina Kim and Yunzi (Rae) Tan

EMERGING 12

Conducting phenomenon-driven rapid-response research to explore disruption and its impact on the minority experience Jennifer Y. Kim and Zhida Shang

261

13

Collaborative Developmental Action Inquiry: a new paradigm for leadership and organizational change research William R. Torbert and Sofia-Jeanne Caring

281

14

Advancing Strong Structuration Theory in organizational change research David B. Szabla and David A. Jarrett

15

Design science for organizational change: how design theory uncovers and shapes generativity logics in organizations Pascal Le Masson, Agathe Gilain, Armand Hatchuel, Caroline Jobin, Maxime Thomas, Chipten Valibhay, and Benoit Weil

16

Longitudinal designs, big data, and social network analysis in organization development and change research Ramkrishnan (Ram) V. Tenkasi, William B. (Bart) Brock, and Donna L. Ogle

355

17

X-Ray Vision: a research tool for uncovering system psychodynamics to advance organization change Debra A. Noumair and Jacqueline D. Jenkins

397

18

Applying data science in organizational change research Joshua Elmore

299

327

431

PART III REFLECTIONS 19

Ethical dilemmas in collaborative action research Tobias Fredberg and Johanna E. Pregmark

20

Reflections on the identity journey of a budding organizational change scholar or insights on constructing a meaningful research path and life Julie Bayle-Cordier

21

Reflections on guiding doctorates in organizational change David Coghlan and Jennifer Y. Kim

452

467 486

Index510

Figures

2.1

Comprehensive framework of action research

21

2.2

Synthesis of action research theory and practice

36

3.1

Towards a processual model of processual research

62

5.1

Map of possibilities in quantitative longitudinal data analysis

113

7.1

The four-leaf clover, representing the complex system at the origin of dysfunctions and hidden costs in organizational change processes

154

7.2

Principle of the ISEOR Laboratory: cooperation with companies and organizations to create knowledge in the field of organizational change and development

158

7.3

Fundamental hypothesis of the Socio-Economic Theory

162

7.4

The three dimensions of the socio-economic approach to organizational change 163

7.5

The step-by-step process of socio-economic intervention

165

7.6

The Socio-Economic star

169

9.1

Sketch of the core idea of learning history

195

9.2

Learning history as a four-stage process with multiple stakeholders

204

10.1

Achieving symmetry between people, work, and technology

225

11.1

The participatory mixed methods research approach for conducting diversity, equity, and inclusion change research in organizations

249

14.1

Duality of structure

300

14.2

Research perspective on SST ontology in change research

307

14.3 Position-practices

310

14.4

Quadripartite nature of structuration

312

16.1

Histogram of outcome variable: Volume of Commentary

373

16.2

Raw ODC network graph, June 30, 2022 to July 9, 2022

378

16.3

37 x 37 network graph

384

16.4

Result of the CNM clustering algorithm

386

vii

viii  Handbook of research methods in organizational change 16.5

Aggregated network graph

388

16.6

Relationships from Org Development to other familial MOS networks occupying the same cluster

389

17.1

The Burke–Litwin Model

398

17.2

X-Ray Vision (formerly referred to as Beneath the Surface of the Burke–Litwin Model) Model

400

18.1

Number of mentions of followership in abstracts by year (hypothetical data)

438

18.2

Framework for data science in organizational change research

439

18.3

Rate of change language and sentiment from all messages by day

446

Tables

2.1

The general empirical method

25

2.2

Elements of quality in action research in organization development

33

3.1

Different classifications of change

50

4.1

Cross-case analysis: grounded theory characteristics

79

4A.1

Use of qualitative methodologies within leading ranked journals

87

5.1

Analytical approaches for longitudinal data analysis

105

7.1

Example of a qualimetric evaluation of hidden costs

166

7.2

Preparation of an organizational change based on a qualimetric evaluation in the case of the economic balance of a socio-economic priority action plan

167

8.1

Learning mechanisms: categories and key features

175

8.2

Phases in collaborative management research process: a generic framework

182

9.1

First-, second- and third-person engagement in learning history

206

10.1

Principles for productive inquiry into ICT-enabled change: evolving a practice of inquiring together

235

12.1

Comparison of phenomenon-driven rapid-response research and theory-driven research

267

13.1

The four territories in different realms of human activity

283

13.2

Leadership, organizational, and scientific developmental action-logics as mapped by CDAI

284

14.1

Ontology-in-situ in organizational change research

306

14.2

Strong Structuration Theory sliding scale of ontological analysis

308

15.1

How the choice of design theory framework impacts on design science research 335

15.2

Two examples of papers that observe new, original design processes and organizations – without a specific model of design rationality

338

15.3

Four examples of papers that experiment with organizational change by designing artifacts

341

ix

x  Handbook of research methods in organizational change 15.4

Examples of papers that uncover a specific form of organizational change: generativity process and generativity building in organizations

345

15.5

Comparison of three streams of research in design science

348

16.1

Example of data structure for longitudinal analysis

361

16.2

Accelerated Failure Time analysis of turnaround strategies on firm speed of “time to emergence”

367

16.3

Accelerated Failure Time analysis of turnaround strategies on firm “time to disbandment”

368

16.4

Summary of data

371

16.5

GEE model results for volume of commentary

374

16.6

Total raw tweet counts

380

16.7

Network assignment examples

381

16.8

Example of counts from network to network

382

16.9

Aggregated networks

387

16.10

Aggregated matrix

387

17.1

Conceptual framework of covert dynamics beneath the Burke–Litwin Model (now X-Ray Vision)

401

17.2

X-Ray Vision tool questions by organizational level

412

17.3

Summary of X-Ray Vision diagnosis process

415

17.4

X-Ray Vision Guiding Principles

418

18.1

A data object with rows and columns

436

18.2

A data object modified for our research purposes

436

18.3

A data object with a new column counting “followership” frequency in the abstract column

437

19.1

Dimensions of dilemmas

459

21.1

List of contributors

486

Boxes 9.1

Guiding questions for the insider/outsider team when planning a learning history project 

207

9.2

Levels of questioning for a learning history dialogue

208

9.3

Guiding principles for presenting a learning history in any form

211

xi

Contributors

Julie Bayle-Cordier, Assistant Professor, IÉSEG School of Management, Univ. Lille, CNRS, UMR 9221 – LEM – Lille, Economie Management, F-59000 Lille, France Marc Bonnet, Deputy Director of the ISEOR & iaelyon, Université Jean Moulin William B. (Bart) Brock, Associate Professor and Program Director Accounting & Finance, College of Adult and Graduate Studies, Colorado Christian University Anthony F. Buono, Professor Emeritus, Bentley University Sofia-Jeanne Caring, Doctoral Student, Kenan-Flagler Business School, University of North Carolina David Coghlan, Professor Emeritus, Trinity Business School, University of Dublin Trinity College Joshua Elmore, Consultant, Court Street Consulting LLC Leonardo Fernandes Martins, Assistant Professor, Pontifícia Universidade Católica do Rio de Janeiro Tobias Fredberg, Professor, Chalmers University of Technology and the Center for Higher Ambition at the Institute for Management of Innovation and Technology Margaret Gearty, Professor, Hult International Business School (Ashridge) and Funding Director, New Histories Ltd. Agathe Gilain, Research Fellow and Lecturer, Center of Management Science, Mines Paris – PSL University Armand Hatchuel, Emeritus Professor, Center of Management Science, Mines Paris – PSL University David A. Jarrett, Assistant Professor, Bloch School of Management, University of Missouri-Kansas City Jacqueline D. Jenkins, Lecturer, Teachers College, Columbia University Caroline Jobin, Assistant Professor of Engineering Management and Co-Head of the Engineering Management Major, EPF – School of Engineering and Research Fellow and Lecturer, Center of Management Science, Mines Paris – PSL University Jennifer Y. Kim, Assistant Professor, Center for the Study of Drug Development, Tufts University School of Medicine Regina Kim, Assistant Professor of Management, Charles F. Dolan School of Business, Fairfield University Pascal Le Masson, Professor, Center of Management Science, Mines Paris – PSL University xii

Contributors  xiii Susan Long, Co-Lead, National Institute of Organisation Dynamics Australia John Loonam, Assistant Professor of Strategy, DCU Business School, Dublin City University Michael R. Manning, Director of Research and Professor of Leadership, Strategy & Change, Benedictine University Joe McDonagh, Associate Professor, Trinity Business School, University of Dublin Debra A. Noumair, Professor of Psychology and Education, Teachers College, Columbia University Donna L. Ogle, Associate Professor of Computer Science, Rockford University William Pasmore, Professor of Practice, Teachers College, Columbia University Johanna E. Pregmark, Chalmers University of Technology and the Center for Higher Ambition at the Institute for Management of Innovation and Technology Elaine Rabelo Neiva, Associate Professor, Universidade de Brasília Henri Savall, President and Founder of the ISEOR & iaelyon, Université Jean Moulin Deepak Saxena, Assistant Professor, School of Management and Entrepreneurship, Indian Institute of Technology Jodhpur Zhida Shang, Medical Student, School of Medicine, McGill University Abraham B. (Rami) Shani, Professor of Organization Behavior and Development, Orfalea College of Business, California Polytechnic State University, San Luis Obispo Inger Stensaker, Professor in Strategic Change and Dean of NHH Executive Programs, Norwegian School of Economics David B. Szabla, Associate Professor of Organizational Change Leadership and Chair, Department of Educational Leadership, Research and Technology, Western Michigan University Yunzi (Rae) Tan, Associate Professor, School of Public and International Affairs, University of Baltimore Ramkrishnan (Ram) V. Tenkasi, Professor of Organizational Change, Ph.D. Program in Organization Development, Department of Organization Studies, Goodwin College of Business, Benedictine University Maxime Thomas, Research Fellow and Lecturer, Center of Management Science, Mines Paris – PSL University William R. Torbert, Leadership Professor Emeritus, Boston College Chipten Valibhay, Research Fellow and Lecturer, Center of Management Science, Mines Paris – PSL University Benoit Weil, Professor, Center for Management Science, Mines Paris – PSL University Véronique Zardet, Co-Director of the ISEOR & iaelyon, Université Jean Moulin

Acknowledgements

This handbook would not have been possible without the help of Sean Gaffney, a doctoral candidate in Western Michigan University’s Organizational Change Leadership program. We would like to acknowledge with much appreciation the crucial role he played in the development and management of this Handbook. We cannot express enough thanks to Sean.

xiv

PART I INTRODUCTION

1. An invitation to revitalize research into organizational change William Pasmore and David B. Szabla

Everywhere we look, there are amazing advances in science. Yet in our science, organization change and development, breakthroughs have been sparse despite the urgent need for our field to contribute on fronts such as climate, international relations, health, and diversity, to name but a few. It is precisely that these things need to change that makes it disappointing that our field, devoted to change in human systems, has offered so little when so much more is needed. Why have we not achieved more? Our view is not that we are lacking for desire or intelligence but that our research methods, derived primarily from psychology, have not been up to the task of studying complex change in organizations and society. We hope that this volume helps to correct that. Surely, Kurt Lewin, one of our founding fathers, would be hard at work on these issues if he were with us today. It is not that we are unaware of these issues; Bartunek (2022), for example, captures the views of a number of leading scientists in our discipline regarding the actions we should be taking to study and contribute more than we currently are. We know better, but we also have to do better. While this volume in and of itself will not provide the answers society seeks, we intend it to enable and perhaps even inspire the work of a future generation of scholars who, because of the urgency of our present situation, may feel compelled to conduct research into what transformational change on such a grand scale requires. Despite our 70-plus-year change research history, our understanding of the elemental psychological, social and organizational enablers of sustainable transformational change remains little advanced over Lewin’s classic unfreeze-change-refreeze and field theory formulations. The phrase “more research is needed” could be included in the discussion section of every article published on the subject. We definitely need more research, but not more of the same research. When physicists wished to study the particles that made up atoms, they needed to invent new tools that would enable them to undertake new research. The particle accelerators at CERN (the European Organization for Nuclear Research) and other research centers are huge, costly, and complex; inventing them was nearly as challenging as the research they were designed to enable. Yet funding was secured and challenges overcome because the potential benefits of the research were immense.

WHY DO RESEARCH IN ORGANIZATIONAL CHANGE? Barbara Kellerman’s book (2018) lays out the case for waste in the $356 billion annual spending on leadership development. Kellerman claims that the leadership development industry, including business schools, has had no impact on the quality of leadership over the past few decades. Is that true, or is it just a claim that is backed by evidence that some CEOs continue to be removed from office or that leaders continue to commit ethical violations? Where is the evi2

An invitation to revitalize research into organizational change  3 dence that these leaders who derailed did or did not receive the kind of training that is available today? And what about the many more who are succeeding who would not have succeeded if they did not have access to training? Yes, more research needs to be done on understanding leadership and preparing people to lead. But has every one of the $356 billion dollars spent this year on helping leaders lead been a waste? There is something called research, and something called science, that was developed to allow us to separate the quacks and medicine men from the real doctors and the real pharmaceutical developers, truth from opinion. There is a tremendous amount of material circulating about what it takes to lead successful change that is no more than speculation dressed up as fact because it is buttressed by an example taken from the headlines that “proves” the author’s point. One of our colleagues recently completed a book based on 340 interviews of executives; an impressive sample, to be certain. However, a large sample of people who occupy a role does not guarantee scientific validity since it is quite possible that none of the 340 were experts on the topic being discussed or were aware of any alternative approaches they should have considered. Each knew what they knew but not what they did not know. A lot of people used to believe the earth was flat because they saw a flat earth with their own eyes. They had a staunch belief in what they knew but did not know what they did not know. Science demands more than strong opinions from laypersons untrained in methods designed to test hypotheses. In their classic book on research methods, Selltiz, Wrightsman and Cook (1976) answer the question “Why do research?” in the following way: One of the functions of social research is to develop and evaluate practices, concepts, and theories of social relations and to develop and evaluate methodologies that test these practices, concepts and theories – in short, to know the limits of one’s knowledge and keep pressing against them. (p. 7)

We need methods in the study of change that enable us to test the limits of our knowledge on a grander scale regarding issues that are crucial to our future. If it is not completely obvious, there are many aspects of our lives that are in urgent need of change and yet not changing. Powerful forces involving capital, wealth, politics, technology, religion, culture, and beliefs seem to be pushing us in directions in which none of us ultimately want to go. Yet even as scientists of change, we are carried along by the racing current, seemingly not able to make headway no matter how hard we swim. We need breakthroughs in not just what we research but how we conduct research, just as the particle physicists needed new tools to understand matter and the origins of the universe. We need new theory but also new methods. Methods are the tools and approaches we use to access knowledge that leads to greater understanding, which in turn lays the groundwork for more effective action. When the Wright brothers decided to pursue the mysteries of flight, their methods were extremely crude compared to the approaches aeronautical engineers use today. The Wright brothers were bicycle makers, and their knowledge of both the secrets of aerodynamics and the design of gasoline-powered engines was limited. Yet they progressed slowly and incrementally, learning from each failed flight something that needed to be addressed. Their method was to test ideas in action, something that Kurt Lewin adopted in his approach to research. The Wright brothers observed birds in flight, read what little was written about aviation at the time, and made themselves aware of what others had attempted. It was not much, but it was enough to keep moving forward with new ideas that led to new tests that failed until they succeeded. It is miraculous that they survived, but they did; their commitment to the dream of flight made

4  Handbook of research methods in organizational change it worth risking their lives. If they could be transported in time to the present day, they would be both totally amazed and completely lost, given the rapid advances that have occurred in all aspects of aviation in the past one hundred years. Perhaps, if we try, we can spark a revolution with our efforts that result in a similar exponential change in organization development. Reason and Bradbury (2007), in their introduction to action research, help us understand how important it is to engage with our topic, just as the Wright brothers did to learn how to fly: Action research is a family of practices of living inquiry that aims, in a great variety of ways, to link practice and ideas in the service of human flourishing. It is not so much a methodology as an orientation to inquiry that seeks to create participative communities of inquiry in which qualities of engagement, curiosity and question posing are brought to bear on significant practical issues. (p. 1, italics in original)

By expanding the circle of inquiry to include those being studied, just as Kurt Lewin did in his study of interracial relations that led to the T-group movement and the founding of the National Training Laboratories, we open our view as scientists not to the truth of others’ opinions but to new hypotheses to be explored. Often, it is an encounter with the unexpected that leads to breakthroughs, as did the study of lighting at Western Electric that led to a new understanding of what has become known as the “Pygmalion effect” or the power of our expectations as scientists to shape the behaviors of our subjects (Roethlisberger & Dickson, 1939). Readers familiar with Kuhn’s (1970) work on the structure of scientific revolutions understand that paradigm shifts occur when basic beliefs that we hold true are challenged and proven either false or incomplete: Discovery commences with the awareness of anomaly, i.e., with the recognition that nature has somehow violated the paradigm-induced expectations that govern normal science. It then continues with a more or less extended exploration of the area of anomaly. And it closes only when the anomalous has become the expected. (pp. 52–53)

When we imagine that interviews and surveys are the only ways to understand what people in the midst of change are thinking, we narrow our focus to collecting data that tell us nothing that is fundamentally different or new than we have known for some time. We operate within the paradigm of normal science. If we keep asking the same questions in the same ways, we should not be surprised if we get the same answers. For a paradigm shift to occur, we must ask new questions in different ways; in other words, we need to develop and apply new methods to see beyond the obvious and to get at the things that people involved in change do not know that they know or do not yet know that they know because they have not yet experienced the phenomenon. We need to search out the anomalous. In introducing grounded theory, Glaser and Strauss (1967) argue that Ignoring this first task – discovering substantive theory relevant to a given substantive area – is the result, in most instances, of believing that formal theories can be applied directly to a substantive area and will supply most or all of the necessary concepts and hypotheses. The consequence is often a forcing of data, as well as a neglect of relevant concepts and hypotheses that may emerge. (p. 34)

In other words, it is a waste of time to assume that we can apply what we already know to solve novel problems of massive social and organizational importance using our existing tools and theories when there is little evidence that they are effective in the first place. Yet we see

An invitation to revitalize research into organizational change  5 study after study repeating the claims made for methods such as survey feedback, appreciative inquiry, creating common goals, or third-party conflict resolution using cases that are selected for best fit and data analyzed using approaches that lead to predetermined outcomes. To break through, we need to be willing to break away. We know that a paradigm shift is needed because we have not yet achieved the scope and scale of sustainable change in many important social and organizational processes with which we are deeply concerned. If we were medical researchers, we would know that a paradigm shift is needed because we have not been able to cure cancer completely or eradicate a number of pernicious diseases from the planet. In organizational change, we know that we need a paradigm shift because we cannot point to scientific evidence that a particular method or combination of interventions will bring about lasting, beneficial change in individuals (coaching, training), teams (team building), organizations (organization design, organization development), or society (community engagement, participatory action research). The answer to the question “Why do research in organizational change?” is just that; we do not know how to bring about beneficial, lasting change, and it is increasingly important that we are able to do so. While a paradigm shift in our research is urgently needed, there are foundational requirements that what we are calling “new paradigm research” must continue to address. Among these are validity, reliability, measurement, generalizability, and impact.

VALIDITY To be useful, according to Selltiz, Wrightsman and Cook (1976), methods must be designed to gather valid information regarding the phenomenon we wish to study. By “valid,” we mean that our way of gathering data reflects true differences caused by the variables we are studying rather than differences due to other factors or random errors. We often speak of “taking the temperature of the room” when we mean we would like to assess the attitudes of participants in a meeting. Yet using an actual thermometer to take the temperature in the room would not tell us much about the attitudes of the people therein. The method we choose must produce valid information about the topic we are trying to study. This is especially challenging when the phenomena under consideration are hidden from direct view. In change work, we often deal with resistance to change, and there are surveys that are designed to help us get at how much or how little resistance to change we might encounter (Holt, Armenakis & Harris, 2007). It is certainly helpful to understand what people anticipate as they enter into a change effort, and a survey is a good point-in-time reflection of what people think, whether they are right or wrong, since it is what they think rather than the truth about the change that best predicts their behavior. At the same time, speculations about change shift over time as people encounter the real thing, and it is equally important to understand how feelings about the change morph as people learn more about it. The challenge of collecting valid data regarding resistance during change is that people are often unclear about what is going on, do not know how they really feel, may not want to expose themselves to criticism if they sound unfavorable, or are influenced by peers to believe things that are not true. Should change agents assume that what people tell them in interviews or surveys is valid data? At a minimum, it would seem wise to triangulate the data gathered directly from statements or ratings with observations of how people are actually behaving. Are they implementing new ways of working? Are they attending change meetings? Are they speaking up

6  Handbook of research methods in organizational change and offering constructive criticism? Behaviors are not always rational or driven by conscious motivations. We need to understand what is going on at an unconscious, autonomic level that helps fill in the gap between what people say and what people do. For example, Schein (1969) developed process consultation methods that guided us in searching for valid data that could tell us whether what appeared to be going on in a group was what was really going on. Who is participating? Who is not? Whose ideas are adopted? Where do people sit? Who is present and who is absent? Schein’s methods were further enhanced by Marshak (2006), who provided a framework for inquiry into covert processes, which he defined as barriers or aids to effective group work that exist but are not acknowledged or even recognized by members of teams. These include the reasons why people are doing things, and their political interests, values and visionary aspirations, emotions, mindsets, and anxiety-based defenses. Just as a microscope enables the scientist to see what the naked eye cannot, Schein and Marshak’s tools for research allowed us to bring to light much that had heretofore remained invisible. We can no longer accept a simplified explanation of change that states, “A survey was given, the data fed back to the leadership team, resulting in the team coalescing around a direction for change.” We know that, in reality, much more than that is going on, that much of what is happening is unconscious, and therefore that the same survey given in a different setting or at a different time may not result either in the team coalescing or heading off in the same direction. If we are going to lead successful change on important and pressing concerns, we have to understand what is really happening so that we know the actions we need to take to keep the change moving in the right direction. At a minimum, in order to advance our science, new methods must help us see more truth about how social systems function in the face of needed change. Then, we need to use our understanding of the more complete picture to fashion interventions that address the barriers we encounter. This is easier said than done because we also need research that helps us understand more completely how the context within which a change occurs affects how our tools perform.

RELIABILITY A second requirement of our methods is that they be reliable. That is, that the same methods, applied in the same way, produce consistent results. If we have truly mastered the collection and understanding of qualitative data, for example, we should be able to send any two researchers into an organization to understand what is happening with regard to change and get back the same analysis and conclusions. Instead, we are more likely to hear different emphases placed on the same data because of the interaction between the researcher’s interests and his or her observations. We pay attention to what we are interested in or what we hope to find, or, at an unconscious level, what we believe we are seeing. Stories, for example, can be a powerful way of explaining what is happening in an organization, but if two storytellers focus on different plots and characters, we should know that each story tells us only a part of the truth and as much about the storyteller as the organization. If there are as many truths as there are storytellers, where is the reliable science that guides us to address the “right” issues in the “right” way? And if all social knowledge is generative and subject to emergent interpretation, transforming even as stories are told, where lies the truth? According to Gergen (1982),

An invitation to revitalize research into organizational change  7 As both psychologists and sociologists often maintain, variations in human activity may importantly be traced to the capacities of the organism for symbolic restructuring. As it is commonly said, one’s actions appear to be vitally linked to the manner in which one understands or construes the world of experience. The stimulus world does not elicit behavior in an automatic, reflex-like action. Rather, the symbolic translation of one’s experiences vitally transforms their implications and thereby alters the range of one’s potential reactions … The validity of traditional science is, in this sense, at the mercy of its subject matter. The scientist’s capacity to predict is precipitously dependent on the conceptual proclivities of the population under study. (p. 17)

Gergen’s (1982) advice, after an exhaustive critique of traditional science, is to abandon the search for permanent truths and instead accept that in social science matters, the truth will never be known because the discovery of truth changes truth. Because we understand something, we choose to behave differently based on that understanding. Instead, he calls upon us to see the role of science as generative, sparking society’s imagination and, in so doing, enabling the consideration of different futures: Let us propose as a chief contender that competing theoretical accounts be compared in terms of generative capacity, that is, the capacity to challenge the guiding assumptions of the culture, to raise fundamental questions regarding contemporary social life, to foster reconsideration of that which is ‘taken for granted,’ and thereby to generate fresh alternatives for social action. (pp. 108–9, italics in original)

In this sense, Gergen is suggesting that we seek reliability in our methods at a meta-level; that choosing to study a system will inevitably result in changing it in some way, and that the change will be a product of the interaction between the researcher and the system. Rather than trying to argue for a particular intervention producing a defined impact or outcome, we should accept that living systems will respond with certainty to being poked at by scientists even though the nature of the response cannot be predetermined. If the poking is done in collaboration and with the intent to create mutual learning that can lead to more productive action, the outcome, more often than not, would be positive, generative change. While it may be hard to wrap one’s head around this larger meta-conceptualization of reliability in social science research, its utility, if adopted, is the avoidance of countless studies that are carefully crafted to reflect the idiosyncratic interests of the researcher rather than pursue the quest for more universalistic truths about change. If we cannot agree on the basic fundamentals, it will be impossible to come together on the messages that our discipline should be sending to organizational and world leaders. As we each advocate for our views in order to establish our research identities, we impede our common progress toward having the impact as a discipline that Kurt Lewin envisioned.

MEASUREMENT It was Golembiewski, Billingsley and Yeager (1976) who helped us question the ways in which we measure the outcomes of change. Their exploration of alpha (intended), beta and gamma (unintended) influences on participants’ reactions to change forcibly removed any notion that measuring the effects of change could be straightforward. We now realize that claims regarding the comparative efficacy of interventions need to be rigorously interrogated

8  Handbook of research methods in organizational change in order to substantiate their validity. Yet many studies published in our field each year violate even the most straightforward canons of evidence. Testimonials from participants are notoriously subject to bias, and yet they are often employed as the ultimate test that a particular intervention has succeeded. Data collected at a single point in time are used to support the efficacy of a change effort, without attention to the degrading effects of time on attention to the maintenance of newly acquired behaviors. Single surveys are used to measure whether what is observed conforms to a priori theory rather than applying multiple measures. Both because participants react to being measured and because organizations and institutional review boards are increasingly opposed to gathering data that could violate expectations of privacy, it may behoove us to rely more heavily on unobtrusive measures, as outlined by Webb, Campell, Schwart, and Sechrest (1966). If we begin inquiry with the question “What should the observable outcomes of this change effort be if it succeeds?” instead of gathering interview data that we code for meaning, perhaps we could cut down on the amount of time we spend making sense of interventions that produced no real change and instead focus on those that make a difference. Of course, there is value in studying change failures as well as successes (Mirvis & Berg, 1977), but once again with an eye toward the measurement of observable outcomes rather than exclusive reliance upon the researcher’s personal interpretation of the dynamics involved. The question of whose truth should we honor should be settled by objective measurement rather than the position or stature of the observer. Too often, conclusions reached should be preceded by “In my opinion …” rather than presented as scientific facts. As we enter the age of big data and artificial intelligence, we are capable of detecting patterns that would have previously gone unnoticed or been altered to fit preconceived notions of what we expect to see. These new tools, along with meta-analyses of an increasingly large body of studies, will help us see beyond our biases and force us to lean into seemingly unpleasant truths. In the end, however, data are always friendly, meaning that to ignore what data are actually telling us only impedes progress rather than hastening it.

GENERALIZABILITY The fourth demand we make of our methods is that they produce generalizable results. What we learn in one setting should apply in other settings; what happens within a group faced with change should be the same dynamic that happens in an organization or on a global scale. That is, the truth revealed through the application of these methods should be the same truth, despite the size, location, or state of the system. We love to use detailed case studies as a method in organization development, as if what happened in one organization could plausibly predict what will happen in many others. But if each case is unique due to its leadership, location, workforce, industry, history, and culture, there is little that case studies can tell us that is universally true. When we repeat the same actions in another organization or setting, we do not observe the same results. We can take heart in studies that indicate we can make progress in a single community or even in addressing a single issue like eradicating smallpox, but thus far we have not been able to replicate the results of small/focused case studies in a larger, more complex context. As

An invitation to revitalize research into organizational change  9 Reason and Bradbury state (2007), we should take heart in the fact that cases demonstrate that change is at least possible even under especially demanding circumstances: action research – which may be quite intimate or may seek influence on a large scale – demonstrates an inquiry-in-action that positively shapes the lives of literally hundreds of thousands of people everyday around the world. Indeed, we might respond to the disdainful attitude of mainstream social scientists to our work that action research practices have changed the world in far more positive ways than has conventional social science. (p. 3)

That change can be achieved at least for a time on such a scale does not tell us that change can occur on an even larger scale in a more sustainable fashion, which is what it will take to alter the course of climate change, international tensions, or race relations. This indicates that our methods have not yet allowed us to understand the fundamental dynamics of change, at either a micro or macro level. The issue, in part, as Gergen states (1982), is that human systems are reflexive, dynamic, and autopoietic. Just as physicists studying particles discovered the Heisenberg principle, researchers at the Hawthorne Works, in the famous studies of the same name conducted by Harvard professors, learned that simply observing workers led to changes in their behavior. As we try to export methods for studying change from one setting to another, our interactions with the system stimulate responses which are uniquely different from those observed in the original case. It is not we who decide how systems will react to our experimental stimulus, but the actors in the system themselves. A search conference that was fabulously successful in uniting inhabitants of one community cannot be replicated exactly in another and may not work at all on a global policy level. Successful methods used to create higher levels of employee engagement in an organization with willing and committed leaders are not readily transferable to one where leaders are actively opposed or even blissfully ignorant when it comes to empowering workers. Our current approach as a discipline seems to be one of accumulating evidence. Each new case study or change effort produces an incremental addition to our body of knowledge, indicating that in the face of some slight variation in conditions, different outcomes were observed. If we study enough cases, we should exhaust the possible contextual variables that should be taken into consideration as we undertake change efforts, leading to higher levels of success and ultimately to a complete understanding of how social systems of all kinds respond to different perturbations of change stimuli. Of course, the permutations of variables in the setting and the approaches used make this a fool’s errand, yet we seem to fill our journals with additional cases nonetheless. We must question whether our current track is taking us anywhere or whether it is time to rethink our approach to studying change.

IMPACT This brings us to the ultimate question of whether or not our work has sufficient impact on people, organizations, or the world. As members of the community of scientists, we seem to subscribe to the dictum that we should illuminate knowledge but not assume responsibility for decisions regarding how that knowledge should be applied. During the development of the nuclear bomb, Oppenheimer and his Los Alamos crew on the Manhattan Project saw their

10  Handbook of research methods in organizational change responsibility ending with the development of a successful device. Its use, they believed, was best left to the military and politicians. While we might be well advised to adopt the medical Hippocratic oath that we should “first do no harm,” we should at least look into options for using what we learn through research to help the world. We could even go as far as to compare the strength of various intervention techniques, something we have rarely done, even in individual-, team-, and organizational-level change efforts. While we may not want to force our recommendations for change on society, we could at least make it easier for those in positions of influence to do the right things. For example, research by Reeves, Faeste, Whitaker, and Hassan (2018) used data analytics on a large sample of corporate transformation efforts to tease out what separates the successful turnarounds from the less successful ones. Turnaround artists make confusing claims to support their own approaches. Leaders would find it difficult to choose among them based on their success stories alone. With the help of science, however, the picture becomes clearer. Quantitative evidence is hard to argue with; in examining available data from approximately 300 firms over a 12-year period, Reeves et al. found that the more successful transformations required changes in leadership, initial cost-cutting, and sustained investment in innovation and programs designed to improve organizational effectiveness. Leaders would be well advised to use this information to ensure that their efforts have a real impact rather than to select advisors based on the information on their websites alone. More broadly, our field would benefit greatly from efforts to discern the real impact of what we do. Kirkpatrick (1959) proposed a framework for assessing the impact of learning interventions based on the level of rigor involved. If we were to apply this framework to efforts to extend our work into the realm of climate change, for example, the challenge and importance of demonstrating impact becomes clear. At the first level of impact, we might ask the question: what percentage of policymakers, environmentalists, regulators, or state and national officials are aware of our work and consider it important? At the next level, we would be interested in knowing why they thought the work was important and what implications it held for their future actions. At the third level, we would search for evidence that our work had been adopted and put to practical use. Finally, we would measure the amount of carbon in the atmosphere to see if the actions taken had an impact on the climate that would lead to a slowing of global warming. If this seems hard to imagine, then we are back to where we began. If our research on change is to contribute to addressing the world’s most pressing problems, we must take responsibility for designing and conducting research that has the potential to do just that despite the enormous challenges involved. We should note that we are not the only science concerned with discovering the keys to change at a global level. Others in the natural sciences, economic and political realms have put a great deal of effort into the search for the holy grail. Yet it is our discipline that claims change as its primary focus and should rightly be at the forefront of researching the motors and barriers to large-scale lasting transformation. We must ask ourselves what methods will allow us to uncover theories and processes that we can use to reliably generalize from the level of individual change to change on a planetary scale. It is with these cautions and aspirations that we undertook to craft this volume, bringing to the forefront new methods that might overcome current limitations and advance the pace of discovery in the field of organizational change. Our authors have given us their best thinking and challenged us to reflect on our own to discover not only what we know, but what we still do not know, and thus illuminate the path to learning.

An invitation to revitalize research into organizational change  11

FOUNDATIONAL TOPICS In this first section, research methods that are foundational to the field of organizational change are presented: action research, processual research, grounded theory and longitudinal research. In his chapter titled “Action Research as the Social Science of Change and Changing,” David Coghlan opens this section by reviewing action research, a method that is grounded in change and changing. He defines action research and reviews its foundations, philosophies, ethics, and quality criteria and then explores the practical side of designing and enacting action research, emphasizing the role played by the context, the quality of collaborative relationships, and the engagement in the dual tasks of addressing the practical organizational issue and generating practical knowledge and producing outcomes of value for the organization and robust knowledge for scholars. A second foundational method in the field of organizational change is processual research. “Conducting Processual Research on Organization Change,” by Deepak Saxena and Joe McDonagh, discusses the theoretical background and practical guidelines of conducting processual research on organization change. The authors discuss the processual nature of organization change and highlight the value of a processual perspective in making sense of change in organizations. Methodological aspects of collecting and analyzing processual data form the core of the chapter, which emphasizes the key role of a longitudinal, qualitative, case study approach in capturing and analyzing the event sequences as they unfold in an organization change initiative. While qualitative approaches to research offer an important methodological contribution to the organizational change domain, grounded theory methodology has received limited attention from both scholars and practitioners alike. “The Grounded Theory Methodology: Over Fifty Years of Inquiry!” by John Loonam explores grounded theory and its application to organizational change research over the past 50 years. The chapter explores the methodology’s origins, characteristics and attributes, and value to empirical inquiry, plus lessons learned from practice, and concludes with a cross-case analysis and a series of recommendations to support greater grounded theory synthesis during inquiry. Elaine Rabelo Neiva and Leonardo Fernandes Martins conclude this foundational section of the Handbook with a chapter on longitudinal research titled “Longitudinal Research Methods for Studying Processes of Organizational Change.” They answer the question: what is the best way to collect and analyze data in organizations over time? The chapter is anchored in theory, research design, and the analysis of change, and addresses epistemological dimensions related to longitudinal research, with discussions about temporal perspectives (how time should be considered in studies), causal inference, and the importance of the context as the cornerstone for conclusions of the studies. Types of longitudinal designs are addressed, considering possibilities of experimental research, cross-sectional designs and repeated measures, and correlational, and qualitative studies. Finally, the chapter reviews techniques of data analysis in longitudinal research: repeated measures with general linear modeling, panel analysis, latent growth curve, other models of structural equations, multilevel models, and time series.

12  Handbook of research methods in organizational change

CONTEMPORARY TOPICS In this second section, more current research methods are presented that range from psychoanalytic and socioanalytic methods to learning history, information and communication technology-enabled, and participatory mixed methods as tactics for studying organizational change. In her chapter, “Psychoanalytic and Socioanalytic Approaches to Organizational Change Research,” Susan Long presents psychoanalytic and socioanalytic approaches to organizational change research. A range of relevant psychoanalytic theories are described, leading to a primary focus on those approaches that prioritize a combination of whole systems and psychoanalytic ideas, known as systems psychodynamic or socioanalytic approaches. Unconscious processes, repression, group mind, group dynamics, free association, social defenses, transference, projection, introjection projective identification, and the institutionalization of psychological processes are described, followed by a discussion of the preferred research designs used by psychoanalytically informed researchers: case study and collaborative action research. Stress is laid on the importance of the practitioner to have in-depth training in the methods employed within these designs, together with personal work on their own internal dynamics. The latter part of the chapter has a focus on the analysis of data and the ethics of such research. A second contemporary chapter, “Qualimetric Intervention-Research as an Approach to Studying Organizational Change,” by Henri Savall, Véronique Zardet, Marc Bonnet, and Anthony F. Buono, presents a specific approach to research in the field of organizational change and development. The object of organizational change is considered as complex, intangible, and transient, which is particularly challenging when trying to capture information through scientific observation. Intervention-research enables the involvement of a large number of informants through cognitive interactivity and contradictory intersubjectivity. It progressively results in a body of information referred to as the Socio-Economic Approach to Management, which is based on the concept of generic contingency that is comparable to scientific approaches in medical research. The complex dynamic of organizational change requires rethinking research orientations, philosophy, design, and methods. Collaborative Management Research (CMR), a Mode 2 (applied) research orientation that integrates a Mode 1 (academic) orientation as the need arises, is advanced in “Collaborative Management Research: Theoretical Foundations, Mechanisms and Practices” by Abraham B. (Rami) Shani. Following a brief review of the theoretical foundation, this chapter discusses the design and management of insider/outsider research teams and the collaborative mechanisms and processes that guide their actions. It concludes by discussing implications for the researchers: the challenge of designing and sustaining a balance between rigor and relevance, the role of learning mechanisms that house the insiders and outsiders’ collaborative discovery process and deliberations, and the quality challenges in CMR. In “Learning History: Engaging Multiple Perspectives for Learning,” Margaret Rose Gearty discusses the role of the learning historian along with a step-by-step guide to the methodological framework. She reviews the decisions and dilemmas involved in learning history practice and discusses how the epistemological position at the boundary between research paradigms opens interesting questions of practice for the researcher and change practitioner. The chapter traces the philosophical underpinnings and origins of learning history in the wider field of organizational learning and its connections to oral history. It sets out learning

An invitation to revitalize research into organizational change  13 history as a methodology of potential interest to research students who are inclined toward action research or narrative approaches or interested in developing their skills and reflexive capacities by adopting a learning historian position. Likewise, the method may be of interest to organizational change practitioners. Throughout the last seven decades, advances in information and communications technology (ICT) have paved the way for the ongoing development of organizations and their broader business networks. Although rapid advances in ICT have accelerated the pace of change across the decades, outcomes from ICT-enabled change initiatives have been consistently disappointing since the 1950s. In “Principles for Productive Inquiry into ICT-Enabled Change in Organisations,” Joe McDonagh offers a deep dive into this enduring dilemma, uncovers the contributing role of occupational communities, and draws attention to their influence on systems of professional practice. Against this backdrop, the chapter introduces six principles for productive inquiry into ICT-enabled change, which together have the potential to both inform and transform professional practice while simultaneously contributing to sound scholarship. The chapter offers advice for researchers on the adoption of these principles, the related evolution of a practice of inquiring together, and the merits of locating oneself at the nexus between sound scholarship and exemplary professional practice. In “Using Participatory Mixed Methods to Study ‘Grand Challenges’: An Illustrative Case of Diversity, Equity, and Inclusion Change Research in Organizations,” Regina Kim and Yunzi (Rae) Tan introduce participatory mixed methods research (PMMR) as an important and suitable approach for tackling grand challenges that are dynamic and complex in nature, with a specific focus on diversity, equity, and inclusion (DEI) as one such challenge. PMMR is a form of research approach that combines both quantitative and qualitative methods in ways that emphasize knowledge co-production and equitable collaboration between researchers and participants. The authors present an illustrative case to articulate how PMMR might be utilized to examine DEI change efforts in an organizational context, and outline a seven-phase process of conducting PMMR to guide doctoral students and junior scholars (and experienced researchers) toward understanding the theoretical underpinnings of PMMR and practical steps associated with this approach.

EMERGING TOPICS In this final section on methods, approaches that are gaining ground in the field are discussed; for example, methods based on structuration theory, design science, and big data. In the first chapter of this section, “Conducting Phenomenon-Driven Rapid-Response Research to Explore Disruption and Its Impact on the Minority Experience,” Jennifer Y. Kim and Zhida Shang present a method that offers rapid responses to the many disruptions that comprise today’s organizational landscape, such as technological advancements, regulatory changes, socio-political movements, and man-made and natural disasters. Specifically, the authors detail their experience conducting phenomenon-driven rapid-response research on the racism encountered by Asian American and Asian Canadian professionals during COVID-19 and the impact it had on work identities. They present the principles, key learnings, and takeaways of phenomenon-driven rapid-response research, a method that allows for a more flexible way to capture and explore a topic of interest rather than traditional theory-driven approaches.

14  Handbook of research methods in organizational change In “Collaborative Developmental Action Inquiry: A New Paradigm for Leadership & Organizational Change Research,” William R. Torbert and Sofia-Jeanne Caring introduce a new paradigm of social science and of organizational change research that, in particular, integrates scientific inquiry and timely action, rather than separating them. In this new paradigm, discovering and validating generalizable (third-person) theories is an indispensable – but secondary – aim. The primary aim is to engage in timely action/inquiry within one’s immediate context and circumstances. In doing so, one is guided in part by third-person theory and data, and in part by first- and second-person inquiry and feedback. The authors offer a method through which we must study ourselves in action (first-person action research); we must also study our interactions with others (second-person action research), and we must study the larger social institutions in which we are embedded (third-person action research). Researchers studying organizational change often struggle to capture the full extent of phenomena, tending to focus on either agency or social structure but not both. Because both agency and structure are implicated in change research questions, having a research method sophisticated enough to incorporate them is essential. “Advancing Strong Structuration Theory in Organizational Change Research,” by David B. Szabla and David A. Jarrett, examines Strong Structuration Theory (SST) and explains how it can represent an improvement for empirical research over older structuration theory and provides guidance on how to organize change research with SST. From a diverse set of published SST studies, best practices in creating effective change research emerge, and the chapter closes by exploring how change researchers might expand the use of SST going forward. Design science research has developed strongly in the last decades with rich contributions to organization science and, more specifically, to organizational change. These contributions were not straightforward and cannot be obtained without strong epistemological and theoretical rigor. In “Design Science for Organizational Change: How Design Theory Uncovers and Shapes Generativity Logics in Organizations,” Pascal Le Masson and his colleagues discuss why it is critical to encompass a variety of design approaches and methods within a unified theoretical framework that underlines the logic and rationality – namely, the design theory – assumed in each type of design science research when studying organizational change. The authors use this framework to compare three families of design science research approaches and underline for each their methodological assumptions and the type of organizational changes they aim to achieve. The chapter concludes with key insights for organizational change researchers and discusses how design science research could further contribute to the development of organizational change theory. Three quantitative methodological considerations in studying organizational change are presented and discussed in “Longitudinal Designs, Big Data, and Social Network Analysis in Organization Development and Change Research” by Ramkrishnan (Ram) V. Tenkasi, William (Bart) Brock, and Donna L. Ogle. The first two methods, Accelerated Failure Time (AFT) analysis and Generalized Estimating Equations (GEE), entail understanding change from a temporal and longitudinal perspective. The third approach addresses the use of big data in assessing organizational development and change through social network analyses. All three quantitative methods are illustrated using grounded examples from the authors’ previous studies. Broader implications for organizational development and change research are discussed. In the contribution by Debra A. Noumair and Jacqueline D. Jenkins, researchers are offered a tool to literally see what is invisible in organizations: the psychodynamic forces that either

An invitation to revitalize research into organizational change  15 pull people together or push them apart. Using the well-known Burke–Litwin model that describes how organizations function as a starting point, Noumair and Jenkins help the reader to "see" what lies behind each of the more visible factors in the Burke–Litwin model, such as “leadership.” By understanding the questions to ask about the psychodynamics surrounding leadership, researchers gain an entirely new perspective on why certain decisions are made or reactions occur. An often-sought goal in organizational change research is to make sense of complex systems. Once understood, change practitioners can effectively intervene, produce change, and monitor the progress of change in a system. Thus, tools well suited to facilitate the activity of developing and maintaining a coherent understanding of complex systems are welcome additions to the toolbox of any change practitioner. In his chapter, “Applying Data Science in Organizational Change Research,” Joshua Elmore integrates data science into organizational change research. In this chapter, he reviews the existing general framework for research in organizational change, introduces the field of data science, develops an integrative framework for data science in organizational change research, and discusses opportunities for applying the framework.

REFLECTIONS The final part of this Handbook focuses on reflections – reflections of the ethical dilemmas in change research, reflections of the identity journey of a budding organization change researcher, and reflections of preeminent organizational change scholars who guided doctoral students researching organizational change. In “Ethical Dilemmas in Collaborative Action Research,” Tobias Fredberg and Johanna E. Pregmark state that as change processes in organizations become more continuous and complex, there will be a greater need to be deeply involved in the organizations under study. However, coming close to an organization and affecting the organization is a delicate matter, which implies specific ethical challenges. In this chapter, the authors present six situational cases where ethical dilemmas arise. They provide researchers taking on a collaborative research approach to investigating large-scale change with a “heads up” around ethical dilemmas that can occur around the delicacy of change. Additionally, the authors provide readers with propositions about how to handle each dilemma. “Reflections on the Identity Journey of a Budding Organizational Change Scholar or Insights on Constructing a Meaningful Research Path and Life” by Julie Bayle-Cordier captures Julie’s identity journey as a budding organizational change scholar and uncovers insights as to how our individual, relational, and collective identities impact our research and vice-versa. She explores how conducting research on organizational change is not only a quest for understanding the nature of organizations but also a quest for self-discovery. The chapter is based on her experience as an ex-manager becoming a student again and taking a Ph.D. journey to embark on a study about the acquisition of iconic ice cream producer Ben & Jerry’s by Unilever and how this acquisition impacted the organizational identity of the social icon. The research quest was part of her own identity journey to construct a meaningful life. Based on her own experience and journey, insights reveal the importance of honoring the dynamic construction of multiple identities and how these may shape our professional identity.

16  Handbook of research methods in organizational change Finally, to close the Handbook, the editors invited colleagues whom they know to be experienced in guiding doctoral dissertations in organizational change to reflect on their experiences directing doctoral students through the dissertation process. David Coghlan and Jennifer Y. Kim capture these contemplations in “Reflections on Guiding Doctorates in Organizational Change.” Each of the musings of these organizational change scholars is reproduced verbatim in this chapter. Readers may study the personal approaches and reflections of these scholars to reflect on their own experiences and to enhance their work as doctoral studies guides and students.

AND NOW, TOWARD THE FUTURE The stimulating formulations and reflections that make up this volume have delivered a challenge to our discipline to rethink the way we approach our work in advancing knowledge regarding organizational change, as well as the training of the next generation of scholars in the philosophy of our science and the methods we use to get at the truth. As Kuhn (1970) so eloquently points out, there can be no real advances without challenging existing paradigms. As we reach the roughly 75-year anniversary of our field, we are in need of a revolution rather than merely an evolution in our methods. The authors who have contributed to this volume have pointed us toward the future, but, of course, there is much work yet to be done. As we are confronted by problems that threaten to alter our very existence on the planet, we are reminded that Kurt Lewin began his work following a similar period of global turmoil, which it seemed might conclude with a nuclear conflagration that would leave only certain species of insects behind to carry on. Perhaps we need the urgent pressure of life-ending challenges to engage our hearts and bolster our courage in what we can and must contribute to the understanding of change and its promise in leading us out of the darkness toward the light of opportunities. What we know, following decades of study, is that our current ways of thinking and existing methods for researching change will not get us to where we need to be. A sign on a London shop window recently proclaimed, “You haven’t come this far just to get this far.” Our obligation, as social scientists, educators, and change agents, is to create the tools that leaders can use to cure the ailment of mistrust and restore faith in the possibility of humankind to care about one another and the future of the world. Let that work commence.

REFERENCES Bartunek, J. (2022). Social Scientists Confronting Global Crises. Routledge. Gergen, K. (1982). Toward Transformation in Social Knowledge. Springer-Verlag. Glaser, B., & Strauss, A. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine. Golembiewski, R.T., Billingsley, K., & Yeager, S. (1976). Measuring change and persistence in human affairs: Types of change generated by OD designs. Journal of Applied Behavioral Science, 12(2), 133–57. Holt, D., Armenakis, A., & Harris, S. (2007). Readiness for organizational change: The systematic development of a scale. Journal of Applied Behavioral Science, 43(2), 232–55. Kellerman, B. (2018). Professionalizing Leadership. Oxford University Press.

An invitation to revitalize research into organizational change  17 Kirkpatrick, D. (1959). Techniques for evaluating training programs. Journal of American Society of Training Directors, 13(3), 21–6. http://​www​.astd​.org. Kuhn, T. (1970). The Structure of Scientific Revolutions. University of Chicago Press. Marshak, R. (2006). Covert Processes at Work: Managing the Five Hidden Dimensions of Organization Change. Berrett-Kohler. Mirvis, P., & Berg, D. (1977). Failures in Organization Development and Change. Wiley. Reason, P., & Bradbury, H. (2007). Handbook of Action Research: Participative Inquiry and Practice (2nd ed.). SAGE. Reeves, M., Faeste, L., Whitaker, K., & Hassan, F. (2018). The truth about corporate transformation. MIT Sloan Management Review, 59(3), 1–7. Roethlisberger, F., & Dickson, W. (1939). Management and the Worker. Harvard University Press. Schein, E. (1969). Process Consultation: Its Role in Organization Development. Addison Wesley. Selltiz, C., Wrightsman, L., & Cook, S. (1976). Research Methods in Social Relations. Holt, Rinehart & Winston. Webb, E., Campbell, D., Schwart, R., & Sechrest, L. (1966) Unobtrusive Measures: Nonreactive Research in the Social Sciences. Rand McNally.

PART II METHODS

Foundational

2. Action research as the social science of change and changing David Coghlan

INTRODUCTION Action research is a form of research where action researchers intervene in organizations and community systems to address real issues and through that action cogenerate practical knowledge in collaboration with those who are affected by the issues. As a science of action, it constitutes a different form of social science from often-used quantitative–qualitative or Mode 1 categorizations with a distinct philosophy, epistemology, methodology and methods (Susman & Evered, 1978; Gibbons, Limoges, Nowotny, Schartzman, Scott & Trow, 1994; Coghlan, 2011). Within this action paradigm, therefore, action research is fundamentally grounded in the theory and practice of change and of changing and, as such, identifies with the subject of this Handbook. The chapter is structured in two parts. Section 1 provides a definition of action research, its foundations in organization development and change, its philosophies, ethics and quality criteria. A particular emphasis on how it is an emergent process that is undertaken in the present tense and how the significant data emerge as a consequence of intervention, and the kind of theory they generate, are explored. It discusses the role and skills of the action researcher. It explores how ‘action research’ has become a generalized term and is used to refer to a family of approaches or modalities, and introduces the more common modalities. Section 2 explores the practical side of designing and enacting action research, emphasizing the role played by the context, the quality of collaborative relationships and the engagement in the dual tasks of addressing the practical organizational issue and generating practical knowledge, and producing outcomes of value for the organization and robust knowledge for scholars. It demonstrates a general empirical method through which the action researcher’s co-inquiry with organizational members draws on abductive reasoning as questions emerge in the unfolding process. As an action research initiative begins from a desire or need to address a real issue facing an organization or community, the opportunity and choice to engage is somewhat opportunistic. Canterino, Shani, Coghlan and Brunelli (2016) describe a scenario where a CEO sought help from a researcher to manage a merger and the researcher gathered a research team which included a doctoral student. That student was able to engage in the collaborative research process to study and enable the merger to work and achieve her doctorate. More generally, a student might choose to engage in action research rather than more traditional research whenever there is an interest by the parties involved to investigate the immediate effects of change on the individuals, groups or organizations involved, with the intention of using the findings to both understand the efficacy of change mechanisms and improve the impact of the changes in question.

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20  Handbook of research methods in organizational change

THE CONTEXT OF METHOD AND METHODS IN SOCIAL SCIENCE The emergence of experimental methods in the eighteenth century was based on rational thought and sought to purge people’s minds of beliefs, prejudices and the influence of tradition by grounding their thinking in rigorous observation and experimentation. What was understood as science was defined by experiments involving trial and error and measurement which set off a scientific revolution. A legacy of this revolution was the creation of a plurality of autonomous specialized disciplines, each with its own heuristic structures, methods, theories and practical applications. As time went on method became a distinctive feature of these disciplines and particular methods were adopted in specialist fields so that now modern scientists place their reliance on method (Lonergan, 2017). A distinction between a method and a recipe which delivers a further instance of the same product is important in action research as it challenges tendencies to replication, a tool which works in other research traditions. Coghlan (2010) explores how the key to method is the relationship between questioning and answering. He shows that method in action research is a collaborative activity that deals with different types of questions: what is happening? What does it mean? What might we do? These three questions lead to a social science of praxis and how action research in organization development and change may be explored. The emergence of a distinction between social science and the natural sciences came through the work of Wilhelm Dilthey (1833–1911), who argued that human life could be understood only in terms of categories that do not apply to knowledge of the physical world, aspects of meaning such as ‘purpose’, ‘value’ or ‘development’ (Hollis, 2002; Coghlan, Shani & Hay, 2019). Dilthey emphasized that the sensible data of the human sciences, in contrast to the empirical data of the natural sciences, are intentional carriers of meaning and value. From Dilthey we have come to understand social science as essentially critical and interpretive as it explores the meaning of how people understand themselves and their active role in the world, described as intentional consciousness. As social science has developed its identity from the natural sciences, and engages with the world of meaning, so has its methods, as the variety of chapters in this Handbook demonstrates. A consequence of understanding social science as a science of meaning is that the whole of human consciousness must be taken into consideration; that is, not merely the data of sense but also the data of consciousness. This is central to action research as those engaged are consistently questioning their experience, interpreting it, making value judgments of what is best to do and taking action. Consequently, paying attention to both the data of sense and data of consciousness is critical. This theme will be developed later under the rubric of interiority. In order to do that a generalized conception of empirical method must include the data of consciousness on an equal par with the data of sense. As Coghlan, Shani and Hay (2019) argue, the entire field of human consciousness falls within the scope of intentionality analysis. As human consciousness is polymorphic and unfolds in distinctive patterns of experience – intellectual, practical, aesthetic, artistic, dramatic, religious – which Heron and Reason (1997) term the ‘extended epistemology’, the methods of social science need to be inclusive of the entirety of human consciousness. In engaging in action research participants may be moving across different ways of knowing: assessing technical data in propositional/scientific mode, attending to building collaboration in a dramatic mode, relationship building and making organizational changes in a practical mode.

Action research as the social science of change and changing  21

1.

INTRODUCING ACTION RESEARCH IN ORGANIZATION DEVELOPMENT AND CHANGE

Section 1 introduces action research under seven headings: definition and characteristics, its foundations in organization development and change, its philosophies, quality criteria, ethics, modalities, and the role and skills of the action researcher. Definition and Characteristics of Action Research The field of action research extends beyond the context of organizational change and development and is prevalent in creating social change (Burns, Howard & Ospina, 2021). While there are many definitions of action research that capture its applications across a wide range of contexts, one provided by Coghlan and Shani (2014) captures its application for the field of organizational change and development: Action research may be defined as an emergent inquiry process in which applied behavioral science knowledge is integrated with existing organizational knowledge and applied to solve real organizational issues. It is simultaneously concerned with bringing about change in organizations, in developing self-help competencies in organizational members and in adding to scientific knowledge. Finally, it is an evolving process that is undertaken in a spirit of collaboration and co-inquiry. (p. 191)

Several core critical themes may be gleamed from this definition. Action research is an emergent inquiry process as it constructs an evolving narrative in the present tense, where data shift as a consequence of intervention and where it is not possible to predict or to control what

Figure 2.1

Comprehensive framework of action research

22  Handbook of research methods in organizational change occurs or the responses to intervention. It focuses on real organizational issues, what Schwarz and Stensaker (2016) refer to as phenomenon-driven research, rather than issues created particularly for the purposes of research. It operates in the domain of people-in-systems and applied behavioral science knowledge is both engaged in and drawn upon. Its distinctive characteristic is that it addresses the twin tasks of bringing about change in organizations and in generating robust, practical knowledge, in an evolving process that is undertaken in a spirit of collaboration and co-inquiry, whereby research is constructed with people, rather than on or for them. Shani and Pasmore (1985) present a comprehensive framework for action research process in terms of four factors (Figure 2.1): ● Context: The complexity of the context, what is referred to as VUCA (volatile, unpredictable, complex and ambiguous), typically has a significant impact on how the action research project is framed, the relationships that are established and nurtured, the inquiry and action mechanisms that develop, the quality of the inquiry and discovery process within and across levels of the organization, and the shared knowledge that is generated. Socio-economic and ecological forces in the global and local economies and societies provide the broad and local context in which action research takes place. Organizational characteristics, such as history, structure, resources and culture and how the organization responds to the external threats and opportunities, affect the readiness and capability for participating in action research. The external and internal context grounds the rationale and purpose of the action research project and its outcomes seek address relevant issues arising in that context. ● Quality of relationships: In action research, the action researcher and organizational members are co-researchers working collaboratively to address an organization’s issues, and to contribute to the body of knowledge. Accordingly, the quality of relationship between members of the system and the researchers as they engage in developing shared understanding of the issues, shared planning, taking joint action and shared evaluation is central. ● Quality of the action research process itself: The quality of the action research process is grounded in the dual focus of the organizational action and the development of practical knowledge. The enactment of iterative cycles of collaborative planning, taking joint action and co-evaluating action in the present tense and an attentiveness to emergent learning mark the progress of this dual focus. ● Outcomes: The dual outcomes of action research are some level of sustainability (human, social, economic, ecological) and the development of self-help and competencies out of the action and the creation of practical theory through the action and inquiry. The Origins and Foundations of Action Research in Organization Development and Change The term ‘organization development and change’ (commonly referred to as ODC) refers to an approach to organizational change that is a philosophy, a professional field of social action, a mode of scientific inquiry and an array of approaches to enable change and learning to take place in organizations (Porras & Robertson, 1987; Cummings, 2008; Schein, 2010; Burnes & Cooke, 2012; Woodman, 2014; Cummings & Cummings, 2020; Shani & Coghlan, 2021a;

Action research as the social science of change and changing  23 Burke, 2022). It is understood to refer to a specific values-based approach that has its roots in the social psychology of Kurt Lewin, and those who subsequently took up his work (Pasmore, 2001; Burnes, 2007; Bradbury, Mirvis, Neilsen & Pasmore, 2008; Lippitt, 2016). Schein (2010) points to Lewin’s work as the tap root of OD and he grounds Lewin’s work as being rooted in the practical social science that Lewin practiced. He reflects that for Lewin, it was not enough to try to explain things; one also had to try to change them. This insight, in Schein’s view, led to the development of action research and the powerful notion that human systems could only be understood and changed if the members of the system were involved in the inquiry process itself. He reflects that what ‘created OD was a combination of a new inquiry approaches based on a willingness to gather data in the field by non-traditional methods, with the vivid concerns of a set of practitioners who wanted to improve organizations’ (2010, p. 93). Accordingly, the tradition of involving the members of an organization in the change process which is the hallmark of OD originated in a scientific premise that this is the way (a) to get more relevant data and (b) to effect change. As Schein points out, the roots of OD are in science and Lewin built a cadre of colleagues and students whose work in group dynamics and organizational research became the foundation for what emerged later as OD. In Schein’s view OD was a ‘quiet revolution’. Argyris, Putnam and Smith (1985) provide a summary of Lewin’s concept of action research. Action research involves change experiments on real problems in social systems by focusing on a particular problem and seeking to provide assistance to a client system. It involves iterative cycles of identifying a problem, planning, acting and evaluating. The intended change in an action research project typically involves re-education; that is, that the change intended is typically at the level of norms and values expressed in action. Action research challenges the status quo from a participative perspective, which is congruent with the requirements of effective re-education. It aims to contribute simultaneously to basic knowledge in social science and to social action in everyday life. Accordingly, high standards for developing theory and empirically testing propositions organized by theory are not to be sacrificed nor the relation to practice lost. New forms of ODC and action research emerged in the late twentieth century, influenced by the new sciences and postmodern thought and constructivist philosophy and views of organizations as meaning-making systems (Coghlan, 2012). Accordingly, contemporary OD views reality as socially constructed with multiple realities which are socially negotiated rather than a single objective reality that may be diagnosed. Data collection is less about applying objective problem-solving methods and more about raising collective awareness and generating new possibilities which lead to change. Contemporary OD emphasizes changing the conversation in organizations by surfacing, legitimating and learning from multiple perspectives and generating new images and narratives on which people can act (Shani & Coghlan, 2021a). Bushe and Marshak (2009, 2015) explore the emergence of new forms of OD in the postmodern world and frame classical OD as ‘diagnostic OD’. This is where reality is understood as an objective fact and diagnosis infers collecting and applying data and using objective problem-solving methods to achieve change to an articulated desired future. As an alternative, they propose what they called ‘dialogic OD’, where organizations are viewed as meaning-making systems, containing multiple realities, which are socially constructed. Accordingly, the focus of OD is to create the space for changing the conversation on what is happening in the external environment and how the organization can both respond and reshape itself in an environment of continuous change (Pasmore, 2015).

24  Handbook of research methods in organizational change In a parallel vein, the premises underpinning action research began to change. Now the purpose of research is understood as forging a more direct link between intellectual knowledge/theory and action so that each inquiry contributes directly to the flourishing of people and their communities (Bradbury, 2015). This development has found expression in the emergence of different modalities of action research which I will discuss later in this chapter. The Philosophies of Action Research Action research is not only a methodology and a set of tools, but also a theory of social science (Peters & Robinson, 1984). There is a great deal written about ontology, epistemology and methodology underpinning action research (Susman & Evered, 1978; Greenwood & Levin, 2007; Coghlan, 2011). Susman and Evered (1978, p. 601) make the case that action research ‘constitutes a kind of science with a different epistemology that produces a different kind of knowledge, a knowledge that is contingent on the particular situation and which develops the capacity of members of organizations to solve their own problems’. In using the term ‘scientific’, they argue, there is a need to move away from adopting frameworks from natural sciences in order to engage with the world of practice. They emphasize that the conditions from which people try to learn in everyday life are better explored through a range of philosophical viewpoints, such as Aristotelian praxis, hermeneutics, existentialism, pragmatism, process philosophies and phenomenology. They propose that action research provides a corrective to the deficiencies of positivist science by being future-oriented, collaborative, agnostic and situational, implying system development and so generating theory grounded in action. In a parallel mode, Gibbons, Limoges, Nowotny, Schartzman, Scott and Trow (1994) have argued it is time for a mode of research (which they call Mode 2 knowledge production) that is transdisciplinary, heterogeneous, socially accountable and reflexive, and is produced in the context of a particular application, which Coghlan, Shani and Dahm (2020) apply to producing knowledge in ODC. Interiority Coghlan, Shani and Hay (2019), in their reflection on the field of social science philosophy for ODC, conclude that the key to unraveling the pluralism and conflicting theories of knowledge that persist in the philosophies of social science is interiority; that is, to attend to how we know as well as to what we know. Understanding the activity of human knowing is the main characteristic of interiority. Awareness of how understanding unfolds reveals that there is a structure to human cognition. Human knowing comprises an invariant series of distinct operations: experiencing, understanding and judging (Dewey, 1933; Lonergan, 1992; Cronin, 2017). Experience occurs at the empirical level of consciousness and is an interaction of inner and outer events, or to express it alternatively, data of sense and data of consciousness. People not only experience external data through their five senses, but they also experience internal data as they imagine, remember, feel and think. They also experience themselves as seeing, hearing, thinking, feeling, remembering and imagining. Sensory data are what they experience but do not yet understand. So, they ask questions about their experience, and the answers come in the form of insights, which are creative acts of understanding, of grasping and formulating patterns, unities, relationships and explanations in response to questions posed to their experience. While they might not know yet if a particular current search is intelligent, they anticipate intelligent answers. Understanding occurs at the intellectual level of consciousness

Action research as the social science of change and changing  25 as they move beyond experience to explanation. While insights are common, they do not always provide satisfactory answers to questions. The question then is, does the insight fit the evidence? This opens up a question for reflection. Is it so? Yes, or no? Maybe. I do not know. I need further evidence. So, people move to a new level of the cognitional process, namely that of judgment, where they marshal and weigh evidence and assess its sufficiency. This is the rational level of consciousness. Furthermore, they do not merely know; they also make decisions and act (the responsible level of consciousness). At this level they ask what courses of action are open to them and they review options, weigh choices and decide what action to take. The responsible level of consciousness is added to the empirical, intellectual and rational levels. These operations are invariant in that they pertain to all human knowing. Interiority is the mechanism by which researchers can move between the realms of theory and practical knowing, valuing both while recognizing their different contributions. Interiority is a philosophical theory and method that enables action researchers attend to the data of their consciousness; that is, how they experience, question, understand, test their understanding, make judgments, make decisions and act. From the cognitional activities of experience, understanding and judgment, and the valuing activities of deciding what is worthwhile and then taking action, a general empirical method which is simply the enactment of operations of human knowing may be derived (Coghlan, 2010; Coghlan, Shani & Hay, 2019). The activities of the general empirical method are: be attentive (to the data), be intelligent (in framing understanding of that data), be reasonable (in making a judgment as to the best explanation of the data) and be responsible (for one’s actions). This method is grounded in being attentive to data of sense and of consciousness, exploring intelligently what the data might mean, and coming to judgment as to the best or most probable explanation for the data (Table 2.1). Table 2.1

The general empirical method

Levels of consciousness

Activities of human knowing

General empirical method

Empirical

Experiencing

Be attentive (to data of sense and data of

Intellectual

Understanding

Rational

Judging

Responsible

Deciding

consciousness) Be intelligent (in envisaging possible explanations of that data) Be reasonable (preferring as probable or certain the best explanations for the data) Be responsible (for one’s actions)

Many themes in contemporary philosophy point towards interiority, through an emphasis on the subject. Modern philosophy, under the influence of positivism, neglected the issue of the subject in his or her acts of consciousness. For instance, positivist science which focuses on externalized data of sense cannot consider how the researcher’s cognitional process enables a revision of a work in progress; for instance, how a researcher makes a judgment as to the nature of evidence available. In such an instance the researcher turns inward and asks whether there is sufficient evidence for making affirmations about the data and then makes a judgment. In other words, there is a turn to the researcher’s data of consciousness. Interpretist social science focuses on meaning and how meaning is conducted through language and culture, and researchers in this mode attend to how meaning is constructed and carried in groups and organizations. In this mode, researchers also turn inward and examine the evidence for forming

26  Handbook of research methods in organizational change a judgment. The point from these two examples is that the general empirical method (be attentive to experience, be intelligent in understanding, be reasonable in judging and be responsible in taking action) is applied to the data of consciousness as well as to the data of sense. Sensible data, such as the content of acts of seeing and hearing, do not occur in a cognitional vacuum, but in the context of the researcher’s interests and preoccupations. Hence, the case being made in this chapter for an understanding of the role of interiority explicitly. Within a framework of an extended epistemology practical knowing integrates experiential, presentational and propositional forms of knowing in the acts of practical engagement (Heron & Reason, 1997). Action research operates in the realm of practical knowing, a form of knowing that has been underemphasized and downgraded in the academy since the seventeenth century (Toulmin, 1990). In an effort to readdress this shortcoming, Coghlan (2016) elaborated a philosophy of practical knowing in terms of four characteristics: (a) knowing in this mode is concerned with the everyday concerns of human living, (b) much of knowing in this mode is socially constructed and reconstructed continuously, (c) attention to the uniqueness of each situation is critical and (d) practical knowing and action is driven by values and is fundamentally ethical in that taking practical action is grounded in judgments about what is the appropriate or best thing to do. The third characteristic of practical knowing identified by Coghlan is that it takes place in the present tense and each present moment is unique (Coghlan & Shani, 2017). Action research builds on the past, and takes place in the present with a view to shaping the future. Accordingly, engagement in the cycles of action and reflection performs both a practical and philosophical function in its attentiveness and reflexivity as to what is going on at any given moment and how that attentiveness opens up questions for co-inquiry and leads to purposeful action. In the realm of practical knowing each situation is unique as no two situations are identical. What works in one setting or worked at an earlier time may not work now. What took place on a previous occasion is irretrievable and obsolete and accordingly needs to be revisited, questioned and modified in the light of the present unique situation. Remembering what worked or did not work before is an insight into situations which are similar but not identical. If the uniqueness of the present situation is ignored, then there is a serious threat to changing and learning. Challenging assumptions such as ‘we have always done it this way’ or ‘we have done this previously so we know how to do it now’ is critical. Therefore, practical knowing needs be differentiated for each specific situation and action researchers need to be attentive in the present tense and engage in listening, questioning and testing assumptions as the project unfolds. The core insight that is at the heart of the process of changing is that practical knowledge is generated in the present tense. In the action research literature this process is typically expressed in terms of cycles of action and reflection. Action research’s emphasis on cycles of action and reflection is paramount in the context of working within the realm of practical knowing where knowing is always incomplete and where reflexive attentiveness to unfolding contextual dynamics is central to both understanding and action. In its original Lewinian and simplest form, the action research cycle comprises a pre-step and three core activities: planning action, taking action and fact-finding. The pre-step involves naming the general objective. Planning comprises having an overall plan and a decision regarding what the first step to take is. Action involves taking that first step, and fact-finding involves evaluating the first step, seeing what was learned and creating the basis for correcting the next step. Accordingly, in Lewin’s words (1947, pp. 147–8):

Action research as the social science of change and changing  27 Planned social action (intentional change) usually emerges from a more or less vague “idea”. An objective appears in the cloudy form of a dream or a wish, which can hardly be called a goal. To become real, to be able to steer action, something has to be developed which might be called a plan … It should be noted that the development of a general plan presupposes “fact-finding” … On the basis of this fact-finding the goal is somewhat altered … Accepting a plan does not mean that all further steps are fixed by a decision; only in regard to the first step should be the decision be final. After the first action is carried out, the second step should not follow automatically. Instead it should be investigated whether the effect of the first action was actually what was expected.

Not only does the pattern of the action project change through the enactment of the cycles of action and reflection but the research questions themselves may change (MacIntosh, Bartunek, Bhatt & MacLean, 2016). Canterino et al. (2016) provide an example of how the collaborative process of helping a merger to work went through phases and cycles as specific issues emerged and were addressed. The American philosopher Charles Peirce (1997 [1903]) describes three forms of reasoning: deductive, inductive and abductive. Deductive reasoning draws on generalizable theory to craft particular arguments whereas inductive reasoning proceeds from particular observations to clarify more generalizable theory. Abductive reasoning yields tentative answers and produces exploratory hypotheses during the process of inquiry. It operates at the level of understanding before confirmation through judgment. In other words, it expresses the form of reasoning that takes place as the action research progresses through the collaborative cycles of action and reflection as they unfold in the present tense in anticipation of more definite answers when the research is completed (Shani, Coghlan & Alexander, 2020; Saetre & Van de Ven, 2021). Three voices/practices The theory and practice of action research creates a profile for those conducting action research. The profile of the action researcher is one who can learn-in-action and can work collaboratively with others in the context of the uncertainty of the unfolding story of the project and the expectations of scholarship. Accordingly, the researcher needs to be knowledgeable in the organizational disciplines of, for example, organization dynamics, strategy, change and teamworking; be skilled in collaborative work and in making interventions; and be able to articulate emergent theory. The dynamics of action research for researchers are captured in an integrating framework of first-, second- and third-person voices/practices (Sherman & Torbert, 2000). First-person practice What marks out the distinctiveness of the action research from other research approaches is that action researchers act as engaged scholars who are agents in the generation of data, rather than being mere observers or data collectors and analysts. Accordingly, a high level of self-awareness is required of them as they need to learn how they themselves are agents and instruments in the generation of data and that their active presence is itself an intervention. ‘First-person practice’ refers to the personal and professional learning and practice of action researchers as they engage in action research and in how they attend to the data of their consciousness. Action researchers’ learning in action is grounded in the inquiry-reflection process (Kolb, 1984). Here, some of the core skills are in the areas of self-awareness and sensitivity to what they observe, supported by the conceptual analytic frameworks on which they base their obser-

28  Handbook of research methods in organizational change vations and interpretations. Their inquiry can be focused outward (e.g., to data of sense of what they see and hear in the organization, or the team) or inward (e.g., to data of consciousness of what is going on in themselves, how they are thinking), named above as interiority. When they inquire into what is going on, when they show people their train of thought and put forward hypotheses to be tested, when they make suggestions for action, they are generating data. People’s responses (as organizational team members and fellow researchers) to these interventions generate further data. Within the action research literature, Argyris (2004), Torbert and Associates (2004) and Marshall (2016; Gearty & Marshall, 2020) provide frameworks for exploring first-person inquiry. Second-person practice Second-person practice addresses action researchers’ engagement in collaborative work in co-inquiry and shared action with others on issues of mutual concern, through face-to-face dialogue, conversation and joint decision-making and shared action (Shani & Coghlan, 2021a). The general empirical method involves: being attentive to experience, being intelligent in envisaging possible explanations of the experience, being reasonable in preferring as probable or certain the explanations provide the best account for the experience, and being responsible for one’s actions. This general method underpins the collaborative inquiry orientation of action research as it seeks to generate the practical knowledge of addressing the core issues and of contributing to knowledge. In the second-person exchanges action researchers and organizational members engage in dialogical conversations to interpret events and discuss the variety of actions that envisage ends, select means, evaluate outcomes and capture learning. The conversations focus on strategies and actions, how participants figure out what they mean, how data are understood, what is considered to be of value and what might need to be done, what happens when change is attempted and what might be learned. The co-inquiry process engages participants to discuss the multiple meanings that exist in a collective venture and seeks to enable participants to express the meanings they hold, and to listen to, understand and appreciate the meaning other participants hold to create new meaning in response to external and internal opportunities and threats. In this context action researchers employ a variety of engagement skills (Coghlan, 2018). When ODC researchers and practitioners attend to what has been taking place in the organization and try to reach a common understanding of what it might mean (however provisional that understanding may be), and then consider which appropriate ODC research interventions to take, they are enacting the general empirical method. In this manner they are embodying rigor in a science of action and addressing explicitly the pitfalls of working from untested inferences and attributions (Argyris, 2004; Coghlan & Shani, 2013). Drawing on Schein and Schein’s typology (2021) of helpful conversation, action researchers work with the knowing processes of the participants (Coghlan, 2009, 2018). Schein and Schein describe several types of inquiry. Their first category is humble inquiry, which is an exploratory mode of inquiry in order to understand the situation and how it is perceived by organizational members. This is where experience is elicited by generating the story of what has taken place and is taking place in the organization. Their second type of inquiry is diagnostic inquiry, in which understanding is elicited by exploring how the experience is understood and what causal interpretations are being made. Their third type of inquiry is confrontive inquiry, where the conversation moves to a more explicit sharing of different ideas that new perspectives have generated. Schein and Schein argue that if sufficient time is not devoted

Action research as the social science of change and changing  29 to exploratory and diagnostic inquiry, confrontive inquiry closes down the conversation and traps the participants in dependence and a debating mode which does not help the collaborative inquiry process. Third-person practice Third-person practice is an impersonal practice and is actualized through disseminating the action research’s contribution to an audience beyond those directly involved, such as through dissemination by reporting and publishing. Action research intentionally merges theory with practice on the grounds that practical or actionable knowledge can result from the interplay of knowledge with action. It demands an explicit concern with theory that is generated from the conceptualization of the particular experience in ways that are intended to be meaningful to others (Cartwright, 1978). Austin (2017) describes her doctoral action research work in the field of national women’s health in New Zealand. The everyday concern was about how critical incidents were handled. She reports how health workers engaged in cycles of action and reflection to address the issues in the present tense and how they decided to develop a support package to facilitate health professionals after a critical incident. Action research projects are situation-specific and do not aim to create universal knowledge. Action research does not lend itself to repeatable experimentation; each intervention will be different to the last. Action research generates emergent theory, in which the theory develops from a synthesis of the understanding which emerges from reflection on the process of changing within the organization, in contrast to positivist science, where the theory to be tested is defined from the outset. An action research project unfolds through cycles of action and reflection as the problematic issue(s) being addressed is confronted (or the opportunity exploited) and members of the organization attempt resolution with the help of the action researcher. The enactment of the cycles of planning, taking action and evaluating can be anticipated but cannot be designed or planned in detail in advance. The philosophy underlying action research is that the stated aims of the project lead to planning and implementing the first action, which is then evaluated. The second and subsequent actions cannot be planned in detail until evaluation of the earlier actions has taken place. Hence the centrality of engaging in abductive reasoning in the present tense as the project unfolds. Interiority provides a foundation for understanding how theory is developed. Swedberg (2014) argues that attention be given to the process of theorizing, i.e., that what one does when producing a theory tends to be ignored, with the emphasis being generally placed on the theory as the outcome of inquiry. Hansen and Madsen (2019) explore how the process of theorizing is as important as focusing on theory as an outcome and it involves attending not only to external data but also to the internal data of one’s own thinking and assumptions and engaging in a community through reading, talking, listening, questioning and writing. The act of theorizing or model creation turns the attention from the outcome to the act of generation itself. It places the issue firmly in the question, ‘How do we come to know?’, the starting point of the exploration of the practice of social science as argued by Coghlan, Shani and Hay (2019). While other research traditions emphasize first-, second- and third-person inquiry separately, action research has long adopted an explicit integrative approach incorporating the three inquiries and voices: the first-person voice of individuals inquiring into their own thinking and learning, the second-person inquiry into the collaborative engagements between the actors as co-researchers and the third-person contribution to knowledge for a wider audience (Sherman

30  Handbook of research methods in organizational change & Torbert, 2000; Bradbury, 2015; Coghlan, 2019). Coghlan and Shani (2021) explore how attention to the process of human knowing – that is, how people move from experience to understanding to judgment and to decision and action and to affirming theory, and in particular to the process of abductive reasoning in the present tense – provides the integrating thread between the three voices and practices. By attending to both the data of their consciousness (how they are experiencing, questioning, understanding and judging) as well as to the data of sense (what they see and hear in the external data) action researchers can engage with the empirical data of their experiencing, the intellectual data of their understanding (by abductive reasoning in the context of discovery) and the rational data of their judgments (by inductive reasoning in the context of verification) as they engage in the collaborative process. Coghlan (2021) provides an illustration by showing how Schein created his change model. From his exploration of Schein’s work, Coghlan provides a structure for understanding the cognitive processes of creating a model or theory. It begins from experiencing what one observes and posing questions to those experiences. The answers or understandings that come have to be subjected to rigorous scrutiny in the light of how they fit the evidence and whether there are alternative explanations. The outcome is a judgment that it is indeed so and the model/theory is affirmed. If not, the process of experiencing. inquiring and testing continues. Attending to how these processes are unfolding is an act of interiority and foundational for the framing of theory emergent theory. Quality Criteria in Action Research What constitutes good-quality action research? Schein (2010) comments that, regretfully, action research has often been diminished by being a glib term for involving clients in research and has lost its role as a powerful conceptual tool for uncovering truth on which action can be taken. Pasmore, Woodman and Simmons (2008) postulate that action research and collaborative research needs to be rigorous, reflective and relevant. Under ‘rigorous’, they group: data-driven, multiple methodologies; reliability across settings; co-evaluation; causality; underlying mechanisms; and publishability. Under ‘reflective’, they group: historical impact, referential, co-interpretation, community of practice, collection and repeated application. Under ‘relevant’, they group: practical, codetermined, re-applicable, teachable, face-valid, interesting, true significance and specific. Eden and Huxham (1996) provide their characteristics of good action research. These include: the intentionality of the researcher to change an organization, that the project has some implications beyond those involved directly in it and that the project has an explicit aim to elaborate or develop theory and be useful to the organization. Theory must inform the design and development of the actions. Eden and Huxham place great emphasis on the enactment of the action research cycles, in which systematic method and orderliness is required in reflecting on the outcomes of each cycle and the design of the subsequent cycles. Accordingly, rigor in action research typically refers to how data are generated, gathered, explored and evaluated, and how events are questioned and interpreted through multiple action research cycles. Coghlan and Shani’s (2014) definition of action research above provides another framework for assessing quality in action research. Good action research may be judged in terms of the four factors from that definition: (a) how the context is assessed, (b) the quality of collaborative relationships between researchers and members of the system, (c) the quality of the action research process itself as cycles of action and reflection are enacted and (d) how the

Action research as the social science of change and changing  31 dual outcomes reflect some level of sustainability (human, social, economic and ecological), the development of self-help and competencies out of the action, and the creation of new knowledge from the inquiry. An action research intervention is unlikely to be replicable as the exigencies of a particular situation may not be repeated; however, the learning needs to be transferable and the process may be transportable to other situations. Also addressing the issue of quality, Reason (2006) focuses on choices. As action research is conducted in the present tense, attentiveness to the choices being made and their consequences, and being transparent about them, are significant for considering the quality of action research. Reason argues that action researchers need to be aware of the choices they face and make them clear and transparent to themselves and to those with whom they are engaging in inquiry and to those to whom they present their research in writing or presentations. The explicit attention to these questions and to the issues of rigor, relevance, reflexivity and quality of collaboration is an exercise in interiority and takes action research beyond the mere narration of events to rigorous and critical questioning of experience leading to actionable knowledge for both scholarly and practitioner communities. Ethics in Action Research Ethics may be defined as a practical science focused on how we put values into action. It is the study of ethical relationships we have with human beings, sentient creatures and the physical world in which we live. It is the study of what we value in these relationships and the decisions we make based on those values. (Yoak & Brydon-Miller, 2014, p. 306)

As action research is grounded in a philosophy of practical knowing, it is driven by a series of choices people make in deciding what to do. Choices are grounded in assessments about what researchers judge to be worthwhile and valuable as they ask what courses of action are open to them, and how they review and weigh options, reach decisions and choose to act (Coghlan, 2013). Action research is full of such choices, and it is important that how these are made be transparent, to the action researchers themselves, to those with whom they are working, and to those who read their work (Reason, 2006). In this realm of generating practical knowing, action researchers are confronted with an imperative of having to act ethically in concrete situations (Barden, 1991; Coghlan, 2013; Nielsen, 2016). Hilsen (2006) suggests covenantal ethics as a foundation for an approach to research ethics for action research. Covenantal ethics are grounded in the unconditional responsibility and the ethical demand to act in the best interest of our fellow human beings. Covenantal ethics provides an approach to research ethics more in line with the basic tenets of action research, including a focus on the development of caring and committed relationships with partners, a respect for people’s knowledge and experience, and a commitment to working together to effect positive change in organizational, educational and community settings. Assessing the ethical challenges and reflecting on their own values are exercises in interiority for the action researcher. Action Research Modalities What is noticeable in contemporary action research is that there is a wide diversity, not only in practice but in the discourse on action research practice. ‘Action research’ has become

32  Handbook of research methods in organizational change a generalized term and is used to refer to modalities or a family of approaches. Examples of these modalities are action learning, appreciative inquiry, clinical inquiry, cooperative inquiry, intervention research and learning history, to name some common ones. Coghlan (2010) provides a way of viewing the diversity of approaches. He does this in terms of a generative insight. By this he means an insight by the person or group that first constructed it that was foundational in framing that modality and which led to the development of further insights and methods of working within each modality (Table 2.2). ● Action learning: Action learning has traditionally been directed toward enabling professionals to learn and develop through engaging in reflecting on their experience as they seek to solve real-life problems in their own organizational settings through participation in a peer group (Coghlan & Rigg, 2012). ● Appreciative inquiry: Cooperrider and Srivastva (1987; Cooperrider, 2017) criticized how action research is viewed as a form of problem solving. As an alternative, they propose appreciative inquiry as a form of action research which focuses on building on what is already successful, rather than what is deficient, thus leveraging the generative capacity for transformational thinking and action. ● Clinical inquiry/research: Clinical inquiry/research is articulated by Schein (2008), who argues that when researchers gain access to organizations at the organization’s invitation for help, Schein’s insight is that the most fruitful way of understanding an organization and facilitating change to occur is through their interventions to be helpful. ● Collaborative management research is defined by Shani et al. as an effort by two or more parties, at least one of whom is a member of an organization or system under study and at least one of whom is an outside member/external party, to work together in learning about how the behaviour of managers, management methods, or organizational arrangements affect outcomes in the system or systems under study, using methods that are scientifically based and intended to reduce the likelihood of drawing false conclusions from the data collected, with the intent of both proving performance of the system and adding to the broader body of knowledge in the field of management. (Shani, Mohrman, Pasmore, Stymne & Adler, 2008, p. 20)

These authors understand that collaborative management research is unique and different from action research, in that it seeks to add value to the action research approaches through the way in which practitioners and researchers engage in a joint undertaking, where each partner takes some responsibility for the others partners’ learning and knowledge. Shani’s chapter in this Handbook provides a detailed introduction to collaborative management research. ● Cooperative inquiry: Heron (1996) describes cooperative inquiry as a process in which the participants work together in an inquiry group as co-researchers and co-subjects. The participants research a topic through their own experience of it in order to make sense of their life and to learn how to act to change things they might want to change. ● Developmental action inquiry: Developmental action inquiry is an expression of action research where Torbert adds insights from developmental psychology. He argues that as leaders progress through the stages of adult development, they may intentionally develop new ‘action-logics’ (Torbert & Associates, 2004). Torbert and Caring’s chapter in this Handbook provides an extensive account of this approach.

The essence

Design

the development of the quality of the relationship?

laboratively and rigorously generated, collected and explored?

those of academic rigor?

organization’s needs, as well as ● To what extent is attention paid to

● To what extent are the data col-

design directed to meet the collaboratively?

ensure rigor?

● Building relationships

To what extent is the research designed and implemented

● To what extent is the project

designed and implemented to

● To what extent is the project

research selected and justified?

● Are appropriate modes of action

with sufficient details?

and inquiry process described

● Cycles of action research

● Data collection and generation

scholarly criteria?

● To what extent are the methods

the organization’s needs and

collaborative?

● Establish learning mechanisms

of action and inquiry driven by

methods of action and inquiry

● Contracting

To what extent are the methods

research cycles described?

of contracting, selection of

● Ethical issues

inquiry

central to the issue studied? To what extent are the action and

organizational experience that is

● The role of the action researcher

● To what extent is the process

understand the context?

of the study?

way?

● Does it build on past and present

analytical frameworks applied to

research that is central to the focus

a rigorous, systematic and holistic

academic context

To what extent are relevant

achieve what for whom?)

is necessary or desirable? (To

Is the contextual data captured in

study?

the purpose and rationale for the

● Does it display the data to justify

● Does it build on past and present

ness and organizational issues?

address a gap in the scholarly literature?

● Is it linked to contemporary busi-

● To what extent does the focus

literature?

Does it describe why action

for inquiry and action?

Understanding the business, organizational and

● What contribution is intended?

are necessary or desirable?

● Is it linked to past research and

● Does it provide a clear rationale

Methodology and method of

Context

and inquiry

Relevant

Reflective

Rigor

Elements of quality in action research in organization development

Purpose and rationale for action ● What is the case for why action and research

 

Table 2.2

Action research as the social science of change and changing  33

Describe the story and outcomes, (intended and

Narrative and outcomes

● Make judgments on the process and outcomes description of outcomes meet the

outcomes

design, narrative and outcomes, reflection) contribute to sustainable outcomes for the organization and actionable

the outcomes and reflection) fit the quality of the action research process and the quality of relationships?

reflection, the quality of the action research process, the quality of relationships) contribute to

of relationships and sustainability of the

outcomes

practice

● Articulate the contribution to both theory and

and effort worthwhile?

returns that make the process

research approach demonstrate

● To what extent does the action

knowledge for scholars?

methodology and methods,

narrative, outcomes, sustainability of

design, narrative and outcomes,

● Discuss the action research process, quality

knowledge and practice?

account (purpose/rationale,

methodology and methods, design,

methodology and methods,

● Discuss the story and outcomes

● To what extent does the entire

scientific needs?

account (purpose/rationale,

focused on addressing the

units/communities of practice take

account (purpose/rationale,

and outcomes’ meaning

among different org’l groups/

● To what extent are the story

needs?

focused on the organization’s

meaning and possible actions

● To what extent did dialogue about

created?

and outcomes’ meaning

To what extent does the entire

ing theory)

● Link the story to theory (existing and emerg-

Source: Coghlan & Shani (2014, pp. 529–30).

knowledge

● Articulation of actionable

context

● extrapolation to a broader

reflected on collaboratively? ● To what extent is shared meaning

intended and unintended? ● To what extent are the story

● What were the outcomes, both

place? To what extent does the entire

standards/criteria of research?

● Analyze the story and reflection

Discussion

and action in the present tense?

what happened?

● To what extent does it capture

To what extent does the story demonstrate collaborative inquiry

Relevant

Reflective

values distinguished? To what extent do the narrative and ● To what extent is the story

● To what extent are facts and

an appropriate level of detail?

● how well is the story told, with

Rigor

Reflection on the story and

unintended)

The essence

 

34  Handbook of research methods in organizational change

Action research as the social science of change and changing  35 ● Intervention research: Intervention research has emerged out of France and is built on a detailed analysis of an organization’s performance and the consequent development of management tools and actions to address deeply embedded problems (Hatchuel & David, 2008; Buono & Savall, 2017). In this Handbook, Savall and colleagues elaborate the Socio-Economic Approach to Management (SEAM) within the intervention research modality. ● Learning history: Rather than presenting the univocal voice of a single author or group of researchers, the learning history presents concurrent, multiple and often divergent voices in an organizational story (Bradbury & Mainmelis, 2001; Gearty & Coghlan, 2018). Presenting the jointly told tale is enabled by the format, whereby columns of narrative text are juxtaposed with the interpretative voice of participants (often disagreeing) and the voice of the learning historian. Gearty’s chapter in this Handbook provides a rich introduction to the learning history theory and process. This selection of action modalities provides a flavor of contemporary action research. The emphasis is on exploring subjective experience and how the participants construct the meaning of the situations in which they find themselves, which they seek to change and how they frame and implement action strategies. Selecting a modality as appropriate to a given situation requires an insight into both a given modality and what might be required to inquire rigorously in a given situation (Coghlan, 2010). Insider action research While not fitting into the category of an action modality, insider action research has emerged as a significant development within the action research family of approaches. As the term suggests, insider action research explores the process whereby the action research is conducted by a ‘full member’ of an organizational system, rather than by one who enters the system as an external researcher and remains only for the duration of the research. Insider action research challenges the notion that being ‘native’ is incompatible with good research (Brannick & Coghlan, 2007). Coghlan (2019) explored how attention to the three core elements of insider inquiry – managing the tensions between closeness and distance (pre-understanding), organizational and researcher roles (role duality), and managing organizational politics – are critical to the development of effective action and the generation of actionable knowledge. The insider action research approach provides a methodological grounding for the growing prevalence of doctorates by practitioners (Coghlan, 2007). Williander and Styhre (2006) present an account of Williander’s doctoral action research work at the Volvo Car Corporation, where he was a senior manager tasked with the development of environmental strategies and the ‘eco-benign’ car. In a parallel vein, Roth, Shani and Leary (2007) provide an account of Roth’s doctoral work as a senior manager in his firm in addressing organizational learning challenges and institutionalizing learning mechanisms. Synthesis of Section 1 To draw this Section 1 to a conclusion, several key characteristics of action research may be identified. Action research constitutes a distinct philosophy, epistemology, methodology and methods from the commonly used quantitative–qualitative polarization. As a social science of praxis, action research is fundamentally grounded in the theory and practice of change

36  Handbook of research methods in organizational change and of changing. It is driven by a real concern of an organization, community or group and is conducted in real time. It always involves two goals: to address a specific issue and to contribute to knowledge. Action researchers take action. They do not merely observe something happening; they work actively at making it happen. As agents or instruments of the changing and knowledge-generation process, action researchers attend to both data of sense as to what is happening around them and their data of consciousness as to how they are thinking, feeling, interpreting and conceptualizing. These activities are captured by their enactment of the general empirical method (be attentive to experience, be intelligent in understanding, be reasonable in judging and be responsible in taking action) in their first-person attention to their data of consciousness, their second-person collaborative work with members of the organization and in the articulation of actionable knowledge (Figure 2.2). As a distinct social science philosophy, the action research paradigm requires its own quality criteria and should not be judged by the criteria of other research paradigms. Finally, action research requires an understanding of its ethical framework, values and norms within authentic relationships between action researchers and organizational members in their collaboration.

Figure 2.2

Synthesis of action research theory and practice

It can be seen that action research is challenging in a number of respects. Action researchers need a willing organization, a worthwhile cause and partners willing to engage in co-inquiry. They need to be willing to undertake an uncertain journey where questions may shift in the face of unintended events. Engaging in cycles of action and reflection using interiority may evoke critical first-person inquiry as well as adjusting second-person engagements and relationships. What knowledge is being cogenerated and how that knowledge is useful for the organization and robust for scholars is emergent and uncovered by rigorous and reflective attentiveness to the unfolding narrative.

Action research as the social science of change and changing  37 Action research is not without controversy. It has often found itself excluded from the forum of organizational scholarly research, especially in the Anglo-Saxon academy, led by the United States (Greenwood, 2002; Levin, 2003). The dominance of an Anglo-Saxon philosophy, particularly positivism, denigrates research that incorporates action as smacking of subjectivism and inimical to the canons of what constitutes research. Action research continues to be perceived in some quarters as not being ‘scientific’. This perspective perpetuates although, as is well argued, its methods are far more scientific in generating knowledge that is tested in action and in mobilizing relevant knowledge from people in a position to know their conditions better than conventional research (Greenwood, 2002). More importantly, understanding action research as a social science of change and as a practice of changing requires a philosophical shift and a grasp of the notion of interiority. Interiority’s attentiveness to data of consciousness enables action researchers to engage their own thinking and the collaborative endeavors with co-researchers through the process of changing to both effect change and to cogenerate practical knowledge as a social science of praxis. Accordingly, it would seem that it is risky for a young scholar who believes that research is about making a difference in the world and not merely contributing an academic paper. It is not accidental that published doctoral action research works have been undertaken by experienced practitioners (Williander & Styhre, 2006; Roth, Shani & Leary, 2007; Austin, 2017) or as members of research teams (Canterino et al., 2016).

2.

ENACTING ACTION RESEARCH IN ORGANIZATION DEVELOPMENT AND CHANGE

There is an extensive literature on how to conduct research across the various research paradigms (e.g., Van de Ven, 2007; Mirvis, Mohrman & Worley, 2021). Building on the preceding section that introduced the philosophical and practical values of action, collaboration and reflexivity that are engaged through first- and second-person practice and enacted through interiority and the general empirical method, this section explores how an action research project may be designed and enacted so as to make a contribution to practical knowledge. Seven core activities are offered as a structure (Coghlan & Shani, 2014). Table 2.2 offers some questions to assist action researchers to bring rigor, reflectiveness and relevance to the design and implementation of their project. Completing these questions is an exercise in interiority. Purpose and Rationale of the Research The starting point for action research is a situation in an organization, community or group that is of concern and on which the members desire to take action. When action researchers are framing the purpose and rationale of an action research piece of work, they are, in effect, presenting the case, and stating why the action chosen was worth doing for the organization, why it is worth studying and what it is that it seeks to contribute to the world of theory and of practice. At the outset of an action research project, it is critical for action researchers to make both a practical and an academic case for what they are doing, and to declare their intentionality to both enable change and generate actionable knowledge. This is not just an argument for credibility, but a formal effort to locate their work in both a practical and an academic context.

38  Handbook of research methods in organizational change Accordingly, Table 2.2 poses questions as to how being rigorous, reflective and relevant is present in the presentation of the purpose and rationale of the action research project. ‘Context’ here refers to the business, social and academic context of the research. Action research requires a breadth of pre-understanding of the corporate environment, the conditions of business, the structure and dynamics of operating systems and the theoretical underpinnings of such systems. There are three context areas: the broad general business context at global and national level, the local organizational/discipline context (i.e., what is going on in the selected organization) and the specific topic area. In action research framing the business and social context is very important. Therefore, action researchers need to describe the business context in which the organization operates, and the organization with which they are working. This would include details of the competitive environment; an introduction to the organization and what it does; some historical background about the organization and its evolution and history with OD efforts, if any; what its concerns are and what engaging in the issues means; and what is intended and hoped for out of the action research project. Academic context is also important. Not only are action researchers framing the business context of their project, but they also review and critique the research that has been conducted in that context. Locating their action research in a tradition lays the ground for their hoped-for contribution that extends beyond the immediacy of the particular organizational setting and the people involved in the project. Table 2.2 poses questions as to how being rigorous, reflective and relevant are present in the presentation of context of a project. Methodology and Methods of Action and Inquiry As in all accounts of research, the selection of a methodology requires justification. The context, purpose and contribution to be served by the action research design and approach and the methods of inquiry to be used are described. For example, if the project is built on an appreciative inquiry modality, then a definition, some history and the main philosophical tenets of appreciative inquiry would be provided and justified for this context. Table 2.2 poses questions as to how the philosophical grounding of methodology and methods of inquiry build in structures of rigor, reflection and relevance. Design There needs to be a general plan of how cognitive, structural and procedural learning mechanisms are designed to address practical issues, generate knowledge and attend to ethical issues (Shani & Docherty, 2003). For example, the design might be built around project teams who meet to address the issues confronting the organization who would work in an action learning mode to articulate their learning in-action. As the project proceeds in the present tense, ethical issues of obtaining consent, ensuring anonymity and confidentiality and balancing conflicting and different interests are grounded in the cycles of planning, taking action, and reflection (Walker & Haslett, 2002). Design needs to be informed by theory as well as by the exigencies of the situation. Action research can include all types of data-gathering methods such as interviews and surveys. However, action researchers keep in in mind that data collection tools are themselves interventions that generate data. For example, a survey or interview may generate feelings of anxiety, suspicion, apathy and hostility or create expectations in a work force. If action

Action research as the social science of change and changing  39 researchers do not attend to this and focus only on the collection of data, they may miss significant data that may be critical to the success of the project. What is important in action research is that the planning and the use of these tools be well thought through in advance with the members of the organization and be clearly integrated into the action research design. Selecting an appropriate research intervention involves two processes of interiority. First, collaborative process is established between OD researchers and organizational practitioners in the mode of ‘dialogic OD’ and collaborative management research (Shani & Coghlan, 2021). Second, a method is identified to assess the experiences that lead to an OD intervention and theory generation. These describe how the design was collectively constructed to meet the requirements of rigor, reflection and relevance. The action researchers also need to locate themselves in the project; i.e., as external or internal OD consultants, or senior or line managers. They identify themselves in terms of their roles in the project and position themselves to face the challenges of the project. Action researchers who act as OD consultants need to explain how the research role was negotiated, especially if the initial contract was more oriented toward helping than toward research (Schein, 1995). If they are insiders to the organization then they need to show how they can inquire into and exploit their insider knowledge, experience, role and relationships (Coghlan, 2019). In term of the questions posed in Table 2.2, to ensure rigor action researchers might ask, ‘To what extent is the project designed and implemented to ensure rigor? To what extent are the data collaboratively and rigorously generated, collected and explored?’ To display reflectiveness, they might ask, ‘To what extent is the project designed and implemented collaboratively? To what extent is attention paid to the development of the quality of the relationship?’ For relevance, ‘To what extent is the research design directed to meet the organization’s needs, as well as those of academic rigor?’ Narrative and Outcomes The heart of any action research paper is the narrative or story of what took place. Here the cycles of action and reflection reflect a systematic method and order in constructing, planning action, taking action and reviewing outcomes and processes, and generating understanding. A critical issue in presenting the narrative is to distinguish the events which took place, about which there is no dispute, and the meanings attributed to these events. This form of presentation gives the evidence in a factual and neutral manner. The action researchers’ view of these events and their theorizing as to what these events mean should not be mixed in with the telling of the story. By separating the narrative from its interpretation, and by clearly stating which is story and which is interpretation, action researchers are demonstrating how they are applying methodological rigor to their approach. Combining narrative and interpretation leaves them open to the charge of biased storytelling and makes it difficult for readers and editors to evaluate their work. The questions in Table 2.2 challenge the narrative of the events of the project in terms of being rigorous, reflective and relevant. Reflection on the Narrative and Outcomes Here action researchers present their understanding of the events of the narrative and their theorizing as to what these events and outcomes mean and judgments about them. The outcomes, both intended and unintended, desired and undesired, are judged in terms of the intention

40  Handbook of research methods in organizational change of the project to address the organization’s needs and the extent the collaborative processes are rigorous, reflective and relevant in coming to judgment about the project’s success or otherwise. The Discussion/Extrapolation to a Broader Context and Articulation of Practical Knowing A key issue that requires attention is that the action research study must have implications beyond the remit of the immediate project. As commented earlier, one of the most common criticisms of published action research is that it lacks theory. In other words, action research accounts tell a story but do not address issues of emergent theory, and so do contribute to practical knowing. Action research projects are situation-specific and do not always aim to create universal knowledge. At the same time, extrapolation from a local situation to more general situations is of utmost importance. Action researchers are not claiming that every organization will behave as the one studied. But they can focus on some significant factors; e.g., they can consider what is useful for other organizations, perhaps similar organizations or organizations undergoing comparable change processes, or offer a contribution to methodology. A shared reflection on the action research process itself is central. Action researchers work to help the organizational members to understand what is going on and to take action based on that understanding (Coghlan, 2009, 2018). Through their interventions, both those who take action and those who generate knowledge, action researchers employ the general empirical method in engaging with organizational members, whether as clients or fellow organizational colleagues. They do this to draw out their clients’ or colleagues’ experience, their insights, their judgments and their actions in the settings where things change as a consequence of intervention, and where perceptions and meanings shift as people interact and enact strategies and actions for change (Coghlan, 2018). The focus is firmly on acts of knowing and doing in the present tense as the project unfolds. Hence, the discussion needs to show the integrity between the purpose of the research and action, how the context is assessed, the quality of the relationships whereby through abductive reasoning the participants have engaged in cycles of action and reflection on a real-life issue, how the outcomes are workable and how practical knowledge is generated. To conclude, Section 2 has aimed to demonstrate how to design and enact an action research project and ensure that it meets the quality requirements of being rigorous, reflective and relevant. The seven core activities – grounding the purpose and a rationale of the research; describing the business, social and academic context of the research; articulating the methodology, methods and mechanisms of action and inquiry; framing the issue to be addressed and the design to be followed; carrying out the action research process, capturing the narrative of what took place and its outcomes; reflecting on the narrative and outcomes and exploring how the particular situated action research project may be discussed and extrapolated to a context beyond that local situation; and articulating practical knowing – provide a set of specific guidelines and criteria to enhance the overall quality of an action research project as rigorous, reflective and relevant. There is also a structure for a dissertation document, as instanced by Canterino, Shani, Coghlan and Brunelli (2016).

Action research as the social science of change and changing  41

3.

TOWARD A SYNTHESIS: ACTION RESEARCH AS A SOCIAL SCIENCE OF CHANGE AND CHANGING

As this chapter has demonstrated, action research is a form of research where, because action researchers intervene in organizations to address real organizational issues and through their actions cogenerate practical knowledge in collaboration with those who are affected by the action, it constitutes a different form of social science from the commonly used quantitative– qualitative categorization. Action research is grounded both philosophically and historically in the theory and practice of ODC, which is a theory of change and a practice of changing. The four factors identified by Shani and Pasmore (1985) – context, quality of relationships, quality of the action process and outcomes – provide a comprehensive framework through which first-, second- and third-person practices are enacted in the present tense. Shani and Coghlan (2021b), who engage in a joint act of interiority in their reflection on action research in business and management, note that, in their view, action research has failed to realize its potential for generating robust actionable knowledge. They comment that while debates as to whether action research is ‘real’ research have largely abated, action research has not become mainstream and is frequently marginalized. They attribute this to several factors; for example, the dominance in the academy of a philosophy of social science that is modeled on that of the natural sciences, and published accounts of consulting projects claiming to be action research merely because they were collaborative and followed cycles of action and reflection but failed to address the intricacies of generating valid knowledge. They conclude that what has been lacking has been a rigorous reflection on the choices that are made, in relation to, for example: contextual analysis, design, purposes, degrees of collaboration, planning, implementation and review. They propose that in keeping with Table 2.2, (i)

the presentation of context be captured in a rigorous, systematic manner so that the rationale for the action and the research is solidly grounded (ii) the action research relationships be formed, built and sustained to meet a standard of collaborative endeavor that action research espouses (iii) the account of the action research process itself demonstrates a rigorous and collaborative engagement in the action research project’s design, and subsequent enactment of cycles of planning, taking action and reflection so that the paths to the organizational and theoretical outcomes are transparent (iv) both the practical outcomes for the organization and the robust knowledge cogenerated be presented. Underpinning both Shani and Coghlan’s reflection and what this chapter is proposing to readers of this Handbook is the engagement in interiority. Interiority is the activity whereby data of consciousness is afforded equal attention as data of sense. Coghlan (2017), who shows his train of thought in his review of twenty-five years of publications in the volumes of Research in Organizational Change and Development, and in attending to how he came to judgment in his categorization, provides a practical example of interiority in action. It is through interiority that action researchers can employ the general empirical method to consider the key factors of (a) understanding the context, (b) quality of the relationships, (c) the quality of the action research process itself and (d) the twin outcomes of practical value to the business and the generation of practical knowledge, acting on the questions posed in Table 2.2 so as to ensure the quality of their action research work. By attending to their data of consciousness action

42  Handbook of research methods in organizational change researchers may avoid the traps of philosophical debates about the nature of research, attend to their first-person knowing process, co-inquire with others on issues of mutual concern and build collaborative relationships for change in their second-person practice and through their third-person practice cogenerate knowledge. In their exploration of the nature of impact in management research, MacIntosh, Mason, Beech and Bartunek (2021) have offered a model of impact in management research that is ‘processual, contextual and that incorporates different impact types that enable the actors to make choices about how they proceed’ (p. 81). They propose that practice-based research, practice-as-research and designed-in impact research are core constituents in achieving impact and that different types of scholarship can produce new knowledge. The emerging emphasis on research impact is opening the door for action research. Mirvis, Mohrman and Worley (2021) explore the subject of designing and conducting relevant research, which they define as ‘studying the real issues, problems, and demands facing organizations and the people that work in and manage them. It means generating knowledge that is 1) applicable to practice, 2) useful to practitioners, and 3) actionable’ (p. xvii). They argue that theory-driven and practice-driven management research need to be integrated through what they call the ‘sweet spot’, ‘an ideal or most favorable location, level, area, or combination of factors for a particular activity or purpose’ (p. 7).

CONCLUSIONS This chapter has sought to provide an overview of action research as a social science of change and changing, a distinctive integration of the actions of changing and the generation of knowledge through studying changing. As this chapter has demonstrated, it constitutes a different form of social science from other research traditions and presents a radical alternative to those that seek to create knowledge only. In the 1930s and 1940s, from his experiences of the Nazi regime in Germany and of racial and religious prejudices in the USA, Kurt Lewin articulated action research as a social science to address these pressing issues. In our volatile, unpredictable, complex and ambiguous world, where we are confronted daily with ecological, healthcare, social and political challenges where social scientists can play a key role (Bartunek, 2022), a re-imagining of Lewin’s vision of the power of action research is called for and is within our power to deliver.

ACKNOWLEDGEMENTS I thank the fellow members of my action research writing group – Vivienne Brady, Geralyn Hynes and Denise O’Leary for their supportive critique of the earliest version of this chapter, and Bill Pasmore, Rami Shani, David Szabla and Jennifer Kim for helping me take it further and deeper.

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Action research as the social science of change and changing  45 Hollis, M. (2002). The Philosophy of Social Science: An Introduction, Revised & Updated ed. Cambridge University Press. Kolb, D. (1984). Experiential Learning. Prentice Hall. Levin, M. (2003). Ph.D. programs in action research: Can they be housed in universities? Concepts and Transformation, 8(3), 219–38. Lewin, K. (1947). Frontiers in group dynamics II: Channels of group life, social planning and action research. Human Relations, 1(2), 143–53. Lippitt, R. (2016). Kurt Lewin, action research and planned change. In D. Coghlan & A.B. (Rami) Shani (Eds.), Action Research in Business and Management (Vol. 1, pp. 23–8). SAGE. Lonergan, B.J. (1992). The Collected Works of Bernard Lonergan, Vol. 3. Insight: An Essay in Human Understanding. F. Crowe & R. Doran (Eds.). University of Toronto Press. Lonergan, B.J. (2017). The ongoing genesis of methods. In R.W. Doran & J. Dadosky (Eds.), The Collected Works of Bernard Lonergan: A Third Collection (Vol. 16, pp. 140–160). University of Toronto Press. MacIntosh, R., Bartunek, J.M., Bhatt, M., & MacLean, D. (2016). I never promised you a rose garden: When research questions ought to change. In D.A. Noumair & A.B. (Rami) Shani (Eds.), Research in Organizational Change and Development (Vol. 24, pp. 47–82). Emerald. MacIntosh, R., Mason, K., Beech, N., & Bartunek J. (2021). Delivering Impact in Management Research. Routledge. Marshall, J. (2016). First Person Action Research: Living Life as Inquiry. SAGE. Mirvis, P., Mohrman, S.A., & Worley, C. (2021). Doing Relevant Research: From the Ivory Tower to the Real World. Edward Elgar Publishing. Nielsen, R.P. (2016). Action research as an ethics praxis method. Journal of Business Ethics, 135(3), 419–28. Pasmore, B. (2015). Leading Continuous Change. Berrett-Kohler. Pasmore, W.A. (2001). Action research in the workplace: The socio-technical perspective. In P. Reason & H. Bradbury (Eds.), Handbook of Action Research (pp. 38–47). SAGE. Pasmore, W.A., Woodman, R., & Simmons, A.L. (2008). Toward a more rigorous, reflective, and relevant science of collaborative management research. In A.B. (Rami) Shani, S.A. Mohrman, W.A. Pasmore, B. Stymne & N. Adler (Eds.), Handbook of Collaborative Management Research (pp. 567–82). SAGE. Peirce, C.S. (1997 [1903]). Pragmatism as a principle and method of right thinking. In P.A. Turrisi (Ed.), The 1903 Harvard Lectures on Pragmatism. State University of New York Press. Peters, M., & Robinson, V. (1984). The origins and status of action research. Journal of Applied Behavioral Science, 20(2), 113–24. Porras, J.I., & Robertson, P.J. (1987). Organization development theory: A typology and evaluation. In R.W. Woodman & W.A. Pasmore (Eds.), Research in Organizational Change and Development (Vol. 1, pp. 1–58). JAI. Reason, P. (2006). Choice and quality in action research practice. Journal of Management Inquiry, 15(2), 187–203. Roth, J., Shani, A.B. (Rami), & Leary, M. (2007). Facing the challenges of new capability development within a biopharma company. Action Research, 5, 41–60. Saetre, A.S., & Van de Ven, A. (2021). Generating theory by abduction. Academy of Management Review, 46(4), 684–701. Schein, E.H. (1995). Process consultation, action research and clinical inquiry: Are they the same? Journal of Managerial Psychology, 10(6), 14–19. Schein, E.H. (2008). Clinical inquiry/research. In P. Reason & H. Bradbury (Eds.), The SAGE Handbook of Action Research, 2nd ed. (pp. 226–79). SAGE. Schein, E.H. (2010). Organization development: Science, technology or philosophy? In D. Coghlan & A.B. (Rami) Shani (Eds.), Fundamentals of Organization Development (Vol. 1, pp. 91–100). SAGE. Schein, E.H., & Schein, P.A. (2021). Humble Inquiry: The Gentle Art of Asking Instead of Telling, 2nd ed. Berrett-Kohler. Schwarz, G.M., & Stensaker, I.G. (2016). Showcasing phenomenon-driven research on organizational change. Journal of Change Management, 16(4), 245–64.

46  Handbook of research methods in organizational change Shani, A.B. (Rami), & Coghlan D. (2021a). Collaborative Inquiry for Organization Development. Edward Elgar Publishing. Shani, A.B. (Rami), & Coghlan, D. (2021b). Action research in business and management: A reflective review. Action Research, 19(3), 518–41. Shani, A.B. (Rami), Coghlan, D., & Alexander, B. (2020). Rediscovering abductive reasoning in organization development and change research. Journal of Applied Behavioral Science, 56(1), 60–72. Shani, A.B. (Rami), & Docherty, P. (2003). Learning by Design: Building Sustainable Organizations. Blackwell. Shani, A.B. (Rami), Mohrman, S., Pasmore, W.A., Stymne, B., & Adler, N. (Eds.) (2008). Handbook of Collaborative Management Research. SAGE. Shani, A.B. (Rami), & Pasmore, W.A. (1985). Organization inquiry: Towards a new model of the action research process. In D.D. Warrick (Ed.), Contemporary Organization Development: Current Thinking and Applications (pp. 438–48). Scott Foresman and Company. [Reproduced in D. Coghlan & A.B. (Rami) Shani (Eds.) (2010). Fundamentals of Organization Development (Vol. 1, pp. 249–60). SAGE.] Sherman, F., & Torbert, W.R. (2000). Transforming Social Inquiry, Transforming Social Practice. Kluwer. Susman, G.I., & Evered, R.D. (1978). An assessment of the scientific merits of action research. Administrative Science Quarterly, 23(4), 582–601. Swedberg, R. (Ed.) (2014). Theorizing in Social Science. Stanford University Press. Torbert, W., & Associates (2004). Action Inquiry: The Secret of Timely and Transforming Leadership. Berrett-Koehler. Toulmin, R. (1990). Cosmopolis. Chicago University Press. Van de Ven, A.H. (2007). Engaged Scholarship. Oxford University Press. Walker, B., & Haslett, T. (2002). Action research in management: Ethical dilemmas. Systemic Practice and Action Research, 15, 523–33. Williander, M., & Styhre, A. (2006). Going green from the inside: Insider action research at the Volvo Car Corporation. Systemic Practice and Action Research, 19, 239–52. Woodman, R.W. (2014). The science of organizational change and the art of changing organizations. Journal of Applied Behavioral Science, 50, 463–77. Yoak, S.D., & Brydon-Miller, M. (2014). Ethics and moral decision-making. In D. Coghlan & M. Brydon-Miller (Eds), The SAGE Encyclopedia of Action Research (pp. 306–9). SAGE.

3. Conducting processual research on organisation change Deepak Saxena and Joe McDonagh

INTRODUCTION Change can be understood from a variety of perspectives. In their seminal paper, Markus and Robey (1988) note two types of logical structure of change theories. A logical structure may be defined as the temporal aspect of the theory and the logical relationships between the causes and the outcomes. This is a question of whether the explanation of change is measured in terms of the difference in variables (variance theory), or the explanation is continuous and follows the gradual change in differing contexts (process theory). They observe that the majority of theories underpinning organisation change employ variance-based conceptions, with relatively less attention given to process perspectives. A similar trend is noted by Paré et al. (2008) in their review of theories on information systems (IS)-enabled change during 1991–2005. This chapter speaks to this gap by offering some guidelines on conducting processual research on organisation change. Process research offers an opportunity, particularly for doctoral researchers, to investigate organisation change holistically over an extended period of time. In this regard, this chapter is particularly useful for doctoral researchers who are starting their research journey, by serving as a reference to different elements of process research. The chapter is organised as follows. First, interpretations of the term ‘process’ by scholars from diverse domains are explored. Then the focus is on the processual nature of organisation change, highlighting its contextual, multi-level, continuous, and complex nature. This is followed by a discussion on recommended data-collection strategies when conducting process research. A discussion of analysis strategies and guiding assumptions for making sense of process data follows. This is trailed by a discussion on three types of theoretical contribution offered by process research – patterns, meanings, and mechanisms – with extended discussion on the mechanisms identified for organisation change. Subsequently, a practical contribution by advancing a processual model to guide the conduct of processual research is reviewed. This is informed by our own experience in conducting such inquiry over many years. This chapter draws to a close by reaffirming the appropriateness of processual research for doctoral students and the programmes of research they are engaged in.

THE NATURE OF PROCESS The etymological roots of the word ‘process’ date back to around fourteenth-century French, from the word ‘proces’, meaning ‘journey’, and also from Latin, from the past participle stem of ‘procedure’ – ‘go forward’ (Hernes, 2008). The essence we can take from its etymological roots is that ‘process’ denotes movement in time. However, how to study this movement in time is approached in diverse ways by scholars of organisation change. In his influential 47

48  Handbook of research methods in organizational change article, Van de Ven (1992) notes three different connotations of the term ‘process’. While all interpretations of ‘process’ conceive process as a sequence in some way, the three connotations define process in slightly differently ways. In the first interpretation (Mohr, 1982; Mackenzie, 2000), process is defined as a sequence of probabilistic events. Contrasting the process approach with the variance approach, which measures change in terms of change in variables, a process theory in this conception deals with discrete states and events. This approach to the study of change may suitably be viewed as an effort to include the time dimension in traditional variance theories. Used in this sense, process is conceived as a mechanism that changes the state (i.e., the value of a set of variables) of a system, though not with certainty but with a certain probability. This is why Mohr (1982) stresses that if it is a true process model, the same input will be capable of leading to more than one outcome. One example of this approach can be found in Mackenzie (2000), where he suggests different process frameworks in the form of Y = F (C) where Y is the set of outcomes, C is the set of considerations, and F is the network linking considerations with each other and with the outcomes. This interpretation of process is essentially on the lines of variance theories that aim to capture the process dynamics by incorporating a set (in contrast to single values) of variables and outcomes. In this manner, there may be some explanation offered, but as Van de Ven and Huber (1990) note, such process explanations typically entail highly restrictive and unrealistic assumptions about the order and sequence in which events unfold in organisations. Thus, by compressing the reality into a set of variables, this approach leaves aside the rich details which could be captured by other qualitative approaches. The second interpretation of the term ‘process’ (often referred as ‘business process’ in operations management and IS research) defines it as a sequence of predetermined events. It is understood as a category of concepts or variables that refers to actions of individuals or organisations (Van de Ven, 1992). Garvin (1998) refers to this interpretation as a ‘work process’. Work processes involve sequences of linked interdependent activities that together transform inputs into outputs, and have clear beginnings and ends with boundaries that can be defined with reasonable precision and minimal overlap. Examples of this kind of usage include processes such as a production process, order fulfilment process, or supply chain process. Used in this sense, a process is essentially conceived as a sequence of activities which produces some fixed outcome. Work processes provide a useful framework for addressing common organisational problems of fragmentation or the lack of cross-functional integration (Garvin, 1998). The movements like total quality management or business process reengineering relied on this conception of process for organisational improvement. However, this conception of process explains what happens to outcomes if something happens or does not happen in an intermediate stage, but does not explain how the transition takes place. This interpretation of process may also be equated with logico-scientific thinking (Langley & Tsoukas, 2011) that seeks accurate prediction, but often lacks a comprehensive explanation. Although Weick (1979) did not use the term ‘process’ per se, his shift from the term ‘organisation’ to ‘organising’ underscores the third interpretation of the term ‘process’. In this interpretation, process is understood as a sequence of events that describes how things change over time. Essentially, this definition of process takes an historical, developmental perspective, and focuses on the sequences of incidents, activities, and stages that unfold over time (Van de Ven, 1992). Garvin (1998) terms these processes as ‘change processes’ highlighting their dynamic and temporal nature. He includes organisational lifecycle and Darwinian evolution as examples of change processes. In essence, this interpretation of process is what amounts

Conducting processual research on organisation change  49 to capturing reality ‘in-flight’ (Pettigrew, 1985, 1987) or ‘as-it-happens’ (Dawson, 2003a, 2003b). In this chapter, processual research in the context of organisation change corresponds to this interpretation of process. Strong Versus Weak View of the Process Some scholars (Chia & Langley, 2004; Hernes, 2008; Tsoukas & Chia, 2002) make an additional distinction in terms of a weak versus a strong process view. Mainstream organisation theory tends to view processes as flows occurring within the confines of organisation goals and structures (Hernes, 2008). The weak view1 assumes organisation and processes as different entities. According to this view, though an organisation shapes and is shaped by ongoing processes, it has its own existence apart from processes in terms of actors, rules, routines, and structure. This means that an organisation can interact with the process with the intention of managing it. In other words, in this view organisational actors have a role to play in enactment and change of processes. Processes can be studied with the weak ontology by focusing on how things change over time (Bizzi & Langley, 2012). In contrast, a strong process view treats organisation as constituted by process (Hernes, 2008). It assumes organisation is something which is constantly in flux and always in the state of becoming. In the strong view, even actors are created and changed by process. Therefore, there is no question of managing a process. With a strong processual view, organisational phenomena are not treated as entities, as accomplished events, but as enactments – unfolding processes involving actors making choices interactively within their contextual conditions (Tsoukas & Chia, 2002). The adoption of a strong process ontology requires a focus on how continuous and non-deterministic flows of activities reconstitute relatively stable phenomena such as organisation routines or structures (Bizzi & Langley, 2012). Bizzi & Langley (2012) find the strong perspective intellectually promising, but also note that it poses pragmatic challenges for researchers since it is assumed that the phenomena they are studying are constantly in flux. Caldwell (2006) also finds this understanding of process and time ontologically sound, but at the same time he finds it epistemologically vacuous. Taken to its extreme, research based upon strong process ontology will lose its generality since everything will be unique in terms of its context, time, and processes. Perhaps that is why, while emphasising change and temporal evolution, most change scholars doing processual research tend to lean towards a strong process view to answer empirical questions (Bizzi & Langley, 2012; Hernes, 2008; Pettigrew, 2012). Ontology of the weak processual approach is suitable for studying the process of change since by differentiating between organisation and process, it allows us to focus on the object of change, giving us an anchor around which we can study the process of change (Avgerou, 2019; Chia and Langley, 2004). This is not to say that the strong process view is irrelevant, but as Chia and Langley (2004) acknowledge, while the strong perspective enables us to appreciate the sui generis nature of process, the weak perspective helps us more in empirical research. Armed with this understanding of process, the next section explores the processual nature of change in more detail.

1 The terms ‘weak’ or ‘strong’ refer to the scholars’ focus on the processual dynamics and not to the theoretical strength of the processual approach.

50  Handbook of research methods in organizational change

PROCESSUAL NATURE OF ORGANISATION CHANGE Change has been classified in numerous ways. By (2005) compiles different classification schemes based upon different criteria, as shown in Table 3.1. Table 3.1

Different classifications of change

Change parameter

Change types

Rate of Occurrence

Discontinuous, Smooth Incremental, Bumpy Incremental, Continuous, Continuous Incremental, Punctuated Equilibrium

Initiating Mechanism

Planned, Emergent, Contingency, Choice

Scale and Scope

Fine-tuning, Incremental Adjustment, Modular Transformation, Corporate Transformation

Source: By (2005).

If one tries to make sense of the classifications, it seems that these classifications are trying to capture all possibilities of organisation change. But a pertinent question arises: to what end? While it is an important intellectual exercise, such classification schemes look like an exercise to fit the change to a particular model, so that appropriate change management strategies can be prescribed. Collins (1998) argues against this type of simplistic accounts of change, even if these approaches are not gross distortions of the dynamics of organisation change, as they fail to give sufficient attention to key dynamics of change such as power, conflict, and resistance. He finds much of the study of change theoretically weak, lacking a view of the context, and devoid of real understanding of change as a complex and dynamic phenomenon. It is true for practitioner-oriented change literature where the term ‘change management’ gets precedence over the term ‘organisation change’. The former has the essential connotations that change is an orderly sequence with clearly defined outcomes and that it can be managed. Clegg and Walsh (2004) find the term ‘change management’ inappropriate and misleading as it conjures up a focus on the implementation phase, does not take account of the full lifecycle perspective of change, and places the focus on managerial issues. Kuipers et al. (2014) also note that change management theories belong to the rational-managerial perspective and find such literature to have relatively limited capacity in terms of providing details regarding how contexts and processes affect the change. Hughes (2011) adds that the belief that organisation change is a code which once cracked misrepresents the ambiguous and dynamic practice of managing change and the process of changing. It is argued that unless we have an image of change as an ongoing process and as a stream of situated actions, it will be difficult to overcome the implementation problems of change programmes (Tsoukas & Chia, 2002). Building on this, this section explores the processual dimensions of change phenomena in more detail. Contextual Dynamics The term ‘context’ comes from Latin and means ‘to weave together’ (as in textiles). For House et al. (1995), the distinctive competence of organisational behaviour scholars is the ability to study behaviour in and of organisations in context. They stress that ‘context’ refers to the whole structure of connections between components that gives those components their meaning. ‘Context’ refers to both the external setting in which individuals, groups, or organisations operate as well as their constituent parts (Caffrey & McDonagh, 2015). If the change process is the stream of analysis, context is the terrain around the stream that shapes the field of

Conducting processual research on organisation change  51 events, and is in turn shaped by them (Pettigrew, 1992, 1997). Dawson (1997, 2003a, 2003b) also stresses that change does not occur in a hermetically sealed bubble. Instead, choices of the actors are influenced by values and beliefs developed and modified during a lifetime of interaction with their context. In the organisational setting, while external context may be influential in promoting certain change initiatives, internal context helps to explain variation in employee experience of change. The term ‘context’ has deliberately been chosen in processual research as opposed to the term ‘environment’. A processual perspective does not view organisations existing independently of their environments, but views context as permeating processual phenomena (Pentland et al., 2020). Collins (1998) observes that those who make use of environmentally based, as opposed to contextually sensitive, analysis miss many of the key dynamics of organising. Unlike the notion of environment made familiar by contingency theories, where it tends to exist as a pre-existing backdrop for internal working of an organisation, the contextualist viewpoint acknowledges a complex set of relations and interactions (Pettigrew, 1985). Unlike environment, context is not simply a barrier to action. Rather, allowing for choice and manoeuvre, context is involved in the production of action (Pettigrew, 1985, 2012). Moreover, context itself is an interactive process rather than a backdrop of change, and change is differentially impacted by configurations of characteristics that constitute context (McDermott et al., 2008). This also highlights the multi-level nature of the change context and process, which is discussed next. Change as a Multi-Level Process Hughes (2006) observes that the literature that seeks to inform understanding of organisation change has often appeared to be preoccupied within a single level of analysis – sectoral, organisational, group, and individual. However, Burnes (2014) argues that it is not the case that researchers at a particular level ignore the other levels, but rather they see their level as being the lynchpin that holds the others together. Each level of analysis has strengths and weaknesses, and in general, understanding of change has been advanced by the analysis at each level. However, any overall understanding requires a synthesis of understanding at all levels. The idea of multiple and multi-level contexts is inherent in processual approach. A contextualist analysis of change draws on phenomena at diverse levels of analysis and the interconnections between those levels through time (Avgerou, 2001, 2019; Pettigrew, 1990, 1997). The vertical levels refer to the interdependence between higher or lower levels of analysis based upon phenomena to be explained at some further level; for instance, the impact of an economic recession on sectoral dynamics or interest-group behaviour. Indeed, studying processes across a number of levels of analysis, noted as ‘embeddedness’ (Pettigrew, 1997; Pettigrew et al. 2001), is one of the important guiding assumptions of processual research. Depending upon the research problem and the level of access, a processual researcher may focus upon multiple levels of analysis – e.g., micro–meso–macro, individual–group–organisational, or organisational, sectoral–national. For an example, the reader may refer to Leavy (1991), who offers a process study of strategic change in the Irish dairy industry, linking multiple levels of analysis.

52  Handbook of research methods in organizational change Continuity and Change Apart from the idea of layered contexts (Avgerou, 2019), processual research also highlights temporal interconnectedness of the change process. While classifying change as episodic or continuous has been common in change management literature, processual research challenges this classification. Chia (2002) observes that if change is to be considered as a flow, it would mean that the influence of the past will be on the present and the present will influence the future. For Dawson (2003a, 2003b, 2012), the process of change is a result of interweaving and sometimes contradictory processes. These processes have an ongoing history that is never static, but open to change as the past is rewritten in the context of the present and in the light of future expectations. Brown and Eisenhardt (1997) invoke the metaphor of ‘links-in-time’ to stress the continuous nature of change. Pettigrew (1997) and Pettigrew et al. (2001) also include the idea of ‘temporal interconnectedness’ to study processes in past, present, and future timeframes. Instead of contrasting between continuity versus change, Malhotra and Hinnings (2013) talk about continuity and change, whereby old and new forms of organisation and ideology coexist as change unfolds. They assert that organisational change occurs through a process of both continuity and change, rather than that of indiscriminate rapid transformation. Weick and Quinn (1999) note that the understanding of a change as episodic or continuous change depends upon the perspective of the observer. From a distance (the macro level of analysis), when observers examine the flow of events that constitute organising, they may note relatively stable organisational routines with occasional episodes of revolutionary change. However, a closer view (the micro level of analysis) suggests ongoing adaptation and adjustments. Pettigrew (1985) observes that it is easier to identify change if we look at the present-day events. On the other hand, the longer we stay with an emergent process and go further back to disentangle its origins, the more likely we are to identify continuities. Thus, change and continuity are not alternative objective states, and they are typically co-existent and coterminous (Sturdy & Grey, 2003). Stability and continuity will be evident in organisations even during the time of radical change, since even in radically changing times organisations would need some stable routines (Hughes, 2006). Complex Nature of Change Outcomes It is usually suggested to study change outcomes in processual studies (Pettigrew, 2012; Pettigrew et al., 2001). For Pettigrew, building a performance outcome into a change process research design has a number of practical advantages. First, the outcome provides a focal point, an anchor for the whole investigation. This is particularly valuable when the process involves the collection and analysis of a long time series. Furthermore, it allows for exploring how and why variations in context and process may underpin certain outcomes. On the other hand, for Van de Ven (1992), while historical analysis is necessary for examining many questions and concerted efforts can be undertaken to minimise bias, it is generally better, if possible, to initiate historical study before the outcomes of a strategic change process become known. It is even better to undertake real-time study of strategic change processes as they unfold in their natural field settings. Within these two extremes, Langley (2007) reminds us that outcomes are often rather artificial staging points amid never-ending processes and should be researched keeping this in mind.

Conducting processual research on organisation change  53 However, Dawson (2003b) reminds us that the essential unforeseeable nature of change means that the process cannot be predicted, and the outcomes are often understood in retrospect. Hughes (2011) argues that outcome determination will always be incomplete since they are often measured against espoused and publicised rationales for change, whereas organisation change results in many unintended outcomes (Jian, 2007; MacKay & Chia, 2013) which are never taken into consideration during the planning stage. Clegg and Walsh (2004) also find it oversimplistic just to focus on the apparent manifest goals as other goals will always be present. Thus, MacKay and Chia (2013) stress incorporating the possibility of multiple outcomes over time if we wish to undertake a serious processual analysis of changing. For instance, Saxena and McDonagh (2019) present a processual case study of a systems implementation that was deemed a failure in terms of project management criteria of budget and time constraints; it was deemed successful in the longer run due to its extended use in the organisation. It is not just a problem of multiplicity of outcomes in organisation change; there is also the problem of multiple understandings of the outcomes of change. Depending on the vantage points of the evaluators, the outcome of the change may be construed in various ways with unclear distinction between success and failure (Hughes, 2011; Saxena et al., 2016).

DATA COLLECTION STRATEGIES FOR PROCESSUAL RESEARCH Langley (1999) mentions four properties of process data: (1) they deal with sequences of events, (2) they often involve multiple levels and units of analysis, (3) their temporal embeddedness often varies in terms of precision, and (4) despite the primary focus on events, process data tend to be eclectic. Based on the nature of process data, this section discusses methodological issues in process research. Longitudinal Research Since a process study tries to understand a sequence of events unfolding over time, time is a crucial consideration in the research design. Longitudinal data obtained with archival, historical, or real-time field observations are necessary to observe how processes unfold over time (Pettigrew, 1985, 1987, 1990, 1992, 1997; Pettigrew et al., 2001; Dawson 1997, 2003a, 2003b; Langley 1999, 2007, 2009; Langley et al., 2013). From a processual research perspective, this boils down to reconstructing a chronology of events (Langley, 1999; Sminia, 2009) based on the data collected.2 Consistent with the contextual perspective, process studies collect data at multiple levels, corresponding to the events from the past and present. Langley (2007, 2009) suggests three possible temporal orientations in studying processes – tracing back, following forward, and reconstituting the evolving present. While retrospective research designs are particularly useful to study processes and evolution over large periods, real-time research design is of particular value in capturing the ongoing developments in rich details as they emerge (Bizzi & Langley, 2012). Taking a dual perspective, Leonard-Barton (1990) suggests While the term ‘longitudinal research’ is used by quantitative researchers to denote repeated measurements of the variables of interest, for qualitative and processual researchers the term means the coverage of the sequence of events, even though the data is collected as a single point of time. 2

54  Handbook of research methods in organizational change simultaneously combining both research designs; i.e., comparing one longitudinal case studied in greater detail with several retrospective cases. In fact, many process studies do this sequentially; i.e., undertaking retrospective analysis up to the point of entry into an ongoing change and following in real time thereafter (Bizzi & Langley, 2012). The final choice, among other factors, would also depend upon the level of access granted to the researcher. Qualitative Case Study Langley (1999, 2007, 2009) observes that scholars have utilised quantitative as well as qualitative approaches for process studies. Studies using a quantitative approach (such as Eisenhardt, 1989; Ferlie et al., 2005; Garud & Van de Ven, 1992) often compare among a large number of (generally 6–10) cases and look for patterns in the sequences of events. While these studies often begin by collecting processual data about temporally evolving phenomena, they end up generating variance-based theoretical formulations. In this sense, these studies are closer to Mohr’s (1982) concept of process in which he introduces time and probabilistic dimensions into variance theories. Thus, while the data collected may be longitudinal, the theory derived from it may not always be processual in the sense of providing temporally ordered explanations. Pettigrew (1997) terms this kind of research design ‘comparative statics’; i.e., re-evaluating quantities at specific intervals, which may not essentially be processual. Langley (1999) also notes that a quantitative time series constitutes rather coarse-grained outcropping of events and variables over time and barely skims the events. In contrast, Dawson (2003a, 2003b) and Pettigrew (2012) highlight the role of qualitative research in understanding and explaining the process dynamics of the organisations within the contextualist framework. Langley (2007) finds forms of process research that incorporate narrative, interpretive, and qualitative data more appealing for the richness of process detail they provide. Since process research gives primacy to the context, the research method needs to be able to capture context-specific details, as opposed to a cross-sectional method. Therefore, case study is often a preferred method for conducting processual research on organisation change. Indeed, Pettigrew’s (1985) case study of Imperial Chemical Industries (ICI) is a hallmark of processual research. With regard to the number of cases to be considered, different recommendations exist in the process literature. Eisenhardt (1989) and Van de Ven (1992) suggest multiple cases for pattern identification and theory building from cases, looking for external generalisation. Pettigrew (1992, 1997) and Pettigrew et al. (2001) prescribe a matched pair of cases such as cases with similar context but dissimilar outcomes, or cases with dissimilar context but similar outcomes. The matched pair design is specifically suitable for connecting context to outcomes (Pettigrew et al., 2001). However, Dawson (2003a, 2003b) warns us that researchers should not equate more case studies with higher-level data and the focus should be on finding richness of details. Indeed, as case scholars argue (Yin, 2017), the aim of a case study is not to make predictions about a sample population, but rather to explore phenomena in all its richness. Langley (1999) stresses that the number of cases should be driven by the nature of the research question. If the research question aims to provide a holistic understanding of specific events and processes, even one detailed case is deemed sufficient. George and Bennett (2005) note that the single-case design allows the researcher to look for a large number of intervening factors and inductively observe any unexpected aspects of the operation of a particular processual mechanism, or to identify the contextual conditions that activate the causal mechanism. They note that statistical methods, which omit all contextual

Conducting processual research on organisation change  55 factors except those codified in the variables selected for measurement or used for constituting a population of cases, essentially leave out many contextual and intervening variables. They also note that by using a single case researchers may also use theories on processual mechanisms to give historical explanations of the case. Thus, in the final consideration of research design, the number of cases should be driven by the nature of the research question and access to the data. If the research question aims to find common patterns in certain processes, a number of case studies may be required. If the research question aims to provide a holistic understanding of specific events and processes, one or two detailed cases should be sufficient (Langley, 1999). Multi-Level and Multi-Method Data Collection Multi-level data collection and subsequent analysis is a hallmark of good processual research. In a processual study, the researcher seeks to collect and analyse data at multiple levels over time. In this regard, the researcher is interested in exploring both intra- and inter-level considerations. Collectively, these levels of analysis capture continuity and change as they relate to individuals, groups, organisations, and institutions. The precise choices about how many levels of context to bring into the study are likely to be shaped by the content of the research problem, the research themes and questions driving the study, the availability of relevant data sets, and the ambitions and resources of the researchers engaged in the research process (Pettigrew, 1997). The processual approach to the study of change is characterised by the use of multiple methods (Dawson, 1997, 2003a, 2003b; Pettigrew, 1990, 1992, 1997; Pettigrew et al., 2001), which includes the use of in-depth interviews, an analysis of documentary and archive data, and the collection of observational and ethnographic material. These methods have complementary strengths and weakness (Bizzi & Langley, 2012; Yin, 2017). Observations are useful for understanding behaviour, but they are localised and ephemeral, relying critically on the researcher as the instrument. Documents and archives are an important source for key event chronologies, but they tend to gloss over conflict and complexity. Interviews are temporally versatile as respondents can draw on their memories and link temporal phenomena across time. Their unique strength lies in the capacity to access the understanding and interpretations made by participants. At the same time, however, interviews are artificial interactions that may be influenced by memory lapses, impression management, and the quality of the rapport between the interviewer and interviewee. With the advent of new technology, some new data sources have also emerged. For instance, Pentland et al. (2020) illustrate the use of digital logs to capture internal context in processual analysis. Thus, a multiple-method approach makes it possible to triangulate different types of data in constructing a credible narrative on the process of change. Triangulation using multiple sources is preferable in processual research since the weaknesses of one source can be compensated by the strengths of others. Analysis strategies, discussed next, also reflect a similar orientation in terms of the use of multiple analytical methods.

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ANALYSIS STRATEGIES IN PROCESSUAL RESEARCH When analysing processual data, researchers may draw from two important considerations. First, some practical strategies to make sense of process data. Second, certain guiding assumptions when developing a process theory. Making Sense of the Process Data At a practical level, Langley (1999) notes seven specific strategies to make sense of process data. Narrative This strategy involves construction of a detailed story from raw data. The theorist who adapts this strategy avoids excessive data reduction and tries to present as completely as possible the different viewpoints on the process studied. Research validity in this context comes from the detailed description of the process. This strategy usually forms the basis of further analysis. Pettigrew (1985) is an excellent example of this approach. Quantification This is just the opposite of the narrative strategy. In this approach, researchers start with in-depth process data and then systematically list and code qualitative events as per predetermined protocol. Thereby, they reduce the mess of data into quantitative time series which could then be analysed using statistical methods. This approach may be deemed suitable if there is sufficient clarity on the variables of interest. Mackenzie (2000) outlines processual analysis following such an approach. Alternative templates In this sensemaking strategy, the analyst analyses the same set of events with different theoretical perspectives, and then assesses the extent to which each theory explains the process. It is assumed that, taken together, all these explanations provide a complete and comprehensive picture of the process. Markus (1983) is an excellent example of the application of this strategy in which organisational resistance to system implementation is examined from three diverse perspectives – individual, systems design, and interaction – and offers a more holistic account of the phenomenon of interest. Grounded theory Grounded theory is a systematic approach for inductive theorising that steers clear of predetermined theory and ‘discovers’ the theory from the data (Glaser & Strauss, 2017). Though there are different variants of grounded theory methodology, it is essentially a set of structured steps to develop categories and concepts from raw data after successive cycles of refinement. In the context of processual analysis, a grounded-theory-type coding approach (such as the one outlined in Saxena, 2019) may help scholars in making sense of their process data. Visual mapping Rather than using words (as in narrative) or using quantities (as in quantitative), this strategy represents process data in the form of visuals and then derives patterns from the visuals.

Conducting processual research on organisation change  57 Visual graphical representations can be used to show precedence, parallel processes, and the passage of time. Leavy (1991) successfully uses visual representation to illustrate his analysis. However, this strategy should be treated more as a sensemaking and representation device and should be used in conjunction with other analysis strategies. Temporal bracketing In this strategy, events are divided into successive adjacent time periods, which allows examination of how events from one period/level may influence the events in the subsequent period or at another level. It also permits the constitution of comparative units for analysis for the process. Saxena and McDonagh (2019, 2020) successfully use this strategy in conducting process analysis of three implementation projects in an organisation. This strategy is best suited in conjunction with a narrative approach; i.e., by temporally bracketing the narrative to assist in the analysis. Synthetic In this strategy, the original process data is transformed from stories composed of ‘events’ to ‘variables’ that synthesise their critical components. These variables are then used to predict the outcome. This strategy is closer to Mohr’s (1982) concept of process analysis than to the processual research discussed in this chapter. Brown and Eisenhardt (1997) is an excellent example of this approach. It may be noted that these strategies are not mutually exclusive and may be employed simultaneously/sequentially in a research project. For instance, narrative strategy may include temporal bracketing and visual mapping. This may be followed by grounded-theory-type coding on synthetic analysis. Guiding Assumptions for Processual Analysis In terms of linking processual analysis to theory, Pettigrew (1997) and Pettigrew et al. (2001) offer five guiding assumptions – embeddedness, temporal interconnections, focus on context, holistic explanation, and the linkage with outcomes. Embeddedness A central assumption of processual research is that the events are embedded in their outer and inner context. The source of change is often an asymmetry between contexts and entwinement of the processes operating at different levels. Therefore, it is suggested that any study of change should be conducted across different levels of analysis in which the events are embedded. Temporal interconnectedness Understanding the sequence of events over time is a crucial requirement for the process scholar (Pettigrew, 1997). Antecedent contextual conditions have an impact on the process unfolding at present, and the process unfolding presently will affect future outcomes. As Dawson (2003b, p. 10) puts it, ‘the process of change is continuously influenced by the interplay and conflict between historical reconstructions, current contextual conditions, and future expectations’. Therefore, to search for recurrent patterns of events, a process scholar has to capture processes in past, present, and future time.

58  Handbook of research methods in organizational change Focus on context and action Pettigrew (1997) reminds us that context is not just a stimulus environment, but a nested arrangement of structures and processes. The actions of the actors help shape processes and in turn, actors’ actions are also shaped by the unfolding processes. Therefore, it is not about supremacy of the context or action. Rather, it is about the context and action (Pettigrew, 2012) mutually interacting in creating an outcome. Holistic explanation In processual analysis, the researcher looks for holistic explanation of processes, rather than merely providing a linear narrative. This is how a processual case study differs from case histories. The explanation should not be limited to pattern recognition, but should also look for underlying mechanisms which drive those patterns. This holistic explanation requires data over a lengthy period of time and across levels and involves both induction and deduction (Pettigrew, 1997). The linkage with outcomes Pettigrew (1997) argues that the ambition of a process scholar should not merely be just about recognition of patterns or explaining the mechanism – ultimately a process scholar should be able to link the analysis to the outcomes of the process under investigation. Pettigrew (2012) identifies that one of the major limitations of much of the process research has been the failure to conduct empirical studies which link changes in context and process to outcomes. At the same time, he also reminds us that since all outcome variables are contestable, the researcher has to be extremely clear about the choice and rationale for outcomes under investigation. When conducting processual analysis, it might be useful to remember that it involves both induction and deduction (Dawson 2003a, 2003b; Pettigrew, 1997), which draws from both our existing theoretical knowledge and the empirical data. A purely inductive approach might seem to do justice to the contextual nature of a process, but will most likely result in ‘data asphyxiation’ (Pettigrew, 1990). Some amount of deduction is believed to help in guiding the researcher through the research process without posing too much of a preconceived understanding on the particular course of events (Sminia, 2009).

THEORETICAL CONTRIBUTION OF PROCESSUAL RESEARCH Unlike variance theory that tries to predict change outcomes based on input conditions, processual theory addresses why and how observed phenomena occur, and develops a causal reconstruction of events (Avgerou, 2013). A processual theory is an explanatory theory that focuses on causal processes that bring about theorised phenomenon (Markus & Robey, 1988). As Sutton and Staw (1995, p. 378) note, ‘theory is about the connections among phenomena, a story about why acts, events, structures, and thoughts occur … the nature of causal relationships … as well as the timing of … events’. Thus, a processual theory moves beyond surface-level description and explains how and why one event leads to another (Anderson et al., 2006). Indeed, the outcome of processual analysis is a ‘sometimes-true’ theory (Coleman 1964, p. 516) that moves beyond the description and provides an explanation of unfolding events within a contextual condition.

Conducting processual research on organisation change  59 Langley and Tsoukas (2011) identify three types of conceptual products emerging from processual research – patterns, meanings, and mechanisms. Particularly with multiple cases (or with multiple iterations with a single case) of organisation change, processual scholars look for patterns within the events. Performed in isolation, the search for patterns may result in some form of stage-based model of change. For instance, Bullock and Batten (1985) developed an integrated, four-phase model of planned change based on a review and synthesis of over 30 models of planned change. The four phases in their model are exploration, planning, action, and integration. During the exploration stage, an organisation investigates and decides whether or not it wants to make specific changes in its working, and if so, commits resources to plan the change. The planning stage involves an understanding of the organisation’s problems and how these problems can be solved. During the action stage, an organisation implements the changes derived from the planning stage. Finally, the integration stage is concerned with consolidating and stabilising the changes so that they become part of organisational routines. One may note a close resemblance with the cyclical process generally followed by both organisational development (OD) and action research, which is (a) diagnosing the situation, (b) planning action, (c) taking action, and (d) evaluating the action, leading to further diagnosing, planning, and so on (Coghlan & Shani, 2014; McDermott et al., 2008). However, a potential risk with the application of such stage-based models is in painting the change in broad strokes and ignoring the context and dynamics of change. For instance, change management consultants are often accused of drawing too much from Lewin’s (1947) model of unfreeze–move–refreeze in a simplified manner and ignoring the wider connotations of his works on group dynamics and field theory (Burnes, 2004; Burnes & Cooke, 2013). Hence, processual scholars complement the identification of patterns with their attempt to capture meanings expressed by the participants (Langley & Tsoukas, 2011). Dawson (2003b, 2012) argues that there can never be a single authentic story of change as there will always be multiple narratives and competing histories of change. He finds that absences of the less powerful in the presentation of the story of change remains a major flaw and a common weakness in much of the change management literature. Competing and erroneous perceptions of organisation change outcomes may simultaneously exist within organisations (Hughes, 2011). Multiple realities of the actors involved in organisation change imply that understanding of change can only stem from an appreciation of ‘competing definitions of organisational effectiveness and organisational needs’ (Collins, 1998, p. 194). Thus, it is suggested that one include competing voices for a holistic understanding of organisational change (Buchanan & Dawson, 2007; Graetz & Smith, 2010). Scholars need not present single interpretations of events. Instead, they should strive for capturing the sequence of events and associated interpretations as comprehensively as possible. While patterns and meanings may be intermediate conceptual products of a processual analysis, processual research ultimately seeks to find the underlying mechanisms which shape the patterns and outcomes of the observed processes (Langley & Tsoukas, 2011; Pettigrew, 1997, 2012). In a processual analysis, the researcher’s repetitive questioning about ‘how’ embodies this constant search for underlying mechanisms which drive the processes (Pettigrew, 2012). Mechanisms are seen as building blocks of middle-range theories that are useful for explaining problems around organisations and organising and to form more general process theories (Pettigrew et al., 2001). Social mechanisms are about ‘the wheelwork or agency by which an effect is produced. In this way, mechanisms do not merely address what happened, but also how it happened’ (Hernes, 2008, p. 74). Anderson et al. (2006) argue that a focus on

60  Handbook of research methods in organizational change mechanisms enables one to move beyond individual variables and their linkages to consider the bigger picture of action in its entirety. A mechanism-based explanation thus moves beyond describing what to explaining how, and thereby clarifies causal ambiguity (Pajunen, 2008) surrounding a phenomenon. Mechanism-Based Theorising in Organisation Change Research In this regard, the reader may find it useful to consult four generic mechanisms (termed ‘process motors’) introduced by Van de Ven & Poole (1995) to explain development and change in organisations. The four process motors are lifecycle, teleology, dialectics, and evolution. Lifecycle The typical progression of change events in a lifecycle model is a unitary sequence which is cumulative (characteristics acquired in earlier stages are retained in later stages) and conjunctive (the stages are related such that they derive from a common underlying process). This progression is pre-configured because the developing entity has within it an underlying form, logic, program, or code that regulates the process of change and moves the entity from a given point of departure towards a subsequent end that is prefigured in the present state. In lifecycle theories, each stage of development is seen as a necessary precursor to succeeding stages. The sequence of events in a lifecycle theory is assumed to be linear and an irreversible sequence of prescribed stages. Most of the stage-based theories of organisation change can be understood as illustrating lifecycle-based assumptions (Bullock & Batten, 1985). Teleology According to teleology, development of an organisational entity proceeds towards a goal or an end state. It is assumed that the entity is purposeful and adaptive. Under the teleological assumptions, the organisation constructs an envisioned end state, takes action to reach it, and monitors the progress. Unlike lifecycle theories, teleology does not envisage a predetermined sequence, but a sequence depending upon goal setting and assessment. An event sequence in teleology is a recurrent and discontinuous sequence of goal setting, implementation, and adaptation of means to reach a desired end state. Most of the theories of OD may be considered following teleological assumptions. Williams et al. (2013) is an excellent example of the application of a teleological approach. Dialectics In a dialectical process theory, stability and change are explained by reference to the balance of power between opposing entities. Change occurs when these opposing values, forces, or events gain sufficient power to challenge the status quo. The relative power of an antithesis may mobilise an organisational entity to a sufficient degree to challenge the current thesis and set the stage for producing a synthesis. Over time, this synthesis can become the new thesis as the dialectical process continues. By its very nature, the synthesis is a novel construction that departs from both the thesis and antithesis. An event sequence in dialectic theory is a recurrent

Conducting processual research on organisation change  61 and discontinuous sequence of confrontation, conflict, and synthesis between contradictory values or events. Most of the political theories of organisation are assumed to follow dialectical processes. One such example of this approach is Bacharach et al. (1996) in the area of organisation transformation. Evolution Evolution explains change as a recurrent, cumulative, and probabilistic progression of variation, selection, and retention of organisational entities. Although one cannot predict which entity will survive or fail, the overall population persists and evolves through time. An event sequence in evolutionary theory is a recurrent, cumulative, and probabilistic sequence of variation, selection, and retention events. This mechanism essentially privileges the environment over the organisation. Institutional theory (Dimaggio & Powell, 1983; Meyer & Rowan, 1977) and resource dependence theories (Pfeffer & Salancik, 1978) may be understood as applying evolutionary principles to organisations. It is important to note here that the process of evolution works at the level of the population rather than the entity level. Van de Ven and Poole (1995) demonstrate that most specific theories of organisational development and change are actually composites of two or more ideal-type motors and the juxtaposition of different theoretical perspectives brings into focus contrasting worldviews of social change and development. Working out the relationships between such seemingly divergent views provides opportunities to develop new theory that has stronger and broader explanatory power than the initial perspectives. Moreover, the four models of change can be viewed not only as alternative perspectives on a single phenomenon, but also as different temporal phases in the journey of change in a complex organisation (Van de Ven & Sun, 2011). Thus, a processual scholar may find the prevalence of certain mechanisms in certain contexts, and the absence of those mechanisms in some other contexts. The presence, absence, or interaction of the mechanisms help in explaining organisational change outcomes. Moreover, the processual researcher may need to employ or conceive some new mechanism(s) if existing mechanisms do not offer a holistic explanation of the events under investigation. For instance, processual studies at the macro level may invoke institutional mechanisms (Meyer & Rowan, 1977; Suchman, 1995), or the ones investigating technological phenomena may find the affordance mechanism (Volkoff & Strong, 2013; Zammuto, et al., 2007) supporting processual explanation.

TOWARDS A PROCESSUAL MODEL OF CONDUCTING PROCESSUAL RESEARCH Having developed a robust theoretical understanding of processual research on organisation change, this chapter concludes by offering a processual model of conducting processual research. Here we draw from our own experience in conducting processual research in offering some practical guidelines for new researchers.

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Figure 3.1

Towards a processual model of processual research

Selecting Case Studies and Securing Access When looking for case studies on organisation change, it might be a good idea to focus on a change initiative that is either recently finished or is towards its end. Investigating an evolving change3 runs the risk of an incomplete and incoherent understanding since the participants may not have a fuller understanding of the unfolding events. When requesting access, it is preferable for the researcher to approach top management first in order to have buy-in to the research process. Securing research access from middle/lower management without top management buy-in may create problems later in the process when collecting comprehensive data on change. Additionally, when conducting multiple case studies, it may be beneficial if the change initiatives follow similar timeframes, allowing the researcher to compare the evolution of change over time. Collecting Secondary Data It is advised that researchers engage in secondary data collection even before they start the primary data collection. Change-related documentation may be requested once access is granted. In this regard, researchers may focus on strategy documents (not necessarily related to specific projects), project proposals, project plans, and/or minutes of the meetings to capture the internal context. In some cases, publicly available material, such as industry outlook reports, government reports, and newspaper and magazine articles, may also be consulted to understand the external context. Collecting and analysing secondary data not only enables researchers to understand the contextual features of the change, but also helps them to identify key actors who could be interview participants. We suggest that processual researchers write a short narrative capturing the sequence of events based on secondary data even before they start collecting primary data. This enables researchers to identify key events around

3 This is not to say the evolving change should not be studied. Our recommendation is that a processual approach may not be best suited to study an evolving change.

Conducting processual research on organisation change  63 which interview questions can be anchored or whether or not more documentation should be requested. In this regard, requesting an office space in the case organisation premises may help researchers to be grounded in the research setting (Dawson, 1997) and the subsequent collection of rich data. Collecting Primary Data As noted earlier, interviews are possibly the most effective data-collection strategy in processual research. In this regard, we suggest a responsive interviewing technique (Rubin & Rubin, 2011). In responsive interviewing, the researcher generally starts with an open-ended question, usually asking about the interview participant’s background and their relationship with the change initiative. This has the added benefit of easing the participants into the interview process. The researcher then follows up by asking questions anchored around their roles and their interpretation of key events. A typical challenge that processual researchers face is in ensuring the multi-level and the longitudinal nature of a processual study. In an ideal situation, the researcher would interview participants from multiple stakeholder groups that have an influence on or are influenced by the change. Similar considerations may be adopted to capture longitudinal elements. Participants may be asked questions about the key events of the change and how earlier events influence the latter ones. Though this approach runs the risk of post hoc rationalisation (Yin, 2017), it still can comprehensively capture the longitudinal element when data is triangulated across participants. Writing the Case Narrative Once the data is collected, the change narrative should be written describing the events from external and internal contexts. This form of process tracing (George & Bennett, 2005) should be highly specific to the case and should attempt to present the case as comprehensively as possible. This also involves including the events and narrating their interconnectedness as different levels of analysis. While the theoretical background of the researcher may not be denied, the case narrative should ideally provide pure description (Walcott, 2008) without any recourse to theory. The narrative may further be clarified by using temporal bracketing and/or visual mapping. In addition, a good processual case study presents the events without compromising the richness of the case (Langley, 1999), including competing narratives from diverse stakeholders (Dawson & Buchanan, 2005). This is particularly relevant in multiple case studies since it would not force the researcher to leave out certain details so that the cases ‘look similar’. Written in this manner, the case narrative serves as an additional data source when conducting processual analysis. Conducting Processual Analysis This stage involves theoretical redescription of the sequence of events by moving from the specific to general. For instance, when analysing an enterprise resource planning (ERP) implementation, the events may be similar to one of many implementation models. This immediately situates the analysis in its theoretical context. Qualitative and process researchers often use qualitative, multi-cycle coding to make sense of their data. To make sense of the wide tapestry of data, the researcher may assign codes to specific stages of the model, based

64  Handbook of research methods in organizational change on their applicability. As part of multi-cycle coding, these codes are then clubbed together into higher-order codes, with final codes often representing the building blocks of the theoretical framework (for an example, see Saxena, 2019). These building blocks are then used to explain the sequence of events during the change under investigation. The strength of the theoretical framework may be assessed based on how well it explains the events as well as how it aligns with existing understanding of theoretical building blocks. Hence, the final theoretical framework need not be a completely new grounded theory (Glaser & Strauss, 2017); it can also be a theory based on repurposing existing theoretical concepts, often drawn from diverse levels of analysis in offering an explanation in process terms. For instance, Van de Van and Poole (1995) arrived at their four process motors after analysing theories across diverse research domains from biology to sociology. For a more recent discussion on different styles of process theorising, the reader is encouraged to refer to Cloutier and Langley (2020). Publishing the Results from Processual Studies If conducted as part of a PhD, one outcome of process research is obviously in the form of a PhD thesis. The thesis, and potentially a book format, allows the researcher to present the richness of an organisation change and the associated process analysis in great detail. Indeed, Pettigrew’s (1985) now classic study in book form on ICI is one of the hallmarks of processual research. However, the academic community also requires publications in the form of journal articles. Here, the word limit of a typical journal article makes the job of the process researcher difficult. In this regard, the authors may focus on specific theoretical concepts as they relate to various stages of change; for instance, on the evolving role of top management support throughout the stages of change (see, for example, Elbanna, 2013). Alternatively, a specific change stage may be examined across multiple instances of the case (Saxena & McDonagh, 2019, 2020). While it may compromise some of the richness of the case, it presents a significant theoretical contribution by uncovering the dynamic nature of change. It is understood that individual research projects would vary in terms of context and resources. Nevertheless, we believe that the research model presented above would be helpful for the researcher in making sense of the complexity of researching organisation change and (to some extent) streamline their processual research.

CONCLUSION To understand the complex nature of organisation change, scholars often recommend process research since it captures the contextual, temporal, and multi-level nature of organisation change. However, first-time researchers, particularly doctoral students, may find it difficult to sift through the literature surrounding process research and organisation change. This chapter serves as an introduction as well as a guide towards understanding the nature of process research, planning data collection, executing the analysis, and reporting theoretical contributions. Merits of a longitudinal, qualitative case study approach are highlighted along with a focus on multi-level/multi-method data collection strategies. The chapter also offers a discussion on various theoretical contributions of process research, highlighting its role in developing a middle-range sometimes-true theory. A key strength of this chapter is in weaving the discussion around key writings in process research in organisation change, as well as

Conducting processual research on organisation change  65 offering practical suggestions to successfully conduct the research. We sincerely believe that this chapter serves as a starting point as well as a faithful companion in conducting process research on organisation change.

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4. The grounded theory methodology: over fifty years of inquiry! John Loonam

For more than fifty years the grounded theory methodology (GTM) has sought to expand empirical understanding and theoretical development. The methodology is most often deployed when a clear lack of empirical understanding exists or when a fresh perspective on current knowledge is sought. This chapter seeks to reflect on the methodology’s contributions to business studies over the past fifty years, with reference to organizational change inquiry. In seeking to build a theoretical understanding of phenomenon from the ground up, the methodology is particularly suited to holistic inquiry exploring organizational patterns and sequences over time. The chapter begins with a brief introduction to the origins of the GTM and its current use by prospective researchers. The next section explores the nature of grounded theory and its corresponding characteristics and attributes. The value of grounded theory is then discussed, specifically focusing on the use of the methodology within the general management, strategy, and organization literature. The chapter then conducts a cross-case analysis of three grounded theory studies to explore key lessons from the methodology’s application. Finally, the findings from the cross-case analysis are developed with specific emphasis on how grounded theory synthesizes methodological paradoxes through holistic inquiry.

THE ORIGINS OF GROUNDED THEORY The grounded theory methodology (GTM) first emerged in the 1960s with the publication of The Discovery of Grounded Theory by Glaser and Strauss (1967). The authors developed the methodology in response to the methods and practices they followed in their study of dying in health institutions throughout the 1960s (1964). The methodology, in no doubt influenced by the personal and intimate nature of the research topic, sought to reposition the sociological researcher “on the ground”, close to emerging data, and unburdened by the dominant hypothetico-deductive culture prominent within scholarship at the time. As Locke notes, framed as a polemic against this verificational enterprise, The Discovery of Grounded Theory was written with several specific purposes in mind: to encourage researchers to use their intellectual imagination and creativity to develop theories relating to their areas of inquiry, to suggest methods for doing so, to offer criteria to evaluate the worth of discovered-as distinct from tested-theory, and to propose an alternate rhetoric, that of generation, to balance out the rhetoric of verification in journal articles and monographs. (1996, p. 239).

Embedded in its foundation is the call for researchers to “protest against the methodological climate of verification” (Charmaz, 1983, p. 109) and start afresh. Central to this call is the pursuit of theory that is built, by data, from the ground up, to explain practice. The original authors, in particular Strauss, who came out of the University of Chicago with “its strong 69

70  Handbook of research methods in organizational change tradition in field observation, intensive interviewing, and pragmatic theorizing” (Locke, 1996, p. 239), were influenced in their thinking by the perspective of symbolic interactionism. The perspective derives from the pragmatist tradition, which assumes that the value of theories or beliefs rests on effective practical application. “Pragmatism views reality as fluid and somewhat indeterminate, and as open to multiple interpretations. In this perspective, facts and values are linked rather than separate and scientific truth is relative, provisional, and assessed through what works in empirical practice” (Charmaz, 2017, p. 264). In other words, through action, meaning, and as a consequence understanding, is derived. In generating meaning from this world of practice and action, the tools of language and interpretation are critical components for prospective researchers. Interaction takes place when individuals interpret one another’s behaviours, which is symbolic of the pre-existing symbols of words, meanings, and language deployed by social entities. Symbolic interactionism “assumes a view of social life as open-ended and emergent, fosters studying action and process, and takes temporality into account” (Charmaz, 2017, p. 265). This pragmatic perspective is what brought the original authors to lean on lessons learned from symbolic interactionism in developing the concept of grounded theory. A new methodology, deployed with the tools of symbolic interactionism, afforded researchers the ability to explore complex organizational environments, which sought to advance the domains of “theory”, through data-centric interpretation, and practice, through an action-derived view of process and change built from the ground up. Despite the methodology’s novelty, or indeed perhaps as a result of it, almost thirty years after the original publication an interpretive schism emerged. After the release of Basics of Qualitative Research: Grounded Theory Procedures and Techniques by Strauss and Corbin (1990), Glaser took umbrage with the publication and formally rebuked it, in his none too subtly titled reply, the Basics of Grounded Theory Analysis: Emergence Vs. Forcing (1992), stating that their work was a “book without conscience, bordering on immorality … producing simply what qualitative researchers have been doing for sixty years or more; forced, full conceptual description” and offered his publication as a “corrected version of Strauss’ book” (1992, p. 3). As Alammar et al. note, Glaser contends that Strauss and Corbin’s procedures force data and analysis into preconceived categories, which strangles the emergent theory, or as Glaser puts it, forces it (Glaser, 1992). On the contrary, Strauss and Corbin (1990) believe that the lack of structured design in the original grounded theory approach made it difficult to know how to make sense of data and develop a theory. (2019, p. 229)

Goulding (2002) offers a more prescriptive understanding of the divergence, noting that Strauss, as he examines the data, stops at each word to ask, “What if?”, whereas Glaser keeps his attention focused on the data and asks, “What do we have here?” As a consequence, this academic dissension created two methodological schools, the Straussian and the Glaserian. Prospective researchers would now be required to declare up front which approach their respective studies would take in conducting grounded theory. Yet, as with all things theoretical, practice reveals a somewhat more opaque view of actual events. According to Smit (2000), for example, on examining sixteen grounded theory studies throughout the 1990s, the authors found that fifteen out of the sixteen studies made no mention of the divergence between Strauss and Glaser. Perhaps these authors took a more Straussian view of it all, and while acknowledging “that there are differences in specific terminology and even in specific procedures” (Locke, 1996, p. 239), Strauss contended that both perspectives “express

The grounded theory methodology  71 an identical stance toward qualitative analysis and suggest the same basic procedures” (1990, p. 8). In other words, the differences were too few to justify calls for new “schools of thought”. Yet, this schism would ebb away at calls for methodological unity, with authors noting philosophical differences, differences in how literature is used prior to field engagement, where the Glaserian approach strongly advises against using extant literature in shaping the emergent theory while the Straussian approach sees literature as an appropriate tool in placing boundaries around theory formation (Strauss & Corbin, 1990). Similarly, data coding is frequently cited as another notable difference between both approaches, where the Glaserian approach deploys two rounds of coding (open and selective) to allow for greater “theoretical imagination” whereas the Straussian approach invokes a more structured process with three coding rounds (open, axial, and selective). However, despite the perceived differences between the original authors, a third perspective has emerged that seeks to unite such methodological disharmony. Constructivist grounded theory is built on the premise that neither theories nor data are discovered but rather “constructed” by the researcher based on their interactions with the field. Charmaz notes, “constructivist grounded theory adopts the inductive, comparative, emergent, and open-ended approach of Glaser and Strauss’s (1967) original statement. It includes the iterative logic that Strauss emphasized in his early teaching, as well as the dual emphases on action and meaning inherent in the pragmatist tradition” (2014, pp. 12–13). In other words, the constructivist approach seeks to “re-ground” grounded theory’s “symbolic interactionist” tradition and as a consequence act as a bridge between Glaser and Strauss. As Alammar et al. remind us, the “two approaches share fundamental characteristics, namely: theoretical sampling, theoretical saturation, constant comparative analysis, memoing, and developing a theory” (2019, p. 230).

THE NATURE OF GROUNDED THEORY While acknowledging the schism between the original authors, it is in this vein of “shared fundamental characteristics” that this chapter will explore the nature of grounded theory. In particular, from a review of both texts, five key characteristics emerge, which uniquely support researchers in conducting grounded theory; these include: (i) “theoretical sensitivity”, (ii) “theoretical sampling”, (iii) “constant comparison”, (iv) “theoretical sorting and coding”, and (v) “theoretical saturation and theory development”. For the purposes of clarity and methodological rigour these characteristics will now be introduced. “Theoretical Sensitivity” A core element in the formation of grounded theory is the necessity for creativity. Glaser and Strauss were aware in their original text for this requirement in building theory, noting “the sociologist should also be sufficiently theoretically sensitive so that he can conceptualize and formulate a theory as it emerges from the data” (1967, p. 46). Yet as Suddaby wryly noted, theoretical sensitivity and in particular the concept of “creativity” would create a tension amongst “those who find comfort in trusting an algorithm to produce results” (2006, p. 638). As a consequence, Glaser felt compelled to retort to such strongly held empirical positions with the unambiguously titled book Theoretical Sensitivity: Advances in the Methodology of Grounded Theory in 1978. Glaser clarified the importance in developing “sensitivity to the

72  Handbook of research methods in organizational change data”, noting “it is necessary for the grounded theorist to know many theoretical codes in order to be sensitive to rendering explicitly the subtleties of the relationships in his data” (1978, p. 72). The concept of theorizing, from a grounded theorist’s perspective, requires thinking afresh on ideas and events, probing, and questioning to reveal further insights, reflecting on how ideas and events fit together, and imagining how things could be interpreted or perceived. As Charmaz notes, “to gain theoretical sensitivity, we look at studied life from multiple vantage points, make comparisons, follow leads, and build on ideas. Because you chart your direction through acts of theorizing, you may not be able to foresee endpoints or stops along the way” (2014, p. 244). A simple standard Oxford Dictionary translation for “creativity” states that it is “the use of imagination or original ideas to create something” (OED, 2011); it is, therefore, understandable as to why the original authors of grounded theory developed the concept of “theoretical sensitivity” to aid researchers in theory generation. As Corbin and Strauss declared, “if the researcher simply follows the grounded theory procedures/canons without imagination or insight into what the data are reflecting … then the published findings fail on this criterion … Creativity depends on the researcher’s analytic ability, theoretical sensitivity, and sensitivity to the subtleties of the action/interaction” (1990, p. 19). Yet, the effort required to explain the concept of “theoretical sensitivity” would result in a departure between the original authors, with Glaser opting for greater “creativity and openness to unanticipated interpretations of data while Strauss and Corbin became advocates of adherence to formal and prescriptive routines for analyzing data” (Suddaby, 2006, p. 638). “Theoretical Sampling” According to Glaser and Strauss, “theoretical sampling is the process of data collection for generating theory whereby the analyst jointly collects, codes, and analyses his data and decides what data to collect next and where to find them, in order to develop his theory as it emerges” (1967, p. 45). In fact, the grounded theory methodology is built upon two broad iterative processes that guide researchers in collecting data; i.e., the constant comparison of data (which will be discussed below) and “theoretical sampling”. According to Suddaby, “both concepts violate long-standing positivist assumptions about how the research process should work … Theoretical sampling violates the ideal of hypothesis testing in that the direction of new data collection is determined, not by a priori hypotheses, but by ongoing interpretation of data and emerging conceptual categories” (2006, p. 634). This concept is quite unique to grounded theory over other qualitative methodologies and is central to the emergent nature of the theory. Essentially, theoretical sampling can help researchers to focus their investigations, placing boundaries around emergent data of relevance while avoiding and not pursuing data cul-de-sacs. As Glaser noted, “theoretical sampling brings explicit systematic checks and refinements into your analysis. You conduct theoretical sampling by sampling to develop the properties of your categories until no new properties emerge. Thus, you saturate your categories with data and subsequently sort and/or diagram them to integrate your emerging theory” (2014, p. 193). Theoretical sampling helps to guide researchers through the potential enormity of qualitative data. Initially, it is easy to feel somewhat overwhelmed with the scale of data available to the grounded theorist. This is only further compounded by the lack of a priori knowledge grounded theorists are typically exposed to at the beginning of their studies. Yet, as Glaser and Strauss remind us,

The grounded theory methodology  73 without theoretical sampling and the constant comparison and assessment of the contribution achieved by new slices of data, it will be impossible to establish how saturated the theory is. In fact, theoretical sampling is the single most important assurance that a theory works, i.e., explains what is actually going on. (1967, p. 35)

Constant Comparison The second part of this iterative research process is that of “constant comparison”, where data are simultaneously collected and analysed. As Glaser and Strauss noted, “the purpose of the constant comparative method of joint coding and analysis is to generate theory more systematically” (1967, p. 102). Similar to theoretical sampling, the process of constant comparison is very much at odds with the positivist perspective, and indeed many other qualitative approaches, where the collection and analysis of data remain separate stages of the research design. Yet, for grounded theory the process of comparing “instances of data” that you have labelled as a particular category with other “instances of data” in the same category to see if these categories fit and are workable forms an integral part of the methodology (Urquhart et al., 2010). In other words, grounded theorists are able to work through volumes of data, breaking units down into codes, concepts, and categories. As Charmaz noted, “comparisons then constitute each stage of analytic development. Grounded theorists use this method to reveal the properties and range of the emergent categories and to raise the level of abstraction of their developing analyses” (2014, p. 342). A significant characteristic of constant comparison is its capacity to deduce findings from an interpretivist process. By analysing data during the collection stage, the researcher is directed towards future data that is emerging during interpretation. As Strauss and Corbin (1998) noted, “an interpretation is a form of deduction. We are deducing what is going on based on data … there is an interplay between induction and deduction … [I]t is, therefore, important that analyst validate [sic] his or her interpretations through constantly comparing one piece of data to another” (pp. 136–7). Therefore, central to grounded theory is the validation process that deduces each emerging “instance of data” to drive future data collection and support the fitting of data in the formation of theory, and by constantly comparing and contrasting data the researcher is delimiting scope and moving towards eventual saturation. “Coding and Sorting” One of the more attractive or, as Suddaby (2006) quips, “neurotic” (p. 638) characteristics of grounded theory, from a qualitative perspective, is its rigorous approach to data coding. Coding provides the researcher with a mechanism to interpret, and as a consequence provide meaning for, the story as it unfolds. Glaser and Strauss originally introduced two coding steps, open and selective, for researchers to deploy, with Glaser (1978, 1992) introducing a third, theoretical coding, while Strauss and Corbin introduced axial coding for their approach in 1990. Open coding, upon which Glaser and Strauss were in agreement, “is the process of going through the data, generally line by line but sometimes word by word, and attaching initial codes to those chunks of data (Seidel & Urquhart, 2016, p. 239). These “chunks of data” begin to form distinct units of meaning through a process referred to by Strauss and Corbin as “conceptualisation” (1990). Iterative conceptualization was introduced to support moving from one phase of data coding to the next, where researchers can induce what they perceive to be occurring in the data (Urquhart, 1997). Strauss and Corbin (1990) propose an alternative

74  Handbook of research methods in organizational change route towards iterative conceptualization, most notably by abstracting data through “axial” coding. During open coding data are fractured and broken into distinct units of meaning; axial coding begins the process of reassembling such data by specifying the emerging concepts’ properties and dimensions and as a consequence their interrelated relationships. This process allows researchers to move to a higher level of abstraction and is achieved by specifying relationships and delineating a core category or construct around which the other concepts revolve (Orlikowski, 1993). According to Strauss and Corbin, “axial coding answers questions such as when, where, why, who, how, and with what consequences” (1990, p. 125). To action this process, the authors propose an organizing scheme, which includes conditions, the circumstances or situations that form the structure of the studied phenomena (answering the why, where, how come, and when questions); actions/interactions, participants’ routine or strategic responses to issues, events, or problems (answering by whom and how questions); and consequences, outcomes of actions/interactions (answering questions on what happens because of these actions/interactions). (Charmaz, 2014, pp. 148–9; emphasis in original)

Another approach to coding is “selective” coding, which has different meanings for both authors: In the Straussian version, selective coding is coding around the core categories and is a final stage. In the Glaserian version, it is a middle stage of coding, where codes are grouped, based on emergent core categories before being related to each other later, in the theoretical coding stage, where a coherent theoretical scheme is developed. (Seidel & Urquhart, 2016, p. 240)

“Theoretical Saturation and Theory Development” Theoretical saturation is the process whereby emerging data no longer present any new ideas or concepts but instead are a repeat of data already collected and categorized. For Strauss and Corbin, this process occurs during “selective coding”, which is about “refining and integrating categories” having reached a point of theoretical saturation (Strauss & Corbin, 1998, p. 143), whereas Glaser points to the need for “theoretical coding” to draw out saturation. The process of theoretical coding supports the final stage of data synthesis through creativity and “new ways of thinking”. In essence, Glaser noted that possessing prior knowledge, as delineated in “theoretical coding”, would facilitate the process of theory formation, stating “it is necessary for the grounded theorist to know many theoretical codes in order to be sensitive to rendering explicitly the subtleties of the relationships in his data” (Charmaz, 2014, p. 150). For Glaser, these theoretical codes help researchers to integrate these “chunks of data” and in so doing elevate meaning towards a story that gradually begins to take shape as a theory. Despite the approach, both authors point to the need to develop a final “core category” that explains the emergent theory. Both final approaches, whether Strauss and Corbin’s “selective coding” or Glaser’s “theoretical coding”, suggest the use of a priori knowledge to support theory formation. One potential source of such a priori knowledge are insights gained from the extant literature. As noted by Loonam, there are three key reasons for comparing the extant literature to the emergent theory, (i) it provides the study with an opportunity to identify and contrast other theories in the literature, (ii) it improves construct definitions, where the researcher is able to ensure the language and concepts of their emergent theory are consistent with the main body of literature, and (iii) it assists in establishing a domain

The grounded theory methodology  75 or field for the emergent theory; i.e. the field of knowledge the investigation is seeking to contribute to. (2014, p. 66)

The researcher, drawing upon a priori knowledge, uses the literature as a signpost towards the journey’s end of theory formation.

THE VALUE OF GROUNDED THEORY A key question to now ask is how has grounded theory contributed to advancing scholarly knowledge and theory after more than a half century since the original publication. This question is all the more poignant when we consider that grounded theory claims to help researchers to build theory, or, as Turner noted, “the method is most commonly used to generate theory where little is already known, or to provide a fresh slant on existing knowledge” (1983, p. 334). In considering “the value of grounded theory,” this chapter reviews the leading international journals ranked by the Association of Business Schools (ABS). Journals ranked 3/4/4* stars were selected from across six business disciplines: (i) general management, ethics and social responsibility; (ii) human resource management and employment studies; (iii) information management; (iv) operations and technology management; (v) strategy; and (vi) organization studies. These disciplines were chosen for two reasons: (i) they reside within the author’s research interest of general management and strategy, and (ii) within the ABS journal rankings these disciplines field a significant volume of inquiry into the topics of “process and change”, which are central issues for many grounded theory studies seeking to build theory. In all, seventy-eight journals were reviewed (see the Appendix). The search focused on the use of qualitative methodologies within these leading ranked journals. Specifically, titles within each journal were searched for the words (i) “case study”, (ii) “grounded theory”, and (iii) “action research”. The subject terms of respective journals were also searched for the same three concepts above. The findings reveal what was perhaps already known anecdotally: that “grounded theory” remains the poor relation within the qualitative family. Case study enjoys a dominant position amongst qualitative researchers, with 1448 studies, using the term “case study”, cited within respective article titles. “Action research”, significantly lower at just over 6 per cent of the number of case studies cited, is in second position. Forty studies cited “grounded theory” in respective article titles, revealing the poorest performance in terms of methodological uptake (less than 3 per cent of all case study articles). In scratching the surface a little deeper, the data reveals that searches of article subject terms returns a reverse of the above findings. The term “case study” receives the fewest mentions within journal subject terms with only fifty-nine citations, “action research” is in second position with 182 subject term citations, and “grounded theory” is in pole position with 223 references across all seventy-eight journals. An obvious question to then ask is: why, with more than fifty years of celebrating the merits of theory generation and development, has the GTM returned such a poor scholarship yield for the field of general management and strategy. The question becomes all the more surprising when we consider the weight attributed to grounded theory investigations. Specifically, in terms of the value attributed to academic research; i.e., the number of citations articles receive. According to Wiesche et al., in examining the research contributions of GTM-based studies published in major information systems (IS) and related discipline journals, the authors found that “the 10 GTM articles that develop theory are highly cited. In total, these articles

76  Handbook of research methods in organizational change are cited 2,852 times. This is 26.3 citations per year compared with 13.3 citations per year for the non-GTM benchmark articles” (2017, p. 694). Yet, despite the acclaimed benefits of theory generation and the expectant high yield from academic citations as a consequence, use of the GTM remains somewhat peripheral when compared to other forms of qualitative inquiry. As a scholar and practitioner of grounded theory over the past two decades, the author will now explore some of the probable reasons behind this outcome, with a view to shedding further light on how researchers can gain greater value from grounded theory inquiries. Upon reflection, three key challenges emerge when undertaking grounded theory inquiries, namely (i) grounded theory remains an “interpretation about reality”, (ii) grounded theory explores “complex social processes”, and (iii) grounded theory involves both “the researcher and the story” where creativity and “theoretical imagination” form an integral part in data interpretation and eventual theory formation. Each of these challenges will now be explored further. Reflecting on grounded theory as an “interpretation about reality”, a central thought emerges on the dominance enjoyed by positivism within the social sciences. This dominance was one of the key reasons as to why Glaser and Strauss (1967) committed initially to the development of grounded theory, seeking to provide scholars with an alternative route to truth and the “interpretation about reality”. Yet, despite more than fifty years of alternative options, this dominance continues within the scholarly community. Generalizable verification via statistical modelling and mathematical algorithms appears to hold a higher premium for scholarship. It is this “more valued” scholarship perception that may have attributed to many qualitative researchers feeling compelled to use positivist language to tell interpretivist stories (Gasson, 2004). Similarly, this may explain, in part, the multi-methodological approach to grounded theory, where researchers start combining qualitative and quantitative approaches in an effort to please the gods of hypothesis and verification. This can be evidenced when we explore the journal subject terms above, with a significant increase in articles citing grounded theory as part of their methodological make up. In fact, Bryant noted that “GTM has been widely misused; often as a catch-all that can be evoked as a justification for methodological inadequacies, or a qualitative loin-cloth to fool the gatekeepers and academies” (2002, p. 37). Many researchers might be more attracted, perhaps encouraged by supervisors or peer community networks, to the “safe harbours” of hypothesis testing than the open road of exploration. Perhaps this is a prudent approach, in particular for novice scholars, but the bipartisan approach of “a foot in both worlds” does little to advance grounded theory scholarship. Glaser and Strauss took aim at such positivist dominance, noting that the real purpose of grounded theory is to help provide a defence against doctrinaire approaches to verification, and to reawaken and broaden the picture of what sociologists can do with their time and efforts. It should also help students to defend themselves against verifiers who would teach them to deny the validity of their own scientific intelligence. (1967, p. 7)

Another challenge for grounded theory is its primary focus in exploring “complex social processes”. Such studies require in-depth exploration to get behind the multi-faceted dimensions associated with complex and messy socio-technical change. This is evidenced from the Appendix, where the majority of grounded theory inquiries are derived from two disciplines, namely information management and general management, ethics, and social responsibility, with eighteen and twelve (or thirty of the forty grounded theory titled studies) respectively. Core to these disciplines is the exploration of such complex processes of change, whether

The grounded theory methodology  77 it is the introduction of IS that will effect large-scale organizational transformation or the deployment of new operational methodologies, such as Six Sigma or Lean thinking, which will structurally and culturally alter the organization. Such study types are reflected in the literature; for example, Bertolotti et al. (2004) and their “Social and organisational implications of CAD usage: A grounded theory in a fashion company” (2004); Schoenherr et al.’s “Enterprise systems complexity and its antecedents: A grounded theory approach” (2010); or Vannoy and Salam’s “Managerial interpretations of the role of information systems in competitive actions and firm performance: A grounded theory investigation” (2010) (emphasis added in each title). The “social and organisational”, “complexity”, and “interpretative” elements mentioned in the above titles demonstrate the type of investigation best suited to grounded theory inquiry. These studies will take time and require significant conceptualization and interpretation, and are building a substantive, not formal (which is guided by hypothetical-deductive processes) understanding of events. As Parkhe (1993) notes, grounded theory was “designed as a method that might occupy a pragmatic middle ground between some slippery epistemological boundaries. Because of this genealogy, grounded theory techniques are inherently messy” (Suddaby, 2006, p. 638). This “messiness” is a challenge for prospective researchers, and invariably the perceived value of grounded theory investigation is likely only overcome through the paradox of methodological adoption and researcher experience. The messiness associated with grounded theory techniques brings the researcher front and centre with the methodology. In fact, grounded theory, invariably, involves both “the researcher and the story”, where the researcher is subjectively involved in interpreting, conceptualizing, and “theoretical imagining” the emerging data. This is another potential challenge for many prospective researchers involved in grounded theory inquiries. Positivist approaches try to objectify the data as much as possible, where the researcher remains separate to the inquiry in order to minimize potential bias and interpretation. For interpretive research, however, the researcher is considered to be an active element of the research process, and the act of research has a creative component that cannot be delegated to an algorithm … The researcher must make key decisions about which categories to focus on, where to collect the next iteration of data and, perhaps most importantly, the meaning to be ascribed to units of data. (Suddaby, 2006, p. 638)

The researcher is, therefore, integral to theory formation. This fact remains a challenge for many prospective grounded theorists, and indeed interpretivist researchers more generally, in part due to the external dominance of positivism across academies but primarily due to the intuitive nature of data interpretation. Yet, the often-challenging nature of intuition and data sensitivity means that such skills and competencies are only honed and developed through learned experience and “rubbing up against practice”. Accordingly, researchers, while deploying the available grounded theory techniques of theoretical sampling and the constant comparison of data, must trust their own instincts and gut feelings to interpret and theoretically imagine (Strauss & Corbin, 1990) meaning. Grounded theory is a practical rather than an absolute methodology, and theory tends to be substantive rather than formal, and as a consequence researcher intuition and creativity should be explored rather than denied. According to Strauss and Corbin, “creativity depends on the researcher’s analytic ability, theoretical sensitivity, and sensitivity to the subtleties of the action/interaction (1990, p. 19).

78  Handbook of research methods in organizational change

THE APPLICATION OF GROUNDED THEORY: LEARNING LESSONS Grounded theory, as is the case with all qualitative methodologies, is best explored through practical application. In order to draw comparisons between different cases, this chapter will conduct a cross-case analysis of grounded theory studies that explore topics within the information systems and organizational change domain. Through the lens of the five key characteristics outlined above, three grounded theory studies are reviewed with emerging findings discussed in the following section. These studies, all published within ABS-cited journals, include (i) Orlikowski’s (1993) article titled “CASE tools as organizational change: Investigating incremental and radical changes in systems development”, (ii) Gasson and Waters’s (2013) article titled “Using a grounded theory approach to study online collaboration behaviors”, and (iii) Gerlach and Cenfetelli’s (2020) article titled “Constant checking is not addiction: A grounded theory of IT-mediated state-tracking”. Each of these studies, which seeks to span the application of grounded theory over three decades, will be reviewed in light of their respective application of grounded theory techniques as highlighted in Table 4.1. Orlikowski’s (1993) article, which won the MIS Quarterly Best Paper Award for 1993, is frequently cited as an example of grounded theory best practice. The study explores the “adoption and use of CASE tools across two organizations” over time, where grounded theory is used to characterize “the organizations’ experiences in terms of processes of incremental or radical organizational change” (Orlikowski, 1993, p. 309). In many ways this study adheres to the classic “purpose” for a grounded theory method,; i.e., an exploration of how processes change over time. Reflecting on the five grounded theory characteristics above (see Table 4.1), while the terms “theoretical sensitivity”, “creativity”, or “theoretical imagination” are not mentioned in the article, Orlikowski notes that “the grounded theory approach was useful here because it allows a focus on contextual and processual elements as well as the action of key players associated with organizational change” (1993, p. 310). The study’s focus on two separate organizations also provides what Charmaz refers to as “multiple vantage points” that enable comparisons to be made, leads to be followed, and ideas to be generated (2014, p. 244) and as a consequence builds a potential depth of theoretical ideas from which the author can draw upon. From a “theoretical sampling” perspective, Orlikowski notes that it “requires paying attention to theoretical relevance and purpose” (1993, p. 312). From a relevance perspective, for the process of “theoretical sampling”, where current data are driving future data sampling, the study aimed to keep organizational choices as similar as possible; for example, both organizations chosen had implemented CASE tools within the previous few years, and were deploying the tools for similar development processes and using similar capabilities. From a purpose perspective, which Orlikowski notes was to “generate theory”, the author chose to explore differences amongst organizational dimensions, such as industry, location, size, structure, and culture, in order to more fully appreciate the complexity and variety of “change processes” involved during CASE implementation. From a “constant comparison” perspective, Orlikowski noted that this grounded theory characteristic occurred “across types of evidence to control the conceptual level and scope of the emerging theory” (1993, p. 311). This sense of control is important in such studies as the sheer volume of potential data can overwhelm such exploration. In this study, the first case site’s data were systematically compared and contrasted with the second site, “where early stages of the research remained more open-ended with later stages being directed by emerging concepts, and hence involving more

micro)

to one another with different or “complementary” concepts developed by

the emerging theory” (data funnel effect

for theory generation)

data”

of behaviour related to socially situated knowledge construction could be derived”

what had been observed at both sites

and when no additional data were being

collected

Positional-centric (unbounded no new insights resulted from additional vs bounded)

when we realized that no new categories

concepts had been defined to explain

theoretical coding used) “[We] ended our data collection when

theoretical coding used) “[W]e achieved theoretical saturation

axial, and selective coding

When enough categories and associated

systematic) was deployed (open, selective, and

selective [of core category], and

Corbin approach to coding; i.e., open,

theory development

Iterative-centric (systemic vs A Glaserian approach to coding

A Glaserian approach to coding (open,

Data analysis adopted the Strauss and

new insights during theory generation

inconsistencies with a view to supporting

authors) in order to consciously seek out

Process-centric (macro vs

of data”

process used (compare similar concepts

control the conceptual level and scope of

separate iterations of analysis

dimensions)

from next “Constantly compared different slices

data were needed and where to sample

to data sampling allowed for four

differences amongst organizational A “complementary comparison”

different)

guided us in terms of what additional

start of the study; evolutionary approach

similar patterns) and purpose (sampling

Occurred “across types of evidence to

Context-centric (similar vs

Intermediate results of data analysis

Did not design a sampling strategy at the

Theoretical relevance (sampling

imagination and creativity

openness to the data and theoretical

preconceptions allows for study

a more detailed granularity of interaction

of theoretical ideas to support researcher

with interpretations

in the text of the article; managing

the article; authors became sensitized to

two separate organizations to build depth

Theoretical saturation and

Coding and sorting

Constant comparison

Theoretical sampling

deductive)

“creativity”, or “theoretical

“creativity”, or “theoretical imagination”

or “theoretical imagination” are not imagination” are again not mentioned

Creative-centric (inductive vs

The terms “theoretical sensitivity”,

The terms “theoretical sensitivity”,

“Theoretical sensitivity”, “creativity”,

mentioned in the article; study focused on are again not mentioned in the text of

Grounded theory findings

Gerlach & Cenfetelli, 2020

Gasson & Waters, 2013

Orlikowski, 1993

Theoretical sensitivity

Cross-case analysis: grounded theory characteristics

 

Table 4.1

The grounded theory methodology  79

80  Handbook of research methods in organizational change strategic selection of informants and more structured interview protocols” (1993, p. 313). This funnel effect, where data are constantly compared, helps very broad interpretative studies to place boundaries around data and to move emerging concepts towards conceptualization. From a “coding and sorting” perspective, data were analysed within each site as well as across both sites. Most of the data come from interviews (159 in total) across senior-, middle-, and line-level managers. Data analysis adopted the Strauss and Corbin (1998) approach to coding; i.e., open, axial, and selective coding, where concepts are grouped into categories and these categories in turn are causally connected via a framework. Through iterative conceptualization, the author was able to “examine a set of broad categories and associated concepts that described the salient conditions, events, experiences, and consequences associated with the adoption and use of CASE tools” (p. 314). By “opening the data” in the first case site, emergent concepts emerged that allowed the process of data collection, coding, and analysis to be more targeted for the second site. Yet, central to this process of iterative conceptualization is the ability to constantly redefine and “reimagine” concepts as they become more evolved and developed during data generation. For example, Orlikowski notes that initial concepts that emerged from the first case were redefined when they were at odds with data in the second site, stating that “in order to incorporate considerations of PCC’s (second site) experiences [it was necessary] to return to the SCC (first site) data and [re-sort and re-analyse] them to take account of the richer concepts and more complex relations now constituting the framework” (1993, p. 315). Finally, the last characteristic of “theoretical saturation and theory development” was completed “when enough categories and associated concepts had been defined to explain what had been observed at both sites and when no additional data were being collected at PCC or found at SCC to develop or add to the set of concepts and categories” (Orlikowski, 1993, p. 315). Once saturation has been reached, the focus now turns to the extant literature in order to support theory development and empirical placement. In the context of this study, extant literature on change and radical change were reviewed with a view to strengthening and refining the emergent theory. The second grounded theory study reviewed is Gasson and Waters’ (2013) article, which provides a Glaserian or “classical method for generating grounded theory” (p. 98) view on the application of grounded theory. The authors note that “the paper discusses how an interpretive theory of action was explored and developed through iterative cycles of grounded theory generation” (p. 95). The rationale for choosing grounded theory resides in its ability to generate a “theory that is grounded in evidence rather than developed from existing conceptual frameworks”, with the authors “wishing to challenge the existing orthodoxy in IS [information systems] where GTM provided us with the means to explore socially situated learning in a way that would avoid the equally limiting preconceptions and assumptions inherent in the current technology-centric eLearning literature (2013, pp. 97–8). The authors are clear that a grounded theory method was selected to offer a fresh perspective in understanding for online collaboration behaviours. Reflecting on the grounded theory characteristics outlined in Table 4.1, while the terms “theoretical sensitivity”, “creativity”, or “theoretical imagination” are again not mentioned in the text of the article (as was similar with Orlikowski’s [1993] article above), the authors do make reference to Glaser’s (1978) book on “theoretical sensitivity” and note that they became “sensitized to a more detailed granularity of interaction … that enabled them to detect a more complex set of discussion threads than had previously been apparent” (2013, p. 108). This sense of deepening one’s understanding throughout the process of data engagement is central to theoretical sensitivity, where viewing events from “multiple vantage points”

The grounded theory methodology  81 (Charmaz, 2014) allows the researcher to become more versed in the language and symbols of the respective case organization and accordingly plays a key role in influencing researcher interpretation of emerging data. The article discusses the application of the “troublesome trinity” of theoretical sampling, constant comparison, and theoretical saturation, which according to Hood “generate considerable explanatory power that is not found in other approaches” (2007, p. 152; see also Gasson & Waters, 2013). From a theoretical sampling perspective, the authors noted that they “did not design a sampling strategy at the start of the study but asked what type of data was needed at each stage to develop or validate emerging categories of analysis, or to explore gaps in the findings” (2013, p. 98). This evolutionary approach to data sampling allowed for four separate iterations of analysis, where “the theoretical sampling strategy that we employed evolved as our theory became progressively more complex and better understood” (Gasson & Waters, 2013, p. 99). The authors provide an example of theoretical sampling in practice, where both researchers articulated and justified their own understanding of the emerging data to one another, which “in turn led us to question our unit of analysis” with the authors noting that “data analyzed is adjusted to fit with an emerging understanding of what is going on in the data” (Gasson & Waters, 2013, p. 106). The researchers clearly illustrate that a sense of ambiguity and serendipity must be tolerated throughout the sampling process. A formal sampling strategy would not allow “a thousand flowers to bloom” and as a consequence would confine and control theory generation. From a constant comparison perspective, the study deployed a “complementary comparison” process to support constant comparison. The authors deploy a series of techniques to support constant comparison, namely “enhanced by researcher debate around emerging themes and categories, co-coding of data samples, coding of researcher theoretical memos, and reflection-in-action during explicit explanations of coding schemes to research assistants and the review of research process memos” (2013, p. 95). These differing techniques are viewed as “complementary” to the constant comparison method in order to consciously seek out inconsistencies with a view to supporting new insights during theory generation. As a result, the authors were able to compare similar concepts developed in one data sample and concepts developed for similar situations in previous and ongoing data samples (as suggested by Glaser & Strauss, 1967) with different, or “complementary”, concepts as developed by the authors above. Reviewing both similar and differing concepts allowed the authors greater insights from the emerging data. From a “coding and sorting” perspective, as the study followed a Glaserian approach, data were moved from identifying broad categories of behavior (open coding) to identifying a core category that represents the central idea or construct of the study, then employing selective categories that analyze concepts in relation to the core category (selective coding), then onto theoretical coding that generates concepts to explain the integrated set of relationships between the core category and other elements of the situation. (2013, p. 98)

The authors note, however, that this approach to coding sounded tidier in theory than it tended to be in practice. The study began by asking three key questions as recommended by Glaser (1978): (i) what are these data a study of? (ii) what category does this incidence indicate? and (iii) what is actually happening in the data? A key initial challenge, in adopting the Glaserian approach, can be the identification of a “core category”. As a consequence, the authors developed multiple categories to allow a meta-understanding of the core category to emerge. The second step was to conduct selective coding where an initial core category was defined. The selection of a core category helps the study to draw out the unit of analysis and centre data

82  Handbook of research methods in organizational change relationships and interrelationships around a core axis. Following the emergence of a core category, theoretical coding takes place where concepts are compared and contrasted to one another using both researcher experience and insight and reference to the extant literature. Finally, theoretical saturation and theory development takes place. According to the authors, “we achieved theoretical saturation when we realized that no new categories of behavior related to socially situated knowledge construction could be derived across three very different courses, with different students, and different domains of knowledge” (2013, p. 116). In other words, all concepts and data that could be derived from the chosen sample had been selected, theoretically codified, and positioned within the extant literature. The third study reviewed, by Gerlach and Cenfetelli (2020), recounts the use of the GTM to explore “constant checking of digital devices”. The authors note that “given the lack of theoretical insights reported, we chose a grounded theory approach that allowed us to explore the phenomenon of constant checking without force-fitting data to a priori hypotheses” (2020, p. 1709). The choice of the grounded theory method offers the study an opportunity to “iteratively develop an emergent theory” (Gerlach & Cenfetelli, 2020, p. 1709). Reflecting on the grounded theory characteristics outlined in Table 4.1, while the terms “theoretical sensitivity”, “creativity”, or “theoretical imagination” are again not mentioned in the text of the article (similar to the other two articles above), the authors do make reference to Glaser’s (1978) book on “theoretical sensitivity”. The authors also note the importance of “managing preconceptions”, where existing theories are used not as a lens to interpret the data but to relate the emergent theory to the prior literature (Gerlach & Cenfetelli, 2020). This management of preconceptions allows the study to remain open throughout data emergence and plays a pivotal part in theoretical imagination and creativity. From a theoretical sampling perspective, the authors note that “intermediate results of data analysis guided us in terms of what additional data were needed and where to sample from next (Gerlach & Cenfetelli, 2020, p. 1710). The study started with an evolving open-ended broad-level approach to questioning, where, based on participants’ responses, the authors’ increasing understanding of the topic, and the emergence of preliminary findings with existing research, new questions were added over time while others were removed. For example, the authors note that “we started to ask questions such as what are examples where you constantly check digital channels for new information, when and where do you do it and how often, and why do you check for this kind of information and why is that important to you” (Gerlach & Cenfetelli, 2020, p. 1709). From a constant comparison perspective, the authors note that they “constantly compared different slices of data” (Gerlach & Cenfetelli, 2020, p. 1710). An example is provided where data were compared from different individuals using different IT in different situations that led to different outcomes. The authors also note that findings were compared with prior literature in order to further develop these findings and integrate them with the extant literature. In particular, the authors note that “these comparisons helped us to identify similarities and differences in our data, and thus to categorize our data and achieve abstraction” (Gerlach & Cenfetelli, 2020, p. 1710). Finally, in order to increase the scope of the emergent theory, comparisons were drawn from participants with different ages, genders, geographic regions (European and US), and occupations. From a coding and sorting perspective, a Glaserian approach (Glaser, 1978) to coding was deployed, using the software ATLAS.ti to support the process. Initially, the study deployed open coding “by analysing our material line-by-line, providing descriptive and often preliminary conceptual labels to meaningful units of text at the phrase or word level” (Gerlach & Cenfetelli, 2020, p. 1710). Data were collected from ninety Internet

The grounded theory methodology  83 users, deploying different collection techniques, most notably (i) semi-structured interviews (forty-six in total) and anonymous online surveys using open-ended questions (forty-four participants). According to the authors, this process allowed a deeper understanding of the emerging data and the ability to discover initial themes. Selective coding followed, in order to focus the coding process on more core categories. Finally, theoretical coding followed, where the authors “theorized about relationships between the emergent categories and validated these relationships against our data” (Gerlach & Cenfetelli, 2020, p. 1710). Finally, theoretical saturation and theory development was conducted by the authors. According to them, “we tried to elicit new aspects other than those we already knew of to enrich, confirm, or disconfirm our ideas but eventually ended our data collection when no new insights resulted from additional data and our theory had reached saturation” (Gerlach & Cenfetelli, 2020, p. 1709).

TOWARDS A PARADOXICAL SYNTHESIS! Reflecting further on the five characteristics from the cross-case analysis above, the findings reveal that a number of distinct grounded theory paradoxes emerge. These paradoxes enable holistic and dynamic inquiry, where studies can focus on the evolution of patterns and processes over time. As Glaser and Strauss remind us, “grounded theory facilitates the generation of theories of process, sequence, and change pertaining to organizations, positions, and social interaction” (1967, p. 114). As a consequence, grounded theory is particularly suited to change inquiry, exploring dimensions over time and across multiple levels (e.g., strategic, institutional, cultural, and technical; i.e., a socio-technical perspective of change). As Orlikowski noted, “the grounded theory approach was useful here [in the study] because it allows a focus on contextual and processual elements as well as the action of key players associated with organizational change” (1993, p. 311). Of particular concern to change inquiry are the many competing socio-technical tensions. Organizations can often engage in polarizing decisions of strategic choice with resultant paradoxical consequences. Instead, successful change seeks a balanced approach to strategic choice that fosters synthesis between competing and contrasting socio-technical dimensions. Similarly, the GTM seeks to act as a catalyst for synthesis, uniting disparate dimensions (as illustrated in Table 4.1) on the pursuit towards theory generation. In fact, the concept of “grounded + theory”, where an understanding of a phenomenon rises up from the ground to generate a theory, is somewhat paradoxical in nature. The “grounded” element illustrates the scale and scope of data dimensions required to explore a phenomenon, while the “theory” element illustrates the formal outcome of verification. This paradoxical nature of the GTM supports the multi-faceted nature of organizational change and the socio-technical tensions involved and accordingly allows researchers to gain a more cohesive understanding for change processes. The five characteristics reviewed in the section above reveal the nature of these paradoxes (see Table 4.1) and how grounded theory synthesizes a response. Firstly, creativity forms a critical role during theoretical sensitivity and imagination. In order to support the creativity process in an empirically valid manner, researchers are able to apply both “inductive” and “deductive” approaches throughout theory generation. During the “inductive” process, researchers are able to nurture an initial interpretation of events “by developing a theoretical account of the general features of a topic while simultaneously grounding the account in empirical observations or data” (Martin & Turner, 1986, p. 141). Yet, Glaser reminds us that

84  Handbook of research methods in organizational change “deductions from grounded theory, as it develops, are the method by which the researcher directs his theoretical sampling” (1967, p. 32). As a consequence, grounded theory applies different techniques to unite both inductive and deductive perspectives in order to support researchers in gaining a deeper and more holistic understanding for organizational change events and patterns. The second paradox is contextual in nature, where emerging patterns of change are compared and contrasted in order to support the process of theoretical sampling. While this process is deducing an understanding of the emergent data, as noted above, it is also assisting researchers to better cope with the complexities of qualitative inquiries. In particular, these complexities play out in terms of data enormity; i.e., what is the primary research challenge, who are the key participants (i.e., who should we talk to in the organization), and what is the future direction to be taken (i.e., what data do we need next from the organization). As Martin and Turner noted, in order to produce accurate results “the complexities of the organizational context have to be incorporated into an understanding of the phenomenon, rather than be simplified or ignored” (1986). To support greater understanding of the complexities of the organizational context, the GTM embraces the paradox of sampling similar and different data with a view to encouraging “theoretical relevance and purpose” (Orlikowski, 1993). The third paradox is process-centric, where the technique of constant comparison is used to move between macro and micro levels of understanding. Macro-level process understanding requires data from different types of evidence or “different slices of data” (Glaser & Strauss, 1967) in order to explore the scale of the phenomenon, whereas micro-level process understanding constantly compares emergent data in order to narrow the process’s scope. As Pettigrew notes, this “provides an opportunity to examine continuous processes in context in order to draw out the significance of various levels of analysis and thereby reveal the multiple sources of loops of causation and connectivity so crucial to identifying and explaining patterns in the process of change” (1990, p. 14). The fourth paradox of grounded theory is iterative-centric, where during coding the researcher views the data from a systematic and systemic perspective. It is a balance between the rigour of coding and the relevance of meaning. During systematic coding, data are “fractured” and “broken down” into singular forms. Words are broken into units of nouns, verbs, adjectives, and their associated properties and dimensions. This rigorous process forms the bedrock for data coding, with techniques such as open, axial, selective, and theoretical coding suggested by the original authors. Yet, words require syntax in order to form meaning, which involves a more systemic approach to coding. As a consequence, and through the process of “iterative conceptualization”, meaning is acquired by causally interconnecting concepts and raising them up to reimagine the phenomena under study. The final paradox of grounded theory is positional-centric, which is polarized between unbounded and bounded dimensions of knowledge. “Unbounded knowledge” refers to the constantly evolving and dynamic nature of the emerging data and the phenomena under study. “Bounded knowledge” refers to the extant literature and current empirical explanations. Grounded theory, in particular Glaserian approaches, must contend with this paradox throughout the investigative process. The tensions created by this paradox are synthesized during theoretical saturation, where the researcher ends data collection when no new insights are gleamed from additional data and using the extant literature places a boundary around the emergent theory.

The grounded theory methodology  85

CONCLUSION This chapter seeks to evaluate the contributions made by the GTM within business studies over the past fifty years. Specifically, the fit between grounded theory as a methodological approach and organizational change inquiry is explored. The findings reveal that grounded theory’s focus on building an understanding of phenomenon from the ground up enables more holistic and dynamic empirical development. The methodology’s accommodation of opposing elements, as denoted by the paradoxes discussed above, can support inquiry for complex socio-technical change. Methodological techniques guide prospective researchers to theoretically imagine emerging data, theoretically sample such data to drive further change exploration, constantly compare and contrast data to allow processes of behaviour and sequences of events to envelop, iteratively conceptualize these behaviours and events to develop emergent patterns that can explain change circumstances, and finally, guided by the extant literature, to position the emergent theory within discipline boundaries. Finally, the GTM, as noted above, offers significant value to both academia and practice for prospective researchers. From an academic perspective, grounded theory studies receive twice as many citations as other methodological approaches while offering practice with a more holistic understanding of phenomenon. As we look to the next fifty years, with expected social, political, cultural, technological, environmental, and economic disruption on the horizon, the GTM can play a significant part in encapsulating empirical deficits and building new and alternative insights for the century ahead.

REFERENCES Alammar, M.F., Intezari, A., Cardow, A., & Pauleen, D.J. (2019). Grounded theory in practice: Novice researchers’ choice between Straussian and Glaserian. Journal of Management Inquiry, 28(2), 228–45. Bertolotti, F., Macrì, D.M., & Tagliaventi, M.R. (2004). Social and organisational implications of CAD usage: A grounded theory in a fashion company. New Technology, Work and Employment, 19(2), 110–127. Bryant, A. (2002). Re-grounding grounded theory. Journal of Information Technology Theory and Application (JITTA), 4(1), 25–42. Charmaz, K. (1983). The grounded theory method: An explication and interpretation. In R. Emerson (Ed.), Contemporary Field Research: A Collection of Readings (pp. 109–26). Waveland Press. Charmaz, K. (2014). Constructing Grounded Theory (2nd ed.). SAGE. Charmaz, K. (2017). Special invited paper: Continuities, contradictions, and critical inquiry in grounded theory. International Journal of Qualitative Methods, 16, 1–8. Corbin, J.M., & Strauss, A. (1990). Grounded theory research: Procedures, canons and evaluative criteria. Qualitative Sociology, 13, 3–21. Gasson, S. (2004). Rigor in grounded theory research: An interpretive perspective on generating theory from qualitative field studies. In M. Whitman & A. Woszczynsk (Eds.), The Handbook of Information Systems Research (pp. 79–102). IGI Global. Gasson, S., & Waters, J. (2013). Using a grounded theory approach to study online collaboration behaviors. European Journal of Information Systems, 22, 95–118. Gerlach, J.P., & Cenfetelli, R.T. (2020). Constant checking is not addiction: A grounded theory of IT-mediated state-tracking. MIS Quarterly, 44(4), 1705–31. Glaser, B.G. (1978). Theoretical Sensitivity: Advances in the Methodology of Grounded Theory. Sociology Press. Glaser, B.G. (1992). Basics of Grounded Theory Analysis: Emergence Vs. Forcing. Sociology Press.

86  Handbook of research methods in organizational change Glaser, B.G., & Strauss, A.L. (1964). The social loss of dying patients. American Journal of Nursing, 64(6), 119–21. Glaser, B.G., & Strauss, A.L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Aldine Transaction. Goulding, C. (2002). Grounded Theory: A Practical Guide for Management, Business and Market Researchers. SAGE. Hood, J.C. (2007). Orthodoxy vs. power: The defining traits of grounded theory. In A. Bryant & K. Charmaz (Eds.), The SAGE Handbook of Grounded Theory (pp. 151–64). SAGE. Locke, K. (1996). Rewriting The Discovery of Grounded Theory after 25 years? Journal of Management Inquiry, 5(3), 239–45. Loonam, J. (2014). Towards a grounded theory methodology: Reflections for management scholars. Irish Journal of Management, 33(1), 49–72. Martin, P.Y., & Turner, B.A. (1986). Grounded theory and organizational research. Journal of Applied Behavioral Science, 22(2), 141–57. OED (Oxford English Dictionary) (2011). “Creativity”. Oxford University Press. Orlikowski, W.J. (1993). CASE tools as organizational change: Investigating incremental and radical changes in systems development. MIS Quarterly, 17(3), 309–40. Parkhe, A. (1993). “Messy” research, methodological predispositions, and theory development in international joint ventures. Academy of Management Review, 18(2), 227–68. Pettigrew, A.M. (1990). Longitudinal field research on change: Theory and practice. Organization Science, 1(3), 267–92. Schoenherr, T., Hilpert, D., Soni, A.K., Venkataramanan, M.A., & Mabert, V.A. (2010). Enterprise systems complexity and its antecedents: A grounded theory approach. International Journal of Operations & Production Management, 30(6), 639–68. Seidel, S., & Urquhart, C. (2016). On emergence and forcing in information systems grounded theory studies: The case of Strauss and Corbin. In L.P. Willcocks, C. Sauer & M.C. Lacity (Eds.), Enacting Research Methods in Information Systems: Volume 1 (pp. 157–209). Palgrave Macmillan. Smit, J. (2000). Grounded theory methodology in IS research: Glaser versus Strauss. South African Computer Journal, 24, 219–22. Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. SAGE. Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). SAGE. Suddaby, R. (2006). From the editors: What grounded theory is not. Academy of Management, 49(4), 633–42. Turner, B. (1983). The use of grounded theory for the qualitative analysis of organisational behaviour. Journal of Management Studies, 20, 333–48. Urquhart, C. (1997). Exploring analyst-client communication: Using grounded theory techniques to investigate interaction in informal requirements gathering. In A.S. Lee, J. Liebenau & J.I. Gross (Eds.), Information Systems and Qualitative Research: Proceedings of the IFIP TC8 WG 8.2 International Conference on Information Systems and Qualitative Research, 31st May–3rd June 1997, Philadelphia, Pennsylvania, USA (pp. 149–81). Chapman & Hall. Urquhart, C., Lehmann, H., & Myers, M.D. (2010). Putting the “theory” back into grounded theory: Guidelines for grounded theory studies in information systems. Information Systems Journal, 20(4), 357–81. Vannoy, S.A., & Salam, A.F. (2010). Managerial interpretations of the role of information systems in competitive actions and firm performance: A grounded theory investigation. Information Systems Research, 21(3), 496–515. Wiesche, M., Jurisch, M., Yetton, P.W., & Krcmar, H. (2017). Grounded theory methodology in information systems research. MIS Quarterly, 41(3), 685–701.

The grounded theory methodology  87

APPENDIX Table 4A.1

Use of qualitative methodologies within leading ranked journals

ABS journal

Number of

Case

Case

Grounded

Grounded

Action

Action

discipline

journals

study title

study ST

theory title

theory ST

research

research ST

title

3/4/4* General

17

224

18

12

73

17

40

15

250

35

4

13

5

7

21

257

0

18

50

37

50

12

623

0

6

30

22

34

9

67

6

16

54

21

48

management, ethics and social responsibility Human resource management and employment studies Information management Operations and technology management Organization studies Strategy

4

27

0

0

3

2

3

 

78

1448

59

56

223

105

182

5. Longitudinal research methods for studying processes of organizational change Elaine Rabelo Neiva and Leonardo Fernandes Martins

AN OVERVIEW OF LONGITUDINAL DESIGN IN RESEARCH: THE MAJOR CHALLENGES Advantages and disadvantages of longitudinal designs are largely discussed in literature (Rajulton, 2001). On the top of being expensive, such studies demand accurate analysis of the research problem and dynamic nature of a phenomenon in relation to time (Ployhart & Vandenberg, 2010; Wright & Markon, 2016). Longitudinal data collection poses challenges that relate both to time waste and to required financial resources. Typically, this analysis demands adopting complex and statistical procedures very specific to the type of problem and characteristics of the data in question. Moreover, there are few statistical software packages available to support such analyses considering the wide range of challenges they pose. Additionally, access to longitudinal information is quite difficult because of privacy and confidentiality issues, and even some other types of losses inherent to course of time (Collins, 2006). For example, respondents may give up (or become inaccessible) participating in the survey over time, which leads to difficulties in comparing groups (size and demographic characteristics), and between survey moments. Another loss refers to the withdrawal of financial support for the project or sudden changes in the research context (pandemic context, for example), which makes difficult comparisons, monitoring of scenarios, and inferences about the studied phenomena. Challenges are even greater when it comes to longitudinal studies on organizational change. Processes of organizational change involve contexts “in” contexts that should be considered as nested from different levels over time, in an interactive process. In that sense, understanding change demands procedural comprehension of how a higher hierarchical level, as the external context, is related to organization context, and how the latter is connected to what happens in work context. This leads to the acknowledgment that all phenomena of change are conditional to these processes over time, including the organizational events that impact the very study in question (Pettigrew, 1990, 2012; Pettigrew et al., 2001). For example, an electric power generation and collection company may implement a new technology for digital control of substations that profoundly alters the way work is done. In addition to the variables to be monitored in the organizational context (work design, previous change processes, type of activity, power groups involved, culture, etc.), other events may occur (such as the need for remote work in the face of a health crisis) that directly affect the organizational change process to be implemented. Associated to that, there is a great debate about how to select the best methods that allow understanding longitudinal changes considering their complexity. These questions range from conceptual and theoretical questions about the nature of the problem (Ployhart & Vandenberg, 2010), to countless decisions about the research design and data collection method, to analyti88

Longitudinal research methods for studying processes  89 cal issues about the most suitable type of analysis, and to different types of longitudinal questions (Collins, 2006). Some of these issues may even arouse suspicion about the possibility of effectively observing a real change in a variable, considering that its measurement in different moments in time may be biased and, thus, make it unfeasible (Collins, 2006; Golembiewski, 1989; Golembiewski et al., 1976; Ployhart & Vandenberg, 2010; Wright & Markon, 2016). Even if these challenges exist, and even if in organizational and social sciences the tests of hypotheses with cross-sectional research designs prevail, most research questions that drove organizational studies over time disclose that organizational theories are explicitly or implicitly longitudinal (Pettigrew, 1990, 2012; Van de Ven & Huber, 1990; Wright & Markon, 2016). Longitudinal designs are especially efficient when the purposes are to investigate time-related psychological, social, and organizational constructs; study time-sensitive organizational processes; specify proper time intervals to detect and measure a process or result (Timmons & Preacher, 2015); and analyze processes in different contexts or with different focuses (historical, economic and others), past and present, and at different levels of analysis – environmental, organizational, group, individual (Sonnentag, 2012). Literature increasingly acknowledges the relevance of considering the longitudinal dimension of organizational phenomena. It includes studies aimed at capturing the dynamic nature of relations between variables referring to different contexts, and considered from different levels of analysis (macro, meso, and micro). Some studies, when acknowledging this fact, temporarily detach data collection from predictive and criteria variables (Ployhart & Vandenberg, 2010; Wright & Markon, 2016). That temporal detachment is used to prevent biases inherent to the method, such as the problem of common method variance. However, it does not provide for analysis of the dynamic nature of phenomena and their interrelationships (Hassett & Paavilainen-Mäntymäki, 2013; Wright & Markon, 2016). Evaluation of trends, seasonality, and occurrences of associations by virtue of one-off disturbances, as well as the understanding of paths at the individual level (such as the process of attitudes formation, change reactions, or of new skills learning), for example, are not considered, despite being elements that characterize several organizational phenomena (Wang et al., 2017). Such aspects, however, should be considered when planning any longitudinal design research. Multiple organizational phenomena require explaining the description of their dynamics, and understanding of their causal mechanisms, as well as their prediction in the light of a dynamic organizational theory. In hypothetic-deductive terms, it means formulating time-bound hypotheses, signaling implications on what is inferred about the nature, magnitude, and direction of relationship between many organizational phenomena. Thus, organizational phenomena longitudinal investigation requires the adoption of methods that enable reviewing these variations over time. Although very common, cross-sectional research designs are not sensitive to these changes as they are based on a single observation of variables, leading to the need to consider longitudinal studies (Ployhart & Vanderberg, 2010; Taris & Kompier, 2014). Information deriving from cross-sectional studies approaches the situation in a moment, while longitudinal information concerns the process over time that induces change or stability in relation to one or more moments in time. It implies that in many areas the term “longitudinal data” suggests the idea of recurrent measurements of the same individuals over a period of time long enough to encompass a detectable change in their developmental status (Wright & Markon, 2016). In the field of organizational change, the key goal of longitudinal studies is precisely the search for what occurs after a fact, in a prospective way, having as a parameter the detection of something other than the investigation baseline over time. Here, typical stand-

90  Handbook of research methods in organizational change ardized tests, many of which are designed for cross-sectional investigation and identification of a status, may be insensitive in identifying changes in longitudinal studies (Rajulton, 2001). The extension of time observation in a longitudinal study is indeed a crucial issue, as changes or even change-based stabilization processes may occur within a specific time frame (Sonnentag, 2012). It brings about considerations on timing and the duration of measurements, leading us from intensive studies, that despite being longitudinal and with recurrent measurements may be restricted to recording numerous dependent observations over a short period of time, to studies with few measurements but that last for decades. To each of the cases, issues related to measurement error and attrition of individuals during observation may impact inferences to be made (Menard, 2008). Therefore, studies of change demand considering a specific topic about longitudinal measures and missing data. There is a consensus that longitudinal information is needed for causal studies on individual and group behavior in particular (Ployhart & Vanderberg, 2010; Taris & Kompier, 2014). It is based on the understanding that longitudinal studies may unveil the nature of a phenomenon’s development, establishing patterns of change, and possibly providing the researcher with a credible picture of cause and effect over time, allowing one to understand what a phenomenon is a function of (Taris & Kompier, 2014). Social processes have become increasingly complex and dynamic. If we are to understand them, we need both theories that represent such characteristics and designs that allow us to represent them. Longitudinal studies are a good choice as they allow us to establish temporal order, measure change, and test causal hypotheses derived from theory. They also allow developing insights about phenomena that could substantively support the interpretation and influence of these phenomena in the practical context (Ployhart & Vanderberg, 2010; Taris & Kompier, 2014; Wright & Markon, 2016). For example, a 10-month study made a conceptual distinction between inertia, the momentum associated with strategic persistence, and the momentum of strategic change. Building on this distinction, a theoretical framework was developed that examines the influence of various change-related events and social processes on momentum during the early stages of organizational change (see Janssen, 2004). The issue of causality inference also recurs in the debate about longitudinal studies. With accurate measurement of the current state, and in possession of precedent and consequent information to a given point in time, this context provides a core element for causality; i.e., the analysis of change in one variable emerging as precedent to the change in another variable (Kelloway & Francis, 2013; Taris & Kompier, 2014). Thus, hypotheses about possible causes can always be inferred, although conclusive testing of a cause as a necessary and sufficient condition for the occurrence of a phenomenon requires the control of other confounding factors (Hassett & Paavilainen-Mäntymäki, 2013). A content analysis comprising 203 longitudinal studies of strategic management by Bergh and Holbein (1997) revealed that most researchers do not test or control for violations in the assumptions underlying longitudinal analysis, nor do they test stability or shape (normality, linearity, collinearity, uncorrelated errors, etc.) of empirical relationships between variables over time. Researchers in the field of organizational research are continually urged to use longitudinal research designs rather than relying on cross-sectional data, because these do not allow for causal inference (Taris, 2000) and may result in misleading or biased parameter estimates (Maxwell & Cole, 2007). For example, cross-sectional approaches to mediation typically generate substantially biased estimates of parameters even under the ideal conditions when mediation is complete. In other words, you may find a mediation effect where there is none.

Longitudinal research methods for studying processes  91 Most empirical tests of mediation utilize cross-sectional data despite the fact that mediation consists of causal processes that unfold over time. Cross-sectional data can also provide, at best, evidence for covariation, although difficulty in ruling out other explanations, such as common method variance, makes even this modest contribution questionable (Kelloway & Francis, 2013; Maxwell & Cole, 2007). Longitudinal research goes beyond cross-sectional data and allows temporal order to be established. Other variables’ effects remain a competing explanation even in the case of longitudinal research (e.g., Zapf et al., 1996). Therefore, cross-sectional designs absolutely fail to establish a causal relationship, or to allow for causal claims (Taris, 2000). However, the ability to make a causal inference may be enhanced by proper use of longitudinal methods. In a straightforward sense, establishing temporal order simply means that the predictor must occur before the outcome. Furthermore, two-wave studies (studies with two moments/waves of data collection) can confuse measurement error with substantive change (Kelloway & Francis, 2013; Ployhart & Vandenberg, 2010; Singer & Willett, 2003). As a result of these concerns, many reviewers do not see much value in two-wave studies. However, two waves are much better than one, with three waves being better still (Kelloway & Francis, 2013; Ployhart & Vandenberg, 2010). Generally, longitudinal studies require: prior definition of how the variables of interest and relationships between these variables change over time; signs of when (on which occasions) these changes may be observed; and specification of how long it takes for variations and relationships to become observable in these processes and for how long an observation should take place for their cycles (increase, stabilization, or decrease) to be observed and described, how often and at what times these changes should be measured to enable describing the direction and shape of the relationships between the variables of interest, and how long and on which occasions it is necessary to describe and control context variables, external to the relationships studied, that may threaten the validity of the causal inferences obtained through the analysis of data from experiments, quasi-experiments, or even from prediction based on surveys applied in the panel model1 (Abbad & Carlotto, 2016; Hassett & Paavilainen-Mäntymäki, 2013; Ployhart & Vanderberg, 2010; Taris & Kompier, 2014; Wright & Markon, 2016). Indeed, the length of observation time is crucial for a longitudinal study, coupled with issues related to measurement errors, and attrition of individuals throughout observation (Menard, 2008). Zyphur et al. (2020) recognize that these limitations still persist, resulting in a relevant impairment for testing hypotheses and making inferences about processes (such as organizational context and well-being, work income and well-being, etc.) found as a rule in the organizational literature. There is no consensus about the definition of longitudinal designs; rather, there are simple and complex definitions. “In longitudinal research, data are collected with one or more variables for two or more time periods, thus allowing at least the measurement of change and possibly the explanation of change” (Menard, 2008, p. 3). Longitudinal research may also be defined as techniques, methodologies, and activities that enable observation, description, and/ or classification of organizational phenomena in such a way that a process can be empirically identified and documented (Kimberly, 1976). From this observation, there is the importance of defining what the process is. Van de Ven (1992, p. 169) defines process as “a logic that explains a causal relationship between independent and dependent variables, a category of Panel data models provide information on individual behavior, both across individuals and over time. Panel data can be balanced when all individuals are observed in all time periods or unbalanced when individuals are not observed in all time periods. 1

92  Handbook of research methods in organizational change concepts or variables that refer to actions of individuals or organizations, and a sequence of events that describes how things change over time.” The number of waves (how many times data collection is repeated) is crucial for the inference about change, for describing the process, and for evaluating the evolution/stability of relationships between variables. Thus, some authors advocate that a minimum of three waves of data collection is crucial to evaluate a change or a process (Kelloway & Francis, 2013; Ployhart & Vanderberg, 2010). Studies on process could play a valuable role as they can produce the important “how-to” knowledge, so crucial to support the managerial practice (Pettigrew, 2012). In its nature, longitudinal research aims to capture the dynamic nature of processes and relationships between variables. It comprises a minimum of three observations of at least one substantive construct of interest, allowing the analysis of individual profiles and paths over time, rather than the mere prospective analysis of change (Kelloway & Francis, 2013; Ployhart & Vandenberg, 2010; Taris & Kompier 2014). Thus, the methodological and theoretical definitions adopted in longitudinal studies turn out to be more specific. They depend on the nature and context studied and cannot be directly generalized from one construct to another, nor from one context to another, since they require a theory of change underlying the context–individual interaction across time (Abbad & Carlotto, 2016). A major example could regard the study of effects that often analyzes them in a non-contextual perspective, being insensitive to such dynamics. It demands the theoretical definition on how affects eventually emerge in greater or lesser magnitude over time following continuous or intermittent exposure to a given context (Wang, et al., 2017). Social processes are characterized by stability and change (Monge, 1990; Pettigrew, 2012; Pettigrew et al., 2001; Van de Ven & Huber, 1990; Van de Ven & Poole, 1995). Research on how organizational systems develop and change is shaped at every level of analysis by traditional assumptions about how change works. Theories in social processes are conceptualizing change as a punctuated equilibrium: an alternation between long periods when stable infrastructures permit only incremental adaptations, and brief periods of revolutionary upheaval. Punctuated equilibrium is not made of smooth trajectories toward pre-set ends because both the specific composition of a system and the “rules” governing how its parts interact may change unpredictably during revolutionary punctuations. Conflicting theories about organizational adaptability (such as resource dependency; Pfeffer & Salancik, 1978), and organizational rigidity (such as population ecology; Hannan & Freeman, 1977, 1984) are applicable at different times, depending on whether a system is in a period of transition or equilibrium (Gersick, 1991). Normal and reverse effects constitute another issue in longitudinal research. In studies on work, well-being, and health in the context of organizational change, normal effects usually refer to the lagged effects of job characteristics on safety, health, well-being, and performance-related variables. Reverse effects refer to the effects of the latter categories of variables on job characteristics. When a study supports both normal and reverse effects, researchers refer to reciprocal effects. Studies on job-related stress also point first to normal effects – from context to the individual – and, in a second moment, to reverse effects – from individual to the context (Ford et al., 2014; Garst et al., 2000; Houkes et al., 2003). Moreover, relevant models, such as demands and resources at work, include in their postulates the idea of reciprocal interactions, which are not able to be investigated in a cross-sectional model. An example in this context is that of Job Crafting behavior, which emerged as a result of a given set of resources available at work and that, in turn, can affect how such resources are used

Longitudinal research methods for studying processes  93 (Bakker & Demerouti, 2017). In longitudinal designs literature, full panel designs2 are often recommended: designs where presumed “outcome” and “explanatory” variables are assessed across all study waves, allowing the modeling of recursive effects and interdependence relationships bound to activation mechanisms in cross-lagged designs such as positive emotions, cultural aspects, stress levels, work design, work engagement, and performance and their interactions across time (Taris & Kompier, 2003, 2014). This type of study assists in understanding normal and reverse effects, among other mechanisms that may be present. Longitudinal research designs are often very challenging for a number of reasons. The main challenges associated with longitudinal research are the cost, time demands, and cooperation between the researcher and the studied organization(s) (Buckley & Chapman, 1996; Menard, 1991). Since the research project focus is on the phenomenon of interest and change is part of the daily life of the organization, a number of unanticipated events may happen and disrupt the project’s progress. The organization may face financial problems or even go bankrupt in the middle of the process, leaving the researcher with incomplete data (Leonard-Barton, 1990). Furthermore, when data are collected while events, attitudes, or activities are under way, results are unknown (Van de Ven, 1992) and the initial objectives may have to be changed (Alfoldi & Hassett, 2013; Van de Ven, 1992). These authors suggest two approaches for dealing with unpredictable events while conducting longitudinal studies: the a priori focus approach and the progressive focus approach (Alfoldi & Hassett, 2013). The a priori focused longitudinal research refers to a research strategy in which the longitudinal dimension is embodied in the research design phase (Dawson, 1997). From a temporal perspective, an a priori focused strategy may be real-time and/or retrospective (Van de Ven & Huber, 1990; Van de Ven, 1992). The need for real-time and/or retrospective data largely depends on the research questions and is assessed by the researcher well before entering the field. This approach considers the organization’s and the researcher’s perspective focusing on the phenomenon, and a clear definition of the aspects of the phenomenon to be addressed, which engenders robustness to data collection by virtue of the systematic approach. The major weakness of the study may be the rigidity that prevents reaction to emerging data in the process. The a priori collection design creates predictability in data collection and allows the researcher to manage the expectations of the organization involved (Alfoldi & Hassett, 2013). The progressive focus approach requires researchers to be well acquainted with the complexities of the problem before going into the field, but not too committed to a study plan. The study is conducted in several stages: first the site observation, then a deeper investigation, beginning to focus on relevant issues and then seeking to explain them (Stake, 1981). Interaction takes place between theory, data, and comparison of groups and contexts (Pettigrew, 1990). The researcher maintains flexibility and openness to modifications, without losing the overall purpose of the study. Despite the complexities that emerge from the consideration of longitudinal models, with both theoretical consequences and impact on the substantive understanding of phenomena, and their consequent impact on decision making, several authors reaffirm that longitudinal designs are scarce in studies on organizations (Kelloway & Francis, 2013; Ployhart & Vandenberg, 2010; Taris & Kompier, 2014), especially in studies on organizational change and its under2 The same set of cases is investigated multiple times (with the exception of missing data) involving explanatory and response variables.

94  Handbook of research methods in organizational change lying processes (Langley & Stensaker, 2012). One likely reason for that persistent gap in organizational research is related to a still precarious advancement regarding the dissemination of methodological advances available today to model interactions between variables over time, allowing the detection of individual, organizational, and social patterns of behavior that emerge only from this perspective (Lehmann-Willenbrock & Allen, 2018). Thus, this chapter aims to contribute a little to close this gap by presenting the main topics that make reference to methodological aspects. Missing Data, Attrition, or Dropout The presence of missing data and loss rates for observation units over time in a longitudinal study can be considered a distinguishing feature of any study, being more the rule than the exception (Fitzmaurice et al., 2012). Thus, in many longitudinal studies the existence of missing data is postulated as a process naturally related to the passage of time, ranging from complete censorship of an individual’s information because of unmanageable events, such as death, to other events linked to the phenomena under study or even to the method employed, giving rise to what is called response mechanisms or missing case generation (Greenacre, 2018; Van Buuren, 2018). Panel studies on wage and participation in income improvement programs report rates of 75–80 percent attrition (Laurie, 2008). To the extent that respondents are lost in further waves of data collection, measuring change can be confounding because lost respondents may systematically differ from those who are retained (Collins, 2006). This is particularly serious if losses are disproportionately originated by those with extreme values on the variables focused by the research. Thus, not only the magnitude of attrition but also the pattern of attrition in relation to critical variables in the study can be problematic. To keep low attrition rates, substantial resources should be available to track respondents, monitor and plan for the acceptable dropout rate, and implement specific designs predicting its occurrence (Rhemtulla & Hancock, 2016). Authors recommend an optimal number of observations, which takes into account subject loss, before performing the study, and consider how to handle a problematic loss of approximately more than 50 percent of subjects from the first to the second measurement wave (Abbad & Carlotto, 2016; Laurie, 2008). Moreover, it is important to assess to what extent missing data are random or follow a pattern, as well as modeling the cause for missing data (ideally theorized and measured a priori) and considering them not only when planning data collection, but also in the strategies of analysis (Kelloway & Francis, 2013; Ployhart & Vandenberg, 2010). In this sense, it is quite common for literature to highlight specific strategies for each type of attrition, depending on the process that gives effect to missing cases. This is the case when the probability of a missing response is the same for all cases, justifying data treatment considering missing cases to be completely random (MCAR) (Little & Rhemtulla, 2013). In actual scenarios, missing cases are commonly related to variables present in data itself, as it could be predicted in the case of an increased missing response rate among poorly engaged professionals, by virtue of elements related to demotivation, which explain not only the related organizational behaviors, but also the very probability of voluntarily participation in a work-related survey. In this case, this type of missing case is identified as missing at random, and modern techniques may be adopted for imputing missing cases, such as those that use auxiliary variables or multiple imputations, among other inference methods that allow controlling to some extent bias arising from an unbalanced sample (Foster & Krivelyova, 2008;

Longitudinal research methods for studying processes  95 Little & Rhemtulla, 2013). A last scenario is when attrition is not random but rather where the probability of a missing value occurring varies as a function of unknown parameters, often requiring specific studies to discover the process that gives rise to missing cases (Greenacre, 2018; Van Buuren, 2018). Thus, it becomes important to assess the extent to which missing data are random or follow a pattern, in addition to modeling the cause of missing data (ideally theorized and measured a priori). Planning should consider the occurrence of missing data in data collection (Kelloway & Francis, 2013; Ployhart & Vanderberg, 2010). Classification of Longitudinal Designs Some argue that longitudinal studies fall into two major trends – qualitative and quantitative (Hassett & Paavilainen-Mäntymäki, 2013; Thomson & McLeod, 2015), with special emphasis on studies of process (Pettigrew, 1990; Van de Ven & Huber, 1990). These studies typically adopt a qualitative research approach, employing narratives, interpretive research, and ethnography to produce temporal accounts of interaction and change (see, for example, Pettigrew, 1987). They are generally used to describe patterns of change as well as to establish and explain the direction and magnitude of causal relationships and change. “Longitudinal research” is a broader term than “process research” and is often used to describe research projects and programs within which studies of process are conducted and processes studied (Bergh & Holbein, 1997; Hassett & Paavilainen-Mäntymäki, 2013; Kimberly, 1976; Leonard-Barton, 1990; Pettigrew, 1990; Van de Ven 1992). Longitudinal research is often related to long-lasting research projects that may comprise several processes or aspects of the process, while research of process is more often related to process thinking (Hassett & Paavilainen-Mäntymäki, 2013). The most commonly used longitudinal research designs are repeated cross-sectional (trend studies), prospective longitudinal (panel) studies, and retrospective longitudinal (event history or data duration) studies (Hassett & Paavilainen-Mäntymäki, 2013; Ruspini, 2002). Repeated cross-sectional designs demand regular studies, using a very different or a completely new sample every time data is collected. A prospective longitudinal study is based on repeated data collections from the same subjects over a period of time. In retrospective studies, respondents are asked to recall and reconstruct events and aspects of a period, life course, or social or organizational experience (Ruspini 2002). This differentiation between prospective and retrospective studies outlines how events are described from follow-ups at the actual time they happen, or through memory resources and recollections of events a posteriori. Theoretical and methodological proposals show how retrospective and real-time analyses can be combined to shed light on patterns and mechanisms of change, enabling a more complex understanding of the phenomenon (Pettigrew, 2012). However, any study is only one picture in a process (Little & Rhemtulla, 2013; Pettigrew, 1990, 1992, 1997, 2001) or an analysis of multiple moments in a process. Many other projects may be defined as longitudinal. For example, Taris (2000) identifies seven basic strategies of designs, such as cross-sectional concurrent studies (data collected to describe change across different ages of groups), trend study (repeated cross-sectional studies), time series analysis (multiple repeated measurements of the same set of participants), and intervention study (experimental pre-test, post-test, control group designs). The literature diverges regarding the classification of longitudinal studies, but four basic types of designs can be listed: full-population studies, cross-sectional designs with repeated measures, rotating panels, and longitudinal panel designs (Menard, 2008). These differen-

96  Handbook of research methods in organizational change tiations are important when choosing the type of analysis of data collected in longitudinal designs, as each design requires a specific type of analysis. Designs may vary, but variations should be evaluated for their sensitivity to show short- and long-term trends. Full-population designs allow for a quasi-full comparison between observations, and other designs differ in the degree to which samples (or comparable cases) overlap from one data collection to another. Repeated cross-sectional designs involve independent samples for each observation period, and comparison provided the groups are equivalent, as research with aggregate levels may unveil changes in variables and relationships over time. Finally, rotating panel designs are characterized by retention of a group of cases over time, measurement of small changes over time at individual or case level, analysis of small changes in intracohort developmental change, and panel analysis. Subject repositions allow the analysis of changes over time with aggregate data, provided that time lags are not large. Combining longitudinal data with repeated measures in some cases, and others without repeated measures, allows detection of situations in which bias originated from repeated measures. Longitudinal panel design occurs when the same set of cases is investigated multiple times (except for missing data), and is known as genuine longitudinal study. Of the designs mentioned, this is the most affected by attrition, but the great advantage is that it enables the analysis of intra-individual changes. A major classification-related issue concerns the use of repeated measures (obtained among the same participants), because measures of this nature allow disaggregating intrapersonal and interpersonal effects in longitudinal models of change (Curran & Bauer, 2011). For example, when an individual engages in effective coping, this is thought to mitigate the effects of work stress for this individual (e.g., Ford et al., 2014). Only longitudinal data allow for the proper separation of between- and within-person effects, and it is critically needed for fully evaluating many theories in organizational studies. From a substantive perspective, it is sometimes difficult to fully articulate precisely in what ways a given influence on an outcome might vary in magnitude and form when looking within persons versus across persons. For example, one might be interested in studying the relation between occupational stress, organizational support, and group climate. It may be quite challenging to unambiguously articulate the theoretically derived expected relations between variability in overall level of stress and organizational stressors-strain across individuals (the between-person effect) and a specific individual’s variation in stress and variation in organizational support perception (the within-person effect). This is further exacerbated by the fact that these two levels of influence may operate simultaneously, and even in opposite directions. We are quite sympathetic to this challenge, having wrestled with these same issues in our own substantive research.

TEMPORAL PERSPECTIVES IN ORGANIZATIONAL RESEARCH AND LONGITUDINAL DESIGNS The discussion of time in longitudinal studies cuts across theoretical, analytical, and pragmatic perspectives in terms of design. All are nested and are essential for understanding the process of change and constructing causal inferences (Collins, 2006). The more pragmatic perspective begins by defining the intervals between data collections aimed to detect when change is pertinent. In terms of data variability, there are two types of changes of interest in longitudinal studies: the first concerns intra-unit changes that reflect paths of growth or attenuation in the organizational phenomena; the other refers to differences between units of the change and the

Longitudinal research methods for studying processes  97 function it establishes with events antecedent to it and other consequents (Curran & Bauer, 2011; Ployhart & Vandenberg, 2010; Wright & Markon, 2016). Dormann and Zapf (2002) and De Lange et al. (2004), for example, conducted studies using multiple-wave longitudinal designs to review how the strength of effects in longitudinal surveys depended upon the length of the interval between study waves. Intra-unit (or intra-subject; intra-person) measures can be detected using a sensitive and well-calibrated instrument, while differences modeling may follow a variety of strategies. Repeated-measures multivariate analysis of variance (MANOVA) is one of the oldest and most classical methods for the treatment of longitudinal data, allowing one to consider the factors explaining changes between assessment time points, using generalized linear models (GLMs) (Curran & Bauer, 2011). Latest developments such as mixed models involving random coefficient modeling and latent growth modeling using structural equation analysis have significantly advanced in the field (Zyphur et al., 2020). In the context of mixed models multilevel modeling, in addition to the suggestion of fixed effects to explain the outcomes reviewed, the aggregated levels that give rise to the random component of the model allow not only considerations about these measures pooled at the level of the individual, but also allow one to take into consideration other levels that interact with this first one, such as the level of the organization and the external context, assuming hierarchical dimensions that allow deepening the analyses (Schonfeld & Rindskopf, 2007). For the second case mentioned, the possibility of building complex models that comprise considerations of latent variables and structural models allows not only the modeling of latent growth, but also the consideration of other latent phenomena. For example, relationships between psychological contract breaches, organizational commitment, and innovation-related behaviors (generating, spreading, implementing innovative ideas at work) were studied over a six-month period using latent growth modeling. Results of latent growth modeling indicate that the effects of psychological contract breaches on employees are not static. Specifically, perceptions of psychological contract breaches strengthened over time, and were associated with decreased levels of affective commitment over time. Increased perceptions of psychological contract breaches were further associated with decreases in innovation-related behaviors (more details in Ng et al., 2010). In the meantime, elements such as the manifestation of missing cases or even structural relations that point to complex conditional processes indirectly measured end up being incorporated, as occurs in well-established models such as the trait and latent state, or even cross-lagged models (Newsom, 2015; Yang et al., 2020). In addition, several contemporary proposals ensuing from the flexibility present in the modeling of latent variables begin to add dynamic elements, expanding the modeling of growth typically linear to consider stochastic processes typical to time series, and often emerging in intensive studies or with experienced sampling (Zyphur et al., 2020). It should be noted that in substantive terms, building causal inferences depends on the length of intervals to be modeled by means of different techniques. They must correspond to the underlying “true” causal delay, at the risk that intervals organized in a reckless way may fail in pointing to a causal relationship between events, when in fact they exist in time, but are delayed or have already been attenuated with delay by the time of evaluation. If this is much shorter than the true causal lag, it is likely that the antecedent has not yet had sufficient time to affect the outcome variable. On the other hand, if this lag is too long, the effect of being exposed to the antecedent variable may have already been attenuated enough to be considered absent. To complicate matters further, in the interim period between the study waves, all sorts of other events may occur, competing with the aforementioned exposures,

98  Handbook of research methods in organizational change affecting the outcome variables. This consideration is to be considered and controlled for whenever possible (Raudenbush, 2001). All this implies that (1) the magnitude of longitudinal effects may strongly vary accordingly to the interval length used; (2) for the advancement of longitudinal research, scholars should carefully consider the possible underlying causal lags before conducting their study; and (3) they should also consider the causal model underlying the testing of the hypotheses present in the study. When this true time lag is unknown or cannot be reasonably assumed, researchers should preferably employ multi-phase designs in which measurements are made at multiple points in time (Ployhart & Vandenberg, 2010; Taris & Kompier, 2003), checking at this stage whether the intervals between measurements are appropriate for the variables under study. We believe these recommendations are suitable because, to date, the number of study waves and the length of the interval between these waves are often chosen for pragmatic reasons, but they gain much in terms of rigor when considered in light of the theory of change and the analyses to be employed. The establishment of intervals depends on the temporal characteristic of the phenomena, with the possible exception of diary study (Bolger et al., 2003) and experience samples of high intensity (Bolger & Laurenceau, 2013). Diary studies tend to focus on relatively volatile processes in which the phenomena of interest (e.g., mood or fatigue) rapidly change over time. Typically, a relatively small number of participants recurrently respond to short questionnaires during the day on several consecutive days. Due to the design of these repeated measures, intra-participant changes in the phenomena of interest may be related to the “short experiences of everyday life” that precede these changes (Wheeler & Reis, 1991, p. 340), requiring here both special resources for their collection, often technological measures such as smart gadgets (Shiffman et al., 2008), and additional analytical resources that have achieved recent prominence in the literature, such as dynamic structural equation modeling itself (Zhou et al., 2019). There are longitudinal studies with variations of hours, days, months, or years. When this true time lapse is unknown or cannot be reasonably assumed, researchers may preferentially employ multi-phase designs in which measurements are made of the same set of participants at multiple points in time (Ployhart & Vandenberg, 2010; Taris & Kompier, 2003), with changes in the lengths of intervals between measurements. To date, the number of study waves and the length of the interval between these waves are typically chosen for pragmatic reasons and often little is known about the phenomenon’s development (Ployhart & Vandenberg, 2010; Taris & Kompier, 2003; Wright & Markon, 2016). Finally, the theoretical, or perhaps philosophical, perspective that integrates such an analytical and pragmatic dimension is crucial for understanding and explaining the processes of change. More directly and in counterpoint, time is usually nothing but a metric or scale for representing change processes in longitudinal studies (Ployhart & Vanderberg 2010). The analysis of the concept of time shows that the word “time,” as a physical measure, cannot be confused with the concept of “cause” of processes of change studied in organizations. There are also the meanings relating to timing, which designates the exact moment when one variable starts to influence or ceases to influence another, or the instant or moment when one should observe and measure a relationship between variables. The psychological meaning of time cannot be confused with that of physical time. This technical use of the concept of time refers to people’s opinions, perceptions, attitudes, feelings, and emotions about time. Most constructs do not change or develop because of time, but over time, such as the formation and development of a group. Time is only a metric or scale for representing processes of change; it is not configured as a cause of the detected effects (Ployhart & Vanderberg

Longitudinal research methods for studying processes  99 2010; Wright & Markon, 2016). The longitudinal study time also serves as a factor in time validity of the phenomenon in that the information collected will inform the conceptual and procedural evaluation of the phenomenon (Hassett & Paavilainen-Mäntymäki, 2013). Time is used to make inferences about the processes of interest with the use of ordering events over time to better understand causality; the use of a time interval to understand the stability of a psychological, social, or organizational construct within developmental dynamics; or the use of repeated measurements over time to understand how different patterns predict the outcome (Abbad & Carlotto, 2016). Other authors conceive time as a variable in itself, as distinct from its function as a delineator of temporal constructs of interest, and the time scale during which those constructs occur and can be observed. Time would serve as a variable in repeated measures and could shed light on what time represents in trying to discover or understand a process. Time offers evidence for a process; it is not the process itself, as differences over time suggest different mechanisms between groups that are not explained by the group itself, but by other variables (Ployhart & Vanderberg, 2010). “Adopting a time perspective in organizing research can mean at least four different things: (1) study time-related constructs, (2) investigate time-sensitive processes, (3) specify appropriate time delays (i.e., addressing the question of “when things happen”; Mitchell & James, 2001, p. 530), and (4) take the temporal context into account” (Sonnentag, 2012, p. 362). Adopting a temporal perspective, also means that rather than studying snapshots (single or multiple) of behavior, it is necessary to study actual interactions as they unfold over time (Sonnentag & Kruel, 2006). This can generate insights into the complex social dynamics at the heart of many organizational phenomena, as most employee behaviors are embedded in dynamic social contexts (Johns, 2006; Mehra et al., 2001; Porath et al., 2012; Spreitzer et al., 2005). The goal is to get closer to the phenomena of interest, to investigate the actual behaviors we are trying to explain, and to understand the temporal dynamics surrounding them. For example, rather than static descriptions of a leader’s general style, behavioral interaction research may produce much more specific answers about what, when, and how a leader needs to communicate in order to motivate their team toward a specific goal (Sonnentag, 2006). On a macro-organizational level, it is important to focus on the actions taken in the organizational environment, and on the repercussions of such actions on the dynamic quality of human conduct in organizational settings. This perspective is encompassing, not only to reveal the temporal nature of human conduct, but also to expose the relationship between human behavior and change and various levels of contexts in which it is embedded (Pettigrew, 2012).

CONTEXT ANALYSIS IN LONGITUDINAL RESEARCH: THE SCRUTINY FACTOR FOR TESTED HYPOTHESES Many authors argue about the need for monitoring and understanding context when analyzing data from longitudinal studies (Pettigrew, 1990, 2012; Sonnentag, 2012). Such analyses involve how the historical, economic, political, cultural context external to the organization affect the phenomena of interest, as well as how internal processes (previous and current) occurring within the organization or in the broader organizational environment and less comprehensive contexts linked to teams and individuals affect the relationships studied (Sonnentag, 2012).

100  Handbook of research methods in organizational change Similarly, Pettigrew (1990, 2012), adopting an organizational science perspective, argues for the importance of studying any specific change within the context of changes at other levels of analysis; locating change in past, present, and future time; and understanding that the context itself helps in shaping the process and that change has multiple non-linear causes. Understanding change over time at the individual and social levels draws on historical, psychological, and sociological perspectives, underpinned by different philosophical approaches as to what can be known about the social world and how it can be discovered. Research in organizational studies is nested in various contexts (group, organizational, social, etc.), and the analysis performed by the researcher is conditioned by social context and time (Pettigrew, 2012) and must uncover the connections between changes in context, process, and outcomes. This requires essential analytical features to be developed in longitudinal or process research (Pettigrew, 1990; Pettigrew et al., 2001; Van de Ven & Huber, 1990). The importance of the context as an explanation of the action and its outcomes is enabled by handling context as a multilevel interactionist field of analysis. Explanations of change in organizational performance, for example, should be linked to higher levels of analysis, such as industry operation and political and economic change, and lower levels of analysis, such as disputes over ideas and power and influence within the organization. Just as levels of analysis are nested, so are processes (Pettigrew, 2012). Actions and actors drive processes, but actions are embedded in different levels of context and both actors and context are shaped and being built. Crucially for any procedural analysis, the exchange between agents and contexts over time is cumulative. The legacy of the past is always shaping the emerging future (Pettigrew, 2012; Pettigrew at al., 2001). Research hypotheses and potentially connected explanations depend on how well information is available in research (Sonnentag, 2012). The most challenging tasks lie in establishing the causal links between context, process, and outcome, acknowledging the research of process as the dynamic study of behavior within and across organizations, with a focus on context, activity, and actions that unfold over time (Pettigrew, 1997; Tsoukas & Chia, 2002). One does not study change itself, but the dynamics and the interactive processes surrounding it. Evaluation at different waves is needed in order to describe activities and experiences, and to obtain reliable metric data on the effects of the interventions undertaken (Shah et al., 2017; Stouten et al., 2018). In addition to monitoring (often qualitatively) the context in which interventions occur, longitudinal studies require evaluation with different measures over time (Sonnentag, 2012). In Pettigrew’s (2012) context-process-outcome approach, there is always the issue of selecting the variable of result. The variable of result implies the definition of outcome variable that involves a result associated with the process of change. Does the variable of result represent any intermediate effects, or any final effects? Final and intermediate effects entail advantages when included in longitudinal studies. The result or effect provides a focal point, an anchor for the entire investigation, but the focus should be on the variations in context, the form of the process, and variability in results observed in a comparative investigation. The proposal is to set up a comparative study that exposes variation in some intermediate result or effect, and then explains that result or effect. Here, the challenge lies in analyzing the factors that shape the results of change episodes and producing convincing evidence that a pattern of initiatives on change over time contributes to a final effect, such as organizational performance, which can also be reviewed over time.

Longitudinal research methods for studying processes  101

LONGITUDINAL MEASUREMENTS Some of the measurement-related problems in cross-sectional designs are also present in longitudinal designs. In the search for detecting change based on the analysis of attributes ordered and located in time, issues related to measurement end up taking a prominent role (Menard, 2008). Considering the assumption that in the assessment of an individual over time measures obtained should be reliable and share the same meaning, any difference between an initial measure and the subsequent measure may be interpreted solely as change and not as a fluctuation arising from any source of bias of the instrument itself or process of measurement (Marcoulides, 2019). An instrument can be said to be more reliable to the extent that it produces scores with fewer measurement errors. To some degree, errors in measurement permeate virtually all aspects of empirical knowledge production (Taris, 2008), as well as in research taking place in the organizational context. The error size and the set of its consequences, on the other hand, have an impact on several contexts of analysis and, depending on their magnitude, may even invalidate results and limit conclusions (Ree & Carretta, 2006). In this context, it is relevant to note how losses related to the reliability of an instrument’s scores may echo in terms of threatening the inferences made through longitudinal studies. A measurement instrument to be employed in a longitudinal study is expected to first be able to provide error-free measures, and also be sensitive enough to capture the required level of change, being to provide measures of the same meaning at different points in time when it will be employed. Grimm et al. (2016) indicate that such characteristics should be explicitly considered in every longitudinal study, and be an assumption supported by empirical testing. In order to operationalize such tests, these authors recommend checking the reliability of the instrument, its scale, and sensitivity for change detection, in addition to the assumption of invariance of the measure in-between occasions of measurement (Vandenberg & Lance, 2000). It is possible to get measures that suggest change when in fact there was no change, or measures that suggest no change when in fact there was change. Measurement error can also occur due to changes introduced in the measurement over time. Lack of standardization in data collection over time may arise for legitimate reasons (Collins, 2006; Taris, 2008). The first aspect concerning reliability, usually based on the internal consistency of items, demands care, since a large proportion of instruments, especially psychometric scales used in research, are constructed for inter-individual assessment in cross-sectional designs. Reliability, in terms of temporal stability of the measurement, is also frequent among these instruments. Evidenced from the analysis of repeated measures of the same instrument with the same sample at two different time points, the greater the correlation found between these measures, the greater the stability of scores over time, and thus the greater the reliability of the instrument, often named as test-retest reliability (Zickar, 2020). This idea of measurement stability, while important in many contexts, can pose a threat to the assessment of change in the context of longitudinal studies. An instrument whose score variation is very small between two time points may actually be unable to identify change, even if change does occur (Golembiewski et al., 1976). One of the ways recommended to circumvent such a problem and still somehow ensure fewer measurement errors is through the adoption of a measure of internal consistency at each time point, assuming Cronbach’s alpha values greater than 0.80 (Grimm et al., 2016). In such events, scores on each measure presumably reflect well the score of interest (the true score).

102  Handbook of research methods in organizational change Moreover, there is the error that includes bias (systematic error which reduces validity but not reliability) and random error in which error is not consistently an overestimation or underestimation (both of which reduce reliability and validity), and both can be considered low enough in a setting of high reliability (Cronbach & Furby, 1970). Test-retest reliability is dismissed here for the decision making on the adoption of an instrument in a longitudinal design, considering the basic assumption that the stability of an instrument does not necessarily ensure the achievement of an adequate analysis of score (Taris, 2008). The discussion about the reliability of scores and the existing influences in relation to them has been found in the literature for decades, as well as the evaluation of factors responsible for confounding the construction of causal inferences from the measurement of change (Golembiewski et al., 1976). Even studies involving controlled experimental designs entail problems regarding attribution of causes in changes observed in the pre- and post-test evaluation. Alpha, beta and gamma changes are hypothesized as ways to explain the differences found: alpha changes refer to actual behavioral change, beta changes refer to recalibration of the scale to the subjects’ responses, and gamma change is related to changes in the redefinition of the concept being analyzed. The causal inference, present in the context in which behavioral change is observed as a function of some antecedent factor, can only happen if these elements of change are properly considered and controlled for (Armenakis, 1988; Armenakis et al., 1986; Buckley & Armenakis, 1987). In a first step, even if the effects of beta and gamma changes can be controlled for, the identification of an alpha-type change requires that the measurement tool adopted be scaled and sensitive to the magnitude of the phenomenon of interest. Despite the alleged simplicity of this proposal, a relevant part of the instruments used in the context of academic research focuses on the comparison between groups of people and not on the assessment of intra-individual variability, a characteristic that can lead to important limitations to the identification of individual changes. Collins (2006) reinforces this argument in his review on the topic, rightly pointing out that most traditional measurement theories, such as classical test theory, are directed toward the construction of measures that tend to be sensitive in terms of identifying inter-individual variability, ignoring intra-individual variability, which is the type of variation that allows for the identification of change. A typical example is related to the measurement level of scales when they adopt granular measures, such as a Likert-type scale, for example. A scale with a more continued and less granular level of measurement (e.g., 0–100 points) has more amplitude and information to describe the variability of a phenomenon, increasing the chance of detecting changes – which in turn may indicate that it is a more sensitive instrument. Although continuous scores are estimated from Likert-type scales through the proposal of underlying latent variables that explain the response to items. Yet, specific skill levels – for example, very high or very low ones – are likely to be missed in the mapping made by those items about this variable. This makes the error associated with an unmapped range of scores large enough to prevent the detection of change, bringing about a problem related to the lack of sensitivity to a part of the scale. Thus, the instruments adopted in longitudinal studies should represent the full spectrum of the underlying construct to be assessed, usually with a good mapping of it, as proposed in the context of the item response theory for psychometric scales. It is worthwhile noticing a broader phenomenon named panel conditioning, which occurs precisely when respondents react to previous experiences of participating in the study. Through intentional or unintentional changes in their behavior, participants may exhibit reactions to the data collection process that influence their responses. Insights about what the researcher is

Longitudinal research methods for studying processes  103 looking for, for example, or even the identification of socially desired responses can cause this type of bias to occur. This phenomenon can also manifest itself through respondents’ behaviors related to reducing the cost of responding, thus generating potential abandonment of the study, not to mention less adherence to the stages of the research. Strategies aimed at reducing the respondents’ burden can cause participants to present acquiescent response patterns, or even random responses, such as guessing, on more difficult items. As can be observed, this whole set of factors greatly threatens the validity and reliability of the measures obtained. A rotating panel design can reduce the problem of conditioning, since it will systematically and regularly replace panel members, preventing participants from being invited at every wave or even from answering every block of scales that make up a battery of assessments in a longitudinal study. Similarly, the adoption of complementary strategies has also been shown to be important, such as the use of brief and easy-to-understand instruments, election of forced-choice scales, or even the control of social desirability and random responses by modeling them into specific models. Although less frequent, an opposite effect can also occur with regard to the participants’ engagement in the research. Depending on the type of link built over the course of collections and the value experienced by participants in relation to the study, participants may become more engaged with the research in its subsequent waves, increasing participation and the quality of responses (Collins, 2006; Ployhart & Vandenberg, 2010; Wright & Markon, 2016). Collins (2006) suggests that all measurement when directed at an intra-individual process of growth should also commit to a theoretical model of the process of change, ultimately identifying the inseparability between measurement and conceptual definitions. Thus, the approach to processes of change and growth is not restricted only to modeling the instrument error, but also to the very specification of the theoretical model of change, as well as the constructs underlying it. In this sense, the discussion about gamma errors, referring to changes in the very meaning of the concept used, is even more relevant. Inherent to the theory of the process of change is the idea that the validity of this conclusion may be grounded in the measurement of the same construct at different time points. One of the current ways to integrate these aspects with the others already presented is through the adoption of structural equation models, which hypothesizes invariance in the way a given latent variable is measured, assuming an assumption of equivalence of models of measurement over time (Ployhart & Vandenberg, 2010; Wright & Markon, 2016). Modeling the invariance of a measure can serve as an example of the adoption of Collins’ (2006) recommendation in recognizing an intimate relationship between model of measurement and model of change. In practical terms the invariance analysis and its associated assumptions proceed on a continuum starting from the idea that at different time points characteristics of the measurement model should remain the same (Vandenberg & Lance, 2000). The first characteristic is related to dimensionality, also called configural invariance, where it is assumed that the same latent variables measured will be responsible for explaining the variability of responses to the same set of items at different points in time. Then, it is expected that the contribution of these latent variables will be identical at the item level to explain how they were answered, both in relation to their factor loadings, meeting the assumption of scalar invariance, and in relation to the intercepts and thresholds present in the model meeting the assumption of metric invariance. The support of the invariance hypothesis at all these levels, even if only partial in the face of some of the items, allows rejecting the suspicion that the change observed between two times was originated from a change in the meaning or way of

104  Handbook of research methods in organizational change measuring the concept over time, allowing not only comparing them, but also ensuring that such a comparison is being made in a fair way (Vandenberg & Lance, 2000). Invariance, then, supports the idea that changes observed in scores over time reflect changes at the level of the construct, representing a change in the latent scores of the individual, and not a change in the instrument of measurement in relation to how measurement was performed. Some common problems, however, emerge from this perspective. An example are longitudinal studies using small samples that make it difficult to fit complex measurement models, and processes involving a natural non-equivalence of items over time, something common in developmental cohort studies where ensuring the face validity of a measurement requires an adequacy of items over time, raising important discussions regarding the adequacy of measures (Ployhart & Vandenberg, 2010; Wright & Markon, 2016).

DESCRIPTIVE, INFERENTIAL, AND CAUSAL ANALYSIS IN LONGITUDINAL RESEARCH The analysis of longitudinal data presents relevant challenges and characteristics related to the type of hypotheses to which they are associated, and the very structure of the data emerging from a problem related to the dynamics of an event over time (Collins, 2006). Some common challenges have already been presented here, such as the problem of missing data, problems related to measurement, time dependence of observations, and the consideration of other systematic processes emerging from the follow-up of the same observation units. This set of characteristics, although only partially reflecting the potential complexity existing in the problems related to the analysis of longitudinal data, unveils the existing challenge. Still, it could be said that this is a field in full expansion, both as a result of technological innovations that increase access to the collection of this type of data, and in terms of development and implementation of analytical strategies that allow modeling phenomena that occur over time (Kelloway & Francis, 2013). Strategies range from simply describing a trend to complex issues, such as causal inference in dynamical systems (Menard, 2008). While these possibilities are broad, two large groups of questions that guide the analyses can be distinguished. The first one refers to questions related to inter-individual variation, such as when one tries to understand how, or based on what factors, people change as a function of time. The second group refers to intra-individual variation, when there is interest in investigating how daily fluctuations in given individual characteristics relate to each other over time (Curran & Bauer, 2011; Ployhart & Vandenberg, 2010; Wright & Markon, 2016). Despite this division, intra-individual variations represent fluctuations or changes that occur at the level of the individual and can be identified at a micro or first level of analysis (Curran & Bauer, 2011; Kelloway & Francis, 2013; Kim et al., 2020). These in turn are nested in the manifestations at inter-individual level, also referred to as the macro or second-level variations (Kelloway & Francis, 2013; Kim et al., 2020). A relevant aspect to highlight here is that despite the general trend of intra-individual variations when aggregated at the micro level converges to what is observed at the macro level, the opposite is not necessarily true. Intra-individual paths that follow specific patterns from individual to individual are always possible, bringing an additional component to research in analytical terms to evaluate change as an individual process (Curran & Bauer, 2011; Kelloway & Francis, 2013; Kim et al., 2020). The following figure presents different types of longitudinal analyzes in order of

Longitudinal research methods for studying processes  105 complexity, starting from a descriptive approach followed by the modeling of complex patterns in time, and the control of biases in order to support causal inferences. Therefore, five different approaches are presented along with an integrated analytical framework at the end, as shown in Table 5.1. Table 5.1

Analytical approaches for longitudinal data analysis

Topic

Analysis strategies

Descriptive analysis

Basic descriptive statistics

Classical inferential methods

General linear models, ANOVA, MANOVA

Mixed models or multilevel models

Generalized linear mixed models

Latent growth curves

Structural equation modeling

Intensive longitudinal designs

Time series analysis

An integrated framework

Time series + multilevel structural equation modeling

Descriptive Analysis in Longitudinal Research Historically, the treatment of longitudinal data and their analysis can be traced back to the simple and even graphical description of trends disclosed by measures over time, like those in growth curves of biometric data, such as weight or height (Menard, 2008). This type of analysis allows understanding the pattern of change in that data set (Fitzmaurice & Ravichandran, 2008). Nevertheless, one may state that this type of descriptive evaluation of longitudinal phenomena is scarcely present in the organizational context, and even classical phenomena, such as stress at work, tend to have few studies related to its manifestation as a function of time in a direct and pure manner. Still, even the univariate analysis of these phenomena, oriented to their description or to modeling intra-individual variability, may have relevant impact not only in relation to the recognition of typical patterns of a variable over time, but also as a step that provides subsidies for hypothesis testing in the context of other exploratory analyses associated with more explanatory variables or even causal models (Ployhart & Vandenberg, 2010; Wright & Markon, 2016). That is true for GLMs, time series, and even structural equation models, and modeling of latent growth structures or even inter-residual dependence. This context of descriptive studies encompasses describing trends through time series (Curran & Bauer, 2011; Kelloway & Francis, 2013; Kim et al., 2020). Classical Inferential Methods for Longitudinal Data We acknowledge there are multiple ways to present longitudinal analyses. We have opted for the commonly accepted practices and techniques, despite the countless ways to classify them. We also recognize the possibility that the text may seem simplistic and incomplete, but we opted for general practices and classifications, disregarding possible nuances and overlaps. In their review, Ployhart and Vandenberg (2010) offer a useful distinction between descriptive and explanatory longitudinal research. Longitudinal descriptive research focuses on “how a given phenomenon changes over time,” while longitudinal explanatory research “seeks to identify the cause of the process of change by using one or more substantive predictive variables” (Ployhart & Vandenberg, 2010, p. 99). Thus, analyses may assess the behavior of variables over time in a descriptive light, in addition to testing prediction relationships

106  Handbook of research methods in organizational change at various times or prediction relationships involving different times (Kelloway & Francis, 2013). A panel survey in which the predictor and the result are measured over several periods may be assessed. The regression model with panel data has a special feature: it consists of a temporal and a spatial dimension. This is because the same cross-sectional unit (groups of people, companies, countries, etc.) is monitored over time. Panel data can also be analyzed using cross-lagged correlations, cross-lagged regression analyses, time series, or structural equation modeling techniques. An example of cross-lagged correlations shows correlation analysis where the magnitude of the correlation between restructuring and shutdown policies at Time 1 and the performance effectiveness at Time 2 (for example) is compared. Cross-lagged regressions are based on the review of predictor– outcome relationships over time while covarying stability in variables (e.g., Kelloway & Barling, 1994). Thus, measures at Time 2 are regressed into measures at Time 1 (stability) considering the prediction relationships. Reverse causality is tested by regressing restructuring and disengagement policies at Time 2 to the restructuring and disengagement policies at Time 1 (the stability). Structural equation modeling offers some advantage over regressions, as the former allows you to incorporate measurement errors, estimate multiple causal relationships simultaneously (Zapf et al., 1996), and incorporate correlated errors (Kelloway & Francis, 2013). Another approach to deal with this kind of data would be to model observations as a time series (Rosel & Plewis, 2008). With its emphasis on description and prediction, the substantive issues underlying time series analysis are not common in organizational change research. They are included here because we believe such questions are of interest in that they offer the potential to help researchers engage in descriptive research and increase our understanding of how change manifests over time. The simplest models for analyzing time series data may be autoregressive models whose substantive assumption underlying these models is that each observation is a function of the immediately preceding observation. These models may be analyzed by hypothesizing a constant autoregressive effect over time or by entering the error terms associated with each measurement with a moving average modeling as well. The constant autoregressive effect is analyzed with a moving average where each variable is hypothesized as a function of the same variable in the previous time period, as well as the previous error (Kelloway & Francis, 2013). Time series approaches usually focus on observed variables, but they may also be implemented using latent variables (Rosel & Plewis, 2008). The models discussed so far were based on the implicit definition of constructs of interest as continuous variables. In contrast, we also want to briefly consider longitudinal forecasting of specific events and suggest that event forecasting has considerable applications in the field of organizational change. For example, measures of workplace characteristics (e.g., insertion of resettlement policies, perception of injustice) may be used to predict the occurrence of illness from such events (Elovainio et al., 2006). The broad class of techniques used to predict events is known as survival analysis. Singer and Willett (2003) suggest there are three elements common to all event prediction studies “(a) a well-defined ‘event’ whose occurrence is being explored; (b) a clearly defined ‘beginning of times’; and (c) a substantively significant metric for timing time” (p. 306). The occurrence of an event is understood as a transition of states – thus, individuals go from not being sick to being sick in the workplace. The “beginning of time” is understood to refer to a moment in time when everyone in the population occupies only one of these states. Although studies should start at a time when all participants are theoretically able to experience the event but have not yet done so, in practice researchers often

Longitudinal research methods for studying processes  107 choose an arbitrary time period with the proviso that the beginning of time is related to the occurrence of the event. Finally, Singer and Willett (2003) note that time should be measured in the smallest possible time unit relevant to the study. In addition, they propose a simple test for researchers to determine when survival analysis is appropriate – suggesting that the techniques should be used for any research question that asks whether a specific event occurs or when a specific event occurs. Modeling longitudinal data, involving the analysis of their association with external factors or even the prediction of growth from the estimate of rates associated with causal events, usually encompasses a set of analyses referred to as explanatory models. Thus, the adoption of general linear models can be historically traced, as a resource for modeling inter- and intra-individual variability, as seen in applications that adopt repeated-measures ANOVA or even repeated-measures MANOVA (Kelloway & Francis, 2013; Kim et al., 2020; Mitchell & Maxwell, 2013). Such models allow one to explain continuous outcomes with normal distribution, despite the possibility of their extension to GLMs by which the modeling of outcomes with other distributions is also possible (Collins, 2006; Curran & Bauer, 2011; Kelloway & Francis, 2013; Kim et al., 2020; Mitchell & Maxwell, 2013). GLMs explain such outcomes based on the effects that specific predictors may have, as is the case when we assume time lapsing as responsible for the effect of growth of an organism resulting from its maturation (Collins, 2006; Kelloway & Francis, 2013; Mitchell & Maxwell, 2013). The effects found in these models can be classified into fixed and random effects. The first type of effect is represented by the parameters associated with the predictors of a model that, through a linear combination, predict the outcomes under analysis (Collins, 2006). In the context of longitudinal data analysis, the so-called unconditional models directly represent the fixed effect of time on outcome, and the estimated parameter of this predictor allows us to know the growth rate existing in the phenomenon under investigation. In this way, the above-mentioned intra-individual variability is modeled based on time, so that it can be identified as an individual’s change or fluctuation in relation to its own measure of the outcome repeated over time (Collins, 2006; Curran & Bauer, 2011; Kelloway & Francis, 2013; Mitchell & Maxwell, 2013). For extension and completeness, a generalized conditional linear model, in addition to the fixed effect of time, also presents other predictors as fixed effects that condition the change. Here, elements of inter-individual variability may be considered as conditioning factors, such as a specific group’s exposure to factors that may contribute to change over time (Collins, 2006). The other part of the model concerning random effects characterizes the stochastic part of the model, related to the variance in the error produced from the portion of variability not predicted by the fixed effects described above. By definition, GLMs have the prediction of a random effect related to the distribution of the residuals predicted a priori (Collins, 2006; Curran & Bauer, 2011; Kelloway & Francis, 2013; Kim et al., 2020; Mitchell & Maxwell, 2013). In the case of repeated-measures ANOVA, for example, this random effect related to error variance is modeled as having a normal distribution. Despite the possibility of being applied to some contexts of analysis, the inflexibility regarding modeling of random effects, defined as following a normal distribution, ultimately limits the model’s ability to actually represent the variability present in the data. This is often the case for longitudinal studies, especially for the case of modeling intra-variability, where emerging patterns of changes or fluctuations over time may not follow a linear pattern of growth, presenting specific features that need to be modeled, such as serial dependence between observations, seasonal effects, or

108  Handbook of research methods in organizational change even fluctuation patterns (Curran & Bauer, 2011; Kelloway & Francis, 2013; Kim et al., 2020; Mitchell & Maxwell, 2013; Zyphur et al., 2020). Several classic problems connected with the use of GLMs arise when their basic assumptions are not applied, such as constant variance of residuals over time, something that does not hold in scenarios of serial dependence where autoregressive models are required (Kelloway & Francis, 2013; Kim et al., 2020; Mitchell & Maxwell, 2013). Furthermore, a basic assumption, but one hardly achievable in the applied scenario, is that this type of analysis requires data to be balanced; i.e., no missing cases. Classic strategies of removing these incomplete cases or loading the closest value temporarily for a missing observation are widely condemnable. It may entail consequences that invalidate any conclusion based on these data, considering to which extent such practices can bias the results (Kelloway & Francis, 2013; Kim et al., 2020; Mitchell & Maxwell, 2013; Wang et al., 2017). It is a fact, hence, that the answer to some essential questions in the context of longitudinal studies precisely depends on a model in which the variance of residuals is not fixed, and which has flexibility for modeling random effects associated with intra-individual variability (Curran & Bauer, 2011). Even a simple question that inquires whether people change over time, based on different rates of growth, requires, for example, that the effect of time is not considered as fixed; i.e., a constant parameter adopted as a single predictor of growth for all times across all domains in the sample investigated (Kelloway & Francis, 2013; Kim et al., 2020; Wang et al., 2017; Zyphur et al., 2020). In that event, when considering the effect of time as random, it is possible that parameters of the effect of time are estimated for each individual, representing the consideration of a growth profile to each subject over time. Such an example can then be considered not only with respect to the variation of a given rate related to growth over time, but also related to the interaction that a specific variable may have in the intra-individual dynamics itself, for example, as an element capable of predicting intra-individual fluctuations (Curran & Bauer, 2011; Kelloway & Francis, 2013; Kim et al., 2020; Wang et al., 2017; Zyphur et al., 2020). Mixed Models or Multilevel Models Generalized linear mixed models are designed to allow both the modeling of fixed effects of their predictors and the modeling of random effects related to the variance of residuals (Collins, 2006; Curran & Bauer, 2011; Zyphur et al., 2020). These models are also described as multilevel-type models or hierarchical linear models. They show great versatility not only for modeling random effects related to intra-individual variability and time dependence of observations, but also for relevant considerations regarding dependence between observations emerging from subdomains of the sample, such as natural groupings or hierarchical levels (e.g., see Curry et al., 2015). The consideration of random effects as representing different levels or multiple dimensions of the sampling process becomes very interesting and pertinent to organizational theory and theories of change, by allowing one to represent sources of intra- and inter-individual variation existing in such theories. One can say that the purpose of multilevel analyses in this context is to quantify in a first step, to then explain each source of variation facing the different dimensions of the sample, allowing that ultimately and whenever pertinent this source of variation is the subject itself (Kelloway & Francis, 2013; Wang et al., 2017; Zyphur et al., 2020).

Longitudinal research methods for studying processes  109 Currently, advances related to the estimation of these models allow a wide diversity of longitudinal hypotheses to be tested, also including the handling of missing data among other relevant components, such as the estimation of effects from interactions between levels. On the other hand, an important limitation of multilevel models is related to the assumption that they are estimated based on observed variables, which should be considered, in turn, as error-free measures. Although this may be the case in a large number of studies, several phenomena present in the organizational context and in change processes cannot be directly observed, such as the motivation for change. Moreover, such models are deficient for not being prepared to the modeling of latent variables, since latent variables can not only represent theoretical constructs in a formal way but can also help in estimating underlying phenomena and processes, such as latent processes of missing case generation, latent growth, or even disturbances that are not directly measured but that can be analytically inferred (Curran & Bauer, 2011; Kelloway & Francis, 2013; Wang et al., 2017; Zyphur et al., 2020). Latent Growth Curves In the face of inherent limitations from generalized linear mixed models, a large family of models called structural equations takes the scene by allowing the modeling of both observable and latent variables. The latter can be represented through a measurement model, usually representing a common factor that, although it cannot be directly observed, can be modeled as responsible for the manifestation of a set of observable variables. The estimation of a latent variable has the property of constructing a measure that has only the common information coming from observable variables, not being confounded from the error portion arising from the unexplained variability of each variable observed. Although this is inappropriate in absolute terms, it is understood that a latent variable constructed from a reasonable set of observable variables is a measure free of error or with modeled error. A similar idea and one that marks a significant part of the adoption of structural equation models in the context of longitudinal analyses is the creation of latent variables that represent a growth factor underlying observations made over time. In these cases, such models are called latent growth curve models through which individual differences at each level and growth rates over time are described by a latent variable that represents an intercept for the first case and a slope of the growth curve over time (Kelloway & Francis, 2013; Wang et al., 2017; Zyphur et al., 2020). Furthermore, structural equation models report great versatility, in addition to modeling latent variables generally related as the measurement model component. These models also present a structural component that allows the representation of relationships between latent and observed variables, including in this set of possibilities all those present in path analysis models, such as the representation of relationship chains between variables over time; conditional processes, involving analyses such as of mediation and moderation; and allowing these models to represent complex causal events (e.g., Augustsson et al., 2017; Drzensky, et al., 2012; Kim et al., 2011). Here it is worth highlighting cross-lagged type models that integrate such features into an autoregressive model intended to find the mechanisms responsible for effects over time that may be conditional on previous events related to activation of mechanisms through mediating variables or effect modification through moderation (e.g., see Kern & Zapf, 2020). Last but not least, in addition to the versatility presented herein, it should be noted that structural equation models also allow for the modeling of random effects through their specification as multilevel models, allowing both modeling of time-repeated observations

110  Handbook of research methods in organizational change as temporally dependent, and also concerning characteristics of sampling pooling into hierarchical levels (Collins, 2006; Curran & Bauer, 2011; Kelloway & Francis, 2013; Wang et al., 2017; Zyphur et al., 2020). Another possibility in mixed models is using multivariate second-order latent change score modeling (LCSM; Ferrer et al., 2008). LCSM enables researchers to examine relationships among within-person changes, which is in contrast to cross-lagged panel models that do not separate within-person processes from between-person differences (Hamaker et al., 2015). Second, in LCSM, the change score spans over one time interval, enabling us to test the notion that prior changes are expressed in subsequent changes (McArdle, 2009; Selig & Preacher, 2009; Timmons & Preacher, 2015).). This contrasts with growth curve modeling, which can be used to examine changes across three or more time points but not across two time points. Furthermore, in LCSM there is no need to subtract two scores from each other to create a difference score or model residual changes, which do not map well with within-person processes and are associated with methodological problems (Henk & Castro-Schilo, 2016). LCSM enables researchers to examine dynamic processes, in which work engagement and cognitive appraisals are expected to foster each other and impact subsequent changes in each other. Intensive Longitudinal Designs Analysis Longitudinal designs in which many collection occasions among people occur (between dozens and hundreds of observations) are often called intensive longitudinal designs. This has been gaining importance as new technologies have allowed for more continuous and easier data collection, such as those using sensors and other Internet-connected devices such as cell phones, smart watches, and other items. The ecological momentary assessment designs adopt these technologies, among other more conventional ones, to obtain real-time measures, making room for a wide range of hypotheses to be tested in the context of intensive longitudinal studies. In this way, time is modeled as a continuous process that can consider varying time intervals from individual to individual, as well as a huge number of records from person to person. Sometimes, by assuming such characteristics the model may actually predict the rate of occurrence of repeated events within each interval rather than assessing what occurs at a given moment in time. The recording of an event of interest at the moment and context in which it occurs becomes the domain for measuring intra-individual variability (Curran & Bauer, 2011; Kelloway & Francis, 2013; Timmons & Preacher, 2015; Wang et al., 2017; Zyphur et al., 2020). The literature from several other areas of knowledge commonly assumes that longitudinal or repeated-measures studies have characteristics of intensive studies, and these are usually modeled through time series, with a focus on describing autoregressive patterns or other types of events with time dependence. Through these analyses, a wide range of resources is available for modeling random effects, including for identifying and decomposing these patterns of intra-individual variability. Based on the identification of general trends through mobile averages over time and the decomposition of autoregressive components, such as time dependence through lags, periodic cycles, seasonal events, or even point disturbances, a broad analytical horizon shows up. Moreover, although time series models can be fitted to explain a single unit or individual in n = 1 type designs, principles related to time series analysis could be potentially integrated with multilevel structural equation models for larger samples, allowing one to consider the modeling of fixed effects and random effects in an integrated manner.

Longitudinal research methods for studying processes  111 Here, it gives rise to what is called dynamic structural equation modeling, generally applied to intensive longitudinal studies, in which the modeling of random or stochastic time series effects is integrated with latent variable modeling for the analysis of dynamic patterns, such as oscillations, self-regulation events, dyadic influences, one-time external disturbances, and effects at different levels (Collins, 2006; Curran & Bauer, 2011; Kelloway & Francis, 2013; Wang et al., 2017; Zyphur et al., 2020). An Integrated Analytical Framework for Causal Inferences from Longitudinal Studies The integration of these models presented thus far is proposed by Zyphur et al. (2020), based on the justification that the construction of causal inferences from longitudinal studies is a key element for an evidence-based approach in the context of organizations. Despite the recognition of the inherent value of randomized clinical trials as a first-choice design to build evidence and causal inference, longitudinal data modeling can also contribute to build causal inference, provided it considers elements related to controlling the confounding effect of third variables, and the temporal precedence relationship between cause and effect (e.g., see Kaltiainen et al., 2020; Mäkikangas et al., 2019). In the same direction Raudenbush (2001) acknowledges that contemporary theories of causal inference, especially those discussing the role of potential outcomes and counterfactual effects, are all based on the idea that there would be a difference in an individual’s response compared to herself or himself if all evaluative conditions are kept identical by manipulating only one specific cause as an explanatory factor for a given result. Despite the logical sense of this construction, this epistemological principle is not testable, and it is not possible to derive here a testable hypothesis that meets the conditions for identifying a cause in a formal way in the individual. The experimental control and manipulation of an independent variable, along with the random allocation of participants into an experimental group and a control group, aims to create statistically equivalent scenarios, which would differ only in terms of exposure to a certain level of the independent variable. This would represent the best approach to the premises inherent to the identification of a cause. In the absence of direct manipulation of independent variables and random allocation, maintaining a counterfactual condition is thus threatened, such as the property of a causal inference (e.g., see Kern & Zapf, 2020). In contrast, seeking to describe individual paths through multiple time points can be a useful factor for evaluating the individual in relation to the self, as advocated by the idea of a counterfactual experiment. Considering the effect of a given variable as an explanation of a process, through panel studies and causal modeling in longitudinal studies, one can infer whether a particular result or change was caused by exposure or whether it is just part of a natural process of change. To this end, herein lies the key element of decomposing the parts that explain change or stability by means of fixed effects, but also by means of random effects, such as those related to autoregressive effects (intra-individual). In addition, change can also be the effect of a dynamic process, characterized by exposure to a particular variable (inter-individual) and its long- and short-term consequences on the system. In this way and grounded in this rationale, the number of measurements over time and the verification of these associations in the terms presented here allow increasing the validity of a causal inference, even in the absence of randomization (Curran & Bauer, 2011; Kelloway & Francis, 2013; Wang et al., 2017). In their proposal, Zyphur and colleagues (2020) indicate that panel data collected at at least three time moments and involving at least two variables can be modeled in such a way that

112  Handbook of research methods in organizational change components of intra- and inter-individual variability are considered for a causal explanation. Thus, one could infer whether the occurrence of an event explains the manifestation of another even crosswise, something possible in substantive reality. To this end, short-term direct effects and long-term indirect effects in a cross-lagged model are proposed, along with the control of autoregressive effects, mobile averages, and cross-lagged mobile averages, which attempt to represent impulses capable of generating changes. In addition, this general model also proposes including factors responsible for some stability of the system over time, such as personality factors and aspects of an organization’s culture. Considering such modeling in the context of structural equation analysis, it also allows the modeling of latent variables representing other unobservable variables, as well as covariance between residuals, a relevant element for causal inference in some contexts which is crucial for modeling longitudinal data assuming interdependence. In this context, it should be noted that inference by means of maximum likelihood methods and Bayesian inference methods increase the flexibility of the model by allowing the treatment of missing data and the consideration of other dimensions of analysis, such as the presence of mediator variables or even other levels of analysis in hierarchical terms. It is worth mentioning that the authors make freely available the codes for implementing these analyses in R and Mplus statistical analysis software programs, along with other resources such as appendices detailing practical and theoretical aspects of these analyses, including a very enlightening video tutorial. To assist in decisions about data analysis in longitudinal studies, we created this flowchart with some questions to be considered in terms of the study objectives.

QUALITATIVE DATA IN LONGITUDINAL DESIGNS Qualitative research in longitudinal designs can be described from two basic conceptions: the first concerning the data nature, referring to the level of measurement when only the classification of attributes is adopted, and the longitudinal research that starts from an epistemological approach in which the phenomena in question are understood through a qualitative approach (Thomson & McLeod, 2015). Originally, despite the predominance of quantitative approaches among longitudinal studies, the adoption of a qualitative and longitudinal perspective for understanding social phenomena is increasing. The adoption of a longer time perspective for researching process, including a growing interest in secondary data analysis, resulting from written or spoken communication data records, in addition to the increased accessibility to archival research that is now digitized, are part of this expansion of horizons. Intergenerational approaches and revisiting of classic studies, such as ecological field insertions and ethnographic studies, are now of great importance in structuring a qualitative trend in the proposal of longitudinal designs (Abbott, 2001; Andrews, 2007; McLeod & Thomson, 2009). When data are collected through a qualitative measurement approach, they may be coded into categorical variables using indicators of the process of change (Saldaña, 2008). To perform this coding, one can use a matrix (see the Longitudinal Qualitative Data Summary Matrix in Saldaña, 2008) that may in turn become a database used to perform several descriptive and inferential statistical analyses. Data coding allows one, for example, to use Configural

Figure 5.1

Map of possibilities in quantitative longitudinal data analysis

Longitudinal research methods for studying processes  113

114  Handbook of research methods in organizational change Frequency Analysis (CFA3) and identify patterns between variables, as well as temporal patterns between specific groups, which allows one to identify curves of change and analyze trends over time (Von Eye et al., 2008, 2009). A first step in longitudinal data analysis requires a basic description of data (Menard, 2008). The goal of analysis in longitudinal research with qualitative data is a systematic approach with an emphasis on tracking patterns of change (Saldaña, 2003, 2008). When delved into epistemologically, Qualitative Longitudinal Research – QLR – involves exploring the potential of research methods and techniques (Back & Puwar, 2012; Lury & Wakeford, 2012) and the traditions of ethnography and longitudinal qualitative methods in anthropology and community research (Kemper & Peterson-Royce, 2002), as well as exploring temporality within narrative and biographical research (Abbott, 2001; Andrews, 2008). Longitudinal qualitative research is marked by the use of historicizing or situating everything, from theoretical analyses to technical revolutions (Thomson & McLeod, 2015). Articles by Stanley (2011) and Taylor (2015) suggest that QLR has been adopted as a new way of looking at phenomena. For Stanley (2011), it involves claiming QLR as a framework for his major investigations, and as a pathway to broad sociological and historical research agendas (Thomson, 2007). A relevant aspect of such comprehensive approaches is that, unlike quantitative studies, the possibility of a more interactive approach makes room for capturing dynamic aspects of processes of change that may be limited to a specific type of measurement. Thus, the very process of collecting information can lead to discussion about the different ways of building longitudinal evidence. It starts from the use of repeated interviews, but follows with recording narratives in retrospective, with documentary analysis in retrospective studies, and with participant observation, ethnography, and/or oral histories (e.g., see Falce et al., 2017; Molineux, 2013). Each of these entail specific advantages and disadvantages, but upon triangulation may enable deep understanding of the phenomena through different kinds of data collected for each construct or variable, for three or more distinct periods. More in-depth experiences driven by an ethnographic perspective may be longer, making the distinction between periods give way to continuous monitoring of a process of change, sometimes including participatory and action research processes here. The themes or cases analyzed may be broadly comparable in this sense; and their analysis involves the temporal perspective that spans periods predetermined by collection, or defined in the collected process or report, considering that these can even reveal dimensions of a given phenomenon in a retrospective manner (Thomson et al., 2014; Thomson & McLeod, 2015). In analytical terms, such a diversity of information needs to be articulated in the light of previous studies and the view of experts, capable of articulating categories of analysis that allow relating the investigated contents in a way that points to patterns previously described, but that also has enough freedom and criticism to identify new, emerging, and characteristic patterns of idiosyncratic phenomena peculiar to the context of change in question. This means that all methodological proposals in this context involve an in-depth analysis and interpretation of

3 The goal of a configural frequency analysis is to detect patterns in data that occur significantly more often (such patterns are called Types) or significantly less often (such patterns are called Antitypes) than expected by chance. Thus, the idea of a CFA is to provide by the identified types and antitypes some insight into the structure of the data. Types are interpreted as concepts which are constituted by a pattern of variable values. Antitypes are interpreted as patterns of variable values that in general do not occur together.

Longitudinal research methods for studying processes  115 the collected data set, emphasizing the nuances between data collected and the context that produced them, as well as general and specific aspects of the phenomenon in question (Corden & Millar, 2007a, 2007b; Thomson, 2007; Thomson & McLeod, 2015). Qualitative longitudinal research tends to vary across academic disciplines (Holland et al., 2006) including, for example, ongoing research in the same organization over time, follow-up studies or a return to previous research sites, repeated interviews with the same individuals at regular intervals, life course research involving data collection across multiple generations, ethnography involving multiple moments in the same group, and retrospective studies at several time points to build narratives, for example (Thomson et al., 2014). Qualitative longitudinal studies (QLR) that are openly identified as such are those designed as longitudinal since their inception (McLeod & Thomson, 2009; Thomson et al., 2003). They are studies with built-in temporality and a specific focus on change. There is a trend toward examining processes, formations, developments, or transitions, as trajectories in an organizational or social context (Harocopos & Dennis, 2003). Most commonly they involve repeated interviews or a combination of ethnographic work and interviews. Because they involve returning to the field site or revisiting individuals after a period of time, and sometimes multiple times, QLR studies can understand the development of social life, organization, and change as an ongoing process in which structure and agency interact, and individuals and cultures are in an ongoing process of transformation (Morawska, 2011). Therefore, they are often enveloped by a, perhaps implicit, theory of organizational or social change. Frequently, there is also an element of reflexivity, or of recognizing the relationship between the researcher and the researched subject as incremental and recursive (McLeod, 2003; Thomson et al., 2014). QLR therefore involves dynamic, ongoing, and reflexive practices of design and analysis (Thomson et al., 2014; Yates, 2003). In a different vein, there is something of a legacy of re-studying situations, places, or social contexts, as re-studies tend to alter the previous study, with an emphasis on how the situation is altered and/or on what has been omitted (Thomson et al., 2014).

RECOMMENDATIONS ON PLANNING LONGITUDINAL STUDIES In a theoretical light, conducting longitudinal studies requires specifying the theory of change, and looking at the form and duration of change, the intensity, and its predictors. In this case, theory and cross-sectional research may be insufficient to develop a theory about change, and this finding can make a difference in research construction, opening an important space for longitudinal studies (Abbad & Carlotto, 2016; Wright & Markon, 2016). Determining the optimal number of measurement occasions and intervals (amount and spacing between repeated observations) to adequately model the hypothesized form of change implies thorough knowledge of the nature of the phenomenon that will be addressed. It is suggested that one uses previous information about the phenomenon to accurately determine the most suitable interval, but the organizational context will play a major role in this decision. Adopt (time lags) time intervals in-between observations in order to investigate causality issues but ensure that lags are neither too large nor too small, and consider the necessary burden of engagement on participants (Timmons & Preacher, 2015). Diary studies require a great deal of effort to keep participants engaged in the study (Wright & Markon, 2016). As for planning the method, two decisions are key: sampling choice and choice of measures. When possible, choose samples that are most likely to exhibit the hypothesized form

116  Handbook of research methods in organizational change of change, and shy away from convenience samples. Determining an optimal number of observations, which takes into account subject loss, prior to conducting the study (loss of more than 50 percent of subjects from the first to the second measurement occasion) facilitates the achievement of more reliable results. For longitudinal studies, the choice of measures that show consistent evidence of validity in different sample groups facilitates the detection of invariances. With regard to data analysis, model the cause of missing data (ideally theorized and measured a priori) and consider, in planning, the occurrence of missing data in data collection. It is also recommended that one assesses the properties of variables for invariance (configural, metric) before testing whether change has occurred (Ployhart & Vandenberg, 2010; Wright & Markon, 2016). In pragmatic terms, a first goal of studies may have a descriptive nature: to graphically represent the hypothesized form of the change, connecting it to the observed form of change. The notion of process is outlined from this representation (Abbad & Carlotto, 2016; Fitzmaurice & Ravichandran, 2008). For this initial descriptive analysis, it becomes essential to clarify the level of interest of the change: group average change, intra-unit change, inter-unit differences in intra-unit change. In this case, even inter-group differences are accessed to describe change and its nuances (Collins, 2006; Fitzmaurice & Ravichandran, 2008). Next, a relevant element for most longitudinal studies involves describing the causal relationships and mechanisms inherent in the phenomenon. Following Zyphur (2020), modeling of causal relationships should be done considering a cross-lagged type design, making room for iterating relationships between variables, as well as not confounding through autoregressive component modeling, mobile averages, cross-lagged mobile averages, and fixed effects over time. Finally, qualitative research methods can also be considered depending on the objectives of the study, especially in the face of challenges related to the understanding of complex phenomena. An open approach to the construction of hypotheses improves comprehension about phenomenon that develops over time and cannot be predicted. Qualitative research in a retrospective way associated to gathering information in real time is also a strategy particularly appropriate for examining process through its attention to context and particularities. Immersive purposes in the field among other interactive approaches can contribute to the understanding of change processes in their complexity and specificity.

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Contemporary

6. Psychoanalytic and socioanalytic approaches to organizational change research Susan Long

INTRODUCTION This chapter explores the unique contribution of a psychoanalytic vertex to organisational change research. I will outline some of the major theories relevant to this task, with a particular focus on the assumptions embedded in these theories, covering some of the major differences. Following that, I will outline some fundamental methods used to research organisational change from an applied psychoanalytic perspective. Previously I have argued that: the unique contribution (of psychoanalysis) to the field of organisational dynamics lies in the capacity of psychoanalytically trained practitioners to work in a state-of-mind that enables others, through the in between (of the analytic practitioner and analytic subject), to bring forward, study and work through their own states-of-mind. This is the centrality of psychoanalytic discourse that is sustained, I believe, within the institution of psychoanalysis. (Long, 2001, p. 2)

In light of this, the “mind” of the psychoanalytically informed researcher is a critical instrument. This will be repeated in different ways throughout the chapter. I wish to emphasise that the study of organisations from a psychoanalytic perspective requires researchers to be educated and trained in quite sophisticated approaches, including self-reflection and close observation to achieve such a mind. These approaches are not distanced from people as research subjects, but are intensive, and at times involve deeply personal probes into the psyche. Education and training are mandatory to mitigate the risks involved, both to the subjects of the research and the researchers themselves. The ethics of these approaches are considered late in this chapter. Psychoanalysis began and thrived in the clinic, even though Freud’s own mind wandered often into the social, historical, and cultural implications of his work. Applying psychoanalytic ideas to organisational change is no simple transposition from the individual to the group. A whole new perspective is needed: one that sees the organisation as a system with its own dynamics that are in constant flux and evolution. The psychoanalytic study of individuals as they find themselves in an organisation is only one part. In fact, it is a moot point as to whether or not an individual can be isolated. There is always an individual in connection: a person in a role.

CHANGE Change is ubiquitous. It is imposed by contextual and environmental factors as well as by changes within the organisation or the decision-making of its members. According to Bridges (1991), change occurs and transformations result. It is an understanding of these transformations within the thoughts, emotions, ambitions, anticipations, and behaviours – especially 124

Psychoanalytic and socioanalytic approaches  125 decision-making behaviours – of organisation members, that is of primary interest to psychoanalytically oriented researchers in organisational change research (Amado & Ambrose, 2001; Lawrence, 2005b). Organisations are constantly in flux and transition and people in their roles adapt to changing contexts and internal organisational turbulence. Sometimes organisational leaders wish to instigate change as a deliberate process to understand how the change may be taken up and the effects it has on organisational members. At other times changes occur due to unforeseen circumstances. Nonetheless, despite conscious and rational, or even conscious and somewhat irrational, interest-driven change, its effects are often unexpected due to powerful hidden (unconscious) individual and group dynamics (Hirshhorn, 1988; Krantz, 2001; Huffington et. al., 2004). Change, then, deliberative or in response to external forces, brings about disruption to organisations and their members. Psychoanalytically informed research into these disruptions has a focus on “below the surface” dynamics, emotional and unconscious structural resistances, boundary maintenance, and those processes that can contain or safely hold disruption to allow people to continue to work. Important in these issues is the place of emotion because change arouses anxiety together with the various psychological and social defences engaged against it and other unbearable emotions, both conscious and unconscious and individual and social (Menzies-Lyth, 1988; Krantz, 2001; Hinshelwood & Chiesa, 2002; Armstrong & Rustin, 2014; Barababasz & Nadzw, 2016). Researching an organisation as a whole system involves understanding both structure and culture. How these are approached has been varied. For example, in a detailed debate Amado and Jacques present different approaches to organisational change consultation (Amado, 1995; Jacques, 1995). Sometimes criticised as retroactive and bureaucratic, Jacques stresses the importance of organisational structure and the authority relations therein, claiming that a “requisite organisation” is a natural hierarchy linked to human cognitive capacities (Jacques, 1996, 1998). Amado argues for the overwhelming importance of unconscious socio-cultural dynamics and their effects on people, a perspective he and colleagues develop more fully in describing a transitional approach to change. This approach recognises the importance of an “undercurrent of continuous change” and builds on the work of psychoanalyst and organisational consultant Harold Bridger (2001), requiring a deep understanding of what lies beneath the surface of organisational life and aiding people to reflect on the social dynamics of continuous as well as step-like change (Amado & Ambrose, 2001). Researching organisational change from this perspective takes into account continuous adaptation and the presence of temporary transitional spaces for reflective thinking outside the mainstream of day-to-day work. The former Grubb Institute and its offshoots have a model for use in both research and consulting that combines the importance of both unconscious processes and organisational structures (Long, 2016). This model is described more fully later in this chapter.

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A BROAD RANGE OF APPROACHES WITHIN APPLIED PSYCHOANALYSIS1 Psychoanalysis is a discipline in itself, not a sub-discipline (Long, 2001; Armstrong & French, 2005). As such, it has many different and sometimes opposing theoretical and practical perspectives, including a variety of approaches in its application to organisations. Gabriel and Carr (2001) review such approaches to organisational change research and stress the differences between “studying organisations psychoanalytically, and psychoanalysing organisations” (p. 351). The former, they say, involves using psychoanalytic concepts to understand how organisations make demands upon and affect people emotionally and socially, including their decision-making in their various roles (see, for example, Antonacopoulou & Gabriel, 2001). Many studies in leadership take this approach. The latter approach, they argue, treats the organisation itself as the focus of change requiring interventions that highlight unconscious dynamics and endowing organisations with characteristics that in some ways parallel individual psychic systems. See, for example, understanding organisations as having basic unconscious assumptions (Bion, 1961), organisational personalities (Stapley, 1996) or defence mechanisms (Menzies-Lyth, 1970; Armstrong & Rustin, 2014). Rather than seeing this as a transposition from the personal to the organisational, however, I would argue that such an approach can be understood as a general systemic approach; individuals and persons both being realisations of system dynamics at different levels. More recently the psychoanalytic theories of Jacques Lacan have been adapted to group and organisational analysis (Long, 1991; Arnauld, 2002; de Swarte, 2005; Contu, Driver & Jones, 2010; Boxer, 2020; Western, 2020), Lacan’s work being indebted not only to Freud, but also to a long history of European scholars and philosophers including Hegel and Levi-Strauss, and linguists De Saussure and Jackobsen. From this perspective change and transition is seen as successful only if the structural Lacanian Symbolic Order of signifiers in social relations is engaged; immediate psychological experience being regarded as an effect of symbolic structure. The Lacanian Symbolic Order is seen as providing the basic structure of the psyche. It is structured on the human capacity to form symbols such as language and denotes relations between people such as father and son. Immediate psychological experience comes through the Imaginary order. Relations such as father and son are structured by the Symbolic but experienced in day-to-day life as particular Imaginary fathers and sons. Role relations between manager and subordinate can be thought of in this way. The symbolic relation remains the same even if the particular imaginary experience is different for particular fathers and sons/managers and subordinates. Hence, substantial change is needed in the symbolic order. In organisational change, just working with particular managers and their subordinates (horrible word – maybe “workers” is better) might not do much. Changes are required to the underlying structure of the manager–subordinate relation in the wider organisation, or perhaps in the institution of management. (Note: here psychic structure is differentiated from the earlier-mentioned ideas of Eliot Jacques on organisational structure and should not be confused.) Other influences on psychoanalytic approaches to organisational dynamics have been through the work of American psychoanalysts, such as Hartmann, Kris, Loewenstein, I hesitate to use the term “applied psychoanalysis” because psychoanalysis has many forms in terms of theory, meta-theory and practice. But as it began in the clinic, that situation is considered its base ground and applications to other contexts are considered as “applied”. 1

Psychoanalytic and socioanalytic approaches  127 Rapaport, Sullivan, Kohut and Kernberg (Kernberg, 1997), whose work associated with “ego psychology” and narcissism has been used, amongst other things, to understand the personality characteristics of leaders and their influence on organisational change (Kets de Vries & Engellau, 2008). Narcissism or “self-love” is seen by psychoanalysts as an important part of development in order to enhance self-esteem. However, in not recognising the opinions and rights of others, overly narcissistic leaders, in their absolute certainty that their way is “right”, may, through the neglect of other perspectives, lead the organisation towards disastrous ends (Kets de Vries & Miller, 1985; Shapiro, 2020). The highly publicised cases of Enron, Long Term Capital Management (LTCM) and Opes Prime are examples (Stein, 2003; Long, 2008, 2009). Also associated to psychoanalytic and socioanalytic thinking are methods that work with experiential learning (Gould, Stapley & Stein, 2004) and the fields of group relations (Aram, Baxter & Nutkevitch, 2015) and group analysis (Foulkes, 1986; Hopper, 2003). Group relations theory and methods are strongly influenced by the work of psychoanalyst Wilfred Bion on groups (Bion, 1961, 1970) and methods developed by the Tavistock Institute of Human Relations (Rice, 1965; Miller, 1990). Much of the research in this field is based on the group relations conference model (Rice, 1965; Aram et al., 2018), where a temporary organisation is created by staff and further developed by participants within the conference. This temporary organisation is studied by participants in experiential “here and now” events. Such events show the importance of seeing authority as derived from organisational purpose and task rather than simply from the personal power of leaders. This conceptualisation of leadership is critical for effective organisational change (Shapiro, 2020). Group analysis also began within a therapeutic setting, aiding patients with psychological problems from a group psychoanalytic perspective. The theory of group analysis holds the underlying position that there is no such thing as an individual outside of a group context (Foulkes, 1986). The individual both represents and is represented by the groups that he or she belongs to or identifies with. A single voice may represent something of the whole (Redl, 1942); a single group may represent something of an organisation (Armstrong & French, 2005). In organisational change management this is important lest emotions or thoughts go unrecognised. These may be expressed by a single individual who, if treated as an aberration or data error, may in fact be expressing something for the whole. Group analysis has grown into a body of work exploring unconscious whole group dynamics within a variety of settings such as the oil industry, universities, psychiatric clinics, prisons and government departments (Hopper, 2003, 2012). During the 20th century applied psychoanalysis also entered a broad range of approaches in social and anthropological circles. For example, the works of Norman O. Brown (1959), Slater (1966) and Reiff (1966, 2008) apply psychoanalytic thinking to history, religion and broad societal dynamics – all background to organisational theory and context to change. Links with Marxist thinking are made through the work of the Frankfurt School (Adorno, Frankel-Brunswik, Levinson & Sanford, 1993) and later Zizek (1989). Many other contributions have been made by those specifically interested in group dynamics – too many to include here. While psychoanalysis and its organisational and societal applications form a broad and vast field, many of their approaches to organisational change research are also informed by or engage with other disciplines. Nowadays we are aware that many so-called “wicked problems” cannot be solved or even approached by one or two disciplines or change management

128  Handbook of research methods in organizational change processes alone; that we need new ways of working in a cross-disciplinary manner. Palmer, Dunford and Akin (2008), for instance, examine approaches to change management from multiple perspectives, including appreciative enquiry, depth analysis, contingency, processual, sense-making and dialogic approaches. Psychoanalytic approaches have been successfully linked to some, although in a special edition of the journal Human Relations, Neuman and Hirshhorn (1999) deplore the “limited degree to which those working with psychodynamic theories have managed to also relate to organizational theories and vice versa” (p. 683). Nonetheless, Faucheux, Amado and Laurent (1982) did survey research and practice in organisational development and change, citing contributions from the combined systems psychoanalytic work of the Tavistock Institute as well as the socio-technical approaches of Eric Trist that combine psychoanalytic and systems thinking (see also Pasmore & Khalsa, 1993). The psychoanalytic contribution to systemic thinking brings new insights that neither one of these approaches can bring alone. As exampled more recently, Florent-Treacy (2022) examines psychodynamic approaches to organisational change, arguing that logical step-change models are inadequate because many unconscious processes occur simultaneously in response to change anxiety. Using a combination of psychoanalytic, complexity (Stacey, 2003) and group identity approaches (Humphreys & Brown, 2002), she demonstrates how different informal sub-groups take up alternative responses to change, through their differential positioning towards an ongoing organisational identity narrative. Her research found sub-groups with the orientations that she names as: New Rules (change obligation), Own Rules (change engagement), Known Rules (change resistance) and No Rules (change subjugation). To put it more colloquially, she says: I found four distinct themes: 1) New rules: ‘Just give me the tools and tell me what to do’; 2) Own rules: ‘OK, but I am going to do it MY way’; 3) Known rules: ‘WTF? I am NOT going to change!; 4) No rules: ‘I am completely lost and emotionally confused.’ (p. 28)

In the 1990s Trist and colleagues edited a three-volume anthology outlining the work of the Tavistock Institute and its offshoots (Trist & Murray, 1990a, 1990b; Trist, Emery & Murray, 1997). In this anthology many theories and case studies in organisational development and change are presented, bringing together psychoanalytic, sociological and systems ideas. Arguing the need for bringing socio-psychoanalytic ideas together with other disciplines in the face of the turbulence caused by change, they posit: The socio-ecological approach is linked to the socio-technical because of the critical importance of self-regulating organizations for turbulence reduction. It is further linked to the socio-psychological approach because of the need to reduce stress and prevent regression. (Trist et al., 1997, p. 33)

Unfortunately, in recent years their ground-breaking integration of these fields has waned and each of these fields appears to develop separately and be taught in different unlinked programs with perhaps just a nod to the others. Socio-technical theory lives on in organisational design, future search conferences and self-managing team effectiveness; see, for instance, Muthusamy, Wheeler and Simmons (2005), Mumford (2006) and Lewis University Experts Blog (2021). The socio-psychological approach lives on through management education, group relations studies and organisational systems consultancy based in management, strategy and human social psychological processes (Sievers, 2009). Research methods in these areas

Psychoanalytic and socioanalytic approaches  129 focus on unconscious processes in systems (Clarke & Hoggett, 2009; Cummins & Williams, 2018; Long, 2013; Stamenova & Hinshelwood, 2018).

PSYCHOANALYTIC AND SOCIOANALYTIC RESEARCH: KEY CONCEPTS AND TERMS Not able, in the space available, to review the very broad range of ideas stemming from psychoanalytic perspectives, I will examine here the nature and methods of research into organisational change from psychoanalytically informed socioanalytic perspectives (Bain, 1999; Long, 2017). These approaches could be described as coming under the broad banner of applied psychoanalytic systems and social dynamics, where ideas derived from psychoanalytic theories and practices are combined with systems thinking. They are otherwise described as systems psychodynamic approaches (Gould, Stapley & Stein, 2006) and largely overlap the field of psycho-social approaches (Clarke & Hoggett, 2009; Brunning & Perini, 2010; Cummins & Williams, 2018). They build upon psychoanalytic approaches but extend these beyond the study of persons and their interpersonal relations to the study of groups, organisations and larger social systems as entities in themselves. Much of the work done from this perspective has been informed by the British Object Relations School (Melanie Klein, Wilfred Bion and Donald Winnicott, amongst others) and the relational psychoanalytic work stemming from Fairbairn (Izod, 2017). These approaches build on unconscious intrapsychic and interpersonal dynamics as they occur in groups. For example, they explain how one group might understand another group through projection; at times seeing the “other group” as containing the unwanted negative aspects of their own group and hence understanding the “others” through a hostile lens: a dynamic often found in racism. Another projection might see the “other” group as idealised, as found in the idealisation of celebrities in sport, cinema and the media. Psychoanalytic ideas and applications have changed and diversified substantially since their inception in the 20th century. But this section will examine the core approaches that are relevant to psychoanalytic research methods and that continue through their heritage. Psychoanalytic and socioanalytic approaches range from the study of individuals in organisations – especially the characteristics of individual leaders (Kets de Vries & Miller, 1985) – to the study of larger systems (Sievers, 2009). The focus here is on the latter. In 1985, David Berg and Kenwyn Smith edited a book titled Exploring Clinical Methods for Social Research. What was said there still holds today. Clinical methods are applied and intensively involved with their subject. In that book, Lowman (1985) indicates the qualities of clinical approaches as being applied science, working with unconscious processes, having a systemic and developmental perspective, working on multiple levels and interventionalist approaches such that the researcher uses himself or herself as an instrument (Berg & Smith, 1985; Kets de Vries & Cheak, 2014). I will now outline some of the fundamental concepts shared by such applied psychoanalytic methods.

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UNCONSCIOUS AND UNCONSCIOUS PROCESSES While the idea of unconscious desires and their effects on our behaviours, feelings and thinking is central to psychoanalysis and everyday life (Eisold, 2010), it was an idea well established before Freud (Schelling, 1942, 2006; Whyte, 1979; McGrath, 2012). Although earlier conceptions of the unconscious had emphasised its creative potential – a perspective taken up more fully by Carl Jung – Freud focused on the dynamic or repressed unconscious, being concerned with the way his patients selectively forgot events and thoughts. Such ideas as turning away from truths, fooling the self, denying what at some level one knew to be the case, were already in the air during the 19th century and even evident in the works of dramatists such as Shakespeare. What was startlingly new about psychoanalysis was its method of accessing the unconscious. Finding hypnosis unsatisfactory, Freud developed the method of free association.

FREE ASSOCIATION Freud discovered that if the mind is left to free-wheel through links and associations, there will be times where there are gaps in the process – stumbling points where associations stop and blocked thinking or distanced new lines of thinking occur. These are the points of repression; the points where the unconscious contents of the mind are hidden. It is as if the fabric of associations, like the fabric of time space, has come to a black hole; nothing comes out and the mind is lost and then diverted. Such diversions are understood as defences: psychological mechanisms for warding off unwanted thoughts and their associated emotions. Except with the psychoanalytic method, prohibited thoughts may emerge in everyday actions, dreams, slips of the tongue and memories, and in the expression of curious symptoms that appear initially to have no conscious meaning (Freud, 1901). Free association to such events brings them more clearly to consciousness.

REPRESSED UNCONSCIOUS, RESISTANCE TO CHANGE, AND GROUP MIND The idea of the repressed unconscious has been applied to organisations and the question of why change is resisted in many situations, even when it seems logical and advantageous. In this application, the question must first be posed: can we think then of an organisation as having a mind that itself can employ such defences? Many theories regard a group or organisation as having a mind or something equivalent, at a systemic, collective and associative level (Long & Harney, 2013). For example, in group analysis this is regarded as an underlying matrix or network of the group. When a group of people, by which for our purposes I mean a small number of persons, form intimate relationships, they create a new phenomenon, namely, the total field of mental happenings between them all … The point I wish to stress is that this network is a psychic system as a whole network, and not a superimposed social interaction system in which individual minds interact with each other. This is the value of thinking in terms of a concept which does not confine mind, by definition, to an individual. (Foulkes 1990, p. 224)

Psychoanalytic and socioanalytic approaches  131 Bion refers to a group mentality to which individual members anonymously and unconsciously contribute ideas, and from which emerges group basic assumption dynamics (Bion, 1961; Rioch, 1970). According to Bion (1961), “Group mentality is the unanimous expression of the will of the group … an expression of will to which members contribute anonymously” (p. 50). Another way of understanding this from a psychoanalytic and neuroscientific perspective is that taken-for-granted ideas central to an organisational mentality are developed from past experiences, and not necessarily calibrated to new and changed circumstances. Such ideas get unconsciously linked to immediate behavioural habits, and any thoughts occurring post those habits become rationalisations in support of the behaviour, while organisational members see them as “rational” arguments. A past way of behaving becomes unconsciously lodged into organisational practices and regarded as the only way to act. See, for example, the work of Mark Solms (2017), a psychoanalytic neuroscientist who brings together current neuroscientific research with psychoanalytic theory, and who argues that most cognitive processes are unconscious and habitual, while conscious experience is predominantly emotionally based and driven from sub-cortical brain centres. Taking this understanding into the organisational arena, we might consider that organisation members tend to rely on habitually learned responses that become entrenched in the organisational culture. And many conscious decisions are emotionally driven. Finding a path between unquestioned, unconscious habit and emotional reactivity requires reflective space for clear thinking (Krantz 2013) so that reasoned decisions about change are made. Paul Hoggett, with a background in politics and from the psycho-social and psychoanalytic traditions, picks up the idea of the group mind in the form of an internal establishment in the minds of its members. Drawing on the work of Bion and Meltzer he articulates a process within the group that acts as a “kind of gang or mafia” offering a “protection racket” against fears that it unconsciously creates (Hoggett, 1998). This internal establishment can also be understood as an unconscious establishment in the mind of the organisation, creating, for instance, irrational fears that intensify risks during organisational risk assessment and bring about risk aversion that stymies creativity and innovation. It is as if one part of the mind in the case of a person, or one part of the organisation, unconsciously holds exaggerated fears, holding another part of the mind or organisation “to ransom”. Change resistance can be analysed through exploration of such internal conflicts, their genesis and the part they play in cultural patterns. In a step further, some authors argue that unconscious assumptions are not necessarily just held in what we might think of as a “mind”, with its implications of an interior psychology. Arguably, there is an “exteriority” or what Althusser named a structural cause of systemic effects (Morfino, 2021). Assumptions can become embedded in the way organisations structure their physical environments – office spaces, for example – or their policies, such as pay structures, safety procedures, marketing approaches and work practices. Assumptions get institutionalised into the structure of organisations: professional or technical siloes, departments and work groups, divisions and hierarchical segmentation. Boccara (2013, 2014) argues that unconscious assumptions become lodged in government policies at a country level as a defence against a chosen societal trauma (Volkan, 2001) that draws cultural identifications. It can be argued that organisations have their own historical traumas and glories that shape the way roles are developed and taken up (Chapman & Long, 2009).

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UNCONSCIOUS ISSUES IN ORGANISATIONAL CULTURE How is it then that a group or organisation has unconscious processes? This is a vast subject, but I will rest on the idea that groups develop cultures that unconsciously or implicitly contain mostly unquestioned beliefs, assumptions and practices, built on past experiences, traumas, repetitions, defences and hopes. In order to research the effects of change and how organisations transform, a study of culture is essential. Systems psychodynamic and socioanalytic approaches are based on the premise that issues in organisational change go beyond surface appearances and involve unconscious dynamics in the people, groups, contexts and cultures that affect organisations (Kets de Vries, 1991; Obholzer & Roberts, 1994; Huffington et al., 2004; de Gooijer, 2009; Hinshelwood & Stamenova, 2018). Group culture depends on collective agreements and thinking. Sometimes this is deliberate and conscious, although, as previously argued, often emotionally driven. Sometimes agreements are habitual and out of awareness. Sometimes phenomena such as group think (Janis, 1986) occur when organisation members unconsciously collude to avoid the anxieties around the problems, disagreements and conflicts that some organisational tasks give rise to. For example, there may be collusion emanating from fears of the organisation failing in its purpose such that members have unrealistic beliefs about its powers or the capacities of its leaders who become heroic in their eyes. This leads to ignoring warning signs that would normally lead to appropriate risk management actions (Hirshhorn, 1999; Long, 2019). Collusions may take the form of collective defences. These are known as social defences and depend on collective repression of unwanted and uncomfortable ideas. Socioanalytic approaches propose that several factors contribute to defending organisational members from apprehending and working with the anxieties arising from many of their organisational tasks, truths and desires. The defences become institutionalised and support the individual psychological defences of the people in the organisation. Where this does not occur, the individuals will feel uncomfortable in the organisation and may leave or be extruded so the social defensive pattern becomes deeply embedded in the organisational culture (Menzies-Lyth, 1970, 1988; Armstrong & Rustin, 2014). Many examples of how social defences affect organisational change are in the literature; for example, Piterman (2005), Fraher (2011), Miller (2011) and Humphreys (2014). Stein (2000) argues that the paradigm of social defences against anxiety has overlooked the place of envy in organisational dynamics, and indeed the paradigm can be extended to social defences against many uncomfortable emotions, envy being one. Such social defences illustrate how group cultures have assumptions that are shared by the members in an unthinking way (Bion, 1961). For example, a group may have the assumption that only the leader can make important decisions and others just need unthinkingly to follow. Bion calls this basic assumption dependency. Group cultures build into their processes and protocols ways of thinking and working so that assumptions become institutionalised and rarely questioned until a disruptive force enters; perhaps a new idea, perhaps a whistle-blower, perhaps something in the environment that challenges old ideas and calls forth new ways of working (Miller, 1993). Finding an unconscious mind or its equivalence in organisations will become important when we think of how to access that mind – discussed later in the chapter.

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TRANSFERENCE, PROJECTION, INTROJECTION AND PROJECTIVE IDENTIFICATION The psychoanalytic ideas of transference and the processes of projection, introjection and projective identification are a critical part of the observational stance of negative capability – a stance that holds judgement at bay. Transference refers to a person’s perceiving and understanding a new situation primarily through the lens of past experience. It is as if the person transfers their ideas, feelings and judgements about another person or situation from the past (classically an influential figure such as a father or teacher) onto a new person or situation, with little reference to the differences involved. A small cue may trigger the transference, such as some physical resemblance, a tone of voice, a smell or a gesture. Then, quite unconsciously, the new person or situation is treated as if the original were present. If brief, this transferential experience can be helpful. It enables a hypothesis about what the new person or situation might hold, how we might respond. It helps us to bring forward resources for dealing with the new in the light of our learning from experience. But if it persists in the face of evidence to the contrary – that is, that this situation is not the old one but is new – it defensively holds us back from new learning and more realistic engagements. Transference is based on the capacity to project and introject psychological experiences and objects. These capacities enable empathy so that we might not simply just recognise the concerns of others but feel them ourselves. This is part of what is called emotional intelligence (Goleman 2020) and is the basis of communication without words – an everyday experience. Projective identification is where we take in (called introjection) an emotion or thought from another person who projects that feeling or thought into us. This is all unconscious and the projection occurs not magically but because the person projecting rejects the thought or feeling, and by some or other means, perhaps provocative, gets the other person to feel or think it. A simple example is an angry child who misbehaves in such a manner that their carer or parent becomes angry. The feeling of anger is then shared and if the adult has good enough holding capacities (Winnicott, 1993) and they understand how the anger is being spread, they can find ways to understand the child’s own anger. This phenomenon occurs equally in adult organisational life. Understanding the processes of transference and projective identification is basic to psychoanalytic research methods. With reflective training (Krantz, 2013), and supportive professional supervision, the researcher can use monitoring of themselves as an instrument. Self-as-instrument enables the researcher to formulate working hypotheses about the cognitive, motivational and emotional state of the other, and of groups in the case of organisational cultures. Psychoanalytic concepts are not always intuitively understood because they are dealing with experiences outside of everyday consciousness. But through careful observation and training they can be woven into a design that enables unconscious material and patterns to be discerned.

RESEARCH DESIGN: USE OF THE CASE STUDY Whereas there are many psychoanalytic theories about, for instance, the effects of childhood development, psychological defences against trauma and unwanted thoughts, the import of desires, the strictures of authority, and the detrimental and maturational effects of, for

134  Handbook of research methods in organizational change example, guilt, shame, love and work, it is the internal playing out or working through of these in the uniqueness of the individual that counts for transformation and change. The case study is central to the psychoanalytic model, and it is through extrapolation from many cases that theory can be formed and evaluated. It is within the case study that change is charted. This is as relevant to organisational change research as it is to individual change. A case study examines in detail how an organisation reacts to changes internally and in its context. Rather than just document large-scale outcomes, an organisational case study can pick up the nuances of barriers and enablers to those transformations occurring in response to change. Berg (1990) outlines a range of case study forms including illustrative, anomalous, hypothesis-generating and representational cases, the latter type working towards generalisability by testing theory. Case studies have the advantage of working with an organisation longitudinally, across time, and hence understanding the dynamics of change as they occur rather than relying on memories susceptible to error (Allcorn, Stein & Duncan, 2018). I am particularly interested in anomalous and hypothesis-testing cases because they bring up the surprising and the tangential that challenge our locked-in assumptions that in turn make us blind to previously unexplored dynamics. These are the dynamics that may emerge in new and changed contextual circumstances. The Transforming Experience Framework: The Basis for Approaching Organisational Change Research Case Studies From a socioanalytic perspective, the case study is a primary method. Because changes to organisations occur in unique and multiple sets of circumstances, an in-depth understanding of the particular dynamics at play is most helpful in formulating working hypotheses about such dynamics that might then be observed in other settings. The formulation of working hypotheses is the first step in scientific research. With psychoanalytic and socioanalytic approaches, hypotheses are primarily derived neither by deductive logic from established theory, nor from the inductive logic required in quasi-experimental designs or broad field studies across multiple organisations, although these types of studies may support the development of some aspects of working hypotheses. The socioanalytic working hypothesis is primarily derived through the abductive logic involved in the close observation of particular idiosyncratic phenomena. A case study is an ideal method for such observations (Hauser & Kloesel, 1992). The Transforming Experience Framework (TEF) is presented here to guide a discussion about data collection and analysis. The framework looks at experience through the lens of five domains: (i) the experience of being a person, (ii) the experience of being in a role, (iii) the experience of being in a system (Organisation), (iv) the experience of being in a context – social, economic, environmental, etc., and (v) the experience of source – the meaning and value for the person, the role and the organisation, including spiritual meaning. Data for change processes can be discovered and collected at all levels of experience, and at each level there are conscious and unconscious experiences (Long, 2016). The experience of being a person Personal experience comes from our personal internal processing and our interpersonal relationships in a variety of situations, including workplace experiences. They are deeply influenced by patterns learned in childhood and later development.

Psychoanalytic and socioanalytic approaches  135 The experience of being in a role The experience of being in a role is different from, although connected to, the experience of being a person. It is best to think of “persons in roles” because personal experience flavours the ways in which we take up roles. The role has systemic connections to other roles – we are reminded of the Winnicottian saying “there is no such thing as a baby”, meaning the idea of a baby is in relation to the role of mother and father and does not exist without those roles – even if the parents are not present (Winnicott, 1993). Roles “ask” and authorise us to do things that we may not think of, believe in or have experience of as persons. Moreover, we are likely to be in multiple roles, hence the idea of role conflict where different roles themselves demand different thoughts, feelings and behaviours. We have to deal internally with such conflicting demands. In research on change in a hospital, medical and other staff had to struggle with the demands from their professional roles, demands from their roles in hospital management and personal convictions (Long, Penny, Gold & Harding, 2010). The experience of being in a system and a context The experience of being in a system, such as a hospital, or in a context, such as a particular government-regulated broader health system, brings extended pressures and responsibilities. The organisation (the system) and its contexts have both enabling and limiting effects on experience. Organisations, societies and countries have cultural mores and beliefs that are internalised by their members and affect personal habits, values and behaviours. These are also maintained through legislation, policies and procedures. They reflect the institutionalised assumptions described earlier. In addition, contexts such as the recent Covid pandemic and economic recessions also affect experiences and bring about both adaptive and maladaptive transformations in lifestyles and working conditions. The experience of source The final domain of experience examined in this framework is the experience of connectedness with source. One can call this the spiritual domain, a domain of deepest belief and the generator of meaning. Human beings have a yearning to know more about themselves, their origins and their futures. Research itself is lodged in meaning-making and a quest for understanding. Source may be felt as a God, as nature, as humanity or any process that gives a foundational sense of purpose. Our connection, as a group and hence also as persons to our source(s), is a powerful motivating and centring experience. Humans thus have experiences, channelled through different domains, consciously apprehended and often unconsciously internalised. Researching change and the transformations it engenders is fruitfully done through the multiple lenses offered by this framework. For instance, in research in a hospital dealing with colorectal cancer, we, the researchers, could have seen enablers and barriers to attaining the task of working with new technology as residing in the practitioners and the patients themselves – their skills and attitudes. That is, we may have seen some of the dynamics through the lens of the person domain. But from a more systems psychodynamic lens, we were able to derive working hypotheses about the enabling or disabling emotions engendered towards change and transformation as a result of the way roles were understood and enacted, and the way the whole care system was structured. The lens of context of the broader health system, including the various sub-cultures of different professionals grown in the education of these professionals as well as sustained in the hospital culture, was also considered. For example, surgeons tend to be trained as much through

136  Handbook of research methods in organizational change example and a kind of professional apprenticeship as well as through research findings, while following the very latest research around chemotherapy is primary for oncologists. These different approaches to learning had effects on the way the different disciplines took up learning about organisational changes. Working hypotheses developed through a consideration of each domain named in the TEF aided the participants in the research to improve their organisational practices while implementing the new technology (Long, 2008, pp. 11–12).

PSYCHODYNAMIC COLLABORATIVE ACTION RESEARCH Action research has the intent of bringing about action as a result of investigations carried out. These actions are part of a cycle of research, reflection, action and evaluation, often leading to a second cycle. Collaborative action research, sometimes also known as participative action research, is a research design where researchers join with the research subjects (called participant researchers) in designing and working on a project. The degree of participation by participant researchers may vary from being involved at every level to only being part of an overall governance or steering group. The idea behind this design is a belief that (i) the participants know their organisation more than researchers and are hence able to point the researchers towards important aspects of the research, (ii) they can aid interpretation of results consistent with their culture and purpose, (iii) they are more heavily invested in implementing outcomes and (iv) the process has a democratic intent. Psychoanalytically or socioanalytically informed research often uses this design in organisational settings, building an interpretive stance (Shapiro & Carr, 1993; Shapiro, 2020) with working hypotheses into the cycles described above. Interpretation of results is done collaboratively, understanding that defences against acceptance of the findings may be encountered and that work is needed where different interpretations occur. Much as an analyst and analysand work together with interpretations and working hypotheses on the personality and issues of the analysand, research conducted in this way collaborates on the issues in the organisation. The degree to which the participants may be familiar with research methods and theory differs and careful initial negotiation around what is expected and how the project will proceed is needed. Psychoanalytic and socioanalytic methods are not methods where the researcher stands at a distance from that which is being researched. In generating and collecting data (data creation) there are times when more traditional sociological and anthropological methods are employed – such as interviews and questionnaires, field observations and site visits. Although even with these, the stance is to discover hidden unconscious dynamics. And even when using these methods, as described earlier, the researcher uses self-as-an-instrument in the research process. Understanding by the researcher how they, as researcher, are “used or positioned” conceptually, structurally and emotionally by the research systems, as they enter and progress the research, is critical. Such positioning may be deliberate – where the researchers are given specific directions – or unconscious, where researchers are subject to various projections. To best make use of this, a research team is preferable to a single researcher. This is a case of “it takes an organisation (the research team) to ‘catch’ an organisation”, metaphorically. The idea of parallel process is significant in this regard. A research team may find its own dynamics running in parallel to the organisation dynamics. It is as if they have “caught” the dynamics of the organisation through team members identifying with aspects of the organisation and acting them out in their own team. In one way, this “acting out” is problematic if it is

Psychoanalytic and socioanalytic approaches  137 allowed to extend to the research team’s interactions with the research organisation. However, if caught and “nipped in the bud” it can give otherwise unobtainable information about the organisation (Smith, 1989; Mersky, 2001; Bloom, 2010). A Case Study Using Collaborative Action Research In a project concerned with changes to the roles of many custodial officers in prisons toward case management roles, I was part of a team of three. We each found ourselves with specific emotional reactions to the prisons we visited and felt that our reactions were important indications of the emotional climate for prison staff. One felt quite frightened – almost paranoid; another felt sickened in reaction to many of the crimes committed by inmates (our research involved staff in sex-offender prisons); the third felt a kind of puerile curiosity. In our team, we worked with how and where these emotions manifested themselves in us and how we managed them. As the research progressed, we found each of these emotions present in the research participants alongside their desires to help prisoners and make safe the community. The research included looking at how these emotions were managed by role holders in the exercise of their work and how this affected role changes. We used our understanding of ourselves to begin the process of exploration, which, together with data from our interviews and observations, allowed us to formulate working hypotheses about the role changes in the system. The working hypotheses so derived were then discussed with the research organisation members to further explore their dynamics. Working in this way meant that we were part of an ongoing process of change and transformation, and that we joined with organisation members to discover the dynamics and processes under scrutiny. This is what is meant by collaborative action research (see also Long et al., 1997, for a description of another such project).

METHODS OF DATA CREATION AND COLLECTION: ACCESSING UNCONSCIOUS PROCESS DATA Given the importance of unconscious processes, how might these be accessed in organisational change research? In such research it is important to find the hidden assumptions in the organisational system studied. Free Association Access to unconscious assumptions and processes comes through associative processes. In psychoanalysis this is through the method of free association – whatever comes to mind until the free flow of associations is blocked or until a pattern shows itself. This free association method is extended in social contexts. Such associations can draw not simply on language and symbolic understanding but also on iconic and indexical modes of making meaning, arriving through sensation (Hauser & Kloesel, 1992). Iconic meaning is most often exemplified by icons found in art. That is, in pictorial or sculptural depictions of meaning. Emo’s in contemporary texting and road signs are icons. Indexical signs of meaning occur where meaning arises directly from a perceived occurrence. For instance, “smoke” means “fire”. I have argued elsewhere (Long & Harney, 2013) for the idea of an associative unconscious where all the signs, symbols and meanings potentially available to an interacting group

138  Handbook of research methods in organizational change or society are located within and between members of that group (or society). They are in potential because of the structure and processes of language and thought that could potentially give rise to them. The whole of this system of potential thoughts is not available to any one member, but becomes available as the members interact, communicate and share. Access to this pool of meaning comes as a culture develops and meanings are shared. Of course, the meanings and ideas that make up the associative unconscious of any interacting community may be myths, or even deemed falsehoods; the issue here is not their factuality, but that they influence organisational decisions and behaviours. Access is often blocked due to cultural prohibitions, social defences, human physical limitations and the absence of new technologies that give rise to new ideas and associations – who could think of in vitro fertilisation until the technology was available? In organisational change research, the purpose is to discover the transitions taking place internally (and through associative processes) in people, groups, structures and cultures, as well as discovering the blocks and enablers to desired transitions. We can begin to access the repressed and associative unconscious of an organisation through communicative associations and links made to its various sub-cultures and structures. This is done through a variety of socioanalytic methods (Long, 2013) such as in-depth interviewing, where transferences and other projective or introjective processes are noted alongside more conscious themes and patterns (Long & Harding, 2016; Long, 2019). Another popular method for accessing unconscious processes is the use of drawings (Gould, 1987; Mersky, 2013; Nossal, 2016). These are used in role analyses and dream matrices (Hutton, Bazalgette & Reed, 1997; Lawrence, 2005a; Newton, Long & Sievers, 2006; Newton, 2013; Long and Manley, 2019), again where themes and patterns are discerned as pathways to hidden assumptions and unthought habits. Traditionally seen as projective methods, these are more recently understood as collaborative associative techniques where data is created in the researcher/ researched dyad – recognising that psychological and social data is never simply “out there” to be “gathered” but is always formed in a context. Organisational stories are an example of this. Stories that circulate through organisations are an indicator of how members make sense of their experience in the organisation and its history and anticipated future. An analysis of stories, in terms of themes, can bring to light hidden dynamics. But stories are always told in a context and the socioanalytic researcher must provide a safe context for these to be told, recognising that stories of organisational change emanate from different perspectives, given the different roles that storytellers hold. Gabriel’s work on organisational stories illustrates how stories told and retold in organisations, even when fictional, uncover how people feel, and this can throw light on why they resist change (Gabriel, 1991). Tarim (2012) investigates how narratives generated by fund managers working in the Istanbul Stock Exchange helped reduce investor uncertainties during the global financial crisis of 2008–9. Allcorn and Stein (2016) look at the ways in which stories often contain “stories before the story” where the expectations of the players in the story are influenced by a previous story circulating in the organisation. Stories travel and are reinterpreted as this occurs. Allcorn and Stein say, “methodologically the study of the relationship between the storyteller and the story listener offers a potential treasure trove for understanding the experience of a workplace” (p. 36). As indicated earlier in the discussion about transference, the role of the researcher is critical in psychoanalytic and socioanalytic approaches to organisational change research. In brief summary, the researcher is the fundamental instrument (Smith & Berg, 1985). The researcher must design his or her interactions with the organisation such that unconscious processes may

Psychoanalytic and socioanalytic approaches  139 be discerned alongside consciously apprehended responses, thoughts, emotions, themes and patterns of behaviour. And the researcher must be trained in observations and interventions that include careful attention to what is psychologically aroused in himself or herself that is “in” them but not “of” them (D. Armstrong, personal communication, 2013). This is the essence of transference analysis. It allows the formulation of working hypotheses about the nature of the organisational transitions taking place in response to change. These hypotheses might then be verified by a range of methods including more traditional methods such as questionnaires or further observations. Observation Using a Psychoanalytic Lens How can we observe such phenomena as unconscious processes and collusive group cultures? These approaches stress that knowledge comes through observation that includes the observation of the experience of the observer (Willshire, 1999; Hinshelwood & Skogstad, 2000; Skogstad, 2004). Based on the psychoanalytic ideas of projection, identification and projective identification (Sandler, 1987; Grotstein, 2005), the observer is regarded as part of the system being studied and hence the thoughts and emotions of the observer arise as much from the system as from the individual characteristics of the observer. These formulations emphasise the nature of subjective experience and the cultivation of a particular state of mind in the observer. While observation is fundamental to all research methods, it is the stance and state of mind required for observation that is important here. The stance of psychoanalytic and socioanalytic observation has a focus on three factors: (a)

Direct observation of a person, group or situation – what is done, what is said and how this occurs. Yet, often important is what is not said or not done, given the circumstances surrounding the observation; that is, it includes that which surprises the observer, is “out of the normal” run of expectation; (b) the emotional climate of the situations observed and how this develops over time; and (c) observations made by the observer of their own reactions, emotions and thoughts before, during and post the observation. This includes what is surprising. Reflections can then be had as to whether or not the “surprising fact” belongs more to the situation or to the limitations of the observer. This is referred to as self-as-instrument, as discussed earlier (Smith & Berg, 1985), and requires personal work by the observer in understanding their own valencies and unconscious tendencies (Luborsky, 1998). Psychoanalysts have long used the idea of surprising “out of the blue” ideas that emerge from parapraxes, free association and psychological symptoms, seeing them as messages from the unconscious. The technique developed is to be open to such surprises by cultivating a state of mind that can observe and receive them. Freud talks of free floating attention, Bion of being without memory, desire or an irritable search for meaning and Winnicott of reverie. These are unfocused states of mind, yet still with heightened peripheral close observation. This may seem to be contradictory, yet they are states of mind akin to some forms of dispersed attention or meditation. The poet Keats, quoted by Bion, named this state of mind as being in “negative capability” (Simpson & French, 2006). Being available to observing the messages of the unconscious is a critical part of psychoanalytic observation. For socioanalysis, this includes an

140  Handbook of research methods in organizational change awareness of the social system, including its politics, structures and dynamics as they emerge consciously and unconsciously. The idea of the surprising fact also comes from the work of Charles Peirce (Hauser & Kloesel, 1992). He claims that noticing surprising facts heralds the first step in scientific enquiry and the process of abductive logic. Long and Harney (2013) argue that abductive logic (Hauser & Kloesel, 1992) lies at the basis of psychoanalytic and socioanalytic hypothesis formation. This logic, in contradistinction to deductive and inductive logics that presuppose already formed propositions, is based on associative thinking linked to surprising observations.

METHODS OF DATA ANALYSIS Finally, how can researchers using psychoanalytic and socioanalytic methods approach an analysis of their results? This is an area where some serious rethinking is required because the general research context – its funding, general reporting and dissemination of outcomes does not always fit the traditional framework of scientific research. I will diverge here briefly to look at the place of this form of research in the broad context of the philosophy of science. In this I intend to look at ideas of causality, scientific method and scientific ways of knowing, and how these can be understood in psychoanalytic and socioanalytic approaches. How Do We Think about Causes? First, we can identify some major forms of causal thinking. They are: (i) material causes: what something is made of – for example, this body is made of several biological organs, muscles, bones and blood organised into systems; (ii) efficient causes: how something occurs across time (simple cause and effect) – for example, this window is broken because a ball was thrown into it; (iii) formal causes: how form creates structure and dynamics – for example, the shape of this leaf determines how it takes up or expels water, or the building plans for this house determine its shape; and (iv) final causes: how anticipations of the future call things into being – for example, how my desire or need for an object or service leads to a company providing it. Material and efficient causation are primarily used in the physical and biological sciences. However, systems sciences with influence on the physical and biological sciences have come to understand formal causes as important. Final causation is often deemed “teleological” and unscientific, although this is changing with the concept of anticipatory systems in biology and mathematics (Rosen, 2012). Psychoanalysis and socioanalysis tend to focus as much on the latter two forms of causality as the former. While the former two may be invoked to help examine how past events lead to current thoughts, feelings and actions (efficient causation through trauma, for example) or even how physical conditions may also play a role (material causation through biochemistry), a primary exploration is through how internalised assumptions and patterns of thought, emotion and behaviour unconsciously influence present personal dynamics (formal causation), and, importantly, how desire also does this (final causation). In fact, anticipatory desire is the most important causal idea in psychoanalysis. The wish is an anticipation that draws us forward and colours perceptions, cognitions, memories and actions. The past, or more specifically its place in memory and pattern formation, is interwoven with anticipatory desire to unconsciously form those habits, assumptions, beliefs, attitudes and behavioural patterns that constantly guide us. If we focus solely or even predominantly on

Psychoanalytic and socioanalytic approaches  141 efficient causation, especially between atomised components of an organisation, we miss the broader systemic picture of change and the ways in which human wishes and desires shape its progress. The Process of Scientific Research and Scientific Ways of Knowing? Charles Pierce identifies phases of scientific research. The first, he argues, is that of hypothesis creation via abductive logic or reasoning. This, he claims, is the creative part of scientific enquiry; the jump from close observation to a causal hypothesis (Hauser & Kloesel, 1992). This is the kind of jump made by Fleming in the discovery of penicillin, or Freud in the discovery of the Oedipus complex. Abductive reasoning argues: A surprising fact (C) is observed. But if H were true, C would be a matter of course (not surprising). So, hypothetically H is true. In terms of psychoanalytic or socioanalytic thinking, this is a working hypothesis (Long & Harney, 2013). The hypothesis H might then be tested using various traditional scientific methods, but these are often difficult to execute, given the dynamic nature of psychoanalytic and socioanalytic processes. So, a working hypothesis might also acquire verification through, for example, methods that engage free association and interpretation (Long, 2013; Hinshelwood & Stamenova, 2018). This then may lead to theory formation (Swedberg, 2021) and hypotheses may be further derived from established theories. That is a process of deduction by formal logic, not hypothesis formation through abduction. In an action research project with a hospital, a surprising fact was observed. Child patients were required to return to the hospital for clinical visits on a variety of occasions on different days. This occurred even though many had to travel with a parent over long distances, perhaps leaving other family members and necessary domestic activities at home for several days because of the travel. While this was known and noted as inconvenient by clinical and other staff, nothing seemed to be organised to prevent the inconvenience. The acceptance of the practices surrounding this struck the researchers as surprising. In the department it was accepted as an unavoidable inconvenience despite attempts in the past to change the situation and was, in fact, quite disruptive to the whole clinical diagnostic and treatment process. Having completed a cultural analysis, we, the researchers, formed the hypothesis that “if the various siloes of the different professions – cardiologists, radiologists, secretaries and administrative staff – were enabled space and time to work together to sort out issues democratically across their disciplines, a solution would be found”. On establishing a forum using Open Space Technology (Owen, 2008) a solution was achieved in two sessions by a sub-group of cardiologists, radiologists and administrative staff members. Appointment times were able to be co-ordinated across the disciplines. The resistances to change were deeply rooted in disciplinary identifications and splits, requiring group work with unconscious dynamics and assumptions. On hindsight our hypothesis seemed so simple, the surprising fact so obvious. The essential point I wish to make here is that the beginning of scientific research occurs in close observation so that amidst a continuous rigour of observation, surprising facts, even

142  Handbook of research methods in organizational change simple surprises, may be discovered. Psychoanalysts Freud and Bion both stressed the need for a state of mind that would allow for the surprising fact rather than a reproduction of habitually expected observations. As I stated earlier, it is a state of mind of “free floating attention” or being “without memory, desire or an irritable search for meaning”, called by the poet Keats “negative capability”. Psychoanalytic and socioanalytic organisational change research uses abductive logic to develop “working hypotheses” that can then be considered by an organisation within the many other considerations it has for making changes, such as within financial, environmental and political constraints. Further to such working hypotheses, more traditional research may be done in conjunction with socioanalytic methods to validate or further explore them, remembering the fluidity of systemic transition over time. To come back to my question: how can researchers using psychoanalytic and socioanalytic methods approach an analysis of their results? This chapter does not deal in depth with analysis of results, which would take much more space and time. Here I will summarise. I have argued for the design of case studies and the methods of free association and close observation. These all imply a subjective and idiographic approach to research (Conner, Tennen, Fleeson & Feldman Barrett, 2009). Ideographic methods and analyses argue for close study of individual cases because each is individually contextualised and hence unique. Psychoanalysis is, anyway, an examination of the dynamics of subjectivity (Long, 2001). This does not mean, however, that results cannot be generalised. It just means the basis of that generalisation is not through statistical averaging across groups or individuals, but by building detailed pictures of individuals, groups or, in our case here, the organisation in change, for comparisons. I will give an analogy. Imagine that the organisation is a huge jigsaw puzzle. I cannot know the whole picture by looking at the individual pieces. They are all different in shape, size and colour. I cannot “average” their qualities. Only by understanding how they interact with one another can I start to piece them together to find something of the whole. Even then, different pieces may interact with the broader context differentially such that the edges of the puzzle are only understood within that context. So, analysis occurs with understanding a picture of a whole system or domain of organisations, within a broader culture over time, or even a global context. Once I have the picture of an organisation within its context, I can begin to say something about its complex dynamics over time and can begin to make some sense of it through the theoretical lenses that have been built up through the examination of many organisational case studies. Analysis, then, involves detailed descriptions of the dynamics of change and transition within all aspects of the TEF described earlier; i.e., persons, roles, systems, contexts and source. These can be understood in terms of themes (i.e., a cross-sectional analysis), narratives (a longitudinal analysis) and/or systemic maps of the dynamics as they change over time. They also require analysis in terms of concepts, for example, such as unconscious systemic defences against anxiety and other unbearable emotions described earlier (Armstrong & Rustin, 2014); institutionalised structures formed through political ideology, power dynamics and defence systems (Zizek, 1989; Boccara, 2013); and semiotic differences between cultures and sub-cultures, all being manifestations of unconscious processes and assumptions within a group or an organisation-wide associative unconscious.

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THE ETHICS OF EXPLORING UNCONSCIOUS DYNAMICS Before I conclude this chapter, it is important to review the ethics of exploring unconscious dynamics. While there are similarities with ethical issues in all human research, some issues are unique. 1. How does the psychoanalytic/socioanalytic researcher explore structure and dynamics (and they are closely linked) such that no harm is done to individuals and the broader system or context? Usual ethical procedures such as plain statements about the research, gaining individual consent and recognising vulnerable groups are focused on the person. What of the organisation itself? Many research projects are designed to bring forward the flaws in organisational life and, hopefully, how these might be overcome. Sometimes outcomes may well emerge such that an organisation is so transformed that it can no longer exist. Take, for example, those that serve purposes that have become outdated – say, transporting coal in barges – or those with the purpose of producing what are now seen to be damaging products or services, such as cigarettes or asbestos. In the former example, transition to new purposes may be possible. Can we look, for instance, at new forms of energy production? In the latter, what responsibility does the researcher hold? Is it just a matter of personal conscience? 2. What do we mean by “harm”? It is basic to psychoanalytic/socioanalytic practice that emotional pain will be part of an overall transformative process: sorrow is an ongoing part of loss, and organisations suffer many losses of personnel, capacity, resources, structures and processes throughout their lives. Other emotions, such as fear, anxiety, frustration and shame as well as joy, elation and satisfaction, are also inevitable, even though collectively in organisations we attempt to minimise these, sometimes for the better, sometimes the worse. So how might a researcher know if they are bringing forward what is inevitably there, with the idea that such an exposure will, if handled in a contained fashion, be available for serious working through and eventually be creatively helpful? Or whether the research itself promotes pain that is in fact harmful? Addressing these questions requires serious training, education and professional supervision so that researchers recognise the signs of emotional pain and understand ways of containing and working through it, at the level of systems as well as individual persons. For example, employee assistance programs dealing with personal stress are useful at times, but if the stress and subsequent pain is due to systemic structural or cultural issues, such programs are not targeting the right source. Might the pain be due to bad work practices, poor leadership or destructive assumptions? Might the pain be experienced as emotional, physical, interpersonal or through transference or projection into home or other systems? In my experience, our usual ways of considering research ethics – primarily based on a medical research model, because that was where the thinking about research ethics became prominent last century – is a small, sometimes even distracting part of the issue. In this thinking, harm is mitigated by consent. But what of the capacity of the researcher to recognise and deal with the inevitable emotional distress of discovering unwanted truths? Perhaps even joyful and transforming truths. They too may come at a cost.

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CONCLUSION Psychoanalytically informed research, together with a systems approach, understands organisational change research as an ideographic method where the organisation is deeply understood in its uniqueness as it moves through its history into its anticipations. This uniqueness is a combination of contextual conditions, internal systemic processes, structural conditions including the way roles and tasks are organised, and the combination of personalities of organisational members and stakeholders, all linked to organisational purpose. Given the importance of unconscious dynamics and defensive processes developed in the system and enacted by individuals and groups, any organisational change process can only be understood through access to such dynamics. This involves more than a simple metric of observed behaviours, but a sensitive capture of thought and emotion by the observers as instruments.

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7. Qualimetric intervention-research as an approach to studying organizational change Henri Savall, Véronique Zardet, Marc Bonnet, and Anthony F. Buono

INTRODUCTION The continued development of research in the field of organizational change is a major need in the 21st century because of the rapid changes that companies and organizations are facing. The environment that surrounds organizations has become increasingly uncertain and volatile, creating growing risks for organizations that have difficulties anticipating and responding to these changes, preventing them from becoming sufficiently proactive and agile. Advances in artificial intelligence, for example, can quickly render obsolete significant aspects of the operations performed by humans within organizations, while new activities must be developed to meet the immense needs of humanity and its natural environment. Globalization has also created myriad economic and cultural challenges, bringing new competitive advantages to different nations juxtaposed with growing demands to preserve jobs and identities, with populations seeking to choose their destinies. Faced with these challenges, the management sciences have an immense responsibility to conceptualize and guide collective action. Organizations and their management require innovative methods of change management to meet the societal challenges facing humankind. These challenges, which are notably spelled out in the United Nations Global Compact’s Sustainable Development Goals, include nutrition, education, climate change and respect for the natural environment, a harmonious and peaceful approach to diversity and equality, health, and energy, among others. There is thus a growing social demand for the management sciences to take into account their organizational and societal impacts, seen as an indispensable complement to other social sciences such as economics, political science, psychology, and organizational sociology. It is clear that research on organizational change should no longer be limited to a description of the difficulties encountered by organizations and their operations. Such research should also include a much broader orientation, focusing on solutions to help organizations manage and guide change in order to avoid the individual and collective suffering created by haphazard approaches to organizational change. As a way of conceptualizing these challenges, research in the field of organizational change can be compared to research in the other action sciences such as medical research. Indeed, the medical sciences as an action science are not limited to a description of pathologies and their causes. There is a clear imperative to improve health, developing care protocols and relying on other sciences such as biology, epidemiology, robotics, and psychology as needed. Similarly, in management there is a need to support organizations to ensure their health, survival, and development in the face of rapidly changing markets, technologies, and social and environmental realities. As in the medical sciences, it is important that research on organizational change draws on related sciences, such as organizational psychology and sociology, econom150

Qualimetric intervention-research as an approach  151 ics, and statistics. Although prescriptive recommendations are not typically present in many social science works, including management science research, the social demand for engaged research has become more and more pressing over the last few decades. This growing pressure for positive impact and outcome is shaped by the accelerating pace of change in the business environment. The management sciences must be prepared to demonstrate their relevance by making their managerial and societal impacts explicit, especially with regard to research in the field of organizational change. This type of engaged research has been at the origin of the development of qualimetric research-intervention. This approach proposes a specific research method to guide decisions and collective action in the context of the complex phenomena at work in organizational change processes.

ORIGIN OF THE QUALIMETRIC INTERVENTION-RESEARCH METHOD IN THE FIELD OF MANAGEMENT SCIENCE The qualimetric approach was developed in 1973 by Henri Savall. His objective was to move beyond the limits of action research and socio-technical approaches. Qualimetrics has been tested and perfected for nearly 50 years through in-depth experiments in change management by the ISEOR (Institute of Socio-Economics of Enterprises and Organizations) research center, a hands-on learning laboratory in Ecully, France. ISEOR’s experiments have been the subject of rigorous scientific observation in more than 2,000 companies and organizations in all sectors and sizes in 47 countries, and they highlight the core phenomena relating to organizational change that exist across the great diversity of situations and cultures specific to each case (Savall & Zardet, 2021a, 2021b).

GENERAL PRINCIPLES OF QUALIMETRIC INTERVENTION-RESEARCH In defining the concept of qualimetric intervention-research, it is useful to differentiate the approach from action research and other hybrid or mixed qualitative-quantitative research methods. The term “intervention-research” was chosen in contrast to “action research” because of its similarity to medical research, where the aim is not only to explain pathologies, but to also intervene in order to progressively develop care protocols. Of course, intervention-research shares similarities with other action research methods in that it is research in action rather than on action, and that the research is conducted with the actors of change and not on the actors. The term “intervention-research” also reflects the underlying goal that the research is intended to prescribe tools and methods for leading change. As in medical research, the aim of intervention-research is to carry out diagnoses, prescriptions, treatment, and evaluation of effects. Intervention-research consists of describing, explaining, and predicting. Both types of research have a common scientific approach, focusing on (1) the human body, respecting its integrity according to the precept of primum non-nocere; and (2) the social body, whose integrity is to be preserved or constructed. In medicine, the path has been long and discontinuous: it went from Hippocrates, in antiquity, to medical research carried out in partnership with university medical centers. In spite of Averroes’ plea in the 12th century in favor of experi-

152  Handbook of research methods in organizational change mental research in medicine, a parenthesis of obscurantism occurred between Hippocrates and the 19th century, notably with Louis Pasteur (1822–1895) and Claude Bernard (1865–1947), a long period during which medicine was taught through the exegesis of texts. As in medicine, experimentation and intervention-research in the field of organizational change is justified by concerns to alleviate the suffering of stakeholders in companies and organizations. Of course, the management sciences and research methods in organizational change are more recent than medical science (Savall & Fière, 2014) and the degree of maturity is not the same. Thus, there is an urgent need to further develop the field of research methods related to organizational change, as advocated by several presidents of the Academy of Management (Van de Ven & Johnson, 2006; Rousseau, 2012) who coined the concepts of engaged management scholarship and evidence-based management research.

THE CONCEPT OF QUALIMETRIC INTERVENTION-RESEARCH The concept of qualimetrics refers to the integration of qualitative, quantitative, and financial data in change management. Indeed, the observation of decisions in organizational life shows that there is always a need to link words (keywords), numbers (key figures), and financial data (numbers in monetary units). Think about how these factors typically emerge in an organizational change decision. As an example, “We are going improve our customer relationships through the implementation of a new customer software project that will require 5,000 hours of work and training with a goal of a financial return of 35 percent per year over 5 years.” Such words do not make sense if they are not associated with numbers, and the numbers do not make sense if they are not associated with decisions and outcomes (Savall & Zardet, 2003). In the management sciences, it is therefore necessary from an epistemological point of view not to be satisfied with a mere juxtaposition of qualitative, quantitative, and financial information, as is the case in hybrid or mixed approaches, but to integrate them into a coherent whole. The qualimetrics model thus designates a set of variables and links described with qualitative, quantitative, and financial measurement indicators. This approach differs from other management research methodologies that dissociate qualitative, quantitative, and financial data or limit themselves to a juxtaposition of these data (mixed or hybrid methods). Their objective is more to describe situations than to build collective actions in ways that are preferred by the actors. The application of qualimetric intervention-research to organizational change is similar to the medical sciences where a wide variety of physiological and psychological variables must be taken into account – organizational change research is multi-dimensional. It involves strategy, finance and management control, marketing, logistics, and human resource management. Scientific work in the field of organizational change requires identifying the structural elements of knowledge about change and the links between these elements. Scientific rigor thus calls for linking the various sub-disciplines of management, which are typically organized in silos, as well as linking the various dimensions of change. To develop a nuanced understanding of this approach, it is necessary to specify its scientific foundations by successively examining its ontological, epistemological, and methodological aspects.

Qualimetric intervention-research as an approach  153

THE ONTOLOGICAL DIMENSION: HUMAN POTENTIAL AS FACTOR OF COMPLEXITY IN ORGANIZATIONAL CHANGE PHENOMENA As a rich and extensive literature indicates, research on organizational change involves technological dimensions, organizational structural considerations, and human behavior. Unlike management theories that have been implicitly inspired by neo-classical economic theories, the qualimetric approach views people as potential rather than resources or human capital. People can influence or oppose change processes according to their background and objectives. Given the limited rationalities of each actor involved in change processes, the variables that must be taken into account are thus multiple and complex, calling for adapted scientific observation approaches. In particular, the people involved in an organizational change share five basic characteristics: (1) they have their own intelligence, (2) they pursue their own strategy, (3) they behave as comedians, (4) they have disobedient tendencies, and (5) they can be amnesiac with respect to their commitments. In particular, the first factor consists of assuming that each person in a company or organization is endowed with a significant amount of intelligence; i.e., a potential capacity to understand their immediate environment in order to find ways to survive and achieve their goals, regardless of education, hierarchical level, or managerial responsibilities. Consequently, research methods on organizational change require taking into account the intelligence of each actor who has access to the variables of organizational change. All actors have acting skills that encourage them to adapt their behavior to the situations and people with whom they interact. Indeed, a useful metaphor in change management is to consider every work situation as a play: there are intentions, conflicts, and possible resolutions (Boje & Rosile, 2003b). Organizational change research methods must therefore carefully consider the statements of change actors and submit these data to a process of quality improvement and sincerity. Each individual can be considered as a strategist, endowed with one or more mobilizing objectives and following a certain path to reach them, doing so with the resources at their disposal or those they acquire. As a result, research methods in organizational change must take into account the impact of each actor’s strategies on the overall change process. Everyone has a natural capacity and propensity for disobedience; i.e., each company actor spontaneously finds it difficult to comply with instructions or orders from others in the context of organizational change. It results in a comedian-type of behavior consisting of skewing organizational change instructions while pretending obedience. Finally, everyone experiences a certain level of amnesia, in essence, reflecting a limited capacity to remember all details of the complex variables of organizational change and our natural propensity to forget some of them. Therefore, research on organizational change should not be limited to what is declared by a particular actor at a given moment but should keep track of the information and its evolution during the change process. These five natural characteristics (1 – Intelligent, 2 – Strategist, 3 – Comedian, 4 – Disobedient, 5 – Amnesiac) must be taken into account in organizational change research methods to obtain robust and relatively sustainable results. Considering only technological change or financial investments in the context of organizational change research is not enough. A consequence of this observation concerning the nature of the actors in organizations is that the object of organizational change research must be able to peer into the black box of organizational life that economic research has failed to depict. An underlying reality is that

154  Handbook of research methods in organizational change the human factor is the central object of organizational change research, the active factor in the creation of added value. The quantities of labor, financial capital, and technology are only secondary and passive factors, a reality that helps to explain why changes such as automation processes or company mergers do not usually bring the profitability expected by financial calculations (Savall, Péron, Zardet, & Bonnet, 2015). Within the qualimetric intervention-research framework, organizational change is thus viewed as a complex, unstable, and intangible object over time. By “intangible,” we mean that only those actors who are involved in the field can observe organizational change phenomena at work and testify to their existence. The human factor includes a multitude of behaviors that explain the functioning and dysfunctions of organizations in permanent change. In the qualimetric approach, the structures analyzed include variables related to the organization’s physical facilities, the technologies used, the characteristics of the work organization, and the demographic characteristics of the teams and their mental structures. The behaviors to be taken into account are based on individual and group activity, as well as affinity group logics (e.g., affinity groups, pressure groups, collective logics). Such an interactive and complex system of variables accounts for the development of dysfunctions and hidden costs that hinder the efficiency of organizational change, as shown in Figure 7.1.

Source: Savall & Zardet (1987).

Figure 7.1

The four-leaf clover, representing the complex system at the origin of dysfunctions and hidden costs in organizational change processes

Qualimetric intervention-research as an approach  155 Rigor and relevance of research in the field of organizational change require considering organizational change as a living, multi-faceted complex system rather than a mechanism that can be reduced to a limited number of mechanical variables. The results of research on organizational change thus depend on the researcher’s view of the object being studied. This view can be relatively superficial, consisting of a skimming of the object without wanting to affect it, as in the case of surveys carried out at a distance or undertaking a few qualitative interviews. In this case, the researcher’s contemplative approach is privileged, but it cannot then take into account the complexity of the object of research, which calls for a greater quality of scientific observation. As in the medical sciences, it is thus necessary to deepen the knowledge of the material or intangible object by penetrating and transforming it, under the control of scientific ethics. The properties of the object are then progressively revealed based on the intervention of the researcher. Qualimetric intervention-research differs from positivist research on organizational change, which postulates the neutrality of the researcher as a synonym for being scientific. Qualimetric intervention-research uses the same lens as the life and natural sciences. It deliberately transforms certain characteristics of the object studied in order to better reveal its complex properties, with the aim of improving the overall performance of the organization and the response to the expectations of its stakeholders. In essence, the life and natural sciences practice the equivalent of intervention-research (Savall & Fière, 2014). Thus, research in both natural and management science can be viewed through a similar lens.

THE EPISTEMOLOGICAL APPROACH: GENERIC CONSTRUCTIVISM Given the complexity of the subject matter linked to the variability of human behavior, it is necessary to pay close attention to the quality of scientific observation. As found in the epistemological approach to ethnostatistics (Gephart, 2006), it is necessary to be critical of the quality of data collection, data processing, and data interpretation. In particular, one must be aware of the subjectivity of the answers to the questionnaires used in quantitative surveys as well as of statements made by actors in qualitative analyses. In order to improve the quality of scientific observation in the field of organizational change, the qualimetric approach (Savall & Zardet, 2003) is based on three epistemological pillars: cognitive interactivity, contradictory intersubjectivity, and generic contingency.

COGNITIVE INTERACTIVITY Taking complexity into account in understanding change requires the close interaction between researchers and actors in the field. This “cognitive interactivity” principle allows for the creation of knowledge about the object, taking into account variables that are not visible through qualitative or quantitative observation methods that are external to the research object. Knowledge must thus be elaborated and co-constructed, between researchers and practitioners. In comparison to medical research, cognitive interactivity means that by making the patient talk, the hospital doctor (like an intervener-researcher) captures important information as part of the diagnosis. It is key to identify the pathology and then to evaluate the effects of the treatment. Relevant data about the detection of certain symptoms can only be obtained with the

156  Handbook of research methods in organizational change expression and collaboration of the patient and of those who directly observe that individual (e.g., nurses and assistant nurses).

CONTRADICTORY INTERSUBJECTIVITY The qualimetric approach also rejects the possibility of perfect objectivity in the field of organizational change. The concept of “contradictory intersubjectivity” underscores that the points of view of the actors differ according to their role and the issues at stake, and they evolve over time, even within the same day. It is therefore necessary to implement research systems that allow for a partial convergence of representations in order to enable action. In this sense, the qualimetric approach rejects positivist approaches, which assume the stability of variables. It also differentiates from the constructivist approach that rejects the possibility of a common representation of the object. In the qualimetric intervention-research approach, contradictory intersubjectivity replaces the impossible neutrality in research, whereby the subjectivities and differences between actors and between researchers are recognized. Such differences are made explicit and used to construct relevant local generic knowledge. Continuing the comparison with medical research, the principle of contradictory intersubjectivity expresses the fact that it is essential to obtain information from the different points of view of actor-witnesses, situated in the field of the object studied (e.g., patients feel certain sensations, while other persons around them may observe other symptoms). Gathering the variety of information is therefore needed to establish a diagnosis of the pathology and the health conditions of the patient. At the treatment stage of the disease, the same phenomenon is observed: patients must follow the treatment and observe its effects. Without the account of the patient and their entourage, scientific knowledge cannot be built, because it is highly dependent on what the patient agrees to do (e.g., do the patients actively participate in the treatment?). In medical research protocols, patient behaviors are rigorously monitored in order to interpret the relevant information and to describe the evolution of the pathology and the effectiveness of the treatment. Qualimetric intervention-research assumes that the same rigor of scientific observation should be applied to organizational change intervention.

GENERIC CONTINGENCY Finally, each case of organizational change is both contextualized and generic. Some aspects of the organizational learning process are specific to the company and they are not directly transferable to other cases. However, the accumulation of cases makes it possible to highlight invariants relating to the quality of organizational change management. Discovering those invariants contributes to the development of our body of knowledge. In the case of qualimetric intervention-research, this body of information has been structured over the decades, forming the Socio-Economic Approach to Management (SEAM), a robust scientific method for managing organizational change that is in constant development (Buono & Savall, 2015). Therefore, the concept of “generic contingency” recognizes the specific character of each case, as well as the existence of generic elements in each. Thus, a “generic construction” makes explicit both the lineage of a constructivist epistemology inspired by Piaget (1975, 1988) and its closeness to critical realism (Bhaskar et al., 1998). Qualimetric intervention-research applies

Qualimetric intervention-research as an approach  157 to the acquisition of collectively shared knowledge and seeks replicable generic knowledge on organizational change phenomena: cumulative intervention-research allows for the consolidation of generic results through successive iterations of validation. “Generic contingency” thus admits that each case of organizational change is specific and singular, while seeking a core of knowledge that is generalizable and will be found in other cases. In the management sciences, qualitative research methods postulate that any organizational change process is unique and context-specific. Therefore, standardization of organizational change practices is stigmatized. In essence, it would be vain to seek a “one size fits all” organizational change method as it readily limits in-depth observation of the specifics of a unique case. In contrast, other research methods understate the complexity of the object in question through models and algorithms, drawing on a limited number of variables that do not account for the complexity of organizational dynamics. The principle of generic contingency goes beyond these polar extremes, similar to the principles of medical research where all patients have their individuality and specific characteristics while generic treatments, like vaccines, are still efficient and effective. This type of study is the basis of therapeutic trials, which are validated by a statistical study on one case prior to generalizing to a population of 2, then 10, 100, 1,000 patients, and so forth. In comparison, qualimetric intervention-research looks for a core of knowledge regarding the commonalities of solutions to provide efficient and sustainable organizational development.

GOALS OF THE DEVELOPMENT OF A BODY OF KNOWLEDGE THROUGH THE QUALIMETRIC INTERVENTION-RESEARCH METHODOLOGY The implementation of the qualimetric intervention-research methodology enables the activation of a heuristic process of step-by-step learning to create a rigorous and relevant body of knowledge. It has required the development of a specific research scheme devoted to upgrading scientific observation of organizational change phenomena. To this end, in the early 1970s Savall created a research protocol drawing on the concept of job enrichment, founding the ISEOR research center (Savall, 2010). At its origin, the institute was associated with two institutions: the University of Lyon, a public university, and EMLyon, a private business school. A board of directors and scientific council were set up and decided to preserve the scientific independence of the center based on its financial independence. Recognizing the need for a longitudinal research program, which would be implemented over several decades, a goal was to avoid dependence on grants or sponsors, which could change over time. It was also necessary to demonstrate that the relevance of the managerial and societal impacts of the research justified funding by the users of the Center’s work. Figure 7.2 presents the principle of the ISEOR laboratory – a cooperative partnership with companies and organizations to create knowledge in the field of organizational change and development. A method for negotiating qualimetric intervention-research contracts with companies and organizations was developed at the early stage of the research scheme. The aim was to transform the demand, as opposed to proposing ready-made solutions in order to allow a process of reflexivity on the part of the client system. It was necessary to avoid the trap of a superficial response to a non-systemic demand for organizational change, as is often observed in the case of management fads. As an example, a CEO of a company approached ISEOR for

158  Handbook of research methods in organizational change

Source: Savall & Zardett (2011).

Figure 7.2

Principle of the ISEOR Laboratory: cooperation with companies and organizations to create knowledge in the field of organizational change and development

help in preventing staff turnover in the company. Several meetings were organized with the executive to broaden the scope of the project, focusing on all the difficulties encountered by the company, not just staff turnover. As a result, the objectives of the intervention-research were broadened to look at the overall and sustainable performance of the company. ISEOR intervener-researchers proposed a qualimetric protocol involving all categories of personnel in the organization, focusing not only on staff turnover but also on the challenge of change itself. As suggested by this case, all intervention-research carried out by the ISEOR research center involved a lengthy process of contract negotiations. ISEOR would only work with a client if basic conditions were met, particularly with regard to the involvement of the CEO and the visibility of the research outcomes.

INVOLVEMENT OF THE CEO A key success factor is to ensure that the CEO and the top management team understand the research method protocol and objectives. Indeed, commitment of the managers to respect the intervention protocol and to devote time and energy to it over the duration of the research contract is required for one year at least. The company has to sign a financing schedule for the research, spelling out expected costs and performance in the research contract. In some cases, the decision was made by ISEOR to partly self-finance intervention costs based on specific

Qualimetric intervention-research as an approach  159 research objectives, for example, in the case of projects that can hardly be financed by public or private bodies as in the case of the management of trans-organizational projects.

VISIBILITY OF THE RESEARCH PROCESS A commitment to publish the results of the research, cross-case analyses, and testimonies from the users of the research is also required to contribute to the scientific rigor of knowledge. Each year, professional conferences are organized by ISEOR in order to present the outcomes of its intervention-research process. Qualitative, quantitative, and financial impacts are publicly presented by both the companies and the intervener-researchers. The languages of those conferences include simultaneous translations into English, French, and Spanish in order to give access to an international audience. Every year, proceedings of these professional conferences are published in books to give visibility to the qualimetric intervention-research outcomes. Such rules differentiate from habits of the management consulting industry, where testimonies of consulting interventions are scarce and seldom published, as opposed to the practices of medical research.

QUALITY AND RIGOR OF SCIENTIFIC OBSERVATION IN QUALIMETRIC INTERVENTION-RESEARCH Like in medical research, the methodology of qualimetric intervention-research consists of integrating a large amount of data and observations regarding the complex system of processes at work in organizational change: ● Access to written documents such as minutes of meetings, reports, and management accounting spreadsheets, which describe part of the performance and organizational change processes. As an example, it is necessary to keep track of the evolution of the skills of all the members of the staff throughout the change process. ● Semi-directive interviews: during the intervention-research process, researchers have to take exhaustive notes. The goal is to track this information over time to ensure longitudinal analysis of interview notes, which should be compared step by step from a diagnosis at the beginning of the intervention and evaluation of the results after one year. Indeed, it is necessary to compare what the actors say during the diagnosis interviews, and their reaction during the “mirror effect” of diagnoses (as their own words are shared with the actors) as well as during the project and evaluation stages of the actions. ● Direct observation of behavior throughout the intervention-research process. As an example, the analysis of the gaps between what the actors say and what they actually do is an important source of information. The ability to trace and document the sheer amount of data is necessary to allow for a longitudinal analysis of changes throughout the process, in order to interpret the precise nature and characteristics of the organizational change, rather than the appearance of change. Cross-referencing these data is a matter of “contradictory intersubjectivity,” which must be further accentuated by the researcher going back and forth between the scientific field

160  Handbook of research methods in organizational change of observation and the laboratory. To this end, several distancing actions are implemented, similar to medical research: ● The presence of at least two researchers in each intervention-research, so that they can compare their views through a dialectic process. ● Weekly meetings in the laboratory, in order to take a step back from immersion in the field, where the speakers can be manipulated by the actors’ issues and cognitive biases. ● Participation in colloquia and conferences, to compare the points of view of the cases studied with those of other researchers who do not resort to the same school of thought. ● Writing articles and books to position the possible contribution of the results in relation to other theories in the field of organizational change. These immersion and distancing protocols are aimed at improving the quality of scientific observation. This is the essence of contradictory intersubjectivity, which is opposed to the alleged objectivity of positivist research in the field of organizational change. Of course, biases linked to the perceptions and position of qualimetric researchers still exist, but positivist researchers should not think that they are exempt from them. Such biases are shown by the ethnostatistical approach (Gephart, 1988), which makes explicit the defects of the data-collection processes used in the management sciences. Such flaws are further aggravated when researchers work at a distance by exploiting databases based on purely subjective observations, even when they are quantified.

SEAM HYPOTHESES AND RESEARCH OUTCOMES Given the characteristics of qualimetric intervention-research, the performance criteria of the ISEOR research center differs from the traditional research model, which consists of translating the results of academic research carried out in the laboratory to companies. Instead, it consists of co-producing rigorous research with practitioners that has an impact before publishing the results, as is done in university hospitals and medical research. As noted earlier, since its creation ISEOR has carried out more than 2,000 cases of interventions-research in companies ranging in size from two to 30,000 employees. These interventions took place in 47 countries and 73 different economic sectors in the private, public, and non-profit sectors. This body of knowledge is presented in the form of hypotheses; i.e., ideas that are still to be demonstrated period after period, as in medical research. In the qualimetric approach, these hypotheses consist of core hypotheses, which are broken down into three kinds of detailed hypotheses (Savall & Zardet, 2011): ● Descriptive hypotheses, aimed at discovering the pathologies of the complex system and its difficulties in the face of the need for change. They are similar to the medical investigations of the diagnosis in medical research. ● Explanatory hypotheses, aimed at interpreting the root causes of organizational pathologies. They can be compared with the interpretation of the diagnosis. ● Prescriptive hypotheses, intended to structure and evaluate the results of an organizational change action. They can be compared to medical treatment aimed at recovering health and healing from diseases.

Qualimetric intervention-research as an approach  161 These hypotheses are progressively validated, like in the formation of a provisional medical consensus that still needs further developments. They formulate the development of a body of knowledge that constitutes the SEAM.

CORE HYPOTHESIS The core and fundamental hypothesis of the Socio-Economic Theory is that the social and economic performance of companies and organizations are closely intertwined. Social performance is defined as the quality of management applied to six areas of organizational life: working conditions, work organization, communication-coordination-cooperation, time management, integrated training, and strategic implementation. Economic performance is defined not only through immediate results, but also through the creation of potential in the medium and long term, also taking into account the costs and performance external to the relevant environment. It is therefore a complex system that is related to all the structures and behaviors involved in organizational change (Savall & Zardet, 1987). SEAM’s fundamental hypothesis is that dysfunctions in the operation of an organization result in hidden costs that are detrimental to sustainable performance. Dysfunctions stem from the interaction of structures and behaviors considered as a system. Conversely, preventing dysfunctions from occurring is not costly and enables the conversion of hidden costs into value added and sustainable performance. In sum, the improvement of sustainable performance of companies and organizations requires an upgrade of the scope of financial and economic analysis by taking into account hidden costs and their effect on performance. In particular, organizational change approaches that focus on cost cutting can have perverse effects that are not measured through visible cost indicators. Figure 7.3 presents the core hypothesis of the SEAM.

DESCRIPTIVE HYPOTHESES Dysfunctions and hidden costs can be brought to light through an “interactive cognitivity” process, which involves all categories of organizational actors. The “contradictory intersubjectivity” at work in a diagnosis phase and a mirror-effect process show the importance of hidden costs, which can be categorized in five indicators: absenteeism, work accidents and professional diseases, staff turnover, non-quality, and low productivity. Dysfunctions at the origin of hidden costs exist in six domains: working conditions, work organization, communication-c oordination-cooperation, time management, integrated training, and implementation of the strategy. Dysfunctions stem from the interaction between five categories of structures (physical, technological, demographic, organizational, and mental) and five categories of behaviors (individual, trade groups, staff categories, pressure groups, and the collective). This approach has resulted in the creation of a database drawing on the generic contingency principle. This database includes 5,000 dysfunctions broken down in the six main categories mentioned above.

Figure 7.3

Fundamental hypothesis of the Socio-Economic Theory

Source: © ISEOR 2015.

162  Handbook of research methods in organizational change

Qualimetric intervention-research as an approach  163

EXPLANATORY HYPOTHESES The objective of the SEAM is to address the root causes of the dysfunctions. Deciphering those roots is needed to weed out dysfunctions and prevent them from occurring again and again, as is often the case when superficial and copy-and-paste solutions are implemented. The analysis of thousands of qualimetric intervention-research projects has shown that the generic contingency principle regarding the underlying origin of dysfunctions is connected with three in-depth root causes: poor information, operational, and functional systems; a lack of clean-up of the dysfunctions; and flawed synchronization of managerial courses of action.

PRESCRIPTIVE HYPOTHESES Over the past five decades of qualimetric intervention-research in companies, the ISEOR research center has developed a method aimed at enhancing sustainable socio-economic performance. It consists of implementing a three-dimensional process to better steer organizational change. Three axes are simultaneously activated step by step, as shown in Figure 7.4.

Source: Savall & Zardet (1987).

Figure 7.4

The three dimensions of the socio-economic approach to organizational change

Axis A (cyclical process of problem solving) involves a change process that includes a diagnosis phase, a socio-economic project, and its implementation and evaluation. This process activates the “interactive cognitivity” principle during interviews with the members of the

164  Handbook of research methods in organizational change organization during the diagnosis phase. The “contradictory intersubjectivity” principle is at work all over the organizational change process, in particular during the mirror effect of the diagnosis and the focus groups in the project phase. Axis B (socio-economic tools) captures the implementation of socio-economic management tools aimed at sustaining the steering process of organizational change: Internal and External Strategic Planning, Priority Action Plans, Table of Skills, Strategic Piloting Indicators, and Periodically Negotiable Activity Contracts. Such tools particularly draw on the three principles of qualimetric intervention-research. For example, Priority Action Plans are designed through interactive cognitivity, and their economic impacts are assessed according to a “contradictory intersubjectivity” process in order to come up to a shared evaluation of foresighted impacts. These socio-economic tools are based on the “generic contingency” principle. Axis C (policy-making decisions) reflects the political and strategic decisions regarding the objectives, mission, and vision of the organizational change.

ILLUSTRATION OF A QUALIMETRIC INTERVENTION-RESEARCH PROCESS In a subsidiary of a large company listed on the New York Stock Exchange, top management wanted to reorganize the various branches to reduce costs and improve profitability. Instead of applying traditional methods of organizational change through restructuring and the implementation of quality processes, the executive wanted to implement a qualimetric intervention-research approach that allowed the company to create financial value in one year without layoffs, while improving customer satisfaction and the company’s overall and long-term sustainable performance. The step-by-step process in this case is described in Figure 7.5. In the first step (mostly focused on interactive cognitivity), the qualimetric intervention-research approach consisted of analyzing the hidden costs by involving all categories of personnel. Hidden costs reflect outlays that are not captured by traditional accounting information systems. This analysis made it possible to challenge the accounting approach of visible costs in traditional financial management, which represented only a part of the object of the organizational change. Everyone understood that acting on visible costs alone would be a toxic approach to the organizational change process, since reducing visible costs would have resulted in a sharp increase in hidden costs. It was therefore necessary to adopt a global qualitative approach to the object to be transformed. We can illustrate this evaluation of hidden costs by an example taken from the hundreds of hidden costs evaluated, as presented in Table 7.1. This table shows the hidden costs stemming from the departure of engineers in an IT department. The qualitative interviews revealed a variety of reasons for these resignations, such as illustrated by field-note quotes: ● Working conditions: “We work long hours due to visits to our clients during rush hours. We don’t even have a break for lunchtime.” ● Work organization: “Many procedures are useless or overlapping. We don’t understand why they are mandatory.”

Qualimetric intervention-research as an approach  165

Source: Savall & Zardet (1987).

Figure 7.5

The step-by-step process of socio-economic intervention

● Communication-coordination-cooperation: “Too many e-mails become overwhelming, while a weekly 30-minute meeting of the team would move communication from bureaucratic to friendly cooperation.” ● Time management: “We always react on short notice due to lack of anticipation.” ● Integrated training: “Training seminars are provided, but we don’t sign up as it is time-consuming and work backlogs would pile up.” ● Implementation of the strategy: “We have access to the corporate strategic framework, but we don’t understand the connection with our operational objectives and challenges.” The second step (mostly based on “contradictory intersubjectivity”) reflects the presentation of the mirror effect (i.e., getting organizational members to listen to themselves), improving management’s awareness of the complexity of the organizational change. It corresponds to descriptive hypotheses intended to better understand the object concerned by the organizational change. Based on these descriptive hypotheses, an interpretation of the explanatory hypotheses was formalized by the intervener-researchers, in the form of an explanation of unstated ideas based on the ISEOR database on the invariants of organizational change (“generic contingency” principle). This perspective, presented to the entire management and staff of the IT department, emphasized the managerial deficit in the change management process. During the third step (mostly inspired by “generic contingency”), the intervener-researchers moved to the presentation of explicative hypotheses through drawing on the unstated ideas.

$36,600 + risks company image, and former staff being recruited by competitor,

10 lost clients cannot be regained in the following two years: 2 years *

due to 2 clients lost each time an employee has left the agency: 2 clients* 5 employees *

to a one-week training seminar: 5 * $950 = $4,750

devoted to interviews to recruit a new employee: 5 * 10 hours* $61 = $3,050

shift in function * $12 excess salary = $4,800

manager has to replace the employees

employees

resign each

year, mostly

at short notice prior to

overwhelming

workload and

disruptions

Objectives are

$12,000 margin on variable cost per year per client =

Unsatisfied clients due to changing contact person

Managers not

taking care of

career prospects

Source: © ISEOR (2015).

skills * $1,200 = $1,200 average

applicants

unattainable

$12, 000

and loss of 5 employees

recruiting

considered

amount Risk of poor

Half of the

Loss in earnings

Sending employees

Overall

10 hours

Risks

5 * 80 hours

of potential

The branch

Non-production Non-creation

Five

Overconsumption

Disruptions and

Excess salary Excess time

Staff turnover

calculation

Detailed

dysfunction

Frequency

Reason for the

hidden cost

Example of a qualimetric evaluation of hidden costs

Indicator of

Table 7.1

166  Handbook of research methods in organizational change

Qualimetric intervention-research as an approach  167 For example, there was a lack of listening to the staff on the problems (dysfunctions) they were faced with, and a lack of synchronization of change-related actions (e.g., the gap between the rhythm of the software evolution and attempts to upgrade skills). The fourth step (mostly resorting to interactive cognitivity), based on the discussion of the explicative hypotheses, which is the prescriptive phase of the qualimetric intervention-research, consisted of setting up project groups using collective intelligence to experiment with several scenarios for change solutions. For this purpose, the management and the supervisors worked with the staff of the department to develop draft solutions whose expected impacts were evaluated by the qualimetric approach. An example of one of the qualimetric decisions is shown in Table 7.2. In this case, the organizational change action involved three main axes: ● Improvement of communication, focused on better anticipation of workload and priorities to balance long- and short-term objectives. ● Streamlining procedures; for example, using a “buddy system” to regularly visit clients and prospects with junior employees in order to train them in methods and techniques. ● Organization design of agencies, emphasizing delegation and multi-skill development to enrich jobs and streamline processes. Finally, in the fifth step (involving contradictory intersubjectivity) the economic impacts of this socio-economic organizational change proved to be not only highly performing in terms of social impacts, but also regarding short- and long-term economic impacts (see Table 7.2). Table 7.2

Preparation of an organizational change based on a qualimetric evaluation in the case of the economic balance of a socio-economic priority action plan

Content of the socio-economic organizational development project (Priority Action

Investment cost (amortized in only one year)

Performance metrics

Value creation

Plan) Improvement of

4 hours per month * 11

communication within the IT

employees * 12 months * $61

department

= $32,208

Reduction of staff turnover by 2/3

66% * $32,600 = $12,200

Participative process to adapt Streamlining procedures

procedures, training sessions

Less time wasted in useless

and buddy system: 340 hours

procedures and disruptions

* $61 + $8,000 tuition fees =

due to lack of anticipation

1,600 hours * $61 = $97,600

$28,740 Reduction of customer claims: minus 35 per year, Organization design of the

Time devoted to the project:

which resulted in 1/3 loss in

35 claims * 1/3 * $1,200 =

premises

100 hours * 61 = $6,100

earning of margin of variable

$14,000

costs for each dissatisfied client  

 

Increase of market share due

26 new clients * average per

to improved quality of sales

client margin on variable costs

process

of $1,200 = $31,200

168  Handbook of research methods in organizational change Content of the socio-economic organizational development project (Priority Action

Investment cost (amortized in only one year)

Performance metrics

Value creation

Plan)  

 

+ Qualitative impacts: ● Improved work atmosphere  

within the agency ● Enhanced customer loyalty ● Improved agency brand image

Overall amount of the investment (mostly intangible)

$67,048

Overall amount of value creation

$155,000

Source: © ISEOR (2015).

DISCUSSION: CHALLENGES FOR ORGANIZATIONAL CHANGE RESEARCH The project to develop qualimetric intervention-research focused on measuring the impacts of organizational change is the key to connecting the dots between organizational change and economic performance (see Savall, 2010; Zardet & Bonnet, 2021). Although some economists have debated the nature of human capital, they were unable to fully measure it because of the complexity of organizational life and the degrees of freedom that contradicted the postulate of rationality of Homo economicus. Despite attempts at experimental in vitro simulation made since then, we now see the limitations of purely mathematical models. Savall continued to develop this work, drawing on research by the Tavistock Institute’s socio-technical school as well as organizational development (OD) work in the United States. He observed that action research methodologies had a difficult time gaining the same level of academic recognition as theoretical research conducted in the field of organizational behavior. In particular, Savall (2003) identified three challenges for research in the field of organizational change. First, researchers are faced with the challenge of thoroughly analyzing the complex links between organizational change and economic performance in the short, medium, and long terms. This analysis must include the economic dimension of organizational change – it should not be limited to the social and technical aspects of change. The need for a link between economic and organizational change approaches has been emphasized on numerous occasions by organizational change management scholar-practitioners (e.g., Beer & Nohria, 2000; Savall, 2003). There is a need to develop an integral systemic approach to the processes of organizational change that goes beyond qualitative or quantitative evaluations of change actions. It is also necessary to take into account the financial resources necessary for the action and the expected impacts in the short, medium, and long term. In this context, qualimetric intervention-research contributes to the development of an interaction across the fragmented disciplines of management science, as illustrated by the SEAM star (see Figure 7.6). It is also necessary to establish a link between the characteristics of the change itself and the cost of all the operations carried out as part of the change action. Research methods in

Qualimetric intervention-research as an approach  169

Source: Savall & Zardet (1987).

Figure 7.6

The Socio-Economic star

this field, however, rarely take into account the economic dimension, or they seek to establish a questionable link with financial performance. It is understandable that research based on simple questionnaire surveys or isolated case studies is not sufficient to obtain reliable results for the analysis of the links between organizational change characteristics and economic performance. As mentioned earlier, in comparison with medicine, the outcome of a cancer treatment is analyzed not only in the short term through a questionnaire or interview with the patient, but also with very thorough diagnostic methods over time. The quality of the treatment depends not only on the techniques and products used, but also on the combination of many variables of care, variables of the patient’s pathology, variables of the quality of the patient’s living conditions, and the benevolence of his or her entourage. A second challenge focuses on formalizing the rules of organizational change in order to identify the explicative and relevant variables for change management. Research methods relating to organizational change must include the psychosocial aspects of organizational dynamics (Szabla, 2017). The objective is to better understand the evolution of the behav-

170  Handbook of research methods in organizational change iors of all actors during the change process. Researchers try to characterize these processes according to their specificities – how were the actors involved, what are the precise contents of the actions of change on their work – focusing on their individual and collective competencies and strategies. To do this, OD research methods rely on a variety of longitudinal approaches involving actors through participatory mechanisms. They develop a form of collective intelligence to build innovative strategies and organizations. They adopt a variety of terminologies (Coghlan & Brydon-Miller, 2014) – from action research in the sense of Kurt Lewin and Richard Beckhard’s complex change approach, to the Tavistock Institute’s socio-technical approach, Chris Argyris’ Action Science, and Edgar Schein’s Clinical Inquiry, to Bill Torbert’s Action Inquiry, Peter Reason’s Participatory Action Research, and David Coopperrider’s Appreciative Inquiry and so forth. Participation of actors in the change process has been recognized in each of these approaches, but the specific ways in which these management methods were implemented did not appear to be very explicit in the literature analyzed, while the qualimetric intervention-research attempts to address this gap. Finally, the third challenge is to create generic principles of organizational change performance, beyond the specificities of individual cases, by capitalizing on the results of experience. This is rarely done in the field of organizational change, unlike in medical research where each patient case is specific, but where a medical consensus is sought for disease treatments. Savall and Zardet (1987) observed that accounting and management control tools were not adapted to measure the links between organizational change and economic performance. For example, this research found that reorganization with downsizing could result in an apparent reduction in personnel costs, but could also lead to a loss of overall performance due to an increase in non-quality costs and a loss of customers. It is therefore necessary to develop a methodological approach to reveal hidden cost and performance variables, complementary to the analysis of visible cost-performance.

CONCLUSION Despite the ongoing evolution of research methods in organizational change, significant gaps remain. As this chapter has indicated, it is necessary to go beyond quantitative and qualitative research frameworks per se, seeking out more holistic approaches. As a growing number of observers have suggested, all too often organizational members are unprepared for organizational change in the face of an economic and normative environment with contradictory demands. Managers are tempted to resort to inappropriate management methods, which often prove to be toxic because they focus on one part of problems rather than their systemic nature. Enhanced relevance of management research is also needed to sustain the legitimacy of management researchers and management schools themselves.

REFERENCES Beer, M., & Nohria, N. (2000). Breaking the Code of Change. Brighton, MA: HBS Press. Bhaskar, R., Collier, A., Lawson, T., & Norrie, A. (1998). Critical realism. In M. Archer, R. Bhaskar, A. Collier, T. Lawson & A. Norrie (Eds.), Proceedings of the Standing Conference on Realism and Human Sciences (Vol. 4, 30–45). Bristol: Center for Practical Realism.

Qualimetric intervention-research as an approach  171 Boje, D., & Rosile, G.A. (2003b). Theatrics of SEAM. Journal of Organizational Change Management, 16(1), 21–32. Buono, A., & Savall, H. (Eds.) (2015). The Socio-Economic Approach to Management Revisited: The Evolving Nature of SEAM in the 21st Century. Charlotte, NC: Information Age Publishing. Coghlan, D., & Brydon-Miller, M. (2014). The SAGE Encyclopedia of Action Research. Thousand Oaks, CA: SAGE. Gephart, R.P., Jr. (1988). Managing the meaning of a sour gas well blowout: The public culture of organizational disasters. Industrial Crisis Quarterly, 2(1), 17–32. Gephart, R.P., Jr. (2006). Ethnostatistics and organizational research methodologies: An introduction. Organizational Research Methods, 9(4), 417–31. Piaget, J. (1975). Comments on mathematical education. Contemporary Education, 47(1), 5. Piaget, J. (1988). L’équilibration des structures cognitives, problème central du développement [Equilibration of Cognitive Structures: Core Problem of Development]. Paris: PUF. Rousseau, D. (Ed.) (2012). The Oxford Handbook of Evidence-Based Management. Oxford: Oxford University Press. Savall, H. (2003). An up-dated presentation of the socio-economic management model and its international dissemination. Journal of Organizational Change Management, 16(1): 33–48. Savall, H. (2010). Work and People: An Economic Evaluation of Job-Enrichment (2nd ed.). Charlotte, NC: Information Age Publishing. Savall, H., & Fière, D. (2014). Étude comparative des méthodologies de recherche en médecine et en gestion: Cas de la recherche-intervention socio-économique d’ordre qualimétrique [Comparison between medical and management research methods: Case of the Socio-Economic Qualimetric Intervention-Research]. Journal d’Économie Médicale, 32(5), 354–70. Savall, H., Péron, M., Zardet, V., & Bonnet, M. (2015). Le capitalisme socialement responsable existe [Socially Responsible Capitalism]. Caen: EMS. Savall, H., & Zardet, V. (1987). Mastering Hidden Costs and Socio-Economic Performance. Charlotte, NC: Information Age Publishing. Savall, H., & Zardet, V. (2011). The Qualimetric Approach. Charlotte, NC: Information Age Publishing. Savall, H., & Zardet, V. (2021a). Synthèse de la théorie et du management socio-économiques. In Traité du Management Socio-Économique. [Synthesis of the Socio-Economic Theory and of the socio-economic approach to management. In Treatise of the Socio-Economic Approach to Management]. Caen: EMS. Savall, H., & Zardet, V. (2021b). Traité du Management Socio-Économique [Treatise of the Socio-Economic Approach to Management]. Caen: EMS. Savall, H., Zardet, V., & Bonnet, M. (2008). Releasing the Untapped Potential of Enterprises through Socio-Economic Management. Geneva: ILO-BIT. Szabla, D.B. (2017). The Palgrave Handbook of Organizational Change Thinkers. Cham: Springer. Van de Ven, A. & Johnson, P. (2006). Knowledge for theory and practice. Academy of Management Review, 1(4), 802–21. Zardet, V., & Bonnet, M. (2021). Savall, Henri: Connecting the dots between organizational development and economic performance. In D.B. Szabla (Ed.), The Palgrave Handbook of Organizational Change Thinkers (2nd ed., pp. 1509–25). Cham: Springer.

8. Collaborative management research: theoretical foundations, mechanisms and practices Abraham B. (Rami) Shani

INTRODUCTION Collaborative inquiry has a long history of practice in the field of organization development and change. The increasing occupation and preoccupation with management and organizational research impact (MacIntosh et al., 2021) and research relevance (Mirvis et al., 2021) point to the potential added value of research that is more useful (Mohrman et al., 2011) and more collaborative in nature (Shani & Coghlan, 2021). Unlike traditional research orientations, collaborative management research’s (CMR’s) key tenets are the engagement of the human systems in the discovery process, changing, and the managing of change and development. As such, this orientation generates deep knowledge and simultaneously enhances organization development, performance and longevity. Research orientations and practice that focus on organization development, change, managing change and development seem to vary. Coghlan et al. (2019), in their exploration of the development of the philosophy of social science and organization development and change, have argued that the field of organization development is embedded in the concerns of members of a living system and as such the engagement of the system’s members in the production of a knowledge creation process is crucial. Gibbons and his colleagues provided a basic typology that offers a mapping of knowledge production orientations that groups the wide variety of research methods into what they have labeled as Mode 1 and Mode 2 clusters. Mode 1 is characterized by explanatory knowledge that is generated in a disciplinary context that is usually set by an academic agenda, while methods in Mode 2 are generated in a specific context of application and seek to address a practical issue (Gibbons et al., 1994, Gibbons et al., 2011). The majority of the empirical-based published works in the field of organization development seem to magnify either the intervention methods and methodologies or the research rigor that tend to follow a Mode 1 research orientation. Collaborative inquiry by its very nature fits within the Mode 2 knowledge production orientation, yet it integrates Mode 1 methods as needed during the discovery process. CMR is one of the collaborative inquiry modalities. The CMR approach refers to a stream within the collaborative inquiry family that has been identified as a potent method for advancing scientific knowledge and bringing about change in organizations (Shani et al., 2012). This family of inquiry approaches are based on varied degrees of action and collaboration and were advanced during the last (and the current) century (Shani et al., 2018). Each method or research orientation seems to emphasize distinct scientific or collaborative or action features. Such methodologies include action research, participatory action research, action learning, action science, developmental action inquiry, cooperative 172

Collaborative management research  173 inquiry, clinical inquiry/research, appreciative inquiry, learning history, intervention research, collaborative research and CMR, to mention a few. (See Coghlan’s chapter in this Handbook.) The CMR orientations are based on a specific world view (ontology), epistemology that expresses how we seek to know (the theory of knowledge) and methodologies that articulate the approach that is being adopted for inquiry (Coghlan, 2010; 2017). At the most basic level, CMR is viewed as an effort by two or more parties, at least one of whom is a member of an organization or system under study and at least one of whom is an outside member/external party, to work together in learning about how the behavior of managers, management methods or organizational arrangements affect outcomes in the system or systems under study, using methods that are scientifically based and intended to reduce the likelihood of drawing false conclusions from the data collected, with the intent of both proving performance of the system and adding to the broader body of knowledge in the field of management. (Pasmore et al., 2008a, p. 20)

CMR is an inquiry process that occurs in a natural setting within a specific business and industry context, involving true collaboration between practitioners and researchers. It addresses an emerging specific issue of concern while utilizing multiple methodologies that are scientific. CMR involves the creation of a learning system via the establishment of learning mechanisms aimed at improving system performance and adding to the scientific body of knowledge in the field of management (Canterino et al., 2016; Cirella et al., 2012). The collaboration between insider and outside research teams is central to CMR. The engaged scholar that co-designs and co-leads a CMR effort with or within a human system requires an appreciation for the added value of a research endeavor as a collaborative effort (Van de Ven, 2007). Unlike traditional research (that can be found in the Mode 1 research orientation cluster) the mindset that guides the discovery process is of an engaged process (rather than detached). The researcher is engaged in a mutual learning process – that is rigorous, reflective and relevant – with a living system (Pasmore et al., 2008b; Shani et al., 2008). Scholarly contribution is one of the two primary goals of CMR, the other being to enhance the system functioning and performance (Louis & Bartunek, 1992). The learning and insights generated serve as a powerful mechanism for the development of new theories and enhance the researcher’s insights into the process of theorizing (Mirvis et al., 2021; Shani & Coghlan, 2021). This chapter introduces and explores the distinctiveness of CMR within the field of organization development and change by focusing on the critical role that an insider and outsider collaborative inquiry team (or teams) play in creating new knowledge and enhancing the organizational change capabilities. The first part of the chapter presents a comprehensive framework of CMR and its theoretical foundation. The second explores the enactment of CMR with emphasis on the insider/outsider teams, key phases and processes. Such teams are viewed as critical learning mechanisms that serve as the center or engine of CMR initiatives. Finally, implications for engaging in CMR are explored.

174  Handbook of research methods in organizational change

COLLABORATIVE MANAGEMENT RESEARCH: THEORETICAL FOUNDATIONS CMR is embedded in true collaborative endeavor between those who are insiders to the organization and those who are outsiders, with the aim of both addressing relevant organizational issues and creating practical knowledge about organizational change through a collaborative discovery process. As such it takes place in the present tense and the role of the researchers and organizational members is of engaged inquirers. Key theoretical anchors can be found at the foundation of this approach, namely organization as learning systems, learning mechanisms, insider/outsider team-based research, collaboration and partnership, present tense knowledge creation and the dynamic nature of knowledge creation; i.e., the hybrid of Mode 2 and Mode 1 orientation. Organizations as Learning Systems Every living system, by its very nature, is a learning system (de Geus, 1998; Friedman et al., 2001; March, 1991). The origin of the organization learning conceptualization is anchored in the synthesis of contemporary theories that include system theory, adaptive complex systems, sociotechnical systems, group behavior, human development and individual learning theories (Shani & Docherty, 2003). The theoretical foundation of organizational learning can be found in the organizational science, sociological, economics, and organization change and development research (Antal et al., 2001). Regardless of the theoretical point of departure, the perspective of organizations as learning systems is a foundational pillar of CMR. Learning Mechanisms Putting any system under the microscope, one can uncover some elements of learning processes, structures or mechanisms that evolve over time, as systems adapt to continuously changing environments. From a CMR perspective, learning mechanisms are viewed as a formal configuration – structures, processes, procedures, rules, tools, methods, and physical or virtual space – created within or outside an organization for the purpose of developing, enhancing and sustaining innovation, performance and learning (Bushe and Shani, 1991; Fredberg et al., 2011; Popper and Lipshitz, 1998). Shani and Docherty (2008) advanced three broad categories of learning mechanisms: cognitive, structural and procedural. Cognitive learning mechanisms provide language, concepts, models, symbols, theories and values for thinking, reasoning and understanding learning issues. Structural learning mechanisms concern organizational, technical or physical infrastructures that include formal and informal forums such as parallel learning structures, learning or continuous improvement forums, teams, committees or taskforces or technology-based forums of grouping individuals together. Last, procedural learning mechanisms concern rules, routines, methods and tools that have been institutionalized in the organization to promote and support learning. A combination of learning mechanisms evolve in most organization and may be viewed as a tapestry of learning mechanisms that develops organically through the maturation of organizations as complex adaptive systems (Docherty & Shani, 2008; Shani & Docherty, 2008). (Table 8.1 captures in more detail the categories and essence of each learning mechanism.)

Collaborative management research  175 Table 8.1

Learning mechanisms: categories and key features

Categories

Essence

Features – partial illustrations

Cognitive mechanisms

● Cognitive or cultural mechanisms are the

● Company value and mission statements

bearers of language, concepts, symbols,

● Strategy documents, policies and plans

theories, frameworks, models and values

● Employment agreements

establishing thinking, reasoning and

● Intercompany contracts

understanding. ● Cognitive mechanisms are management’s main means for creating an understanding among all employees on the character, needs and priority of the strategy and the learning and changes required to realize it. Structural mechanisms

● Structural mechanisms are organizational, physical, technical and work-systems infrastructures that facilitate CMR and encourage practice-based learning. ● Structural mechanisms house and enable the collaboration and discourse required for collective learning of new practice.

● Communication channels ● Lateral structures to enable learning of new practices across various core organizational units ● Establishment of learning/study teams ● Formal and informal forums for joint exploration ● Parallel learning structures ● Bench-learning structures ● Process improvement teams ● Learning centers ● Technology-based mechanisms for data warehouse, data collection, data sharing

Procedural mechanisms

● Procedural mechanisms are rules, routines,

and shared data interpretations ● Assessment methods and tools

methods and tools that can be institutional-

● Standard operating procedures

ized in the organization for the purpose of

● Action learning procedures

promoting and supporting CMR efforts and

● Debriefing routines

learning.

● Post-project review procedures

● Procedural mechanisms facilitate the creation of learning processes that can be integrated into existing organizational processes and routines.

INSIDER/OUTSIDER TEAM-BASED RESEARCH Bartunek and Louis (1996; Louis & Bartunek, 1992), building on Evered and Louis’ (1981) notions of inquiry from the inside and inquiry from the outside, provide the theoretical foundation for the added value of the collaboration between insiders and outsiders in the conduct of organizational research. Insiders are viewed as organizational members that are employed as full-time members of the organization; they identify with the organization, its mission and purpose, and carry out a specific function or job. They enact work routines, processes and practices in order to accomplish their assigned tasks. They develop working relationships and become a part of both the formal and informal organization. Being an integral part of the living system, the insiders develop a deep level of understanding and knowledge – known only to the insider – about the system or what is viewed as an insider perspective. Most times, the

176  Handbook of research methods in organizational change outsiders are the researchers that have an interest in understanding and developing knowledge about the challenges and opportunities faced by a living system. Outsiders bring with them a perspective that is based on theoretical knowledge, research skills and past experience of knowledge creation. Inquiry from the outside, inquiry from the inside and collaborative inquiry from the outside and inside seem to follow different epistemological assumptions and methodologies. While a deeper exploration of the differences is of added value, for the purpose of this chapter the following captures its essence. The outsider research perspective tends to be guided by the epistemological assumption underlying the importance of the detachment and neutrality of the outside researchers (this epistemological position is held by positivistic researchers and/ or followers of Mode 1 knowledge production orientations). This research orientation tends to generate universal knowledge and generalized laws across contexts that is produced within a discipline. The epistemological assumption that guides the insider researcher(s) is the belief that “knowledge comes from human experience, which is inherently continuous and nonlogical, and which may be a symbolically representative” (Evered & Louis, 1981, p. 389). This epistemological assumption is held by researchers that are embedded in Mode 2 knowledge production orientations. The researcher is immersed and engaged in the discovery process. This research orientation tends to generate practical knowledge that is produced in a transdisciplinary context of the application that is transportable to like settings. The collaborative research orientation that is embedded in true collaboration between outside researchers and inside members of a living system that together compose the research team(s) is anchored in Mode 2 orientation but also integrates Mode 1 in the discovery process and/or at different phases in the collaborative research process. As such, within the CMR practice the two modes (Mode 1 and Mode 2) co-exist. The Dynamic Nature of Knowledge Creation: The Hybrid of Mode 2 and Mode 1 Orientation The discovery process in CMR evolves as the quality of the collaborative relationships in the discovery process develops (Shani et al., 2008). The dynamic nature of social systems coupled with the continuously changing business context impact the nature of the discovery process, the nature of the project, the key relevant issue to be addressed, the actions and at times the research question (MacIntosh et al., 2016). The framework advanced by Gibson and his colleagues (1994, 2011) provides a meta level framework that can aid in such a complex discovery process. The authors describe Mode 1 research as characterized by the explanatory knowledge that is generated in a disciplinary context. It is research that arises from the academic agenda, and that agenda usually takes place within a singular discipline and is accountable to that discipline. The aim of the research is to produce universal knowledge and build and test theory within a disciplinary field by drawing causal inferences from the data to test hypotheses. The type of knowledge acquired is knowledge generalized across contexts. The data are context-free and validated by logic, measurement and consistency of prediction and control. This type of knowledge (Mode 1) is ably demonstrated by many of the chapters in this Handbook. The role of the researcher is that of an observer and the relationship to the setting is detached and neutral. In contrast, Gibbons and colleagues present Mode 2 as the “new” knowledge production and as a “socially constructed and distributed” system-based process. They describe Mode 2

Collaborative management research  177 knowledge production as an emerging paradigm that is increasingly pervasive alongside the incumbent Mode 1. While knowledge production has been traditionally located primarily at scientific institutions (universities, government institutes and industrial research labs) and structured by scientific disciplines, Mode 2 locations, practices and principles are much more heterogeneous (Gibbons et al., 1994; Hessels & Van Lente, 2008). The Mode 2 researcher is explicitly sensitive to broad social consequences. Mode 2 knowledge producers are concerned with solving problems. They produce generalizable knowledge only as a by-product. The Mode 2 knowledge producer, as described by Gibbons et al. (1994), combines theoretical knowledge with applied, practical knowledge to solve particular scientific and organizational problems. In contrast to Mode 1 knowledge producers, who seek to find generalizable laws across contexts taking a disengaged, scientific approach (Gibbons, et al., 1994; Van de Ven, 2007), Mode 2 knowledge producers are closely tied to applied contexts. They are charged with achieving concrete results by creating actionable knowledge that can advance organizational causes. Their point of contact is closer to practice and involves investigating problems of high interest and practical import that sometimes cut across disciplines (Mohrman, Lawler & Associates, 2011; Van de Ven, 2007). There has been a great deal of reflection on the application of the Mode 1 and Mode 2 construct to management and organizational research (e.g., Bresnen & Burrell, 2012; Hodgkinson & Rousseau, 2009; Hodgkinson & Starkey, 2011; MacIntosh et al., 2021; Mirvis et al., 2021; Swan et al., 2010). MacLean, MacIntosh and Grant (2002) in their broad review of Mode 2 argue that the social sciences have an established tradition of Mode 2 research, particularly in research conducted through action research, clinical inquiry and other participatory inquiry approaches. An example of Mode 2 study that incorporated few elements of Mode 1 orientation is reported by Canterino et al. (2016). The authors report on the nature and outcome of a CMR effort that centered on a complex organizational change – the merger process of two real estate investment companies. The study was done in a specific context, that of enabling the integration of two companies into one and aimed at generating actionable knowledge about mergers, and arose from the CEO’s request for help in managing the merger. A research team was formed comprising of external researchers and organizational members from across functional areas. The research team created conceptual mapping that included all the key issues that were associated with the merger and organizational performance. Following the exploration of alternative research designs and research methods by the research team, the decision was made to conduct semi-structured interviews with a sample of organizational members. An interview protocol was developed by the research team, and each interview was conducted by two individuals from this team: one external researcher and one organizational member. The research team analyzed the data, and worked through a collective sense-making process. The analysis led to the identification of the critical role that culture and subcultures played both in terms of the perceived success of the merger and overall organizational performance. The next phase of the study was framed around the research question that focused on the role and the impact of the emerging culture (and subcultures) on the merger and organizational performance. The research team, comprising of external researchers and organizational members, refined the research question, created the conceptual mapping, research design and methods, and decided to use a web-based survey method. Two surveys were created, modified and pre-tested. Following the data collection, top management was engaged in an interactive sense-making exercise to process the data. The process resulted in the identification of specific actions to be taken in order to close the gap between the ‘actual’ and ‘ideal’ state. Four

178  Handbook of research methods in organizational change projects were framed, project champions were identified and agreed to take on the projects, and a timeline for the implementation was established. The discussion of the results identifies and explores some of the characteristics of CMR that could enable mergers and acquisitions (M&As). Furthermore, specific contributions to theory, methodology and practice were presented, proposing CMR as a managerial tool for framing and leading M&As. This study is an example of a Mode 2 orientation, created based on the request for help by the CEO, and was driven by the two aims of helping the two former separate companies to integrate into one and to cogenerate actionable knowledge from and through the experience of the merging actions. The twin aims directed the interventions of cogenerating and making sense of useful information as the project progressed. Though not its intention, this study also provides a springboard for Mode 1 research. This Mode 2 research generated several research questions and propositions relative to cultural identity, learning mechanisms and employee performance and attitudes, as well as the facilitative role of CMR in mergers. The study concludes with contributions to theory extracted through practical ways of knowing, but acknowledges that because the case study extracts its knowledge in the specific context of an Italian real estate merger (N = 1), it is unable to empirically validate these findings. Mode 1 researchers would be wise to capitalize on these study insights to further validate findings in a propositional way of knowing, complementing Mode 2’s practical way of knowing. In an examination of knowledge production practices in the organization development and change field the argument was advanced that both modes (Mode 1 and Mode 2 research orientations) are valuable for managers, organization development practitioners and scholars (Coghlan et al., 2020). The authors concluded that while both are critical to the continuous evolution of the field, they tend to use scientific rigor differently, address issues of relevance and are loaded with relevant reflective practice. Holding both orientations together and finding ways to integrate components from each as situations arise seems to be critical. CMR, as described in this chapter, is embedded in Mode 2 research orientation. Yet, unlike other methodologies it integrates Mode 1 orientation as needed in the emerging collaborative discovery process. Partnerships and Collaboration True collaborations between people, by their very nature, are complex to design, manage or facilitate (Huxham & Vangen, 2005; Mirvis & Marks, 2017). For our purpose, partners are those individuals, groups or organizations who have an interest in the action of an organization and who desire to influence it (Freeman et al., 2010; Shani et al., 2008). Alignment of purpose between different actors is at the basis of true collaboration (Mohrman & Shani, 2008). Partnership is a relationship based on cooperation that entails positive interdependence in which each party, in achieving its own goals, contributes to the other parties’ goals and achievements. As such, true collaboration is only possible if each party also accepts each other’s purposes as well as their own. Partnerships tend to develop over time, go through stages of development and evolve as distinct types (Gray, 1989; Mirvis & Goggins, 2006). Collaboration encompasses a full range of partnerships and relationships among individuals, groups and organizations (Hay & Samra-Fredericks, 2019; Mattessich & Johnson, 2018). In the context of the discovery process in organization change, collaboration implies change and research efforts which include the active engagement of members of the living system

Collaborative management research  179 and researchers in the framing of the action and research agenda, the selection and pursuit of methods, and the development of actionable implications. Collaboration requires collective inquiry, the joint pursuit of answers to questions of mutual interest through dialogue, experimentation, the review and integration of knowledge, or other means (Shani, Tenkasi & Alexander, 2018). Members of a system engage in CMR in order to better understand a certain issue or phenomenon using scientifically valid knowledge and methods. Similarly, researchers engage in CMR in order to better understand a certain issue or phenomenon using practically valid knowledge from practitioners. As such, the nature and quality of collaborative relationships have the most significant impact on the phases of knowledge production and its outcomes (Gray, 1989; Hibbert & Huxham, 2005). The quality of the collaboration, among outside researchers and inside members of the system, is likely to impact managerial actions, the relevant knowledge that was created and new theoretical insights that are generated. The quality of collaboration depends upon the collaborative mechanisms, what we framed earlier as learning mechanisms, that are designed and managed. Mohrman and Shani (2008) theorized that collaborative relationships depend on four factors: (1) the institutional and resource contexts of collaboration, (2) alignment of purpose between the different actors (namely academics and practitioners), (3) the mechanisms that enable learning in collaborative relationships and (4) the convergence of the languages of practice and theory. While the institutional and resource contexts in which collaboration takes place are partially exogenous and thus less under the control of project partners, collaborators actively strive to create a common definition of critical issues and to agree on the scope of research. The complex nature of any business context coupled with intent to transform the organization suggests the need for sensitive structural configurations and processes that sustain the academic–practitioner partnerships (Shani & Docherty, 2003). From a design perspective, in every CMR project the partners create or enhance a tapestry of cognitive, structural and procedural learning mechanisms to fit a particular collaboration within a specific business context in which knowledge gets created in the present tense. Collaborative Management Research and Inquiring in the Present Tense CMR assumes an understanding of organizations as social constructions that are held together by shared meanings and inquiring in the present tense (Coghlan & Shani, 2017). As was suggested earlier, it involves collaboration and partnership between outside researchers and organizational members who work together in addressing real issues and generating knowledge using a research process that is embedded in Mode 2 orientation that also integrates Mode 1 in the discovery journey (Coghlan et al., 2020). CMR is anchored in the context of a social space – the tapestry of learning mechanisms – where through shared inquiry and deliberate action a change takes place. These occur between researchers and organizational members over time, between the researchers themselves and between organizational members, as they experience events, work to understand them, verify their understanding and make judgments on the basis of which they make decisions and take action (Shani & Coghlan, 2021). At the core of present tense inquiry is a collective sense making and meaning creation (Ravasi and Stigliani, 2012; Solari et al., 2015; Weick, 1995). Unlike the individual learning process (Kolb, 1984) and individual reflective practice (Schon, 1986), in CMR the focus is on the creation of shared learning, sense making and meaning creation. We arrange our

180  Handbook of research methods in organizational change understanding of experience so that we can know what has happened and what is happening, and so that we can predict what will happen; it is constructing knowledge of ourselves and the world. Meaning creation in a community of interested and engaged individuals occurs within the learning tapestry mechanisms that were established. When shared meaning is created transformation is feasible. Therefore, collective meaning-making is all about constructing a sense of what is, and what is important for collective action (Drath & Palus, 1994). If development is seen as collective sense making, creating the space for creation, discovery and interpretation is likely to result in new human experience that leads to the creation of new meaning that will be acted upon. The theoretical elements of CMR provide the foundation for the study and practice of research in organization change. The collaboration between those who are insiders to the organization and those who are outsiders, both with the aim of addressing relevant organizational issues and creating practical knowledge about organizational change through their joint discovery process, generates new insights, shared understanding and action. The CMR orientation builds on the view of organizations as learning systems, the notion of insider/outsider collaborative research teams, the essence of partnership and collaboration, the dynamic nature of knowledge creation in the present tense and an inquiry orientation that fits more within the domain of Mode 2 knowledge-production-process practice, yet also integrates Mode 1 as the need arises. Next, we explore the enactment of CMR.

THE ENACTMENT OF COLLABORATIVE MANAGEMENT RESEARCH The CMR process is an emergent inquiry process that unfolds in the present tense. Designing for and managing the honest conversations through the inquiry process between the effort’s partners serve as the cornerstone of the discovery process. Drawing on Schein’s (2013) typology of inquiry types suggests that we can differentiate between exploratory inquiry, diagnostic inquiry and confrontational inquiry. His first category is exploratory inquiry. This is where experience is elicited by generating an understanding of what has taken and is taking place in the organization. His second type of inquiry is diagnostic inquiry, in which understanding is elicited by exploring via inquiry methods how the experience is understood and what causal interpretations are being made. His third type of inquiry is confrontational inquiry, where the inquiry and conversation moves to a more explicit sharing of empirically driven data and ideas that new perspectives have generated. Schein argues that if sufficient time is not devoted to exploratory and diagnostic inquiry, confrontational inquiry closes down the conversation and traps the participants in dependence and debate. As such, the inquiry process requires careful attention to the context, the development of collaborative relationships and the collaborative research process. The context includes the nature of the external business context (such as the state of the economy, the characteristics of the industry in which the effort takes place, and the national and regional characteristics as captured by cultural, political and educational dimensions), the nature of key organizational features (such as business strategy, structure, key processes, technology, social system, economic performance indicators, and management systems and dynamics) and the initial research activities (such as the preliminary dialogue with top

Collaborative management research  181 management about common areas of interests, the perceived legitimacy and added value of a collaborative orientation, and past experiences in collaborative research). The Design and Management of the Collaborative Research Team(s) What differentiates CMR from other specific orientations is the pillar of outsiders and insiders research teams. The quality of the relationships that emerge has the most significant impact on the collaborative discovery process and, in turn, on the outcomes. CMR views the partnership as the mechanism that guides and leads the project. The context in which the collaboration takes place does much to determine the quality of the collaboration that will eventually evolve, but the management of the collaboration is equally important, if not more important. In this sense, the quality of the collaboration depends on different factors. First, the establishments of the collaborative process set in motion the emerging collaborative dynamics. This factor includes different variables, such as the perceived level of need for collaboration, the collaboration potential, and the alignment of interests, values, languages and meanings. Different from other orientations, the CMR process strives towards arriving at a common definition of the critical issue to focus on, and then develops an agreement that outlines the collaborative study and its scope. The organization does not seek help, and the researchers do not impose their studies; the collaboration here is co-determined by the constructive dialogue between the researchers and the top management of the organization about a topic or issue of mutual interest. As a part of the early dialogue with top management, different ways to design and manage the project and the possible mechanisms to carry out the project are explored. The tapestry of the research project that may include a steering group that oversees the study and study team or teams is explored and established. A few of the key variables in this factor include possible criteria for the formation of the project steering committee and collaborative research team(s), the appropriate number of organization and academic members, the structure of and roles in the team, resources (time, spaces) of the team, diversity (for example, in terms of basic demographics, motivation or personality), knowledge base and experience that may be needed, and the development of a shared vision. This factor also includes the development of working processes such as how the study teams and steering teams should work, how the teams should interact with organizational members that are not a part of the steering/study teams, what the most appropriate coordination mechanisms should be and how unanticipated challenges should be handled. Finally, development and possession of the skills and competences that are needed in the facilitation of the collaborative research process are critical to both the quality of the collaborative relationships and the quality of the CMR process. The Collaborative Management Research Phases The development of the collaborative process can be captured by a cluster of different sub-processes and phases. These processes are influenced by, and at the same time influence, the quality of the collaboration among the actors involved in the effort. Since the quality of the collaboration continuously evolves throughout the inquiry process, the delineation of which variables influence which other variables is complex. The following generic phases of the CMR draw on the work of Coghlan et al. (2019), Guerci et al. (2019), Shani et al. (2020) and Shani & Coghlan (2021).

182  Handbook of research methods in organizational change The first phase entails initial conversations among inside organization members and outside researchers about a possible collaborative project. They explore the organization’s context and the essence of Mode 2 research orientation in the framing of a project, purpose, scope, research question(s), inquiry type, inquiry mechanisms and research methodology. The second phase encompasses the actual design and preliminary agreement of the research methodology, data collection tools and methods. The third phase comprises the creation of the collaborative spaces, implementation of the data collection process, the development and capturing of systemic narratives, generative images, meaning creation, the design and facilitation of the data-sense-making process and continuous experimentation within the collaborative spaces of possible changes. The fourth phase includes the design and implementation of the changes inside the organization and the dissemination of the newly created knowledge outside in academic and professional outlets. Table 8.2 provides the generic framework, its generic phases and key activities. Next, we develop further the essence of each phase. Table 8.2

Phases in collaborative management research process: a generic framework

 

Essence and key activities

Phase 1

Conversations about a possible CMR project, agreement about the project topic/focus and inquiry orientation, framing of the project scope, its mechanisms and possible generic phases

Phase 2

Collaborative design of the research methodology, designing and developing collaborative spaces and learning mechanisms

Phase 3

Facilitation of the data collection process while utilizing the collaborative spaces, the development and capturing of systemic narratives, generative images, meaning creation and triangulating the finding via the integration of Mode 1 research and experimentation whenever appropriate

Phase 4

Design and implementation of changes, further experimentation, enhancement and institutionalization of the learning mechanism’s tapestry processes, measurements and new knowledge dissemination

The first phase The first phase starts when the organization experiences some disruption of the status quo and a need for change arises. The stimulus is most likely to come from the external VUCA (volatility, uncertainty, complexity, ambiguity) environment, but it can also come from within the organization. An organization’s management needs to find external researchers who can be of help. For the researcher, having a quest to explore and learn more about an organizational phenomenon can trigger the initiation of conversations with practitioners that can help in framing a project. For example, research curiosity about organizational creativity and how it can be enhanced in a specific industry, such as fashion design, can help motivate preliminary dialogue with members of the fashion industry that can lead to the framing of a project (for more specific detailed examples, see Cirella et al., 2012, and Cirella et al., 2016). The decision to try to collaborate with a company in the fashion design industry had to do with the nature of the industry and the creativity pressure that it entails. Creativity challenge in the fashion design industry is driven by a few principles and practices. A collection is designed by a team and includes between 7 and 21 items, the development cycle of a collection occurs within a 10-week period, most companies are organized around the

Collaborative management research  183 four seasons and thus have four collection development and manufacturing cycles in a year, and repeating a design of an item from a previous collection or previous year is perceived as a failure. As such, the unspoken anxiety and managerial uneasiness in the industry is embedded in the managerial challenge of enhancing continuous creativity. The dialogical practice, whether it is driven from within or outside the firm, led by its very nature to the path of collaborative inquiry vs the more traditional ‘doctor–patient’ consulting firm route. Once that decision has been made, preliminary exchanges between researchers and contact organization members take place. These initial conversations help both parties in arriving at a go/no-go decision and help in the initial framing of a possible project and its scope. Both parties need to gauge the possible added value and the extent that such a project can address their respective needs. In this phase, the parties engage in a preliminary mutual education process as they learn about one another and assess if both parties are ready to undertake a collaborative inquiry project with one another. This set of initial conversations can also serve as an opportunity to have humble conversations and share mutual expectations, the essence of the potential partnership and its meaning, the nature of the emergent process and possible generic phases. Such conversations set in motion the foundation for the outsiders and insiders to form a collaborative partnership in the name of learning and action. Having agreed to proceed, the organization’s management and the external researchers begin to explore the reasons for initiating the change and development project. These conversations usually result in an initial framing or co-framing of the project and its scope around a provisional identification of needs and/or challenges facing the organization. The essence of the conversations and mutual education that take place during this phase among possible project partners allows for the beginning of the establishment of working relationships as individuals get to know one another and begin to appreciate the potential added value of each and the potential added value to the organization for a possible project. The insiders provide a broad review of the organization and its internal and external context – its past, its present and possible future challenges – and possible motives for the change and development effort, while the external researchers provide further prereview of the collaborative inquiry orientation and the nature of its emergent process quality within organization development practice. The second phase During the second phase the focus shifts to the collaborative design of the research methodology, data collection tools and methods. The study insider/outside team(s) determines how and by whom the data collection will be carried out, and once collected deliberate on how to make sense of the data. During this phase, a collective mindset begins to evolve. By the end of this phase, and based on the reflective dialogue and learning, the study team may refine its focus for what they view as the potential for the study that provides the most added value to the organization. The outcomes of the learning and new shared understanding and new meaning creation during the first phase set the stage for the collaborative thinking and possible reframing of the project purpose, scope and research question. Having built the partnership through a preponderance of exploratory and diagnostic questioning, the researchers may draw on confrontational questioning appropriately at this phase. Here the focus is moving towards decision-making and action, and the researchers may allow themselves to offer their interpretations and advice as appropriate. Because the relationship has been formed and the practice of engaged scholarship has been built up, offering interpretations or advice is not taken as an expert prescription. Rather it is taken as an offering of help, which can be assessed on its own

184  Handbook of research methods in organizational change merit. At this stage it may be that conversations become dialogues. Such a shift took place in the collaborative study of creativity mentioned earlier (Cirella et al., 2012; Cirella et al., 2016). Following the initial data collection via extensive interviews, as the insider/outsider study team began to spend time with the raw interview data, beyond attempting to sort out and creating meaning of the qualitative analysis, the team began to explore meaning at the organizational level. The conversation shifted from micro data patterns in the raw interview data to the discovery that creativity in the specific context was more of a collective-based (vs individual-based) phenomenon. The richness of the dialogue led to the realization that the study focus needs to shift from the individual to the collective context and dynamics. As dialogue and deliberation between the partners are taking place in addressing the above content-related issues, deeper-level insights and new shared meaning are created. Deliberation tends to occur in collaborative spaces. Such spaces evolve in organizations in a natural way. Yet they can also be purposefully designed. The study team can explore alternative design forums and make choices about appropriate learning mechanisms (what Shani and Docherty, 2003, labeled as the “learning mechanism tapestry”) that can enhance the ongoing dialogue and deliberations. Since most organizations develop learning mechanisms over time, the leading partners can map up existing learning mechanisms and identify pathways for their utilization as needed. Table 8.1 captured different types of learning mechanisms. If the study team choose to supplement the existing learning mechanisms with some others, or if they choose to design new learning mechanisms that address the need for cognitive, structural and procedural learning mechanisms, they need to address how they would both aid in addressing the practical issue and facilitate the meaning-making process. All the participants are engaging in inquiring in order to understand how events are being understood and the reasoning behind the selection of learning mechanisms; that is, testing the abductive reasoning that is being discussed. The external researchers need to be continually testing the level of agreement or consensus. Critical during this phase is the exploration of options and decisions about the most appropriate inquiry mode, research designs, methodology and timeline. This exploration is also critical in clarifying mutual expectations, desires and goals. This phase concludes with the decision to move forward with the study. Agreement is reached on the project study mechanism, the executive team sanctions the agreement, and the project is communicated to the organization. The third phase The third phase is the actual implementation of the data collection process. It encompasses making sense of the data and constant experimentation. Systemic narratives and generative images are captured, and meaning is created. Team members continue experimentation within the collaborative spaces. Systematic data collection is imperative in the collaborative management inquiry process, as the data needs to meet the criteria of validity and reliability. Furthermore, the act of data collection by its very nature stimulates and engages individuals in new ways of thinking and as such can disrupt the ongoing social construction of reality. As described earlier, the learning mechanisms tapestry’s critical task during this phase is to design the studies based on their understanding of the company and its culture and adhering to scientific methodological rigor and practice. Beer, for example, suggests that what data to collect and how to and who should collect it should be a part of the key missions of the combined “task force” – i.e., the study team(s) (Beer, 2020). He further argues that if the intent is to trigger “honest conversations” the task force should not only decide what data to collect,

Collaborative management research  185 but be the one to actually do so. So, if an interview methodology is used as the main data collection source, task force members should be trained in interview methods and be the ones who conduct the interviews. Similarly, if a generative image methodology is used to trigger new experiences for the exploration of alternative ways of thinking and acting and new meaning creation, what images should be generated and how they should be generated should be the task of the collaborative study team. Bushe and Marshak (2015) argue that generative image methodology is based on “a combination of words, pictures, or other symbolic media that provide new ways of thinking about social and organizational reality” (p. 23). They further suggest that how meaning is made, the language used and the narratives that are created influence the meaning-making process, create mindsets and impact behavior (Bushe & Marshak, 2020). This suggests that a critical part of the collaborative inquiry process is the process of meaning-making of the data that is collected as it will impact the essence, direction and system change implementations. In this phase the researchers are moving in and out of the different inquiry modes as appropriate – sometimes asking exploratory and diagnostic questions and at other times being directly challenging. As always these are done in the spirit of humble inquiry (Schein & Schein, 2021). The issue of validity of the meaning that was created requires special attention by the outside researchers. At the core of CMR, as was described earlier in this chapter, is the importance of integrating Mode 1 research orientations throughout the project. Triangulating Mode 1 research into the study as new insights are generated can improve the project’s validity. For example, in the creativity study that was referred to earlier in this chapter, a couple of Mode 1 methods were integrated: survey research methodology was utilized (i.e., Bailey, 1994) to answer the new research questions in order to validate the collective creativity practice and dynamics in the firm, and experimental and quasi-experimental research designs were deployed (i.e., Campbell & Stanley, 1963) in order to arrive at the decision on the most appropriate way to initiate the first phase of the teams’ new collection development. The fourth phase During the fourth phase of CMR the focus shifts to the design and implementation of the changes. The nature of each study is unique, as no two organizations are alike. This suggests extra attention to rigor and documentation of the evolving inquiry story throughout the collaborative inquiry process. CMR is viewed as an engine for organizational or system transformation. The nature and engagement of a wide number of organization members in the inquiry process sets system-wide transformation in motion. The execution of the transformation strategy is a process that the executive team leads. The implementation process and its impact need to be studied and improved. As a part of the impact assessment, setting in place the continuous learning mechanism to lead such an effort is critical as it provides the organization with the capability to continuously learn and improve. The insights generated in the triangulation of the research in the third phase set in motion this phase. Coming back to the creativity study mentioned above, a few new processes and practices were identified and implemented. For example, each team member was asked to capture periodically throughout the new collection development cycle insights and reflections into a personal website about what is working, what is not working well and what can be improved. The understanding was that at the end of the 10 weeks, a team document would be created for the team to review and discuss. At the end of the new collection development, upon the delivery of the final designs to production, each team set aside 2–4 hours to review

186  Handbook of research methods in organizational change and make sense of the data that was generated. The data was analyzed prior to the meeting by one of the team members – a rotating role. During the sense-making process, ideas for elimination of practices, enhancing some practices and the need to develop new practices were explored and preliminary decisions made. Following approval by management, the teams were encouraged to experimentally implement some of the ideas from the previous collection cycle with the understanding that data will be collected such that continuous improvement will take place. Another example was the discovery of the importance of the face-to-face team launch of the work on the new season’s collection. The research demonstrated that teams that met face to face to start the new collection work were more productive and more creative. New routines were developed and all new collection teams started their work in the firm’s library (or historical archive – the company’s most sacred space), which included all the collections created by the firm over its many years of operations. Teams worked in the library in crafting the skeleton of the new collection; developed the specific design tasks, timelines and delivery milestones; and structured team meetings. Outcomes of CMR Outcomes can be examined in a variety of ways. We chose to focus on four main factors that seem central in capturing the effectiveness of collaborative efforts. The first factor is the change implementation in the organization, which potentially includes organizational improvements, specific learning on the studied phenomenon, improvements in the quality of work life, the development of organizational learning competencies, and the possible observation and analysis of these learning and change processes. The second intended outcome is the creation of new scientific knowledge; i.e., new knowledge about the study’s topic and the collaborative process of the research group. The third possible outcome is the creation of an evaluative system. A post-study review and/or a continuous monitoring program can be developed to generate further reflections and learning about how the collaborative processes and the change actions were performed. The final outcome is the consolidation of a CMR protocol with coherent tools; i.e., a protocol for ongoing organizational learning and the tools and processes required for continuous discovery. These outcome factors and their quality are a result of the complex interactions, relationships, processes and activities that occur throughout the course of the collaborative effort. As this chapter suggests, the outcomes of a CMR effort are influenced by the development of the collaborative process and its quality, which in turn is influenced by the quality of the collaboration, which is itself influenced by contextual factors. To add to this complexity, the outcomes later influence the process itself, the quality of the collaboration and, at times, even the contextual factors; for example, the organizational features or the features of the research group(s). The dynamic nature of the model helps explain the reasons for the variety of approaches and outcomes associated with collaborative efforts. The dissemination of the new knowledge that gets created is an integral element of this phase of the project and is of particular interest to the external researchers. Dissemination is part of the CMR project contract and so engaging organizational participants in the dissemination processes is highly desirable for the credibility of the research. The knowledge generated in collaborative inquiry, while mostly following the Mode 2 paradigm, utilized abductive logic. The collaborative nature of the process suggests that the dissemination of the newly created and rich insights would add value to theory and inform practice.

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IMPLICATIONS FOR THE RESEARCHER CMR is not a new creation, although it continues to be rediscovered by managers, scholars and scholar-practitioners. Building a true collaboration requires developing a shared vision and purpose, commitment to shared goals, and empowerment of participants (Mohrman et al., 2008; Worley & Mirvis, 2013). True collaboration may develop gradually. At the center of CMR is dialogue or honest conversations between outside researchers and inside managers and organizational members. The challenge and the opportunity are to design and manage mechanisms and processes that will be of utmost relevance to the partners, reflective in nature and rigorous such that new valid and reliable knowledge – for practice and the scientific community – can be generated. The system’s context plays a key role in the establishment of a collaborative inquiry-based effort. Usually not much can be done about it as it reflects the nature and systems within a specific context. Yet, as advanced in this chapter, the context for collaboration and dialogue can be designed and managed. In this section we explore the implications for the researcher in how to design and sustain the balance between rigor and relevance, and design and manage learning mechanisms that house the insiders’ and outsiders’ discovery process and deliberations, and ways for researchers to sustain quality in CMR. Designing and Sustaining the Balance between Rigor and Relevance Our point of departure is that the preconditions for initiating CMR are seldom ideal. CMR projects can be initiated by researchers that are intrigued by a phenomenon that they would like to learn more about and build on gaps that are identified in the academic literature to frame their initial interest. For the junior researcher, initiating a CMR project can lead to the development of relationships with a living client system that can yield continuous collaboration, learning and new knowledge generation. The quality of the collaborative relationships that evolve as the project progresses can establish the foundation and opportunities for a wide range of research projects. For example, two of the studies that were shared in this chapter – the one with the fashion design company (Cirella et al., 2012 and 2016) and the one with the merger of the two real estate companies (Canterino et al., 2016) – evolved into seven- and five-year research collaborations with four and three published papers respectively, and had major impact on the companies. Managers that decide to initiate CMR are usually driven by a problem that they are trying to understand better and fix. This may be a result of knowledge gap in their managerial experience. Both managers and researchers live different lives and have different experiences. Yet, regardless of the trigger, they share a common value regarding the added value of collaboration to generate a deeper level of understanding and improvement of practice (Bartunek & Rynes, 2014; Mirvis et al., 2021; Paine & Delmhorst, 2020; Shani & Coghlan, 2021; Shani et al., 2008; Van de Ven 2011). The researcher shoulders the responsibility to lead the initial conversations with a clear eye about the need to craft the project such that the balance between relevance and rigor can be maintained. This orientation sets the stage for accomplishing the goals and needs for both management or practice and new knowledge creation. Collaboration requires collective inquiry between outsider researchers and organizational members (the insider researchers), and the mutual pursuit of answers to questions of mutual interest through dialogue, experimentation, the review and integration of knowledge, or other means (Shani & Coghlan,

188  Handbook of research methods in organizational change 2021). Management engages in collaborative research in order to understand a certain issue or phenomenon using scientifically valid knowledge and methods – the relevance need. Social science researchers engage in collaborative research in order to better understand a certain issue or phenomena using particular valid knowledge with practitioners – the rigor need. The dynamic nature of the research in the discovery process needs to be designed (and redesigned if needed) and managed with the goal of limiting the likelihood of reaching false conclusions about the current state of an event and/or phenomenon. Learning mechanisms play a critical role in providing the collaborative dialogical space where the balance between rigor and relevance is explored, designed and managed, gets created and is continuously discussed. Developing the Learning Capacity One of the key challenges for the researcher is the development of the capacity to conduct CMR in a living system. Learning mechanisms, as was discussed earlier, are viewed as such mechanisms – they provide the collaborative space for dialogue. This arena is the collective space in which outside researchers and organizational members (the inside researchers) meet, design and manage the discovery process such that the needs of relevance and rigor are maintained, while addressing the organizational and the external researchers’ needs. It is the space in which meanings get created (Press et al., 2021). Since most organizations develop learning mechanisms over time, early activity that can be led by the researcher is to map out the existing learning mechanisms and identify pathways for their utilization. For example, in the fashion design firm, over the years a basic routine (i.e., a procedural learning mechanism – see Table 8.1) was established by the organization to have post-collection review sessions. Careful research into the actual post-collection review practice identified a lack of a systematic approach, a wide variety of data types that were collected, wide variation in how and by whom data was captured, and even wider variability of the post-collection review utilization. Following experimentation during two collection development cycles with different collection development teams, a structural protocol was developed (i.e., a structural learning mechanism – see Table 8.1). As such the decision was made to supplement the existing learning mechanisms with some others or even new learning mechanisms that can aid in addressing emergent needs. At their core, learning mechanisms provide the platform, the space and the capability to carry out and facilitate discovery and collective meaning-making processes. For CMR, the need for ‘space’ for collaboration, discovery, learning and meaning creation is critical. Such spaces can be physical spaces (i.e., an office space), a virtual space (i.e., a teleconference or Zoom-based space), a mental space (i.e., shared ideas) or any combination of these kind of spaces (Nonaka et al., 2001). A foundational aspect of the learning spaces is that they provide an arena for interactions between individuals, between individuals and the environment, and between individuals and information. For some, designing the collaborative space is the creation of a learning community within a living system (i.e., Cirella et al., 2016; Coghlan, 2017; Mirvis, 2002). Friedman et al. (2016, p. 114) emphasize the need to create social space that enables individuals “to think, feel, and act in ways that exercise greater choice over the realities they construct and actually construct them”. The collaboration spaces are also places where meaning can be explored and created. McClellan (1983) provided helpful categorization of meaning structures: private meaning structures, accessible meaning structures and collective meaning structures. Private meaning is the meaning which individuals construct for themselves within a system that for a variety of reasons – such as meaning that is of a per-

Collaborative management research  189 sonal nature, meaning that may have been constructed from information that was provided by others, meaning that they may see as providing a competitive advantage or meaning that they believe will not be relevant to others – they decide to not make accessible to others. Accessible meaning is the meaning that individuals do make available to other members of the system and at times to individuals outside of the system. Norms emerge in the system that allow and at times even reinforce the sharing of private meaning. The CMR processes are enacted through exploratory, diagnostic and confrontational questioning so that private meanings are shared and become accessible to the other partners and move to becoming collective. Collective meaning resides in the minds of the system members, and the collective as a whole hold it in common (Dixon, 1999). Collective meaning ability – created within the learning mechanisms tapestry space – enhances the system’s learning capacity. As such, the learning mechanisms serve as a critical engine for both continuous system improvement (relevancy and resiliency) and the generation of new knowledge (rigor). Addressing the Quality Challenges in CMR As with every form of research, adhering to quality standards is critical. In CMR, the social science researcher is the one that shoulders the responsibility for initiating the dialogue between the CMR partners about the quality standards criteria and facilitating adherence to them. Building on Pasmore et al. (2008b), Rajagopalan (2020), Paine and Delmhorst (2020) and Shani and Coghlan (2021), four quality dimensions of CMR – rigorous, reflective, relevant and resilient – guide the CMR implementation process. Under rigorous, Pasmore et al. (2008b) group: data-driven, multiple methodologies, reliability across settings, co-evaluation, causality, underlying mechanisms and publishability. Under reflective they group: historical impact, referential, co-interpretation, community of practice, collection and repeated application. Under relevant they group: practical, co-determined, re-applicable, teachable, face-valid, interesting, true significance and specific. Resilience, as advanced by Rajagopalan (2020), is ultimately about persevering and maintaining stability of the process in the face of continuous challenges. Focusing on all four quality dimensions as listed above, while facilitating the CMR process, is critical in fulfilling the shared vision, purpose and goals of the different parties. The quality roadmap is critical for both the design and management of the inquiry process and being impactful over time as the effort encounters the many challenges placed by changing context, managers and scholars.

CONCLUSION This chapter has advanced CMR as an approach to researching change and development and changing. CMR is captured as an inquiry process that occurs in a natural setting within a specific business and industry context, involves true collaboration between outside researchers and insiders (organization members as co-researchers), addresses an emerging specific issue of concern, uses multiple methodologies that are scientific, involves the creation of a learning system via the establishment of learning mechanisms, improves system performance and adds to the scientific body of knowledge in the field of management. This chapter introduces and explores the distinctiveness of CMR within the field of organization development and change by focusing on the critical role that the hybrid insider and outsider collaborative inquiry team

190  Handbook of research methods in organizational change (or teams) play in creating new knowledge and enhancing the organizational change capabilities. The first part of the chapter presented a comprehensive framework of CMR and its theoretical foundation that includes organizations as learning systems, learning mechanisms, insider/outsider team-based research, the dynamic nature of knowledge creation – the hybrid of Mode 2 and Mode 1 orientation, collaboration and partnership, and present-tense knowledge creation. The second half of this chapter explored the enactment of CMR with emphasis on the insider/outsider teams, key phases, processes and mechanisms. Such teams are viewed as a critical learning mechanism that serve as the center or engine of CMR initiatives. In enacting CMR aiming to produce new practical insights and new knowledge that is rigorous, reflective, relevant and resilient, the design and management of learning mechanisms is central. As such, the context, design and management of the collaborative research team(s), the four phases in the CMR process and the outcomes of CMR are discussed. Last, the implications for researchers are explored. The challenges of designing and sustaining for balance between rigor and relevance, developing the learning capacity via the learning mechanisms that house the insiders’ and outsiders’ discovery process and deliberations, and addressing the quality challenges in CMR are explored. CMR initiative in a living social system is complex. Developing a shared vision and commitment to shared goals and interests are critical to true collaboration. Achieving balance between relevance, rigor, reflection and resilience is a balancing act, yet it is essential for success and goals accomplishments. Developing the learning capacity and designing, and managing the learning mechanisms that house and guide such effort play a critical role for the insiders/outsiders’ collaboration. The added value or payoffs from being engaged in such effort, for both the insiders and outsiders, are invaluable. Yet, as can be seen from this chapter, one must recognize that there are few, if any, shortcuts in the process.

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9. Learning history: engaging multiple perspectives for learning Margaret Gearty

LEARNING HISTORY: ENGAGING MULTIPLE PERSPECTIVES FOR LEARNING In the mid-1980s, the American adventurer and recordist Louis Sarno travelled to the Central African Republic, and while he was there he recorded the music of the Aka people. One of his recordings was of women singing as they gathered mushrooms in the forest. In his recent book about the hidden entangled worlds of fungi, the author and biologist Merlin Sheldrake describes listening to the recording of those women: As they wander around collecting mushrooms, their steps tracing the underground form of a mycelial network, the women sing among the sounds of the animals in the forest. Each woman sings a different melody; each voice tells a different story. Many voices intertwine without ceasing to be many. Voices flow around each other’s voices, twisting into and beside one another. (Sheldrake, 2020, pp. 60–61)

The sound of songs being sung in a forest may seem an unlikely place to start a chapter in a book on research methods. However, for reasons that will become apparent, the story of the Aka women sets the scene perfectly for the method and questions at this chapter’s heart. How might we learn from the past? Humans are storytelling beings – Homo narrans – who have a natural predisposition to order experiences and make meaning of them through stories (Rhodes & Brown, 2005). In organisational life, the experiences of individuals and teams are narrated into stories in a multitude of ways. Some are told in official reports, others are told within the “organisational dreamworld” where desire, anxieties and emotion find expression in, among other things, anecdotes and gossip (Gabriel, 1995, p. 478). Such informal stories interweave with agreed narratives. There is a paradox for organisational learning that results (Rhodes, 1996) where diverse experiences tend towards becoming socialised to the “consensus of organisational culture” (p. 2). So, learning processes can run the risk of homogenisation of narrative. The learning history approach featured in this chapter is fundamentally a pluralistic approach to learning. It is a process that draws on and works with the multiple stories and experiences of participants in a way that acknowledges organisational life is multi-voiced and polyphonic (Hazen, 1993). The story of the women gathering mushrooms that starts this chapter provides a lyrical and highly relevant analogy. In this description, women are going about their work, embedded in the landscape, and singing their song. Their voices “intertwine” without ceasing to be many. Could we liken any project work – whether at work, at home or in our communities – to “women gathering mushrooms”? Where each of us is a protagonist – wandering around, gathering mushrooms and singing one’s song. Fast-paced organisational life might not seem compatible with either wandering or singing. Yet, I will hold to the analogy for now, as the polyphonic aspect of learning history is one of its most distinctive features as a research 194

Learning history  195 process. In learning history, the voices of researchers and participants interact in written texts and in convened learning spaces. This art of learning history practice is in enabling these voices to flow around each other’s voices, “twisting into and beside one another” (Sheldrake, 2020, p. 61). In a Nutshell Learning history is a multi-staged, collaborative action research process where researchers work with participants to co-create a history – usually a written text – that documents a project, occurrence or life event that was in some way notable (Gearty, 2014; Bradbury, Roth & Gearty, 2015). This history is not a convergent account. Quotes from the protagonists’ voices appear verbatim and are woven through a reflective narrative that is created by the researcher(s). The result is a “jointly told tale” (Van Maanen, 1998) that works close to the grain of lived experience. Learning arises by inquiring into and embracing the multiplicity of perspectives in that co-created tale. As the sketch in Figure 9.1 conveys, this is a form of insider/outsider research (Bartunek, 2007) whereby the “outsider” researcher works with the “insider” protagonists within the field of study towards actionable learning and outcomes that are of practical value to all parties involved (Roth & Bradbury, 2008). In later stages, researchers often convene reflective learning spaces in which to inquire and reflect together (Gearty, Bradbury-Huang & Reason, 2015). The learning historian(s) orchestrates this entire process – first listening to and brokering the voices of insider participants into a Learning History and going on to engage them and possibly a wider community of “readers” and “learners” in a learning process.

Figure 9.1

Sketch of the core idea of learning history

196  Handbook of research methods in organizational change Conventions and Voices for This Chapter This chapter will present learning history – in practical and philosophical terms – as an action research approach. “Learning history” is an encompassing term that can become confusing: it is in fact both a product and a process (Gearty, 2014). To help distinguish I will adopt the following conventions: on the one hand “learning history,” written in lower case, will refer to the method and process – in other words the identifiable framework for inquiry – sometimes described as an action research modality (Coghlan & Brannick, 2014; see Coghlan’s chapter). On the other hand, the (Learning) History – capitalised and italicised – refers to the written artefact that is produced through the process and around which conversations and inquiry are configured. In keeping with the polyphonic and narrative spirit of learning history, I will bring two different voices to this chapter. One will be my more informing research voice. This voice will set out learning history in a typical and essential way: what it is, what are the philosophical underpinnings and how it has evolved as a method. I am mindful this is a research methods book and so in the second half of the chapter I will focus especially on practicalities: what is involved in the practice of learning history – the steps, skills and ethics involved in being a learning historian. As I write I occasionally pause to materialise the needs and interests of “you” the reader who has found your way to read this chapter. I imagine you might be a doctoral or master’s student who is considering learning history for your dissertation. Or you might be a researcher or organisational practitioner browsing this book, looking for a method that fits a research or learning agenda you have. Perhaps you are interested in research methods or specifically action research. Or maybe the idea of history has led you to read this far. I cannot know, but with my second more storied voice, I do try to address you, my reader, as though we were having a coffee together or sitting down on the sofa for a chat. This more personal voice of mine will tell you something of my story with this method, and the metaphors I find helpful in making sense of and conveying this rewarding and multi-facetted approach to learning. It arises from a different process in the writing. I chose these stories and excerpts instinctively and wrote them in a free-flowing way. In a way we have met this voice already within the prologue with the story of the women gathering mushrooms, and we will meet it a second time once I have introduced learning history as an evolving method and located it philosophically.

THE EVOLUTION OF LEARNING HISTORY AS A METHOD Learning history was first articulated in the mid-1990s by Massachusetts Institute of Technology (MIT) researchers George Roth and Art Kleiner (1995) as a distinctive method within the field of organisational learning. Early applications were in the manufacturing industry and featured the experiences and practices of high-performing, successful teams (Roth & Kleiner, 1998). In parallel, a field manual for the method was created (Kleiner & Roth, 1996) and the method started to be applied in not-for-profit and educational settings. Notably, Hilary Bradbury conducted a learning history with the Swedish environmental charity The Natural Step for her doctoral thesis (Bradbury, 2001) while Rupesh Shah explored NGO–corporate connections through learning history in his (2001). Amidon’s (2007) excellent overview of

Learning history  197 the emerging genre of learning history showed a diversification of application throughout the 1990s and in the early millennium. The gradual establishment of learning history as an action research practice can be tracked through its appearance in the three editions of the SAGE Handbook of Action Research. In 2001, Bradbury described her doctoral learning history as a series of participatory conversations that had an explicit change agenda in tune with her environmental interests. In the second edition of the handbook, Roth and Bradbury (2008) elucidated four stages of learning history method and set out quality criteria for the practice of learning history that centred on there being actionable learning for those involved. In the third edition of the handbook, the practice of learning history was extended further to distinguish organisationally situated (local) and inter-organisational (boundaryless) learning histories (Bradbury et al., 2015). At first learning history was largely represented in the literature as a staged step-by-step method. Over time it has become more firmly established as an action research practice that arises from the dynamic combination of elements of first-, second- and third- person inquiry (Gearty & Coghlan, 2018). These developments notwithstanding, one could not claim that learning history has become a mainstream research method. It is not generally taught on research methods courses, and learning historians are scattered like a diaspora rather than clustered together. There are some exceptions. At the University of Groningen in the Netherlands, learning histories have been part of the curriculum for a master’s course since 2004 (Peters & Thier, 2019). At the University of Bath, between 2006 and 2009, learning histories were the central method for a large-scale action research project called Lowcarbonworks (Gearty, 2009; Reason et al., 2009). Finally, on the action-research-based master’s and doctoral programmes at Ashridge/Hult Business School, learning histories are included in the teaching and research and have featured in the curriculum and in some students’ work. Despite, or perhaps because of, its niche status, graduate researchers at doctoral and master’s level are often enthused when they do find or stumble across learning history, as it fills a gap that they have been looking for. A student wrote to me recently saying, “I am quite amazed that learning history as a method is not used more, or more widely known – especially where interviews with participants/stakeholders are concerned” (A. Dutschke, personal communication, 19 November 2020). My own story reflects this too.

MY STORY WITH LEARNING HISTORY: EARLY DAYS As I embark on writing this chapter, I reflect on my own history and how that has shaped my choices and disposition as a researcher. Action research is an orientation of research that acknowledges the embeddedness of the researcher in their field. Whether implicit or not, the investigative impulses of researchers are shaped by who they are. I grew up in a small town in Ireland in the 1970s as a younger member of a large and voluble family of six. I see now that, at that time, much of social and family life was negotiated and understood, and evolved through the telling of stories. These were everyday stories rather than the set-pieces of folklore. It was normal to tell stories of oneself in one’s life – often for laughs – and often as a way to explain, locate and situate oneself in social life. At the dinner table or standing warming our backs by the fire we told stories of the day, of people we knew, of happenings in the town. There were implicit power dynamics to this and judgements of quality. Some people were known to be “great raconteurs” Pecking orders also played their part in

198  Handbook of research methods in organizational change determining who had a right to tell stories in certain situations and who should listen. Ireland was, at that time, a more patriarchal and unquestioningly religious society than it is today. Some stories were told once and thrown away – others became apocryphal. These were told and re-told and, in their way, became “mythic” – part of a shared mythology – such as the time a drunk man came into our house and slipped into bed in an empty bedroom, only to be found hours later by my father and younger sister, whose knees, it was told, could be heard knocking. These stories helped us to “know” ourselves in relation to each other and the wider community. I see in the above story many clues to my passion for learning history. There may be clues here too for any prospective researchers. I value and enjoy creating stories out of life. My background gave me an instinctive sense that stories not only entertain but that they can impart meaning and knowledge. They also allow for living negotiations, not so much of the truth, but rather of what from life was worth talking about. Finally, I recognised that stories can also be vehicles of stasis and oppression when lifelessly told and re-told. In the early stages of my doctoral studies, I had chosen to investigate what were the human dimensions behind some of the breakthrough low carbon projects in the UK in the early millennium. My work was part of a larger action research project, what would later become known as Lowcarbonworks, that was working with partners from the food sector in the UK with the aim of better understanding the social, psychological, economic and political barriers that were preventing the adoption of low carbon technologies and processes (Reason et al., 2009). In the autumn of 2005, my investigations had taken me to a run-down council building on the edge of London to interview a policy officer about related low carbon projects. As it transpired, this man had a story to tell himself. So, with the tape recorder running, and the batteries thankfully charged, he recounted to me in vivid detail the highs and lows of his own efforts to get a forward-thinking piece of low carbon planning policy through Parliament. Though the subject matter was dry, bureaucratic even, the human story behind this man’s success was not. It involved dead-ends, café plotting, last-minute calls with politicians and a crescendo moment where, following a catfight, he had a chance meeting at the vets, which led him to a breakthrough (Gearty, 2008). I see now that this man was a “great raconteur,” like those I had encountered in my childhood. He was also a nuanced change agent. It was important to me to bring his story into my research in a way that would keep his practice of change intact. “Have you looked at learning history?,” my doctoral supervisor asked when, after three full months, I had not yet made a start on working with the interview material. “It is like I have a delicious cake rotating inside a glass case,” I had said a little dramatically. “I don’t know how to eat it.” I was to find that the learning history approach gave me the framework I needed to work with the fullness and breadth of the man’s story. I went on to do four more Histories, each featuring a low carbon initiative in local government that had achieved notable results. None of my later interviewees told such vivid stories as the first man, but the practices of change they described were compelling nonetheless. At a later stage in my doctoral studies, I convened actors from across local government into a workshop to inquire into the practices described in these five Learning Histories. A lively debate was to erupt at this event. Surely it was not all down to the cat, some attendees protested when we discussed that first History. “No cat, no policy,” insisted the man who’d

Learning history  199 given the original interview (Gearty, 2009), and I recalled how he had once said in our conversations, “The winds of change are at your back one day, and they are against you the next.”

PHILOSOPHICAL UNDERPINNINGS The Narrative Turn The narrative turn in social studies arose out of developments in contemporary literary theory in the 1960s when there had been a shift towards analysing narrative texts in their own right. This marked a departure from traditional hermeneutics that had focussed more on authorial intentions (Czarniawska, 2004). By the late 1970s, scholars across the social sciences, including in psychology, sociology and anthropology, had started developing narrative methods (idem). They did so in recognition of the ubiquity of narrative schemes in our lives and our natural propensity, as humans, to continually develop storied accounts to make sense of experiences and behaviours (Polkinghorne, 1988, p. 4). Organisational life is no different. We tell stories of projects and happenings, instinctively navigating levels of realness and disclosure according to who is listening. An organisational change project might be told as a series of war stories in a restaurant late one night, only to be re-told as a best-practice victory narrative on PowerPoint the next day. Jerome Bruner, an American psychologist and one of the founders of the field of cognitive psychology, gave us an explanatory framework to understand this fluidity. He suggested that humans occupy one of two modes of thoughts at any given time (Bruner, 1987). The narrative mode is concerned with the highs and lows of human experience, with ambiguity, and the “vicissitudes of human intention” (Bruner, 1988, p. 102). On the other hand, there is the analytic paradigmatic mode – which is the abstracted and conceptual mode of thought – that is expressed in the language of project plans, aims and outputs (Bruner, 1987). Learning history work plies between the modes and has a rectifying, rebalancing role, it has been suggested. Our organisations are “mythically deprived,” wrote Roth and Kleiner (1998, p. 55), suggesting that almost all of formal organisational life and language takes place in the analytical mode. In organisation studies, narrative knowledge is now recognised and valued, and a range of research practices have developed accordingly (Czarniawska, 1997, 2004) that include narrative analysis (Riessman, 2008) as well as processes for inquiry centred on narrative (Clandinin & Connelly, 2000). However, as its name suggests, learning history is about understanding history and so is about more than curating and working with stories. The “history” aspect distinguishes it from other narrative research methods, and this has interesting implications in terms of the underpinning epistemology and, therefore, the research practice. To understand this more fully, we need to trace the connections between learning history and oral history. Oral History Oral history is a recognised and distinct branch of history that involves the collection of perspectives from people regarding key events or everyday life. Modern oral history was developed at Columbia University in the late 1940s by Allan Nevins and his colleagues and developed then through the 1960s and 1970s, as video and audio recording became increas-

200  Handbook of research methods in organizational change ingly available due to technological developments (Liamputtong, 2015). It is a research discipline that takes inspiration from but is quite distinct from the age-old oral tradition where cultural history and indigenous knowing are passed via storytelling and song from generation to generation. Modern oral history typically involves formal interviews with individuals who tell their stories about a certain time in recent history. These stories are not passed on orally like in olden days but are then archived and made available as voice and written material. Historians Michael Bravo and Sofia Davis suggest to their students that oral histories are particularly suited to recent events by providing insight into aspects of tacit human experience that are not normally written about; they include here “rumour, personal grudges, local myths, oppressed minorities, conflicting interests” (Bravo & Davis, 2021). Whereas a traditional historian works towards creating a written text that relies on rigour and the reliability of sources and sound references, the oral historian works towards an archive that relies on accounts of human experience. With oral history the sources are the people involved, and reliability relates as much to authenticity as it does to facticity. If the one (traditional history) is converging towards the authoritative account of “what happened,” the other (oral history) is expanding towards the multiplicity of “what it was like” for those involved. Despite the expansion of perspective, oral histories do give account of some aspect of history and are bound, to some extent, by factual reportage. An agreed focus or actual occurrence acts as the crystal through which multiple human experience is refracted. This could be a social history of a particular time – as in the work of Studs Terkel, who charted what life was like for ordinary Americans across different socio-economic classes during the depression (Terkel, 1997). Or it could be a key event in recent history. Recent popular oral histories include Graff’s The Only Plane in the Sky (2020), which features the verbatim accounts of dozens of people during the attacks in the US on 9/11, and that by the Belarusian author Svetlana Alexievich (2006), who won the Nobel Prize for her multi-voiced writings on the Chernobyl nuclear disaster. Historical accuracy matters in these oral histories – so there will be a coherence in terms of agreed dates, times and happenings of note. Interlocutors bring their experiences to bear on an agreed activity or happening. So oral histories are more than a collection of stories, and this is relevant as we turn to consider learning history. From Oral History to Learning History How the seed was sown In the mid-1990s George Roth was consulting with car manufacturers and looking for an approach to learning that would be more of “a craft and not a recipe” (Roth, personal communication, 22 April 2021). As it happened, he was attending an oral history training course at a local not-for-profit at the time, and had also come across Terkel’s acclaimed oral history Working (1974), which captured in their own words the desperation and dignity of a wide range of ordinary American workers. These influences meshed well with what researchers at MIT were looking for, which was to develop a way of doing systems and organisational learning that would still “help people [stay] connect[ed] with their style and what they already knew” (Roth, personal communication, 22 April 2021). This mini-history outlines what led George Roth and Art Kleiner to develop the learning history method for organisational life. Two of the key principles of learning history, configuring learning around “notable events” (Bradbury et al., 2015) and working with multiple ver-

Learning history  201 batim accounts, have been sustained over the years as central to the method and these directly reflect the original connection to oral history.

THREE RESEARCH IMPERATIVES As with oral histories, Roth and Kleiner were keen to get past listing best practice and more into the “thinking experimentation and arguments of those who have encountered the situation” (Roth & Kleiner, 1998, p. 43). Their aim was richer organisational learning and so, in developing the method, they set out to not only chart the multiplicity of organisational life through the stories of those involved, but to also pragmatically work with those stories to convey and communicate them through the organisation. They identified three imperatives that inform the learning historian’s work, suggesting it as a wide-ranging practice that involves cycling between the “mythic” (storytelling), the “pragmatic” (communicating) and the “research” (analytical) orientations (Roth & Kleiner, 1998). The “research” orientation separates learning history from oral history by emphasising the role of the researcher as an analytical outsider rather than solely as a curator of stories. The learning historian not only crafts the voices of insider protagonists into a History, but they also add analytic and reflective content – drawing out themes, adding reflections and sometimes making links to theory.

LEARNING HISTORY: EPISTEMOLOGICAL POSITIONING Matters of “Truth” The links to both oral history and to narrative research place learning history in an interesting or, put more bluntly, a confusing place epistemologically. For example, a Learning History can sometimes be referred to as an action research type “case study” that suggests it documents and analyses a subject matter and presents a negotiated truth about what happened. This post-positivist interpretation is mistaken but understandable. Unlike other action research processes the History is a tangible output around which learning is organised. Furthermore, the evidential, historic aspect means that, as discussed above, the integrity of the written story needs to hold and cohere. Facts, timelines and occurrences around which experiences are configured need to be precise. It is in this way mere stories are turned into histories (Peters & Thier, 2019). This brings a journalistic quality to the work and a need to fact-check. It can never be an aim to get the History right – this is research in an interpretivist paradigm after all – but at the same time a learning historian must avoid getting certain things wrong. Matters of Plurality At the same time, much of the action and inquiry in learning history is social. The learning occurs in an interpretive, constructivist space, where meanings are not so much negotiated as they are placed side by side and reflected upon. Much of qualitative and action research is oriented around socially constructed meaning-making, and the idea of human experience as a valid site of inquiry is not new. It was the father of phenomenology Edmund Husserl who re-instated the “experiencing self” as a primary locus of scientific and philosophical inquiry

202  Handbook of research methods in organizational change (Abram, 1996). What is distinctive about learning history is that by privileging the original voices of participants, and by rendering these for reflection, the research move is as much towards learning from a plurality of perspective as it is from insights, actions and meanings that are collectively agreed and – in some way – made. Matters of Liberation The primacy of the original voice suggests an emancipatory principle at play that allies learning history with feminist epistemologies and participatory action research methodologies. The knowledge being created is not only democratic, the process of knowledge-making is itself democratised. Thus, learning history has the potential to align with critical pedagogies (Freire, 1970) where hitherto quieter, or even oppressed, voices may be amplified. Whether that potential is realised or not depends on many factors, including the researcher’s approach but also the context in which they are operating.

RESEARCH PRACTICE AT A CONFLUENCE POINT At first glance then, learning history might seem to occupy a contradictory place, sitting as it does on a confluence point of research paradigms that Lincoln and Guba identified as incommensurable (Lincoln & Guba, 2017). We might call this “an edgy place” to occupy with participants, where a first promise of a clear process of qualitative research unfolds into a wider practice of participatory pluralism. In earlier work I have reflected on the ethics of this – asking if the learning history is a Trojan horse that smuggles in postmodern ideas by stealth on unsuspecting co-researchers (Gearty, 2009). At its best, a learning history engagement works more by handholding than stealth – providing a guide-rope into new territories of plurality and knowing. The process unfolds learning from a familiar starting point (i.e., the promise of a Learning History document, an “output” in conventional terms) and moves from there, step by step, into less familiar, postmodern space of contradiction and multiplicity. Learning history practice is in many ways about steadily holding the space open for inquiry at a confluence point – for participants and researchers alike – recognising that epistemological tensions will play out in unique and interesting ways – and that when they do, this may point to contested territory of what constitutes valid knowledge. Action Research at the Boundaries As David Coghlan’s chapter in this book outlined, action research has a practical orientation where actionable outcomes are sought for co-researchers in the field of inquiry. Part of that orientation is a critical one, where taken-for-granted assumptions that guide organisational life and practice are opened up to be challenged. The notion of the “lifeworld” can be a helpful angle in further orienting learning history as a critical research practice. Husserl’s “lebenswelt” refers to the “everyday world of practical, lived experience” (Ladkin, 2005, p. 112). As such it is an ambiguous and multiple realm of direct experience where emotions and personal history(ies) influence our perceptions and interactions with those around us. Later theorists and action researchers suggested “lifeworld”

Learning history  203 as a place of experiential and relational encounter where a community may grapple with shared understandings “of ‘who we are’ and ‘who we value being’” (Wicks & Reason, 2009, p. 245). The substantive social theory of “communicative action” of Jürgen Habermas (Kemmis, 2001) suggested such spaces are constantly being colonised by the abstracted “system world” where formal organising and public life takes place. Like other forms of qualitative research (e.g., case studies) learning history brings attention to “lifeworlds” in terms that are tangible and familiar to the “system world.” Stephen Kemmis drew on Habermas’s work to pose that emancipatory action research is conducted in the marshlands where system world and lifeworld collide (Kemmis, 2001). This collision creates “boundary crises” that are often encountered by participants doing action research (idem, p. 97). Opening and sustaining “communicative spaces” at these collision points is the work of any action researcher who locates their research in wider aims of human flourishing (Wicks & Reason, 2009). As with other forms of action research, learning history practice works on this boundary, though does so in a distinctive way. By dwelling longer with private spheres of human experience and reflecting on these in the context of a web of taken-for-granted suppositions, the learning history process shines a light on the lifeworlds of individuals within system world(s) of organising. It is concerned with the dialectic between our responsibility as individuals for the lives we lead, and our collective responsibility for the world we create (Gearty & Coghlan, 2018, p. 467). We could say that, with learning history, the system world is interrogated from the place of lifeworld(s) in a particular way. So where is the action in this? All action research is concerned with knowledge that is actionable in the present. Learning history starts with looking back on the past. In what way can the learning stimulate forward-moving change or informed action? In learning history, the action element most obviously arises within those “communicative spaces” that are convened as part of the process. Here participants not only take stock of what has happened but can explicitly be invited to form action plans and potentially re-frame strategies together. However, learning history practice does vary considerably in this regard. Regardless of how proactive (or not) the facilitation is, the process of learning history itself will distribute a new awareness across any organisational field. By bringing participants’ lifeworlds into the realm of legitimate organisational discourse, a capability for critically informed action and decision-making may be created. Conversely, it must be said, a space can be opened for existing power struggles to be amplified or to become even more knotted. Such dilemmas are common in research and in organisational development practice. I draw attention here to the specific nature of written documents and their potential to arm. We will return to the nuances of learning history practice and the delicate balancing act it sometimes requires in later sections.

LEARNING HISTORY PRACTICE What is involved in actually doing a learning history? We move now to a more practical section that is oriented at those readers who are adopting or considering adopting a learning history approach for their research.

204  Handbook of research methods in organizational change A Four-Stage Process Learning history is a staged process of reflective inquiry with distinct and identifiable phases of research. The number of phases and precise nomenclature of them has varied somewhat in the literature over the past two decades. Key developments have pertained more to expanding the degrees of freedom for the method rather than pinning down and refining definitions.

Figure 9.2

Learning history as a four-stage process with multiple stakeholders

Learning history  205 For the purposes of ease and clarity I will adopt the four-stage description we consolidated for the third edition of the SAGE Handbook of Action Research (Bradbury et al., 2015). That article brought the “organizationally situated” learning history together with recent developments that identified “boundaryless or open system” practice, useful in settings where the learning agenda is less defined by known organisational boundaries (idem). In a later section (see “When to choose learning history”), I will introduce a range of contexts in which you might consider adopting this approach. For now, it is worth noting that learning history projects vary considerably in scale. The four stages that are visualised in Figure 9.2 set out the common pathway through any learning history irrespective of the complexity or scale of the project. These are like structural scaffolds, and they form the design criteria for a researcher embarking on learning history. Figure 9.2 also represents the shifting involvements of stakeholders – in the organisation and in the research team – as the work progresses. In the following section I will set out each of these stages – with a brief description of what is involved – and some key design and practice considerations. Before we do that, it is worth noting that Figure 9.2 is a temporal, sequenced representation that can help with planning and scoping a learning history project. This belies a freer interpretation of learning history that I am keen to promote. A complementary view of learning history practice that is focussed on research and inquiry skills is therefore shown in Table 9.1. The table – which is an adaptation of Gearty and Coghlan (2018) – represents learning history less sequentially – more as a continuous and dynamic enactment of first-, second- and third- person processes of reflective inquiry for the different kinds of co-researchers who become involved. Readers are referred to that article for a fuller explanation. This table is included here to offer a complementary lens onto learning history practice, and it highlights just how all-encompassing a process of inquiry learning history can be. Stage 1: Planning and co-design Description This is the initiating stage of the research – when the idea of conducting a learning history starts to be shaped into possibility. In this stage the initiating researcher(s) will meet with the insider(s) who are supporting the work to discuss and design the learning history effort. The relational basis for the work and the extent to which it is co-created can vary considerably. Questions of access, sponsorship and design will vary accordingly. Design considerations During this stage, the initiating team will scope out the project and agree the broad storyline or “notable results” that this history will be centred upon. Tangible planning questions about gaining access, key actors and governance of the work may seem on the face of it purely practical. However, at its best, this stage is a key site of insider/outsider collaboration where ambitions, resources and learning aims are negotiated. Box 9.1 shows some of the guiding design questions in play during this stage. These are drawn from a planner I use sometimes with clients.

May actively propagate the learning history past completion via websites and other channels. Can adopt the role of “animateur” if taking a third-person intent into the work. Publishes. Tells, re-tells in academic and other settings the learning history as well as the story behind the story.

Co-inquires within commissioning team and insider/outsider team on agendas, learning and intent. During reflective interviews historian co-inquires with participants – drawing out their experiences and reflecting on them. May also co-inquire in peer supervision as to

Notes and reflects on own agenda, practice and

experience incl. subjective responses and choices

in history creation.

Interrogates power asymmetries inherent in the

process.

Practices sustained reflexive attention and

“provisionality”

May adopt aspects of the methodology in their ongoing organisational or community work.

In later stages, co-inquires with participants and reader-learners – making meaning on the history. Where learning events are staged as part of the

questions and insights and reactions of others

opens up site for further reflection and inquiry

into their story and their practice of change in

that. First-person processes are inherent but more

setting. Published books of learning history reach

invited to form judgements and to share their own related experiences.

reader-learner is invited to reflect on their

reactions and draw out the questions and

Source: First published in Gearty & Coghlan (2018).

involved.

a much wider audience than those directly

reader-learner’s context rather than original

insights by questioning the account and will be

When engaging with a learning history the

meanings for them.

Third-person realm shifts to the

into co-inquiry with others. They will seek

personal practices of reflective inquiry.

what they learnt or found interesting from it.

May tell people of the learning history and

project end date.

on that story.

Subsequent mirroring back of his story with

privatised for reader-learners – depending on their process, the reader-learner is explicitly invited

disseminate the learning history artefact past

by historian into inquiry and deeper reflection

experiences during dialoguing stage.

history

Reader-learners

May continue to actively propagate and

arise. During dialogues tells one’s story and is invited

Key organisational participants in learning Participant is prompted to both tell and reflect on

one’s practice – the choices and dilemmas that

Third person

Second person

First person

Learning historian

First-, second- and third-person engagement in learning history

 

Table 9.1

206  Handbook of research methods in organizational change

Learning history  207

BOX 9.1 GUIDING QUESTIONS FOR THE INSIDER/OUTSIDER TEAM WHEN PLANNING A LEARNING HISTORY PROJECT 1.

What is the learning we hope to achieve with this work – for us individually, as a team, for those involved, for unknown others? 2. What are the notable results we will feature in this work; what happenings are worthy of deeper understanding? 3. How will we work together? Who will we interview? Who will we not interview (and why)? 4. What have we the time and resources for? What trade-offs do we need to make now? 5. What will resource me (us) as researcher(s)? Who or what will support me in the practice and my learning? Source: An internal planning guide developed by the author. The questions above are indicative and might recur through the process. They highlight the practical and political nature of this stage as different agendas meet. The requisite culture of participation and insider/outsider collaboration is already being negotiated and modelled at this point. Participants will be empowered to discuss what has already happened, along with the contradictions and disagreements that may have arisen. Similarly, the insider/outsider co-researchers share perspectives and hopes for the work, and clarify what may be multiple learning aims and the stories and experiences that guide them. In this way, reflections start to orient change efforts towards a desired future. The involvement of the initiating insider partner varies considerably from project to project. In some learning histories they may be little more than an interested party willing to provide access and light-touch support. In others, the insider partners might be a key and crucial sponsor and active co-researcher. Where the learning history is being conducted commercially, resources and financing are key questions in this stage. Where the learning history is part of a doctoral or master’s research project, the emphasis will be more on scoping research questions and ensuring a good enough framework of supervision for the work to get underway. Stage 2: Reflective interviewing/dialoguing Description In this stage, members of the research team – which may include insider co-researchers – will conduct reflective conversations with key protagonists identified in stage 1. The choice of who to speak with and who to leave out is not trivial, and the need to set up further conversations may arise during this or later stages. This stage starts to combine practice and scholarship. Perspectives might be gathered from different constituencies: for example, from managers or local leaders, service users or clients, advisors or consultants, collaborating partners and researchers. Practice considerations The research practice here has something in common with a qualitative research interview in that the learning historian will usually provide a semi-formal conversational space for the participant that includes adequately framing the research, outlining the focus of the conver-

208  Handbook of research methods in organizational change sation and agreeing terms of confidentiality that apply. A principle of this work is that the conversations are recorded, and it is important participants understand the implications of that. At the same time, the learning historian seeks to create the conditions for an open and reflective dialogue – a communicative space as earlier described. The means extending an invitation to the participant to step away from standard scripts and to reflect on their experiences in such a way that there is a possibility of relaxed and unrehearsed reflection. The reflective conversation(s) at this stage are part interview, part storytelling and part inquiry. For that reason, I tend to use the term “dialogue” as the term “interview” can connote fact-finding and something more extractive. The words we use bring new worlds into being, Ken Gergen (2009) says. When participants ask me, as they sometimes do, at the end of a conversation: “Did you get what you wanted?” it feels as though we are occupying at least two different worlds. Conducting a learning history dialogue While a learning historian will do background work and might come to the dialogue with some particular questions they want to address, the skill here is to follow the energy in the participant’s story and to move through the different levels of discussion. By opening up a dialogue about someone’s experience of an event you start to inquire with them into where their energy, edges and learning might lie. Box 9.2 shows some of the different kinds of prompting questions that might feature. These are offered as a guide rather than as a sequence to be followed in order.

BOX 9.2 LEVELS OF QUESTIONING FOR A LEARNING HISTORY DIALOGUE • On what actually happened – questions aimed at getting the shape of the overall story being told, what the achievements were and the key elements of it; e.g., Can you give me a rough outline of what happened? How long did it run for? Who was involved? • On what it was like – questions aimed at getting at the human story, the lived experience; these moving to the specifics of their feelings, intentions – the ups and downs; e.g., Any sleepless nights? Can you tell me about a key turning point? • On looking back – these are reflective questions, prompting the person to step back and make sense of the experience from the perspective of now; e.g., Looking back on it, what do you think was happening? What do you see now that you didn’t see then? • On learning now – these are questions aimed to deepen learning in the present, maybe based on things you notice in the dialogue – intuitions, curiosities – or things you feel are worth reflecting back on; e.g., I notice you seem very happy/down talking about that. Can you tell me more? • On widening awareness – questions aimed to open the participants’ viewpoint out and to sow the seed for checking assumptions and seeing things differently; e.g., What do you think A would say about your view of things? Are there others who might see it differently?

Learning history  209 My Story with Learning History: In the Middle Depending on the project, I will transcribe the recording myself or send it off to a service to be transcribed. When, some days or weeks later, I sit down to listen to the recording I enter a new realm of concentration. Broadly, in terms of Roth and Kleiner’s modalities, I have cycled from “mythic” to “research” mode, though in reality my attention is much more pliable. I am subject to distraction and have a habit of taking small walks around my kitchen to “think” and find my focus. It is hard to distinguish preparation from procrastination as I pour one more coffee, and get the recording teed up and my transcript ready. Eventually I start. Listening. Thinking. Attending. I pause the recording at times to highlight key quotes or to annotate the transcript with reflections that occur to me as I go. A quietness descends. At a certain point it becomes just me and the material. When I read Sheldrake’s account of listening to that polyphonic song I am struck by the similarities. He writes: “Whenever I listen to ‘Women gathering mushrooms’ my ears find their way into the music by choosing a single voice and riding with it, as if I were in the forest and could walk up to one of the women and stand next to her” (2020, p. 61). Here too I am tuning into the participant’s voice – listening out for a phrase or quotation that catches my attention. It is as though I listen with two ears. The other ear is listening to my inner conversation in response to the material. Things constantly strike me, and themes and questions start to bubble up while I listen. The process seems to slow as the recording goes on; I find myself stopping more, noting more. I become more deeply immersed as more thoughts and connections are made. I start to sink into the timbre of the participant’s voice. Sheldrake (2020, p. 61) writes, “To follow more than one line is hard. Several streams of consciousness have to commingle in the mind.” As with a polyphonic song, learning history work requires a certain mutability of attention. I need at times to tune deeply into a single voice but always with this wider consciousness. Then, as the history is being woven, this shifts to a more distributed attention as voices start to mingle. “This is interesting, but it’s just one man’s story,” said my doctoral supervisor of that first Learning History I had finally written, months after that first interview with the great raconteur I mentioned earlier. That History was peppered with verbatim quotes from the interview and combined with researcher reflections that posed questions and noticed arising themes. But I was remaining close to that single story – the tune of that one song. After that feedback, I wrote to all the people mentioned in the history and gathered their quotes and included them. “I don’t really see myself in that story,” said one respondent. “That’s fine, tell me yours,” I said, and started to listen to what he had to say. Only then was the history truly becoming polyphonic. Now, even if I am deeply tuned in and listening to the recordings, I remember that wise piece of feedback, “This is just one person’s story.” Sheldrake (2020, p. 61) continues, “Several streams of consciousness have to commingle in the mind. My attention has to become less focussed and more distributed, but when I soften my hearing something else happens. My many songs coalesce to make one song that doesn’t exist in any one of the voices alone.” Once I have listened and annotated the recordings, I move into the distillation and crafting stage. There are so many decisions to make about how to shape the material that has been pro-

210  Handbook of research methods in organizational change visionally selected. Like Sheldrake’s listening experience changes, so do does my attentional focus as a learning historian start to shift and widen. Now, in my mind, I hold a blurred sense of participants’ voices together with possible themes and structures. Standard research props and activities support this: sticky notes with themes, structuring sketches, storyboards and timelines (chunking out key phases in the story), and aggregation of material (quotes, photos, dates, etc.). But the crafting, the weaving of this all together, has an alchemical quality akin to Sheldrake’s softening. I recall once going for a walk in my local woods with the testimonies of three different participants in a History folded in my pocket. I stopped by a familiar waymark for me, a moss-covered fallen tree, and pulled the papers out and read them again. I was seeking the connecting narrative and the themes or learning points to draw out. At a certain moment the stuck feeling shifted. I had read enough – I just folded them and put them away. It was a fine spring day, I remember – early during the first Covid-19 lockdown in 2020. The bluebells had just appeared. It was as though my hearing for the History softened in that moment. Something coalesced and when I returned to my desk I was at last, ready to write. The above is included to highlight that, as with any qualitative research method, working with your material is not just a case of processing your notes or following a step-by-step listing. Though it takes place largely within the “research” modality, the work takes place within a lived life and still touches into the “mythic.” All research methods require a mutability of attention. Drawing on Sheldrake, I have wanted to convey the particularity of how attention is held and shifts during a learning history process – for the researcher and also for participants as later sections will show. And this is “just one person’s story.” We move now downstream (Bortoft, 1996) to a more formal description and guide for this phase. Stage 3: Distilling and crafting Description This stage starts with a qualitative research step in which all the material – fieldnotes and recordings – are gathered, processed and distilled in several directions. Key quotations are selected. Storylines begin to be formed. Researcher reflections are clarified. The researcher works both individually and with peers to distil themes, and to identify possible learning points. Reflexive space is essential to interrogate conclusions and check assumptions. Depending on what is emerging, further interviews may be negotiated – not so much to fill in gaps but to widen the view and bring further integrity to the learning history process, revisiting or reframing as necessary the original learning aims. In parallel the form of the Learning History artefact will be discussed, ideally with insider sponsors, and guided by pragmatic aims of communication and questions of who this work is intended to reach and influence. There are a range of options for presentation, as the next section will outline. This is an intense stage of working, one that can be both demanding and highly creative. As storylines are shaped the “mythic” imperative is at play. I interpret that imperative as not only an invitation to craft a story out of the material, but to find and juxtapose elements that will bring a sense of myth and maybe even magic to the reader. This is an interpretation that is broader perhaps than Roth and Kleiner may have originally intended, but I find it animates the writing process. Meanwhile the research imperative helps researchers “stay honest” and avoid the “road to demagoguery” as Roth and Kleiner describe it (1998, p. 56), where reflections become unteth-

Learning history  211 ered and can be prone to political power-plays and implicit agendas. Having space to reflect – with peers or supervisors – can support the learning historian(s) in their quality processes, giving them space to cycle back to the observable data and reflect on ethical questions. By definition the Learning History is not a confidential document and though quotes are sometimes anonymised, the key actors are often identifiable. The historian must make judgements on the risks in making certain quotes visible and balance this with learning aims. Crafting: practice considerations All learning historians will construct a History with their own balance and set of aesthetic choices. Some will stay very close to the quotes – letting the voices of those involved tell the story – while others might narrate more of the History, opting for quotations that stir life and learning points into that story. Others still may lean towards providing more thematic analysis and insight as a springboard to inquiry. Typically, a Learning History takes the form of a written document, often divided into two columns, whereby original, verbatim quotes from the protagonists are woven together with researcher reflections and narration. Gearty et al. (2015) suggest, however, that learning historians should feel bound neither by the Talmudic two-column format nor even the written form. They encourage learning historians to choose a form that will suit pragmatic and learning aims, and this might include audio-visual or theatrical forms. Box 9.3 shows five principles that could guide a learning historian in presenting their work in whatever form they might adopt.

BOX 9.3 GUIDING PRINCIPLES FOR PRESENTING A LEARNING HISTORY IN ANY FORM 1. Include the authentic insider voices in written or oral form. 2. Offer multiple perspectives on events in ways that allow and invite contradiction. 3. Embrace mixed and creative modes of presentation allowing different kinds of knowing to be juxtaposed (e.g., images, analytical themes, theory bites). 4. Choose a style of presentation that suits the audience and the resources that you have. 5. Present with consideration to the endurance/longevity of the text/artefact. Source: Adapted from Gearty et al. (2015) Stage 4: Validating, diffusing and learning Description Once written, the Learning History is shared back with key actors to validate their quotes and to check the context in which their words have been presented. Subsequently, key actors are convened together, and there is the opportunity to discuss and reflect together. Accomplishments are clarified and diverse stories may need to be acknowledged. Visibility concerns mean some lines may need to be changed during this stage. Diffusion then occurs in widening circles as stakeholders and wider interested groups are invited to engage with the History and reach for new insights that are actionable for future efforts.

212  Handbook of research methods in organizational change Practice considerations Sustaining engagement A key consideration during this phase is sustaining ongoing engagement and inquiry. After the effort of production there can be a dwindling of energy and resources. Roth and Kleiner warned against the pitfall of Learning Histories gathering dust on a shelf (1998) and talk about capitalising on the buzz and gossip that arises during interviewing to attract interest and further engagement. Gearty et al. (2015) suggest a proactive framing for the learning historian at this point who not only disseminates but also convenes “learning events” and considers ways to spread and sustain learning from the effort. Provocation: balancing risk and learning Shared curiosity and appetite for the story being told is often what will sustain this stage. It is the edge of difference – of seeing themselves reflected in a new way in experience – that brings people into the discussion. This stage can be, in many ways, the most provoking step in a learning history where individuals and organisational groupings enter less familiar territory and encounter themselves in a multiplicity of reflection and voice. It can be confronting for a participant to see their words reflected back, and the desire to redact or change the words of others is not uncommon. Judgements on the balance between risk and learning are no longer the sole jurisdiction of the historian but are negotiated between insider(s) and outsider(s) co-researchers at the validation stage. Once it is “in writing” however, the practice of the learning historian shifts towards holding and facilitating and being alert to whatever ripples and disturbances are arising from the account. Forces of closure Forces of closure may start to act as the work increasingly starts to move into public arenas. Recalling our earlier discussion, this is the stage where the system world may clash with or even act to colonise the lifeworlds that are being surfaced. Words like “sign-off,” “review” and “approve” can take on a duality of meaning. They are necessary but can signify a form of agreeing that differs from the pluralistic dwelling that is sought. Learning events become interesting sites then of Kemmis’s aforementioned “boundary crises” where the system world now mingles with the vitality of the lifeworld. Bringing a questioning curiosity to emerging dynamics can help the researcher to facilitate continued learning within this complexity. Learning moments from the field Likewise in workshops, it can be confronting for a reader to be faced with multiple voices and perspectives and they may not wish to “soften their hearing” like Sheldrake did to listen to his polyphonic song. In one memorable learning event, I asked the twenty-five or so participants to participate in a “big read” where, workbooks in hand, the group simultaneously sat down to read the Learning History in question. The rustles of the room were quietening into a communal, studious silence when there was a loud cry from one participant, “I didn’t come here to do this – to be sent back to the exam hall.” So, each step of the validation and the diffusion process has the potential to uncover a sensitive point of learning. Roth and Kleiner talk of a tumbleweed moment in one meeting when an uncomfortable truth was voiced (1998), and Bradbury (2005, p. 239) gives a vivid account of consternation at The Natural Step when participants were given scissors and invited to cut out bits of the Learning History, leading to protest on the part of one participant who said he felt he was back in kindergarten, and others later protesting that the meeting had no purpose. By the end,

Learning history  213 however, participants had agreed it had been a powerful meeting and that the History was bringing attention to process – something they rarely considered up until then. Here perhaps is an example of the “system world” being interrogated by – and moving into conversation with –“lifeworlds.” Such moments of discomfort may not be deliberately sought out, but I do see them as indicative of an unfamiliarity of this stage, that, if it can be held, points towards quality in the practice. Having a means to hold to and revisit learning aims, and a clear sense of your ethical stance, can guide you through such moments, as the next section will discuss. Finishing: giving the History back At a certain point, whether due to dwindling resources or an agreed position that the work is done, it will be time for the formal learning history project to come to an end. Questions of ownership of the archive arise now if not before. An aspiration at this point is to find a way to hand the Learning History back to those whose story it is and to encourage them to continue to bring to life the practices and learnings that are in there. In this way the invitation remains open for learning from the past to remain present and informing of future action.

POWER, POLITICS AND ETHICS It will be clear from the foregoing discussion that the learning historian role is imbued with power and politics. Though it is participatory, the learning process is orchestrated by the learning historian(s) who act(s) autonomously in making a series of judgements and design choices that they see as being in service of the wider learning aims. This carries some responsibility to participants to leave the field in some way “better off.” But who is to judge what is better off? As we have already remarked, the learning history can open the space for critically informed action, but it can also “arm” or amplify existing power struggles. There is no easy answer. I can only bring attention to the tensions and dilemmas that arise and suggest that the choices amount to a craft in the practice that is itself worth exploring, through self-reflection and review with peers or supervisors. Learning history practice is nuanced and wide-ranging in the skills it requires. Writing, facilitation, discernment and political savvy are all part of the practice. The ethical stance is also distinctive. A written “historic” document carries responsibility, as we have discussed. You must be open to factual corrections and adjusting potentially defamatory remarks; at the same time, you must avoid mediating views or getting caught in “he said/she said” arguments. “What can we live with?” is the question I ask when such conflicts arise. I tend to look for a way to put competing perspectives side by side. Polyphony does not imply equity. Throughout you must be mindful of heroic narratives over-running other voices – or indeed your own judgements in choosing to privilege one point of view over another. Finally, as the wider reading and learning events start you must be open to discomfit your participants, though you are not setting out to be deliberately provocative. In choosing what to focus on, I tend to ask myself what I think is the point this system wants to and is ready to explore. You are looking to open out patterns of practice for scrutiny and reflection in a way that is inclusive and generative on the one hand, while seeking learning points that will be actionable and useful. Ethical practice is about picking a path through these tensions and being aware of your choices – as in any action research process (Reason, 2006). This, then, is a practice that is discerning and rich with learning and fascinating questions of practice for any researcher or practitioner whether new to the task or experienced. These

214  Handbook of research methods in organizational change questions may well become a key site of inquiry and learning in the project – and for MSc or PhD students may indeed be where the core “action” takes place. Whatever the nature of the learning history initiative, opening ethical questions for inquiry in peer and self-reflection can enhance the quality of a learning history research process. Structured ethical reflection (SER), for example, where one inquires into the guiding values for one’s research and articulates touchstone questions to visit and revisit, could provide an underpinning process (Brydon-Miller & Hilsen, 2017). Researchers will have personal ethical positions that guide their practice – that have a resonance (Brydon-Miller & Coghlan, 2018) – and these are worth noting and reflecting on.

WHEN TO CHOOSE LEARNING HISTORY Fitting Research Motivations to Method The starting motivation for learning history can, quite simply, be a wish to acknowledge – to make more visible – the tacit practices of how things are done. The individual researcher or commissioning partner might have a sense that there is a system – a group of people – that has learning potential as yet untapped. The start point is often a positive one. There may be an indicative event, a happening, a project that you think has valuable learning for others. Appreciative inquiry places attention on what is life-giving in a system, and learning histories often start here too (Ludema, Cooperrider & Barrett, 2001). Roth and Kleiner’s early work was in this vein – they were seeking a way to get beyond the generalised descriptions or codified “best practice” and into the day-to-day realities and implicit social patterns that guide how things are done (and done well). By getting closer to experiences that occur within relational contexts, the learning history approach provides a way to surface and work with what are often implicit practices, values and unspoken tensions. An itch or feeling that “more could be said” can also be an entry point for some researchers or commissioning partners. This “grit,” as I call it, serves the process well once it can be converted into curiosity. For example, as the last section discussed, a fruitful inquiry can be how politics and power are acting to produce (or suppress) multiple narratives. Arguably an overly harmonious or self-aware system has little to gain from learning history. Learning History Over Other Forms of Action Research A central way in which learning history differs from appreciative inquiry and other modalities of action research is that it also captures something. The History produced as part of the process represents an archive of experience and a possible legacy for future unknown learners. Thus, learning history brings a promise of endurance, and the potential to connect past, present and future stakeholders can be appealing. Another application is in the area of evaluation. In an organisational setting, learning history can be used as a framework for participatory, formative evaluation where teams are involved in assessing what they have been doing in a way that strengthens them for the future. All action research is developed as an emergent process that to an extent must be shaped with co-researchers in the field. The choice of learning history may be made in advance of

Learning history  215 access arrangements or even a clear sense of the partnerships, plans and sponsorships that will underpin the project. These are usual challenges in conducting qualitative research. Learning history work does rely on generative insider/outsider partnerships that will be capable of co-generating useful practical knowledge (Bartunek & Louis, 1996). Though these will evolve over time and may not be fully established, seeing a potential for them is important. Projects of Different Scale Much of the existing literature describes learning history processes that were intensive – involving large research teams and dozens of interviews – conducted over months if not years. Some early learning histories became full published books. Car Launch and Oil Change were two such books that retained the original learning history format to tell stories of transformational change in the automobile industry (Roth & Kleiner, 2000) and in the Dutch global conglomerate Unilever (Kleiner & Roth, 2000). The latter was then re-written for a wider readership by Mirvis, Ayas and Roth (2003). Later Learning Histories have meanwhile featured in lengthy research reports or PhD dissertations. What has been less developed in the literature has been the potential of learning history to support smaller-scale learning and research projects. There is no doubt that learning history can be a labour-intensive process. However, in my years of working with MSc and doctoral students, I have seen the process being adapted successfully for short-term 3-, 6- or 12-month projects. In such cases the learning agenda might combine methodological experimentation with an interest to explore an event of significance in the student’s life. A Guiding Framework for Inquiry Therefore, the choice of learning history may be rooted in personally focussed learning agendas that centre around method and research practice. Learning history is a layered framework for inquiry, replete with distinctive and interesting ethical questions and dilemmas relating to the power and politics of the role. As such it offers an excellent testbed for master’s and doctoral-level research students who are keen to develop their skills and whose research questions and aims fit with the approach.

WHY LEARNING HISTORY: CLOSING REMARKS Not a Well-Trodden Path Why then choose a learning history approach? The foregoing discussion highlights that this can be an elaborate and extensive methodology (though I stress that lighter interpretations are entirely possible). I have shown it to be a practical approach, as in a way all action research is, one that creates actionable learning in the field for those who participate. Those who engage in the process are strengthened, individually and collectively, through the telling of a “jointly told tale” and by moving away from sanitised single narratives and abstracted versions of how change occurs. By engaging in depth with what change is really like, for diverse actors, new pathways of action will open up. Though the field of participation with learning history is wide and systemic, there are still limitations theoretically. The longstanding debate in action

216  Handbook of research methods in organizational change research about the challenge of the “single case” still applies. The fact is that an emphasis on generating locally and situated knowledge in the field – in specific single cases – means that the potential for influential theoretical insights is harder to realise (Gustavsen, 2003). As one reviewer of this chapter remarked, “Where in the organizational studies literature are there references to learning history studies?” The answer is that there are few. Contribution to Research Community In other words, what of the research community? Why adopt this method as a way of exploring or stimulating change? A reader choosing an approach for their MSc or PhD might wonder are there are not other, well-trodden paths to their qualification. This is indeed not a methodology for playing safe – where playing safe means following or even accepting routes to knowledge that have been legitimised. The epistemological marshiness of learning history is, I would argue, what makes it significant. Innovation as we know occurs on the margins. Learning history is located at the boundary of paradigms as we have discussed, and as a result offers possibility in all directions for theorisation, as, for example, I have started to do in drawing on Habermas’s theory of communicative action (see Habermas, 2015). One of the many interesting routes to theory learning history offers the research community is in how it strengthens capabilities for learning within difference. This is a ready-made but adaptable structure for engaging in the diverse and often unspoken realities of organisational life. Written histories may not be reproduced in academic literatures, but accounts of practice have the potential to inform theories of change and ways of organising. The provocations and disruption of existing power structures and orthodoxies that arise from this kind of research are ripe for further theorising. Developing a stronger theoretical sense of the nature of this participatory learning and how it relates to current thinking on organisational learning is another interesting avenue to explore. So, this is a provocative research approach – for researchers and participants alike. It comes with a wide-open frontier for new directions of theorisation and research. At a time of such complex change and turbulence in the world, the question is perhaps not so much “why” but “why not?” We, as a research community, must find new ways of “being real” and theorising the experiences we are living through in relation to the questions that matter as we live through rapidly changing contexts. Here are the seeds of a process that offers one fruitful pathway to theory from the ground up, as my final story shall show. On a More Personal Note I have learnt so much about myself in research and in life by nailing my colours to the learning history mast and experimenting with it for well over a decade now. Ultimately, I think it is a rewarding approach for life-long learning – for someone who wants to explore and learn themselves as they go and who has an appetite to be awed by the stories of others. If you are undertaking this approach, as my stories have shown, it will involve you and you will be challenged and have a place to process your own first-person questions. It is a rewarding process, one in which anything can happen.

Learning history  217 My Story with Learning History: In the End It is December 2020 and I am on a Zoom call with six others. Two are my colleagues John and Jessica and the remaining four each head up an NGO in different countries across southern Africa. These are dedicated people who have worked for many years in wildlife protection and conservation and who, in more recent times, have formed a regional collaboration to combat illegal wildlife trafficking. The field of their practice is dynamic, complex and sometimes dangerous. The context of their work is a flourishing criminal trade in products like pangolin scales, ivory and rhino horn. As we start the meeting, they say that the pandemic has shifted things again – there is an uptick in poaching in the villages and more need for bushmeat. They are operating in such flux and challenge, I think. And yet they make time for this process. Over the past 18 months, and right through the pandemic, we have worked alongside this group to co-develop a series of Learning Histories that reflect on and chart their experiences in relation to some significant events and themes that are significant for this group. One of the aims of today’s meeting is to reflect on a particular History we have recently written. There are forward-looking aims accompanying the learning process too. As we write, we look to tease out the implicit principles that seem to have been at play in the past – the enabling factors, barriers, shocks and learning points – so as to guide future strategy for this adaptive coalition. Looking at John and Jessica making up the six squares on the screen, I think what a rewarding experience it has been to work with them. Doing learning history work is often such a solo endeavour. At first, we did fly solo – we wrote the Histories independently, enjoying our different styles of writing. Then, as we started to distil learning we came together much more often to reflect on what was coming out, to plan workshops, to decide on next steps of engagement. We were, we realised, starting to echo the adaptive nature of those whose stories we are listening to. Over time, working, listening, writing, my admiration for these people has only grown. I know this is a pitfall for a learning historian, so I watch it growing. It seems to grow too in my colleagues. The experiences of our participants and what they do matters in such an obvious way. We write into our Learning Histories in cool terms how elephant populations dwindled by over 60 per cent between 2009 and 2015 in one country. But now and then the reality of the work comes home to us as a research team. Once or twice, we have been moved to tears. It seems the world in all its complexity is funnelling through this project as we discuss governments, poachers, funders and criminal gangs, and then remind ourselves over and again about the fate of the communities and the wildlife species at the heart of the work. One aim of today’s workshop is to decide how to take these Learning Histories forward to inform strategy. One or two of the participants are enthusiastic about continuing to share the Histories further – into their organisations and with trusted partners. Others are less sure. I have never worked on a learning history project so long past production, I think, as I sit there, fretting a little about the internet connection. We are way past stage four! As with any learning history process it has been bumpy. Things have been said that have rattled relationships. The commitment of this group, and my colleagues, to really stick with each other and the learning process means the method is being tested in ways I have never seen before. What has helped it sustain like this? What happens when it does? Will we leave the field better off? These are the questions that always return and that are never quite answered.

218  Handbook of research methods in organizational change In the here and now of this workshop, we have summarised the learning points and are talking now about the idea of developing a “living strategy.” This would be a strategy that marks out a way forward for this group, not as an abstracted theory, but as something that would build on the principles that we have drawn out. These, someone says, are like concentrated essence from their stories. It is how what you are doing on the ground informs where you are going. “Let’s do it,” says one of the women on the call, and there is a sudden sense of lightness and urgency among us – as though anything might happen. It feels like one of those rare moments where the winds of change are at our backs.

ACKNOWLEDGEMENTS I am indebted to David Coghlan for his editorial guidance and care; a sincere thank you to Judi Marshall for her constructive review and encouragement. Thanks also to John Colvin and Jessica Wilson at the Emerald Network Ltd and partners in the regional wildlife trafficking initiative for giving permission to feature their work. I would like to acknowledge my institution Hult International Business School (Ashridge) and thank my colleagues on the doctorate in organisational change (EDOC) there for their support and continued encouragement to keep writing.

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Learning history  219 Clandinin, D.J., & Connelly, F.M. (2000). Narrative Inquiry: Experience and Story in Qualitative Research. Jossey-Bass. Coghlan, D., & Brannick, T. (2014). Doing Action Research in Your Own Organization (4th ed.). SAGE. Czarniawska, B. (1997). A Narrative Approach to Organization Studies. SAGE. Czarniawska, B. (2004). Narratives in Social Science Research. SAGE. Freire, P. (1970). Pedagogy of the Oppressed. Continuum International. Gabriel, Y. (1995). The unmanaged organization: Stories, fantasies and subjectivity. Organization Studies, 16(3), 477–501. Gearty, M.R. (2008). Achieving carbon reduction: Learning from stories of vision, chance and determination. Journal of Corporate Citizenship, Summer(30), 81–94. Gearty, M.R. (2009). Exploring Carbon Reduction through Tales of Vision, Chance and Determination: Developing Learning Histories in an Inter-Organizational Context (Unpublished doctoral dissertation). University of Bath. Gearty, M.R. (2014). Learning history. In D. Coghlan & M. Brydon-Miller (Eds.), The SAGE Encyclopedia of Action Research (Vol. 2, pp. 492–6). SAGE. Gearty, M.R., Bradbury-Huang, H., & Reason, P. (2015). Learning history in an open system: Creating histories for sustainable futures. Management Learning, 46(1), 44–66. Gearty, M.R., & Coghlan, D. (2018). The first-, second- and third-person dynamics of learning history. Systemic Practice and Action Research, 31(5), 463–78. Gergen, K.J. (2009). Social construction: Revolution in the making. In K.J. Gergen (Ed.), An Invitation to Social Construction (2nd ed., pp. 1–30). SAGE. Graff, G.M. (2020). The Only Plane in the Sky: An Oral History of 9/11. Avid Reader Press/Simon & Schuster. Gustavsen, B. (2003). Action research and the problem of the single case. Concepts & Transformation, 8(1), 93–9. Habermas, J. (2015). The Theory of Communicative Action, Volume 2: Lifeworld and Systems, a Critique of Functionalist Reason (T. McCarthy, Trans.). John Wiley & Sons. (Original work published 1981.) Hazen, M.A. (1993). Towards polyphonic organization. Journal of Organizational Change Management, 6, 15–26. Kemmis, S. (2001). Exploring the relevance of critical theory for action research: Emancipatory action research in the footsteps of Jürgen Habermas. In P. Reason & H. Bradbury (Eds.), Handbook of Action Research: Participative Inquiry (pp. 94–105). SAGE. Kleiner, A., & Roth, G. (1996). Field Manual for a Learning Historian. MIT-COL and Reflection Learning Associates. Kleiner, A., & Roth, G. (2000). Oil Change: Perspectives on Corporate Transformation. Oxford University Press. Ladkin, D. (2005). The enigma of subjectivity: How might phenomenology help action researchers negotiate the relationship between “self”, “other” and “truth”? Action Research, 3(1), 108–26. Liamputtong, P. (2014). Oral history. In D. Coghlan & M. Brydon-Miller (Eds.), The SAGE Encyclopedia of Action Research (Vol. 2, pp. 574–5). SAGE. Lincoln, Y., & Guba, E. (2017). Paradigmatic controversies, contradictions and emerging confluences revisited. In N.K. Denzin & Y.S. Lincoln (Eds.), The SAGE handbook of Qualitative Research (5th ed., pp. 97–128). SAGE. Ludema, J., Cooperrider, D., & Barrett, F. (2001). Appreciative inquiry: The power of the unconditional positive question. In P. Reason & H. Bradbury (Eds.), Handbook of Action Research: Participative Inquiry and Practice (pp. 189–218). SAGE. Mirvis, P., Ayas, K., & Roth, G. (2003). To the Desert and Back: The Story of One of the Most Dramatic Business Transformations on Record. Jossey-Bass. Peters, R., & Thier, K. (2019). Learning histories: Re-authoring the future in the mirror of the past. In J. Chlopczyk & C. Erlach (Eds.), Transforming Organizations: Management for Professionals (pp. 121–36). Springer. Polkinghorne, D.E. (1988). Narrative Knowing and the Human Sciences. SUNY Press. Reason, P. (2006). Choice and quality in action research practice. Journal of Management Inquiry, 15(2), 187–203.

220  Handbook of research methods in organizational change Reason, P., Coleman, G., Ballard, D., Williams, M., Gearty, M.R., Bond, C., Seeley, C., & Maughan Mclachlan, E. (2009). Insider Voices: Human Dimensions of Low Carbon Technology. Centre for Action Research in Professional Practice, University of Bath. Rhodes, C. (1996). Researching organizational change and learning: A narrative approach. Qualitative Report, 2(4), 1–8. Rhodes, C., & Brown, A.D. (2005). Narrative, organizations and research. International Journal of Management Reviews, 7(3), 167–88. Riessman, C.K. (2008). Narrative Methods for the Human Sciences. SAGE. Roth, G., & Bradbury, H. (2008). Learning history: An action research practice in support of actionable learning. In P. Reason & H. Bradbury (Eds.), Handbook of Action Research (2nd ed., pp. 350–365). SAGE. Roth, G., & Kleiner, A. (1995). Learning histories: “Assessing” the learning organization. Systems Thinker, 6(4), 1–5. Roth, G., & Kleiner, A. (1998). Developing organizational memory through learning histories. Organizational Dynamics, 27(2), 43–60. Roth, G., & Kleiner, A. (2000). Car Launch: The Human Side of Managing Change. Oxford University Press. Shah, R. (2001). Relational Praxis in Transition towards Sustainability: Business–NGO Collaboration and Participatory Action Research. (Unpublished doctoral dissertation). University of Bath. Sheldrake, M. (2020). Entangled Life: How Fungi Make Our Worlds, Change Our Minds and Shape Our Futures. Penguin Random House. Terkel, S. (1974). Working: People Talk About What They Do All Day and How They Feel About What They Do. Pantheon Books. Terkel, S. (1997). The Good War: An Oral History of World War II. New Press. Van Maanen, J. (1998). Tales of the Field. University of Chicago Press. Wicks, P.G., & Reason, P. (2009). Initiating action research: Challenges and paradoxes of opening communicative space. Action Research, 7(3), 243–62.

10. Principles for productive inquiry into ICT-enabled change in organisations Joe McDonagh

INTRODUCTION Since the 1950s, advances in information and communications technology (ICT) have paved the way for the ongoing development of organisations and their broader business networks. Whilst early exploitation of ICT in the 1950s and 1960s emphasised automation of routine processes within discrete business functions, the role of ICT in the 1970s and 1980 expanded to incorporate a distinct emphasis on managerial, executive, and strategic work. By the 1990s and 2000s, the emphasis had incorporated the role of ICT in integrating and extending the work of organisations. By the 2010s and 2020s, the exploitation of ICT focused on capturing its transformational potential within both organisations and their broader business and digital ecosystems. The impact of ICT has become increasingly pervasive across the decades, and it has been central to the ongoing renewal and reform of private and public organisations alike. Whilst rapid advances in ICT have accelerated the pace of change within and across organisations, outcomes from ICT-enabled change initiatives have been consistently disappointing since the 1950s. As used here, the term “ICT-enabled change” refers to organisational change triggered by the adoption of digital technologies (Beeson & Rowe, 2012; Hanelt et al., 2021; Lanzolla et al., 2020; Nadkarni & Prugl, 2021; Vial, 2019; Wessel et al., 2021). As a recurring pattern, 20 per cent of such initiatives deliver promised business outcomes as intended, a further 40 per cent deliver significantly impaired business outcomes with inflated budgets and timelines, and the remaining 40 per cent are deemed to fail outright (Candido & Santis, 2015; Decker et al., 2012; Gupta et al., 2019; Lyytinen & Hirschheim, 1987; McDonagh, 1999, 2001, 2015, 2022). Irrespective of the era of ICT, outcomes have been consistently disappointing. Unfortunately, all too often, underperformance and failure mar this landscape (Beer & Eisenstat, 2000; Carlton, 2019; Chanias et al., 2019; Clegg, 1995; Clegg et al., 1996a, 1996b; Correani et al., 2020; Crittenden & Crittenden, 2008; Davenport & Westerman, 2018). Whilst much has been written about the factors that contribute to such underperformance and failure, disappointment of this nature is rooted in a distinct inability to effectively coordinate and integrate the multiple facets of strategic, organisational, and technological change as they relate to ICT. Often, technological change reigns supreme at the expense of its strategic and organisational dimensions. Indeed, all too often, people and their pattern of relationships are marginalised and ignored whilst the technical dimensions of ICT-enabled change are afforded privileged attention and related funding. In the context of change, fragmentation rather than integration appears to be the order of the day in professional practice (McDonagh, 1999, 2015, 2022). This is not helped by the equally fragmented nature of scholarly work that seeks to speak to this important realm of organisational life (Beeson & Rowe, 2012; Hanelt et al., 2021; Nadkarni & Prugl, 2021; Vial, 2019; Wessel et al., 2021). 221

222  Handbook of research methods in organizational change The landscape of ICT-enabled change in organisations is shaped by systems of professional practice such as strategy work, portfolio management work, programme management work, and project management work (Martinsuo & Hoverfalt, 2018; Merali et al., 2012;). These higher-order systems of practice set the boundaries for what can be legitimately progressed in relation to ICT-enabled change. If the people and organisational aspects of ICT-enabled change are not surfaced in the first instance by these systems of practice, then subsequent systems of practice enacted by ICT professionals (e.g., systems analysis, systems design, systems development) will do nothing more than reinforce technological change at the expense of its strategic and organisational dimensions. From a socio-technical standpoint, the interlocking and coalescing nature of these systems of practice is such that they produce troublesome outcomes by prioritising and progressing the technical aspects of change at the expense of its social dimensions. This dilemma with ICT-enabled change and its perpetuation through time are of direct interest to organisation development (OD) researchers. Researchers need to understand the dynamics of ICT-enabled change, the propensity to marginalise the people and organisational dimensions of change, the struggle to legitimise and integrate OD-based approaches into ICT-enabled change initiatives, the dominant worldviews of executives and ICT professionals which result in ICT-enabled change initiatives being fragmented as a matter of routine, and the manner in which systems of professional practice predictably produce troublesome outcomes from ICT-enabled change initiatives. Inquiry into the coalescing and interlocking nature of systems of professional practice is without doubt under-researched and worthy of sustained inquiry into the future. Against this backdrop, this chapter seeks to advance a set of principles for productive inquiry into ICT-enabled change in organisations. This is accomplished by attending to the following. First, emphasis is placed on the power of ICT to transform the work of organisations and the related need to achieve symmetry between people, work, and technology. Second, attention is focused on the dynamics of ICT-enabled change and the related propensity for underperformance and failure. Particular attention is given to the contributing roles of occupational communities and the way their influence on professional practice directly contributes to fragmentation of change initiatives in organisations. Third, recognising the equally fragmented nature of scholarly work, six principles for productive inquiry into ICT-enabled change are advanced which together have the potential to both inform and transform professional practice whilst simultaneously contributing to sound scholarship. Fourth, advice for researchers interested in the adoption of these principles is presented, as are insights into the related evolution of a practice of inquiring together. Finally, the chapter concludes with the merits of locating oneself at the nexus between sound scholarship and exemplary professional practice as they relate to the challenge of ICT-enabled change in organisations.

TRANSFORMING THE WORK OF ORGANISATIONS Digital Foundations The current interest in and pervasive influence of digital technologies, as in ICT, in organisations is not new. This interest has been central to the field of computing, which has changed its name several times over the last seven decades (Denning, 2003; Rosenbloom, 2004; Denning,

Principles for productive inquiry into ICT-enabled change  223 2010). In the 1940s it was called “automatic computation” and in the 1950s “information processing”. In the 1960s, as it moved into academia, it was known as “informatics” in Europe and “computer science” in the USA. By the 1980s, “computing” comprised a complex web of inter-related fields including computer science, informatics, computational science, computer engineering, software engineering, information systems (IS), and information technology. By the 1990s, the term “computing” had become the standard for referring to this core group of disciplines (Denning, 2010). Whilst the field of computing has been greatly influenced by the fields of engineering, mathematics, and computational-oriented science, the core principles of computing which define the field have remained stable over time. As defined by Denning (2010), they include: computation (what can and cannot be computed), communication (reliably moving information between locations), coordination (effectively using many autonomous computers), recollection (representing, storing, and retrieving information from media), automation (discovering algorithms for information processes), evaluation (predicting performance of complex systems), and design (structuring systems and software for reliability and dependability). It is important to note that all digital technologies and related systems and platforms reflect a combination of these stable underlying principles. Digital Transformation Since the 1950s and 1960s, each new wave of ICT has emphasised how best to harness the potential of new digital technologies in transforming the work of organisations. In these earlier decades, the emphasis was on transforming manual record keeping systems and replacing these with electronic flat-file databases. In the 1970s and 1980s, the emphasis was on transforming management, executive, and strategic work through harnessing the power of data stored in relational databases. In the 1990s and 2000s, the emphasis was on transforming work through reengineering work processes and supporting systems and technology architectures. In the 2010s and 2020s, the power of digital technologies has extended itself well beyond the boundaries of organisations to include the transformation of supply chains, customer journeys, and the broader business and digital ecosystems in which organisations are embedded. Whilst the above is a quick and very simplified snapshot across time, the central point is that digital technologies have been with us since the 1950s and they have been actively and often aggressively deployed with a view to transforming the work of organisations (O’Regan, 2016). At their simplest, work processes and digital technologies are inextricably intertwined. It is the work process, in either its current or reengineered configuration, which is captured and reproduced in a programmable form on one or more interconnected digital devices. This has always been the case with digital technologies, and it still is the case even in the context of artificial intelligence and machine learning. All digital devices must be programmed to execute a defined set of tasks (O’Regan, 2016; Roberts, 2021). Of course, rapid advances in digital technologies have extended the scope of what is possible from a transformation perspective. For example, the emergence of decentralised computing in the 1970s and 1980s transformed the distribution of work with management and staff operating their own ICT systems. This constituted a radical change at the time. In a similar vein, advances in digital technologies from the 2000s onwards have facilitated the design of globally distributed work systems which are underpinned by complex systems of interconnected digital systems and networks that offer the possibility of creating greater balance

224  Handbook of research methods in organizational change between people, work, and technology (Chen & Clothier, 2003; Haines, 2012; Pasmore et al., 2019; Winby & Mohrman, 2018). This too is radical and is underpinning the current focus on the future of work as we emerge out of the COVID-19 pandemic. Interestingly, the meteoric rise of digital transformation in both the fields of management and organisation studies (M&OS) and IS along with its related set of highly fragmented themes and discourse from the 2000s onwards (Beeson & Rowe, 2012; Hanelt et al., 2021; Nadkarni & Prugl, 2021; Vial, 2019; Wessel et al., 2021) appears somewhat uninformed about the history of computing in and between organisations (Ceruzzi, 2003; Kling, 1996; Mullaney et al., 2021; O’Regan, 2016). The transformational potential of digital technologies has been a matter of concern for organisations from the 1950s onwards. It is important to bear this in mind when considering the challenge of change with the adoption of new digital technologies in organisations. The need for symmetry Harnessing the power of digital technologies in transforming the work of organisations requires a sense of symmetry or balance when progressing change in relation to people, work, and technology as depicted in Figure 10.1. For example, the introduction of smart metering in relation to the use of utilities such as electricity and gas requires some fundamental changes across all three dimensions. From a people perspective, it removes the need for human intervention in the reading and recording of meter readings on the part of the utility provider and it empowers users to make smarter choices in terms of the use of utilities. From a work perspective, it automates certain work processes and removes human intervention on the part of the utility provider. From a technology perspective, it exploits the latest in digital technology to support a seamless end-to-end process without any human intervention from the perspective of the utility provider. This is but one small example of how people, work, and technology must be addressed in a balanced manner to exploit the full potential of digital technologies. Yet, achieving symmetry and pursuing change that treats people, work, and technology in a balanced manner appears much more difficult than one might initially realise (Davenport & Redman, 2020; Sutcliff et al., 2019; Tabrizi et al., 2019). The landscape of ICT-enabled change in organisations is replete with narratives of underperformance and failure (Beer & Eisenstat, 2000; Carlton, 2019; Crittenden & Crittenden, 2008; Chanias et al., 2019; Correani et al., 2020; Davenport & Westerman, 2018). Notwithstanding the great power and potential of digital technologies to transform the work of organisations, the great waves of innovation and change across the decades have produced similar patterns in terms of outcomes from ICT-enabled change. For example, persistent patterns of failure with enterprise systems, digital commerce, business analytics, artificial intelligence, and digital transformation to name but a few are not new (Davenport & Westerman, 2018; Joshi et al., 2021; McDonagh, 2022). In the knowledge that we have been here before, time and time again (Candido & Santis, 2015; Decker et al., 2012; Gupta et al., 2019), let us now turn our attention to exploring the dynamics of ICT-enabled change in organisations.

Principles for productive inquiry into ICT-enabled change  225

Figure 10.1

Achieving symmetry between people, work, and technology

THE DYNAMICS OF ICT-ENABLED CHANGE Underperformance and Failure As introduced earlier, the typical pattern of outcomes in relation to ICT-enabled change across the decades is reflected in the following. Twenty per cent of change initiatives deliver promised business outcomes within agreed budgets and timelines. A further 40 per cent deliver impaired functionality with inflated budgets and significantly expanded timelines. The remaining 40 per cent fail outright. Whilst outcomes of this nature have been and remain a cause for major concern in organisations (Candido & Santis, 2015; Decker et al., 2012; Gupta et al., 2019; McDonagh, 2001, 2015, 2022), the enduring nature of this dilemma suggests that there is something about the dynamics of ICT-enabled change that is either overlooked or poorly understood. Focusing on the 80 per cent of initiatives typically classified as underperforming or failing, what, if anything, is it about the dynamics of change that contributes to such poor outcomes? Whilst the delivery of effective change outcomes requires a highly coordinated and integrated approach to the management of ICT-enabled change (Clegg et al., 1996a, 1996b; McDonagh, 2022), it appears that fragmentation is often the order of the day in professional practice. The emphasis on coordination and integration captures the need to seamlessly weave together the strategic, organisational, and technological dimensions of change (McDonagh, 2001). Yet, the 80 per cent of initiatives classified as underperforming or failing appear to struggle with this coordination and integration challenge. Whilst there is a significant body of research within the fields of M&OS and IS that speaks to this enduring dilemma, much of this work focuses on the identification of contributing factors or what might be considered for the most part as factors-based studies (Gupta et al.,

226  Handbook of research methods in organizational change 2019; McDonagh, 1999, 2001, 2015). Whilst undoubtedly interesting, such studies offer little by way of insights into processes of development and change and how these factors might be addressed in pursuit of positive change outcomes from a processual perspective. It is equivalent to having all the ingredients to make a cake without the actual recipe and related instructions. Notwithstanding the above limitations, it is possible to glean some critical insights from extant literature from the fields of both M&OS and IS (Beer & Eisenstat, 2000; Candido & Santis, 2015; Carlton, 2019; Chanias et al., 2019; Correani et al., 2020; Crittenden & Crittenden, 2008; Davenport & Westerman, 2018; Decker et al., 2012; Gupta et al., 2019; Horny et al., 1992; McDonagh, 1999, 2001, 2015, 2022). In the context of troubled ICT-enabled change initiatives (i.e., initiatives that are classified as either underperforming or failing), six insights emerge: 1. When ICT-enabled change initiatives fail to deliver promised business outcomes, it is rarely for technological reasons alone. No more than 10 per cent of troubled initiatives point to technological considerations as the primary contributing factors (Horny et al., 1992; Isaac-Henry, 1997; Kling, 1996; Long, 1987; McDonagh, 2001). 2. More than 90 per cent of troubled initiatives identify failure to adequately address a range of strategic and organisational considerations as the principal contributing factor (Clegg & Kemp, 1986; Hirschheim, 1985; Hurst, 1991; Kling, 1987; Leonardi, 2020; McDonagh, 2001; Tomeski & Lazarus, 1975). 3. In the context of achieving balance between people, work, and technology in ICT-enabled change initiatives, it is custom and practice to afford most attention and related funding to the technology dimension (Clegg, 1993, 1995; Kling & Allen, 1996; Lunt & Barclay, 1988; McDonagh, 2001, 2015, 2022; More, 1990). 4. The consequences of pivoting towards and prioritising funding for the technology dimension of ICT-enabled change are that its strategic and organisational dimensions are routinely marginalised and ignored, as are its people-related dimensions (Clegg et al., 1996a, 1996b; Coghlan & McDonagh, 2001a; McDonagh, 1999, 2001, 2015, 2022). 5. Marginalising and ignoring the strategic and organisational aspects of ICT-enabled change along with its people-related dimensions nurtures an environment in which change is considered as an afterthought, an add-on, something to be attended to as the implementation of a technology solution commences (Coghlan & McDonagh, 2001a; McDonagh, 2015). 6. The idea that ICT-enabled change should be co-created and co-owned by those involved and most affected and that development and change processes should be based on principles of openness, transparency, trust, inclusivity, affirmation, collaboration, and democracy seems for the most part missing from this literature. There is no rich history of a values-led approach to ICT-enabled change in either the M&OS or IS literature (Beeson & Rowe, 2012; Hanelt et al., 2021; Nadkarni & Prugl, 2021; Vial, 2019; Wessel et al., 2021). Reflecting on the insights above, it is hardly surprising that 80 per cent of ICT-enabled change initiatives end up being classified as troubled. Whilst the factors contributing to asymmetry in change initiatives as they relate to people, work, and technology have been well documented, what is of even greater interest is the way narratives of failed initiatives point to the hegemony of executive management and ICT management and their influence on development and change in organisations (Schein, 1992, 1996). To this we now turn our attention with

Principles for productive inquiry into ICT-enabled change  227 a particular emphasis on the way occupational communities unwittingly create the climate and conditions that promote fragmented approaches to development and change in organisations. The Role of Occupational Communities In exploring the dynamics of ICT-enabled change in organisations, several academics have drawn attention to the role of occupational groups, their related occupational cultures, and the way they shape development and change in organisations. Schein (1992, 1996) draws attention to executives and ICT professionals as part of two very distinct global occupational communities with their respective sets of shared beliefs, values, and assumptions (i.e., occupational cultures). Within any given organisational setting, these two global occupational cultures coalesce to shape the landscape in relation to ICT-enabled change. In coalescing, they engage with more localised sets of beliefs, values, and assumptions, especially those of front-line workers who have the lived experience of knowing how things work around here (Leonardi, 2020). Schein’s work has been ground-breaking in drawing attention to the way occupational and organisational cultures and sub-cultures influence development and change in organisations over time. Drawing on the work of Schein (1992, 1996), McDonagh (1999, 2022) has inquired into the dynamics of ICT-enabled change with particular emphasis on the roles of executive leaders. This work has further illuminated the way executive management exerts its influence on ICT-enabled change through a powerful economic focus whilst ICT management influences change through an equally powerful technical focus (Squirrel & Frederick, 2020). The interlocking nature of these two dominant and powerful foci results in the supremacy of techno-economic considerations in ICT-enabled change initiatives (McDonagh, 2001, 2015, 2022). The consequences are obvious in that the strategic, organisational, and people aspects of change are downplayed and those most influenced by ICT-enabled change are marginalised and ignored in the process. Various aspects of this research have been considered by Coghlan and McDonagh (2001b) and McDonagh and Coghlan (1999, 2000, 2001). The research works noted above clearly point to the centrality of occupational and organisational cultures in shaping the contours of ICT-enabled change in organisations. In and of themselves, competing sets of beliefs, values, and assumptions lay a firm foundation for fragmentation rather than integration. In the absence of a values-led approach to development and change and in the absence of fostering a cohesive and shared set of beliefs, values, and assumptions on the key aspects of an ICT-enabled change initiative, poor outcomes predictably follow. Of course, the way this is played out and enacted in organisational life is through the mobilisation of diverse systems of professional practice with their embedded value systems and supporting bodies of knowledge. Systems of Professional Practice It can be argued that fragmented approaches to change in organisations are a product of interlocking systems of professional practice with each system having its own embedded values and favoured approach to development and change (Abbott, 1988). For example, the dominant economic focus of executive management in ICT-enabled change is underpinned by a belief that strategy work is the starting point in shaping an organisation’s agenda for ICT and related investments (Block et al., 2012; Merali et al., 2012). But, unfortunately, strategy work in rela-

228  Handbook of research methods in organizational change tion to ICT regularly reinforces a narrow techno-economic orientation. As practised, strategy work is often partial and yields a prioritised portfolio of technology-related change initiatives. There is little evidence that work of this nature is either values-led or people-centred or that it gives serious attention to achieving symmetry between people, work, and technology (Merali et al., 2012). Of course, strategy work can make a profound difference and it can promote a holistic and integrated approach to development and change in organisations. But as practised in relation to ICT, it frequently lays the foundation for and contributes directly to fragmentation of change in organisations. Consequentially, it is hardly surprising that the alignment of strategy and ICT has remained one of the most pressing and worrisome issues for executive and ICT management alike (Kappelman et al., 2017, 2020, 2021). In a similar vein, the dominant technical focus of ICT management in ICT-enabled change is underpinned by both a narrow view of strategy work (Block et al., 2012) which marginalises the people and organisational dimensions of change and an equally narrow view of portfolio, programme, and project management work (Martinsuo & Hoverfalt, 2018). As practised in many organisational settings, the practice of portfolio, programme, and project management work does not readily embrace a behavioural approach to change and neither does it easily accommodate the human and organisational aspects of change which account for the difference between success and failure with ICT-enabled change initiatives (Clegg et al., 1996b; Martinsuo & Hoverfalt, 2018). This dominant orthodoxy in relation to ICT-enabled change contributes to an organisational climate and environment in which the discourse and practice of both strategy work and supporting portfolio, programme and project management work reinforce the dominance of a techno-economic approach to change and the marginalisation of its people and organisational dimensions. Distinct systems of professional practice that relate to strategy work and portfolio, programme, and project management work as they relate to ICT capture the attention of executive and ICT management alike. If these systems of practice leave unchecked the propensity to advance a narrow techno-economic focus in ICT-enabled change initiatives, then the die is cast in relation to what follows. These systems set the boundaries for what can be legitimately progressed in relation to change. If the human and organisational aspects of ICT-enabled change are not surfaced in the first instance by these systems of practice, then subsequent systems of practice enacted by various ICT professionals (e.g., systems analysis, systems design, systems development) will do nothing more than reinforce a narrow techno-economic focus. From a socio-technical perspective, the interlocking and coalescing nature of these systems of practice is that they produce troublesome outcomes by prioritising and progressing the technical aspects of ICT-enabled change at the expense of its social dimensions (Clegg et al., 1996a, 1996b; McDonagh, 2015, 2022; Schein, 1996). In summary then, systems of professional practice are shaped by and embody a range of respective occupational and organisational cultures (Schein, 1992, 1996). Essentially, they reproduce the dominant cultural forms at work in occupational communities and organisations. Furthermore, the worldviews of professionals are shaped by their respective occupational communities and their formation is underpinned by the enabling roles of educational and professional institutions with their respective bodies of knowledge (Abbott, 1988). All in all, this lays a firm foundation for fragmentation as the predictable norm in relation to ICT-enabled change (Coghlan & McDonagh, 2001a). Yet, there is reason to be optimistic as the adoption of both fresh perspectives on scholarship and professional practice has the potential to contribute to increasingly positive change outcomes into the future.

Principles for productive inquiry into ICT-enabled change  229

PRINCIPLES FOR PRODUCTIVE INQUIRY A Fresh Perspective Reflecting on the enduring and troublesome nature of ICT-enabled change in organisations, there is a need for fresh approaches to inquiry that somehow accommodate and weave together highly diverse and distributed forms of wisdom, knowledge, skill, and expertise from both academia and professional practice alike. Whilst OD scholars have much to contribute here, their work will be significantly enhanced by a deeper understanding of the history of computing, the world of ICT professionals and their preferred repertoire of professional practices in the conduct of their work. Similarly, IS scholars have much to contribute with their work being significantly enhanced through a deeper understanding of people-centred approaches to development and change in organisations. Ideally, what is required is a marrying together of the worlds of OD and IS scholarship and a commitment to inhabiting the nexus between strategy, change, and ICT in organisations. Following calls from Cummings & Cummings (2020), the inclusion of OD scholars is central to challenging the dominant social systems and embedded ideologies that marginalise the people and organisational aspects of development and change as they relate to the adoption of ICT in organisations. Similarly, the inclusion of IS scholars is central to dealing comprehensively with the technical facets of ICT-enabled change. The adoption of the socio-technical perspective in ICT-enabled change, especially coming out of Europe, is particularly encouraging in this regard (Avgerou, 2001; Avgerou & McGrath, 2007). Harnessing the strengths of both OD and IS scholarship and adopting novel approaches to inquiry and change hold great promise. But there are challenges none the less, the most significant of which is the fragmented nature of academic research and its perceived usefulness to professional practice. For an insight into the fragmented nature of research into ICT-enabled change in organisations, see the recent reviews by Beeson and Rowe (2012), Hanelt et al. (2021), Nadkarni and Prugl (2021), Vial (2019), and Wessel et al. (2021). From a development and change perspective, it takes a lot of thought and reflection to work out how best to draw productively from this reservoir of academic research and to use it in a manner that is perceived as helpful and useful in professional practice. Notwithstanding the enormous value of this body of work, it does not offer a coherent view of the dynamics of ICT-enabled change and neither does it shed light on how organisations foster a coherent and integrated approach to change from a socio-technical perspective. For an OD researcher, fostering a cohesive and integrated view of ICT-enabled change warrants equal consideration to its strategic, organisational, and technological dimensions. The researcher cannot take the technological dimension as a given and then choose to focus on the other dimensions in some sort of supportive capacity. Rather, the OD researcher should seek to mobilise all that is exemplary in the field of OD and promote the adoption of a values-led approach to change that places people at the centre. This paves the way for eventually achieving symmetry between people, work, and technology. The OD researcher must not treat technology as a black box. It is imbued with values and assumptions about people and their work (Broussard, 2018; Eubanks, 2018; Mullaney et al., 2021; Noble, 2018). Noting the challenges, some researchers have sought to integrate the complementary OD and IS worldviews and to appropriately draw from these when inquiring into the dynamics of ICT-enabled change in large complex organisations (Coghlan & McDonagh, 2001a; 2001b;

230  Handbook of research methods in organizational change McDonagh & Coghlan, 1999, 2001). With a sustained commitment to inquiry of this nature over an enduring period (McDonagh, 2022), six guiding principles for productive inquiry into ICT-enabled change emerge. Taken together, these principles have the potential to both inform and transform professional practice as they relate to the dynamics of ICT-enabled change in organisations whilst simultaneously contributing to sound scholarship. The principles are enumerated below. Principle I: The processual principle When inquiring into the dynamics of ICT-enabled change in organisations, the adoption of a processual perspective is pivotal. Whether inquiring into process from a historical perspective, or considering process in the here and now, or contemplating process from a futures perspective, a processual lens reveals both the public and often hidden back-stage dynamics of ICT-enabled change initiatives. A processual perspective is both grounded in and reveals the actions, reactions, and interactions over time of individuals and groups engaged in shaping and delivering ICT-enabled change initiatives. It uncovers the multifaceted nature of change as reflected in its contextual, content, process, and outcome dimensions. In the study of the dynamics of ICT-enabled change, the adoption of a processual perspective offers unrivalled insights into the dynamics of change along with their influence and impact in terms of shaping change outcomes (Huber & Van de Ven, 1995; Pettigrew, 1990, 1992; Van de Ven & Huber, 1990). Grounded in its distinct emphasis on actions, reactions, and interactions, a processual perspective reveals the ways in which multiple human activity systems contribute to change outcomes over time in organisations, be they positive or otherwise (Abbott, 1990; Huber & Van de Ven, 1995; Monge, 1990; Van de Ven & Huber, 1990). It reveals the identity and nature of such systems, their embedded values in relation to development and change, their interlocking nature, and the way in which they either include or sideline the human and organisational aspects of ICT-enabled change. Uncovering such systems and their individual and collective influence and impact is central to explaining underperformance and failure over time whilst also offering a basis for affirmative intervention and action in pursuit of positive change outcomes well into the future. For an up-to-date view on process-oriented inquiry, see Saxena and McDonagh in this volume. Inquiring into the dynamics of ICT-enabled change from a processual perspective requires a commitment to tracing the action, reactions, and interactions of executive management, middle management, ICT professionals, and business users of ICT as they relate to ICT strategy work; ICT portfolio, programme, and project management work; and ICT systems development work as appropriate. Of course, the precise realm of organisational work to be placed under the microscope will depend on the focus of the research initiative being considered. Good processual research will always blend retrospective and real-time accounts of ICT-enabled change in organisations. Principle II: The practice principle The adoption of a processual approach to inquiry reveals the way multiple actors mobilise diverse systems of professional practice to shape the contours of ICT-enabled change in organisations. It is here that one so often finds the roots of underperformance and failure in relation to change made public. Frequently, the way these systems of practice coalesce to create positive or negative change outcomes is not thought through by key actors involved.

Principles for productive inquiry into ICT-enabled change  231 Hidden beliefs, values, and assumptions underpin and sustain these realms of practice, often without a systemic view of their collective influence and impact on development and change as it unfolds over time. Rather than nurturing and supporting a coordinated and integrated approach to ICT-enabled change, the researcher may readily find that many of these systems of professional practice are themselves disjointed and fragmented (Abbott, 1988). Consider the following by way of example as borne out in the work of McDonagh (2022). Strategy work in organisations is all too often highly fragmented with limited if any attention devoted to the alignment of families of strategies either within such organisations or within the broader ecosystems in which they are embedded. The inherent value of OD work in addressing the human and organisational dimensions of ICT-enabled change is rarely if ever considered by seasoned practitioners, be they executive management or ICT professionals. The current dominance of portfolio, programme, and project management practices to guide ICT-enabled change remains unquestioned in professional practice even when it is known that such practices fail to consider the human and organisational aspects of change which are routinely marginalised and ignored in ICT-enabled change initiatives. Similarly, ICT development practices (e.g., systems analysis, systems design, systems development) remain tilted in the direction of technical artefacts rather than seeking symmetry between people, work, and technology. In the conduct of practice-oriented inquiry within an organisational setting, it is essential to identify and explain both the development and evolution of systems of practice over time and the way such systems are mobilised by occupational groups in the context of ICT-enabled change initiatives. Here, the researcher needs to take note of the distinctive ways in which diverse occupational groups engage with organisational practices to guide ICT-enabled change. The researcher needs to develop the ability to trace the evolution and development of an ICT-enabled change initiative over time and the ways in which it has been shaped by often diverse and competing realms of professional practice. For a current perspective on practice-oriented inquiry, the works of Burgleman et al. (2018), Kohtamaki et al. (2021), Mackay et al. (2020), and Seidl and Whittington (2021) offer an interesting starting point here. Principle III: The immersive principle At the heart of productive inquiry into the dynamics of ICT-enabled change in organisations is the need to get up close to change as it unfolds through time in real-world organisational settings. The idea that a researcher can study the dynamics of change from a distance is not particularly helpful. If the researcher aspires to develop deep insights into the dynamics of ICT-enabled change, then there is a need to become immersed in the milieu of professional practice and to witness first-hand the way competing sets of values and competing systems of professional practice coalesce to shape the trajectory of ICT-enabled change initiatives. For the researcher, immersion of this nature is intensive, draws heavily on their time and resources, and always yields a range of rich and often transformative insights. For an up-to-date view on the enabling role of action research, see Coghlan in this volume. Here, it is encouraging to review the recent calls to IS scholars to engage in inquiry of this nature (Avison et al., 2018; Davison et al., 2021; Gable, 2020). When thinking through matters of immersion, the researcher may derive value from the Scholar-in-Residence (SiR) model of inquiry that promotes the inclusion of researchers in interventionist modes of inquiry intended to inform and transform professional practice (Jacelon et al., 2010; Parke et al., 2015). Widely used within the health sciences, this mode of inquiry can be very appealing when engaging with organisations as it helps frame the

232  Handbook of research methods in organizational change boundaries of the relationship between researcher and practitioners. It offers a structured way of thinking through the way this relationship might work best and how the objectives of academic inquiry and professional practice may be aligned to best effect over time. Adopting the SiR model of inquiry strongly supports immersion in professional practice whilst also laying a foundation for close collaboration as a change initiative unfolds. In the context of ICT-enabled change, immersion in professional practice equates with the researcher being present and helpful as the dynamics of change unfold through time. The researcher needs to witness and account for change as it evolves at multiple levels. Of course, this includes accounting for the way strategic, organisational, and technological change are coordinated and integrated and the multiple mechanisms by which this is achieved. Good research requires the researcher to be part of organisational life as it unfolds and to witness first-hand the way individuals and groups shape unfolding change processes. Principle IV: The collaborative principle Being immersed in the milieu of professional practice begs the question, what exactly is the role of the researcher in inquiry of this nature? In summary, and speaking to what is most ideal, the researcher should strive to be a natural collaborator in terms of working with and through others (Shani & Coghlan, 2021a, 2001b) in shaping the trajectory of ICT-enabled change initiatives in organisations (Shani & Coghlan, 2021a; and Shani in this volume). As a collaborator, the researcher most likely needs to possess a level of wisdom, knowledge, skill, and expertise which if drawn on in an appropriate manner offer great potential in terms of supporting others as they seek to shape and direct change in a manner that produces positive outcomes for all involved. As a collaborator, the researcher adds unique value in terms of advancing the case for a collaborative approach to ICT-enabled change, promoting the adoption of a values-based perspective, illuminating the strengths and weaknesses of various approaches to development and change, and clarifying the way various systems of practice can be used to shape the trajectory of an ICT-enabled change initiative in an affirmative and productive manner. The researcher is not an independent observer and a collector of data to be used solely for some purpose other than the change initiative that is underway in the here and now. The researcher is deeply engaged and seeks to promote the engagement of others in a manner that increases the prospect of delivering positive change outcomes. Ideally, working within a clinical inquiry paradigm (Coghlan, 2009; Coghlan & McDonagh, 2001b; McDonagh & Coghlan, 2000; Schein, 1991, 1995), the researcher’s collaborative role is legitimised in the first instance by being invited into an organisational setting with a view to being helpful to others as they seek to shape the trajectory of their ICT-enabled change initiatives. Generally, such invitations stem from organisations who have experienced difficulties in the past and who appreciate that the addition of an external voice will most likely have a positive impact on their change-related endeavours. That the researcher is invited in to help has very significant consequences, not least in the ability to become legitimately immersed in the work of changing and to have access to rich data and insights that would otherwise be inaccessible for all intents and purposes. As an approach to collaborative research, clinical inquiry promotes deep engagement and access to data and insights that might routinely remain hidden in the normal course of events.

Principles for productive inquiry into ICT-enabled change  233 Principle V: The developmental principle The role of the researcher in collaborative change initiatives is akin to that of the action researcher. It is distinctive in that the researcher’s interests are twofold, namely the pursuit of rigorous academic research and inquiry into issues, problems, and challenges that are of immediate concern and relevance to professional practice. As collaborator, the researcher engages with, listens intently, and responds to others as they seek to shape, direct, and deliver change initiatives in their own organisational settings. In fulfilling this developmental dimension, the researcher draws upon and seeks to integrate insights from both sound scholarship and professional practice into emerging development and change processes. The use of insights from such scholarship and their translation into actionable knowledge for practitioners are non-negotiables for the researcher. Well executed, the developmental dimension of the researcher’s role is highly integrative in that it promotes the weaving together of diverse forms of wisdom, knowledge, skill, and expertise that are widely distributed across individuals and groups. It simultaneously seeks to empower others to recognise, reconcile, and integrate diverse sets of beliefs, values, and assumptions and diverse systems of professional practice when shaping the trajectory of ICT-enabled change initiatives in their own organisational settings. Using sound scholarship to inform and transform professional practice is central to this developmental work, as is the need to address the twin demands of rigour and relevance when inquiring into the dynamics of ICT-enabled change. Good research into the dynamics of ICT-enabled change makes provision for learning and changing as integral elements of the research journey. People inquire together and learn and change together as inquiry unfolds. Learning and changing are not add-ons or afterthoughts to be considered in the form of implications for professional practice. In a well-architected collaborative change process, opportunities for learning and changing together unfold naturally. Of course, the researcher who is committed to upholding the developmental dimension of their role has significant influence here, as does their commitment to harnessing the power of peers (i.e., peer learning, peer evaluation, peer coaching, peer networking) in support of evolving development and change processes. For a more detailed treatment of the enabling role of action research, see Coghlan in this volume. In the context of ICT-enabled change, the developmental dimension of the researcher’s role is of paramount importance. It is linked directly with the challenge of re-educating individuals and groups about the dynamics of ICT-enabled change, challenging the propensity for marginalising the people and organisational dimensions of change, challenging systems of professional practice that contribute to troubled outcomes, and challenging individuals and groups to grow their leadership and change capabilities through on-the-job leadership development work. It is in the milieu of unfolding change processes that the researcher can do their best work as they seek to harness and direct the energy of individuals and groups in pursuit of productive change outcomes. Principle VI: The appreciative principle When inquiring into the dynamics of ICT-enabled change in organisations, it is not uncommon to encounter organisational climates characterised by an aversion to ICT-enabled change due to difficulties with delivering change of this nature in the past. All too often, blame for underperformance and failure is attributed to ICT professionals and the ICT community with limited time and effort invested in seeking to truly understand what contributes to poor outcomes. Of

234  Handbook of research methods in organizational change course, the researcher should be fully aware that blame of this nature is most likely unhelpful and does little to create healthy organisational climates that more naturally embrace and work with the dynamics of change associated with the adoption of new digital technologies. Whilst working within a clinical paradigm and adopting a processual lens add immensely to inquiry into ICT-enabled change, so does the infusion of an appreciative perspective into inquiry of this nature. Drawing on the power of positive psychology, appreciative inquiry adopts and promotes a strengths-based approach to development and change in which actors work in unison to determine their collective strengths and to channel these in support of purposeful and productive change (Cameron & McNaughtan, 2014; Copperrider & Srivastva, 1987, 2017). Appreciative inquiry seeks to uncover and harness productive energy and to use this to shape unfolding development and change processes in organisations. Drawing on the power of the positive, the typical phases of an ICT-enabled change initiative can be future-focused and infused with all that is excellent about people and their organisation. In the context of ICT-enabled change, the researcher becomes aware over time of the nature of this landscape from both scholarly and professional practice perspectives. Here, the experienced researcher focuses on the small number of areas in which affirmative action will contribute to disproportionally positive change outcomes. This regularly involves ignoring or at least setting aside for a time a range of considerations which if attended to now will most likely stymie progress. The researcher seeks to draw out all that is excellent in individuals and groups and to direct their energy in pursuit of well-defined and widely shared change outcomes. It takes time for the researcher to develop this ability and to earn the right to use it wisely when working with and through others. Dynamic interplay These high-level principles for productive inquiry are not mutually exclusive. When inquiring into a particular ICT-enabled change initiative, a researcher may wish to draw upon some or all as part of their research design. Of course, the way the selected principles are operationalised in research practice will need to be made explicit. In addition, they must be congruent with the worldview that a researcher is working within. For example, a researcher adopting a process philosophical approach to understanding the nexus between strategy, change, and ICT (Mackay et al., 2020; Seibt, 2021) will need to ensure that the enactment of these principles is congruent with that worldview. Summary Born out of experience in the study of ICT-enabled change in large complex organisations (McDonagh, 2022), the six guiding principles presented above and summarised in Table 10.1 offer a starting point for researchers interested in the pursuit of meaningful and productive inquiry that informs and transforms professional practice whilst simultaneously contributing to sound scholarship. Whilst each principle adds to and enriches the conduct of inquiry into ICT-enabled change, the set of principles offers the basis for evolving a practice of inquiring together into the dynamics of ICT-change in organisations. This challenge is addressed in greater detail in the next section.

Principles for productive inquiry into ICT-enabled change  235 Table 10.1

Principles for productive inquiry into ICT-enabled change: evolving a practice of inquiring together

Principle

Title

Central focus

I

The processual principle

Capturing actions, reactions, and interactions of actors involved in

II

The practice-oriented principle

Identifying the influence and impact of systems of professional practice

III

The immersive principle

Becoming immersed in the milieu of professional practice and

IV

The collaborative principle

Engaging in the discipline and practice of co-inquiry with practitioners

V

The developmental principle

Harnessing the power of peers and infusing development and change

VI

The appreciative principle

Embracing a future-focused and strengths-based approach to

development and change processes on the trajectory of ICT-enabled change initiatives choosing an appropriate supporting framework

processes with scholarly and practice-based insights development and change processes

EVOLVING A PRACTICE OF INQUIRING TOGETHER Some Insights Crafting an approach to inquiry that embodies the principles outlined in Table 10.1 above is a lifetime’s work. Evolving a mature practice of inquiring together with others and living the principles introduced here takes time, resources, patience, and sustained commitment to pursuing inquiry that is meaningful and productive for both researcher and practitioner alike (Mirvis et al., 2021; Shani & Coghlan, 2021a, 2021b). As principles are transformed into living research practices, the researcher becomes aware of the need to build their capability through time and in so doing enhances their level of proficiency (i.e., basic, novice, intermediate, advanced, expert). But where might a young academic researcher start in terms of crafting and evolving such a distinctive research practice, and how might that craft be fine-tuned through time? To evolve a practice of productively inquiring together with others and to be sought out in a helping capacity in the context of ICT-enabled change initiatives in organisations, the researcher must carefully consider what forms of wisdom, knowledge, skill, and expertise are of most value in supporting the creation of helping relationships. Researchers who develop depth in the following key areas will most likely find themselves being invited into numerous helping relationships over time: 1. Understanding the role of systemic approaches to development and change in guiding renewal and reform programmes within and across organisations. 2. Understanding the role of leadership networks and leadership teams in crafting and exploiting strategy work in shaping the direction and development of organisations. 3. Understanding the role of digital technology and related digital systems and platforms in supporting the renewal and reform of organisations. 4. Understanding the role of leadership work and OD work in achieving symmetry between people, work, and technology. 5. Understanding the interplay between strategy work, OD work, portfolio, programme and project management work, and technology development work as they relate to the dynamics of ICT-enabled change in organisations.

236  Handbook of research methods in organizational change 6. Engaging, enabling, and empowering individuals, teams, networks, and communities to work in unison in the areas of professional practice noted in (1) to (5) above. Harnessing Academia The work of academia tends to be organised into three distinct realms, including leadership and administration, teaching and supervision, and research and publications (including related funding). The young researcher can exploit each of these three realms in terms of building their profile and presence and actively crafting and evolving a practice of inquiring together with others. Leadership and administration work offers opportunities to draw close to constituents that are of direct interest to the researcher. Teaching and supervision work offers opportunities to sharpen and deepen the researcher’s competence in the areas outlined earlier. Research and publication work allows the young researcher to engage closely with practice and to generate outputs that speak to both academia and professional practice alike. The doctoral researcher also has the potential to begin to exploit these three mutually reinforcing realms of academic life. The young academic researcher keen to craft and evolve a practice of inquiring together with others should place collaborative approaches to inquiry and change at centre stage in all their work. The adoption of challenge-, inquiry-, project-, and team-based approaches to teaching and learning will prove invaluable in terms of forging the link between academia and professional practice. In essence, there are a multitude of options open to the academic researcher in terms of building their profile, presence, and capabilities thereby paving the way for engaging in helping relationships with organisations. A Personal Journey The central message here is clear. To engage in helping and supportive relationships with organisations over time, the young academic researcher must work on their own development in ways that go well beyond the requirements of academia. In forging and pursing clear career development goals and objectives, the researcher will do well to engage with senior management in organisations to determine their criteria for measuring success in a helping relationship. Of course, what is being referred to here is a helping relationship in relation to the dynamics of ICT-enabled change initiatives in organisations. Concerning the principles introduced earlier, a phased approach to their adoption might prove most practical and beneficial. By way of supporting immersion in professional practice and effective collaboration with others, the researcher might prioritise the adoption of the SiR model of inquiry along with clinical inquiry. By way of supporting the development of others in unfolding development and change processes, the researcher might get up close through processual and practice-oriented inquiry whilst simultaneously weaving together insights from scholarship and professional practice. By way of enriching the inquiry process, the researcher might emphasise the power of affirmation and the process of learning and changing together.

Principles for productive inquiry into ICT-enabled change  237

CONCLUSION This chapter has focused on visiting and unpicking the enduring dilemma with ICT-enabled change initiatives, paying particular attention to the 80 per cent of such initiatives that routinely prove troublesome in organisations. It has explored the ways in which this dilemma is sustained through time by the influence of diverse occupational and organisational cultures and related systems of professional practice in organisations. For researchers interested in focusing on the dynamics of ICT-enabled change in organisations, the chapter offers six guiding principles for productive inquiry into this realm of organisational life. Taken together, these principles offer the potential to both inform and transform professional practice whilst simultaneously contributing to robust scholarly work. By way of embracing and giving effect to these principles, the chapter offers insights and counsel on evolving a practice of inquiring together throughout the stages of one’s career. Locating oneself at the nexus between sound scholarship and exemplary professional practice as they relate to the challenge of ICT-enabled change in organisations is worthy of serious consideration on the part of any researcher. The interplay between scholarship and exemplary practice is critical and must always be kept to the forefront. Researchers who sit comfortably at this nexus develop the ability to work out the implications of their work for both scholarship and professional practice alike. For OD researchers, there is enormous value in delving deeper into the way systems of professional practice coalesce over time and predictably produce troublesome outcomes in relation to ICT-enabled change initiatives. Ground-breaking insights here have the potential to pave the way for the renewal of professional practice whilst simultaneously addressing the challenge of aligning strategy and digital technology in private and public organisations alike. Without doubt, OD has a pivotal and central role to play in addressing what is a most worrisome issue for organisations.

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11. Using participatory mixed methods to study “grand challenges”: an illustrative case of diversity, equity, and inclusion change research in organizations Regina Kim and Yunzi (Rae) Tan

INTRODUCTION Our world is inundated with challenges. Whether it is the COVID-19 pandemic, global refugee crises, climate change-induced natural disasters, social inequities, or poverty, grand challenges remain persistent despite technological and economic progress (George, Howard-Grenville, Joshi & Tithanyi, 2016). George and colleagues (2016, p. 1881) define grand challenges as “specific critical barrier(s) that, if removed, would help solve an important societal problem with a high likelihood of global impact through widespread implementations.” In recent years, more businesses and organizations have become active in vocalizing their concerns and working diligently to address these grand challenges. For example, Patagonia, a clothing company, recognizes that the clothing industry contributes significantly to the climate crisis and has established a goal to reach carbon neutrality across its entire business by 2025. Another retailer, TOMS, states that its mission is to be “in business to improve lives”—since its founding in 2006, the shoe company has given away one pair of shoes for every pair sold, and in 2021 it made a commitment to give a third of its annual net profits to local community-focused organizations, thus investing in grassroots efforts focused on equity. As the scope and complexity of grand challenges continue to grow, organizations today are increasingly expected to contribute or generate solutions that would help address these challenges. One such grand challenge that organizations are often tasked with tackling involves equity and social justice issues. To address social (in)justice and related inequities, organizations tend to focus on their change efforts around diversity, equity and inclusion (DEI) policies and practices. Indeed, organizational attention on promoting workforce DEI has been accompanied by a robust and still-expanding body of research and scholarship over the past few decades (e.g., Roberson, 2019; Shore et al., 2009). Yet, past and current research on DEI change has, in part, been stymied by limitations inherent in the research methodologies commonly employed by organizational change researchers. Our review of the extant research literature on DEI-related organization change has revealed that most of the existing research relied on anecdotal and narrative accounts, case studies, and other qualitative approaches (e.g., Cheng, 1995; McVittie, McKinlay & Widdicombe, 2008; Van den Brink & Benschop, 2018), while a smaller subset of prior research utilized quantitative designs and methods (e.g., Kim, 2018; Strauss, Sawyer & Oke, 2008). Moreover, only a handful of existing studies used a combination of both qualitative and quantitative approaches—i.e., mixed methodologies—to examine DEI-related problems (e.g., Dreachslin et al., 2017; Selzer & Foley, 2018). Given the dynamic and complex nature of DEI, using “traditional” research methodologies—namely, 242

Using participatory mixed methods to study “grand challenges”  243 qualitative and/or quantitative research approaches—to address such grand challenges necessarily limits researchers’ ability to examine these “wicked problems” more fully. While the methodologies that we traditionally employ in DEI organizational change research have served and will continue to serve us well, there is a growing sense that our methodological approaches need to adapt to the rapidly changing complexities of the phenomena we study (Molina-Azorin, Bergh, Corley & Ketchen, 2017). In this chapter, we intend to push the methodological envelope further by introducing participatory mixed methods research (PMMR) as a viable and suitable approach for tackling grand challenges that are dynamic and complex in nature, with a specific focus on DEI as one such challenge. PMMR can be described as a form of research approach that combines both quantitative and qualitative designs and methods in ways that emphasize honest dialogue, knowledge co-production, and equitable collaboration between researchers and participants (or organizational stakeholders) and that promote stakeholder-driven input, action, and change throughout the organization (Olson & Jason, 2016). In the following sections, we first expound on why addressing workforce DEI is considered a grand challenge, and follow this with a review of the extant literature on DEI change research in organizations. Then, we discuss the PMMR approach in greater detail and elaborate on its relevance and suitability for organizational change researchers studying DEI as a grand challenge. Next, we present an illustrative case to articulate how PMMR might be applied by change researchers to examine and study DEI change efforts in an organizational context. Finally, we outline possible barriers that both junior scholars and experienced researchers might encounter when using PMMR in organizational change research, along with key suggestions for overcoming or mitigating such barriers.

DEI ORGANIZATIONAL CHANGE RESEARCH AS A GRAND CHALLENGE: A STEP TOWARD EQUITY AND SOCIAL JUSTICE Much attention has been given to improving DEI in organizations, and at most multinational organizations DEI has become an imperative. There are various reasons that drive organizations to undertake DEI change initiatives. Since McKinsey released a report in 2015 stating that out of 366 public companies studied, those in the top quartile for ethnic and racial diversity in management were 35 percent more likely than others to have financial returns above the industry mean (Hunt, Layton & Prince, 2015), a business case was established for promoting diversity. In this sense, economic reasons drive organizations to develop the ability to serve an increasingly diverse market and to draw human capital from a diverse workforce. Organizational diversity is often used as a valuable, rare, and non-imitable strategic resource and source of sustained competitive advantage (Richard, Murthi & Ismail, 2007). Moreover, legislative forces also drive organizations to comply with government-based equal opportunity and affirmative action legislation to avoid lawsuits (Kalev, Dobbin & Kelly, 2006). Arguably, the most impactful and important reason that is driving organizations to take DEI change initiatives in recent years comes from a moral, social justice perspective. Global movements such as #BlackLivesMatter, #MeToo, and #StopAsianHate have ascribed organizations with the social responsibility of supporting equity and social justice (Dowell & Jackson, 2020; Knights, 2020; Roberts & Washington, 2020). There is an increasing scrutiny on an organization’s management (or lack thereof) of DEI, coupled with growing public demands for companies to be held accountable for perpetuating inequality within the organization

244  Handbook of research methods in organizational change through exclusionary leadership, inequitable access to mentorship and training, biased hiring and promotion practices, etc. In 2018, Starbucks closed its 800,000+ stores in the United States for racial anti-bias training of all its employees in the aftermath of one of its store managers calling the police on two black patrons (Chappell, 2018). The video of the incident went viral on social networks and the angered public demanded a response from Starbucks. Moreover, in the days following George Floyd’s tragic death in 2020, companies were forced to reckon with systemic racial and gender bias within their companies, which led to many oustings and resignations. The CEO of CrossFit resigned after he said in public, “We are not mourning for George Floyd” (Heffernan, 2020). Bon Appétit’s editor-in chief Adam Rapoport also announced his resignation after a photo of him evincing Puerto Rican stereotypes was tweeted (Severson, 2020). The grand challenge of supporting equity and social justice in the workplace extends across borders and is echoed in other parts of the world. According to a recent DEI Pulse survey conducted in Europe, when 573 executives were asked about their perceptions and experiences of DEI in the workplace, most said that embracing DEI will allow their companies to tap into a rich pool of talent; however, they were not always aware of how to incorporate DEI into their everyday practices and were unsure of the return on investments. The report showed that 34 percent of the S&P European 350 have a chief diversity officer or equivalent, and only 15 percent of companies treat DEI as a business and organizational issue that engages all employees (Abate, Sachar, Stuart & Wieduwilt, 2020). Another survey, conducted by the Boston Consulting Group, which involved 6,100 employees in Singapore, Thailand, Malaysia, Indonesia, Vietnam, and the Philippines, revealed that overall, 58 percent of companies in Southeast Asia have some form of DEI program in place. However, expectations are growing among employees in traditionally underrepresented groups: ethnic minorities, women, and LGBTQ people. They want a workplace that is free of bias, and an environment where they can have an equal chance of being hired and promoted, and where their voices are heard and their contributions recognized. In the survey, 57 percent of all respondents—and 90 percent of those from underrepresented groups—said they would consider leaving their job if these baseline criteria are not met (Krentz et al., 2020). As the demand for timely reforms and organizational change related to DEI issues increase globally, there is a burgeoning need to examine and assess DEI change initiatives in organizations. Indeed, this need to advance DEI and to overcome social injustice and exclusion in our workplaces constitutes a grand challenge faced by organizations around the world.

OVERVIEW OF RESEARCH ON DEI CHANGE IN ORGANIZATIONS The literature on DEI in organizational change has grown since the late 1990s and has become more extensive and complex in recent years (Gonzalez, 2010; Stevens, Plaut & Sanchez-Burks, 2008). DEI scholarship suggests that despite numerous initiatives aiming to transform organizations into more inclusive and diverse places to work, these diversity initiatives and practices rarely translate into deep systemic change (Block & Noumair, 2015; Golom, 2015). There also seems to be a lack of consensus on how best to examine and study DEI initiatives within the organizational change context (Stouten, Rousseau & Cremer, 2018). The fragmented literature on change management can make it difficult to identify and apply change management princi-

Using participatory mixed methods to study “grand challenges”  245 ples based on scientific evidence, so instead practitioners rely on expert opinions from popular writers on organizational change (Stouten et al., 2018). As the socio-contextual demands for social justice pressure organizations to adapt to rapidly changing markets, there is also an increasing need for nimble methodologies that can match the complexities of the organizational landscape to tackle and address DEI-related change by providing the necessary empirical intricacy and adaptability. Our review of the existing organizational change literature focusing on DEI issues revealed several methodological limitations. Much of the literature on DEI organizational change continues to have a weak evidence base, relying primarily on anecdotal and narrative accounts, and proposals of conceptual frameworks and practice models (Coleman et al., 2017; Easley, 2010; Kwon et al., 2020). Moreover, DEI work in this literature has largely focused on theory generation as opposed to application (Fainshmidt, Andrews, Gaur & Schotter, 2021). Among those that use a more empirical approach, research has relied on qualitative methods (e.g., case studies typically based on authors’ experiences as consultants) and, to a lesser extent, quantitative methods (Komaki & Minnich, 2016; Mor Barak et al., 2016; Packard, 2013). An even smaller number of studies have used a mixed methods approach using both quantitative and qualitative approaches (e.g., Selzer & Foley, 2018; Stanley et al., 2019). Among these mixed methods studies, most of the research has been done in either healthcare (e.g., Dreachslin et al., 2017) or university settings (e.g., Creamer & Ghoston, 2012). Taken together, the review of the existing DEI literature in organizational change demonstrates that there is a dearth of research using more robust and adaptable approaches to match the dynamism and complexities of the DEI issues we find in organizations today. To this end, we propose that the use of PMMR is a particularly relevant and promising approach in conducting DEI-related change research in organizations. This is a useful and relevant tool for emerging scholars (i.e., doctoral students, junior academics) and change practitioners who wish to conduct evidence-based research in DEI because the participatory nature of PMMR emphasizes the involvement of participants throughout the entire research process, which can help generate key insights and co-create meaningful solutions that the researchers alone may not be able to construct. In the next section, we provide a more detailed treatment of what PMMR is, its guiding philosophy, and its key elements, followed by its application in organizational DEI change research.

PMMR AND DEI CHANGE IN ORGANIZATIONS: WHAT IS PARTICIPATORY MIXED METHODS RESEARCH? PMMR has been applied to study complex social problems in various research domains and disciplines, particularly in the health and social sciences (DeJonckheere et al., 2019), and it is similar to related research approaches, such as participatory action research (PAR; Ivankova, 2014; Plano Clark & Ivankova, 2016), community-driven participatory research (CBPR; Israel, Eng et al., 2013), and mixed methods participatory social justice research design (Creswell & Creswell, 2018; Creswell & Plano Clark 2017). Despite the different terminologies used, these approaches all share in the commonality of leveraging mixed methods designs in a manner that empowers participants and lifts their voices throughout the research process that is guided by an overarching theoretical framework aimed at advocating for social justice and change. They are also grounded in the shared values and principles of fostering equitable

246  Handbook of research methods in organizational change researcher–participant partnerships, of engaging in mutual knowledge sharing and co-creation, and of promoting positive action and social change to improve the participants’ lives and the organizations and communities in which they are situated. In this chapter, we focus our discussion solely on PMMR1 as it pertains to the study of DEI change in organizations. The reader is encouraged to consult prior existing literature for more in-depth treatments of the other participatory, collective-based, and action-oriented research approaches mentioned above (see Ivankova, 2014 for PAR; Israel, et al., 2013 for community-based participatory research, or CBPR; and Creswell & Plano Clark, 2017 for mixed methods participatory social justice designs). What follows is a discussion of the underlying theoretical frameworks, values, and principles that inform the use and application of PMMR more specifically and in the context of supporting organizational change in DEI. Guiding Philosophy, Principles, and Values Underlying PMMR In presenting their model for PMMR, Olson and Jason (2016) described “dialectical pluralism” (Johnson, 2012; Johnson & Stefurak, 2013) as the theoretical frame that underlies mixed methods inquiry and that encourages the integration or synthesis of diverse approaches, across disciplinary paradigms, to arrive at a fuller understanding of truth and knowledge about our social world. Inherent in this theoretical perspective is the notion that multiple truths and meanings exist (hence, pluralism) in complex social phenomena, and researchers should strive to uncover this multitude of truths so they can arrive at a more inclusive, nuanced, and comprehensive understanding of our human and social experiences (Olson & Jason, 2016). Beyond dialectical pluralism, PMMR is also undergirded by Lewin’s (1946) “action research” theory of inquiry, which emphasizes the importance of addressing real-world problems in a participatory and collaborative fashion that produces knowledge and social change through an iterative and cyclical process. In the Lewinian action research tradition, knowledge is viewed as both a producer and a product of change through action—in other words, change is not only considered as the resultant outcome of knowledge; it is an important contributor to knowledge as well (Lewin, 1946). Action research also relies on collective democratic principles by encouraging collaboration and partnership among the researchers, participants, and other relevant stakeholders; it is a process that places a high value on the local input and experiences of the stakeholders and requires the researchers to work with, rather than on or for, the participants as equal partners in the research process. These guiding tenets of action research are intended to operate simultaneously and interactively toward the goal of real-world betterment or improvement for the participating stakeholders and their communities (Lewin, 1946). Key Elements of PMMR As mentioned, the PMMR approach is informed and guided by both dialectical pluralistic and action research traditions in a few ways. For one, unlike in traditional organizational 1 For purposes of readability, clarity, and consistency, we will use the term “PMMR” throughout this chapter. To our knowledge, the different terminologies used in the extant literature are largely reflective of disciplinary preferences, rather than substantive differences across the various participatory mixed methodologies.

Using participatory mixed methods to study “grand challenges”  247 research that is most often driven by the researcher, the PMMR researcher or research team does not determine the focus or direction of the research inquiry; rather, the identification of the research problem and the design of the research process are co-determined by the research team and participant-stakeholders from the partnering organization (Olson & Jason, 2016). Through authentic dialogue and open communication, the participating stakeholders work alongside the research team to identify the presenting research problem that the organization needs to address, jointly develop the research instruments, and then carry out the research procedures and activities together in an iterative and cyclical fashion. In PMMR, knowledge and power are shared evenly and distributed among the research team and participant-stakeholders: while the research team possesses relevant research expertise and technical knowledge about conducting research, an equal emphasis is also placed on the localized knowledge and lived insights that participant-stakeholders have about their organization, and that are used to inform the research process and outcomes. Another principal aspect of PMMR involves the importance placed on obtaining “whole-systems” input, understanding, and involvement (Pratt, Gordon & Plamping, 2005). This means that researchers using the PMMR approach strive for the active and ongoing engagement and participation of the entire organization throughout the research process. Consistent with Gestalt theory and principles (Koffka, 1935; Köhler, 1947; Wertheimer, 1938), PMMR recognizes that the organization is “greater than the sum of its discrete parts,” and as such, any meaningful action or change that produces, and is produced by, research knowledge would necessarily require collective buy-in and commitment from most, if not all, members of the organization. It is expected that active and ongoing engagement from participant-stakeholders from up, down, and across the organization throughout the research process, in and of itself, would also serve as a “change intervention” that influences and shapes the research inquiry and eventual outcomes (Lewin, 1946). Finally, the PMMR approach assumes that with active, collective involvement from the organizational participant-stakeholders, the inputs, throughputs, and outputs of the research inquiry would not only produce a more accurate, valid, and legitimate assessment of the research problem that the organization needs to address, but they also lead to more sustainable action and real change that is intrinsically supported by the organizational members themselves (Lewin, 1946). Aligned with the well-known Lewinian declaration that states, “nothing is so practical as a good theory” (Lewin, 1943, p. 118), one main goal of using PMMR is to generate and apply insights and evidence derived from empirical social science research to drive actionable and practical changes that could improve people’s lives and social conditions in the real world. To that end, researchers using the PMMR approach have to work closely with participating stakeholders from the partnering organization to ensure that not only do the organizational stakeholders have equal and equitable input and involvement throughout the research process, they are also building the necessary internal capacity to translate research insights and findings into viable decisions, implementable policies, and practical solutions for their organization as a whole. Using PMMR to Examine DEI Change in Organizations (Part 1): Adaptation of Israel et al.’s (2013) Seven-Phase Framework In this section, we discuss, in more depth, how the PMMR approach is particularly relevant and suitable for studying DEI-related change in organizations. Notably, we present our

248  Handbook of research methods in organizational change arguments on how the guiding values and principles of PMMR are in keeping with those that inform organizational development and change, and that support DEI. Then, an adaptation of Israel et al.’s (2013, p. 12) seven-phase CBPR framework is presented alongside an illustrative case example to demonstrate how an organization might apply the PMMR approach to facilitate specific DEI change goals and initiatives successfully. Israel and colleagues (2013, p. 12) offered a seven-phase framework for conducting CBPR. The first phase in their framework focuses on the formation of a CBPR partnership, which includes identifying potential partners and communities to be involved in the research effort, establishing trust, building relationships with identified partners and communities, agreeing on shared norms and principles that align with CBPR values for working together, and creating the necessary infrastructure for undertaking the research effort. The second phase of the framework involves “assessing the community’s strengths and dynamics” (Israel et al., 2013, p. 12). This means gathering information to understand the community’s strengths, resources, history, culture, and agents or levers of influence. It is also important for the CBPR researcher to identify and understand which individuals or groups within the community are historically or traditionally excluded, so they can be included and have their voices heard in the research process. The third phase in Israel et al.’s (2013) framework entails identifying the local community’s priorities and concerns. The idea here is to understand the primary concerns and priorities of the community, so key research questions could be generated to address these concerns and priorities. As for the fourth phase in the framework, this involves “designing and conducting etiologic, intervention, and/or policy research” (Israel et al., 2013, p. 13). The focus of this phase is to determine the appropriate research design, strategies, and methods to use; develop appropriate interventions and policies; and implement such interventions and policies in the community. The fifth phase concerns the interpretation and sensemaking of the research findings with the community’s input and involvement, and the sixth emphasizes the dissemination and translation of the research findings for intervention and policy change. As for the last, seventh phase in the framework, this focuses on “maintaining, sustaining, and evaluating the partnership” (Israel et al., 2013, p. 13). It is important for us to note that while we have discussed these seven phases in a linear manner, the application of these phases is expected to occur in a cyclical, iterative fashion, where the start and end of each phase are likely to overlap with other phases at various points in time. Next, we discuss how each of the seven phases outlined in Israel et al.’s (2013) CBPR framework could be adapted and applied in the context of DEI change research. Using PMMR to Examine DEI Change in Organizations (Part 2): Case Illustration of “Fostering DEI at ChangingUp, Inc.” To illustrate how PMMR could be applied to support DEI change research in organizations, we are going to base our discussion around a hypothetical organization named ChangingUp, Inc. ChangingUp is a for-profit organization that provides technological solutions to a variety of clients across the United States. In the summer of 2020, the United States experienced a racial reckoning that reverberated around the globe, following George Floyd’s tragic death. These events, in turn, prompted ChangingUp to take interest in conducting an organizational culture assessment (or cultural audit) around DEI issues across the organization, with the

Figure 11.1

The participatory mixed methods research approach for conducting diversity, equity, and inclusion change research in organizations

Using participatory mixed methods to study “grand challenges”  249

250  Handbook of research methods in organizational change hopes of developing and implementing appropriate change interventions that will help diversify its talent pool and foster a more equitable and inclusive work environment and culture for its leadership and employees. Further, ChangingUp is interested in using the PMMR approach to undertake this DEI change effort (see Figure 11.1). Phase 1: Establish a PMMR partnership ChangingUp approaches a research team to support its DEI change effort using the PMMR approach. The research team intends to apply the adapted version of Israel et al.’s (2013) CBPR framework to support ChangingUp with this change initiative. To engage in the first phase of the framework, the team establishes the PMMR partnership with the involvement of relevant parties or stakeholders from ChangingUp. This includes identifying organizational partners and stakeholders at the leadership, management, and staff levels, as well as across different functional areas or work units throughout ChangingUp. Key partners and stakeholders include the organization’s top or senior leadership (President/CEO, Chief Diversity Officer, Chief HR Officer, etc.), functional leadership (e.g., leaders of the marketing, IT, client service, and product development functions), and staff representatives from various work units. Furthermore, the research team also identifies and collaborates with representatives from ChangingUp’s client base, vendors, external contractors, and other affiliates. As the PMMR partnership is being established, it is critically important for the research team to cultivate trust and honest relationships among the partnership stakeholders. The research team needs to model and encourage candid dialogue and open communication among members of the PMMR partnership, by fostering a psychologically safe environment (Edmondson, 1999) for all to express their commonalities and differences in ideas, viewpoints, and experiences. At this early stage in the PMMR partnership, the research team also needs to help facilitate the development and agreement of shared guiding principles and norms to inform how members of the partnership communicate, work, and engage with one another throughout the research process. Note that it is essential that these guiding principles and norms align with the principles and values of PMMR, which emphasize knowledge co-production, power-sharing, collective involvement, collaboration, and participatory action for equity and positive change. To that end, it might be incumbent upon the research team to educate other members of the partnership about general PMMR principles and values as they convene to develop and establish guiding principles and norms for working together. Finally, as part of the first phase in the PMMR framework, an essential infrastructure, such as clear processes and structures, is created and put in place to support the research tasks, activities, and materials involved throughout the research process. Determining infrastructure needs requires the research team—in collaboration with members of the PMMR partnership— to address several considerations, such as: How will the PMMR partnership access, store, and maintain information, resources, and data associated with the research? How might potentially sensitive information—e.g., employees’ demographic data in relation to salary figures, promotion records, and performance reviews—be accessed, stored, and managed? What are the processes, procedures, and tools for coordinating, sharing, and storing information, content, and resources among members of the PMMR partnership? Phase 2: Assess organizational strengths and dynamics With the PMMR partnership in place, it is then appropriate for members of the partnership to turn their attention to assessing ChangingUp’s strengths and available resources that can

Using participatory mixed methods to study “grand challenges”  251 be applied toward its DEI change effort. The overall goal in this phase is to obtain a comprehensive and “whole-system” understanding of the current organizational state and associated dynamics. Here, the assessment would also involve obtaining a strong understanding of ChangingUp’s history and background, and its prevailing culture, political dynamics, and structural aspects. This might include asking questions, such as “What is the origin story of ChangingUp?” “How did ChangingUp come into existence?” “What does a typical day at ChangingUp look and feel like?” “What is the day-to-day experience at ChangingUp like?” “What are some expressed and unspoken values, beliefs, and assumptions held by ChangingUp leaders and employees?” and “What behaviors and practices are considered as acceptable at ChangingUp, and what are not?” Beyond assessing the historical, cultural, socio-political, and structural dimensions, it is also important for the PMMR partnership to identify key power roles, influential relationships, and intergroup dynamics at ChangingUp. Examples of relevant questions relating to this area of the assessment might be “Who are the ‘power players’ and agents of influence across different levels and functions within ChangingUp?” “Who would be considered as members of the traditionally underrepresented, marginalized, or excluded groups at ChangingUp?” and “Who should have their voices lifted, amplified, and included in shaping and informing decisions, processes, policies, and/or practices at ChangingUp?” To conduct a thorough and comprehensive organization-wide assessment that is in keeping with PMMR values and principles, the PMMR partnership should be mindful of engaging representative stakeholders from different levels, units, and functions across ChangingUp at each step of the assessment process. This includes collaborating with representative stakeholders in designing and using a variety of quantitative and qualitative data collection methods, such as surveys, focus groups, interviews, and field observations. When it comes time to analyze and interpret the assessment data, it would be important for the PMMR partnership to take on a more facilitative and advisory role—rather than a directing or leading one—in guiding and involving representative stakeholders in the data analysis and interpretation process as well. Phase 3: Identify organizational concerns and priorities Over the course of conducting the organizational DEI culture assessment described in the earlier phase, it would be essential for members of the PMMR partnership to also take note of any major concerns, issues, or priorities that ChangingUp currently has. Not only does the PMMR partnership need to identify these major concerns, issues, or priorities, it must also ascertain how these concerns and issues are being prioritized at ChangingUp, along with likely key contributing factors to these concerns and priorities for the organization. Recognizing the organization’s major concerns and priorities would be key to determining the central research questions that the PMMR partnership would help address and be able to corral resources and support around. As a result of the organization-wide assessment undertaken in Phase 2, let us assume that the PMMR partnership has identified the persistent lack of women and people of color among its leadership and managerial ranks as one major concern for ChangingUp. This is also one of the organizational priorities for ChangingUp due to both external and internal pressures: externally, ChangingUp’s client base has become more diverse and progressive in their espoused values, and their clients are expecting to see more diversity among ChangingUp’s leadership and employees. The recent racial reckoning in the summer of 2020 has also amplified these marketplace pressures on ChangingUp. At the same time, these external forces have also

252  Handbook of research methods in organizational change contributed to internal shifts at ChangingUp: its workforce is also becoming more attuned to issues relating to DEI at large, and the assessment findings also pointed to a prevalent sense of discontentment and dissatisfaction among its employees with regard to DEI in the workplace. Some of the key factors contributing to the prevailing sentiment of discontent among ChangingUp’s employees included: a predominantly white, male leadership team that lacked nuanced understanding around DEI issues, as well as an organizational culture with strong norms that tend to maintain white-favoring/white-centric systemic structures and practices. With these concerns, priorities, and factors in mind, the PMMR partnership would have to engage relevant partnering stakeholders—and especially leadership partners—to determine the key research questions that would guide and inform the DEI change research endeavor. Here, the PMMR partnership would serve as a facilitator and advisor in conducting candid discussions with partnering leaders and representative stakeholders toward the goal of formulating key research questions. Further, the research questions developed should not only center around the top concerns and priorities identified from the assessment; they should also be fully supported and owned by the partnering leaders and representative employees. Examples of research questions that pertain to the ChangingUp case might include: “How can ChangingUp better support the recruitment and advancement of women and people of color into leadership and management positions?” and “How can ChangingUp shift its existing workplace culture to one that embraces and supports diversity, inclusion, and equity?” Phase 4: Design and conduct change research As the research goals and questions come into clearer focus, the next phase for the PMMR partnership is to turn its attention to the design and implementation of the research involving the change. In this phase, members of the PMMR partnership engage in participatory discussions and make joint decisions around specific research designs, approaches, strategies, and methods to use, design appropriate interventions to implement, and determine how best to implement the selected research design, methods, and interventions. At this point, at ChangingUp, the members of the PMMR partnership have come to determine that designing and implementing a multi-pronged cultural change program at ChangingUp would be necessary to develop and sustain the advancement and retention of women and people of color among the leadership and managerial ranks. This program would involve various change interventions targeted at different stakeholder groups, organizational structures, processes, and practices at ChangingUp. For example, a series of “courageous conversations” dialogue sessions might be designed and conducted to encourage more open, honest discussions among leaders and employees around topics, such as structural racism, privilege, and social inequity, and how these might manifest and are experienced in the ChangingUp workplace. Concurrently, DEI awareness training workshops would also be designed for and delivered to leaders and other stakeholders with managerial or supervisory responsibilities. Further, changes would be made to existing recruitment, promotion, and leadership development policies and practices; and new coaching and professional development initiatives will be created to better support and mentor both new and tenured employees. Designing and implementing these various change interventions effectively would require the members of the PMMR partnership to work in close collaboration with relevant stakeholders and partners, both within and outside ChangingUp. As an example, to design and deliver the DEI awareness training workshops, the PMMR partnership might collaborate with outside DEI consultants and internal training specialists to co-create suitable DEI content, materials,

Using participatory mixed methods to study “grand challenges”  253 and delivery formats for different stakeholder groups at ChangingUp. Part of this collaboration might also include a repeated measures pretest posttest control group (Campbell & Stanley, 1963) research design, where pre- and post-training survey measures could be used to assess the levels of DEI knowledge, attitudes, and skills among ChangingUp leaders and employees over time, and where the survey data could also be compared with groups of yet-to-be-trained leaders and employees over time. At the same time, the PMMR partnership would also need to work with other ChangingUp stakeholders and partners to design and apply qualitative research methodologies, such as interviews and observations, to other integral aspects of the organization-wide cultural change program—e.g., the dialogue sessions—as part of the broader DEI change research efforts. Phase 5: Get feedback and interpret research findings As the initial findings from the data start to emerge, it is important for the PMMR partnership to coordinate and facilitate the data analysis and interpretation activities with the input and active involvement of representative stakeholders and partners. This means involving representative stakeholders and partners as part of the data analysis and interpretation process, feeding initial results or findings back to participating stakeholders and partners, or engaging all stakeholders and partners across the entire organization to make sense of the data and findings. At ChangingUp, the data analysis and interpretation activities might include having the PMMR partnership conduct qualitative data analysis workshops with participating leaders, employees, and partners representing different levels and functions at ChangingUp, and inviting all to contribute their insights and interpretations of what was shared and expressed during the dialogue sessions. Phase 6: Disseminate and translate research findings Once the research findings and results about the DEI change interventions are generated, the PMMR partnership, in collaboration with relevant stakeholders and partners, addresses the following considerations: First, what are the most important findings to share with the entire organization? Second, what are the most appropriate ways to share the research findings throughout the entire organization? Thirdly, who plays what role in distributing the findings? Finally, how do we translate the research findings into future appropriate change interventions or adjustments to existing change interventions? In the case of ChangingUp, the PMMR partnership, in consultation with relevant partners and stakeholders, might elect to share the qualitative findings derived from the dialogue sessions through both formal communication channels—e.g., work team or departmental meetings, written memos and reports, or town-hall style presentations—and informal occasions, such as lunchtime conversations and after-work happy hours with colleagues. Identifying stakeholders in both formal roles of authority—e.g., senior-level executives, managers, and supervisors—and those with informal but influential relationships—e.g., the trusted executive assistants or the well-connected associates—would also be important in distributing the research findings across ChangingUp. Phase 7: Evaluate, maintain, and sustain the PMMR partnership When it comes to this last phase of the PMMR approach, the emphasis is on engaging in both formative and summative evaluations of the PMMR partnership throughout the research cycle. In other words, members of the PMMR partners address the following considerations: “How

254  Handbook of research methods in organizational change well is the PMMR partnership working?” (i.e., summative evaluation) and “In what ways can the partnership be improved or work better?” (i.e., formative evaluation). Another important aspect of this phase in the PMMR approach is the eventual “winding down” or transitioning of the PMMR partnership’s roles and responsibilities to internal partners and stakeholders of the organization. The goal here is to build internal capacity within the organization—beyond the PMMR partnership—so that the organization itself can continue to manage the DEI change effort and make continuous improvements into the future. At ChangingUp, as the DEI cultural change program becomes more established in the organization, the initial research team would have to begin the process of transitioning out of the research effort and transfer its work to internal partners and stakeholders within ChangingUp, who would continue to manage the ChangingUp cultural change program and make ongoing improvements and changes as needed. Consistent with PMMR values and principles, the transitioning process would require participatory deliberations and shared decision-making among the members of the PMMR partnership and relevant internal partners and stakeholders to ensure successful transitional planning and implementation.

LIMITATIONS OF AND RECOMMENDATIONS FOR USING PMMR IN DEI CHANGE RESEARCH As illustrated in the previous sections, there are numerous advantages of using the PMMR approach in studying DEI change initiatives in organizations. However, like any methodology, the PMMR approach is not without limitations. First, there are practical and logistical challenges associated with using PMMR and other mixed methods in general. Due to its collaborative nature that involves different stakeholders, PMMR requires extensive time, financial resources, and effort (Niglas, 2004), as this approach requires more time and funding in the process of data collection, analysis, and interpretation, which may be considered too great a cost for many researchers (Tashakkori & Teddlie, 2010). Moreover, PMMR—and mixed methods in general—requires researchers’ familiarity and knowledge in both quantitative and qualitative methodologies, yet most researchers are trained in either quantitative or qualitative research. Most researchers do not have the training nor field experiences to be competent in both qualitative and quantitative research, so to use PMMR as a solo researcher, one must develop dual competencies in both qualitative and quantitative methods to be able to know how to utilize each method effectively. One way of overcoming this challenge is to build a diverse team consisting of specialists in both quantitative and qualitative methods (Masterson, Corley & Schinoff, 2016) and researchers with expertise in different methodologies and research training. Mixed methods teams should have members with a range of expertise who respect diverse methodological orientations. In order for this team approach to work in practice though, it is important that each team member has a minimum level of competency in qualitative and quantitative methods, plus expertise in one or the other (e.g., Shulha & Wilson, 2003; Tashakkori & Teddlie, 2010), because without minimum competency in both types of research, team members may not be able to communicate effectively as they lack a common methodological language (Tashakkori & Teddlie, 2010). It is also important to be intentional about team composition, give equal treatment and respect to all methodologies, communicate continuously, and involve all team members in the decision-making process (Molina-Azorin et al., 2017).

Using participatory mixed methods to study “grand challenges”  255 Various disciplinary and training backgrounds and research orientations can also lead to lively group interchanges, and as a result, researchers become more competent in various research methodologies as they work collaboratively on projects where they see others applying problem-solving skills to research issues from a perspective different from their own (Tashakkori & Teddlie, 2010). As an example, Tashakkori and Teddlie’s (2010) research team consisted of 11 team members from education, psychology, statistics, nursing, and research methods when they participated in a longitudinal organizational change project. Five of the team members self-identified as mixed methods practitioners, while three identified as having a quantitative orientation, and three as having a qualitative orientation. In Jang and colleagues (2008), a member of the research team that used mixed methods said, “my participation in a mixed methods project expanded my horizons from research methodology as a debate between paradigms that dealt with ‘people versus numbers’ and from an understanding that abstract debates between ‘either/or’ actually, and quite compellingly, dialectically resolve into an ‘and’” (Jang et al., 2008, p. 243). In addition to these general challenges that are applicable to all mixed methodologies, using PMMR has further challenges given its collaborative approach to research that involves organizational partners or stakeholders. First, given the nature of its process framework and various parties involved in the research, PMMR usually requires deep knowledge and skill competencies in facilitation, coalition building, conflict resolution, and change management. Yet, not all researchers are skilled in these process areas. To address this challenge, it would be useful to collaborate with researchers and practitioners with expertise in different process areas, such as conflict resolution, facilitation, and organizational change management. Moreover, more training and professional development in the areas of facilitation and coalition building may help researchers to gain and hone skills needed to work collaboratively and effectively with various partners and stakeholders involved in the research. Secondly, PMMR necessitates sustained, ongoing collaboration and engagement from researchers and organizational partners; hence, trust and relationship building are critical components and major contributing factors that determine the research project’s success. As such, it is vital to establish trust and foster relationships with all participants and organizational partners from the onset of the research process. To work effectively and build trust, Christopher and colleagues (2008) suggest the following: first, researchers need to be aware of their own biases and assumptions. It is recommended that researchers use tools that build self-reflective awareness and skills, attend anti-bias training, and realize that we will never fully understand the impact of our personal histories on our work. Second, it is also important to understand the historical context of the research; researchers need to gain an understanding of the broader histories of the institution they are researching, and the interactions between and among organizational partners, to better understand the context in which the research is happening. Third, in order to build trust and relationship, it is vital to be present and listen to members of the organization. Members of organizations/communities can easily identify insiders from outsiders, so it is essential for the researcher to be present and actively engage with the organizational community to build trust. Fourth, researchers should acknowledge the expertise of all partners. When researchers trust the expertise of the organizational/institutional partners, they are more engaged and research is enhanced. For example, researchers need to trust organization members to carry out the DEI intervention themselves, as the vehicle for change has to lie within the organization for the effects to be sustainable. Lastly, it is important to be upfront about expectations and

256  Handbook of research methods in organizational change intentions. Various stakeholders involved in the PMMR may engage in collaboration with different expectations and intentions. As an example, researchers may have the expectation and intention of publishing the data and findings, whereas other stakeholders may have a different goal in mind (e.g., identifying strategies for implementing a new DEI initiative in their organization). While the goals of the two parties are not mutually exclusive, communicating clear expectations and intentions at the onset of the research collaboration would help build trust and relationships.

CONCLUSION Inequity and other grand challenges—including DEI issues at both organizational and societal levels—by their very nature require coordinated and sustained effort from multiple and diverse stakeholders toward a clearly articulated goal. These challenges are often extremely difficult to tackle as they are deeply rooted in attitudinal, cultural, and structural elements of the organization (Fassinger & Morrow, 2013). PMMR offers strengths in advancing a social justice agenda and can help accomplish social justice goals (e.g., consciousness-raising, community action, and empowerment) because it requires intensive and prolonged involvement among all key stakeholders, thereby increasing commitment to implement practical and sustainable changes to the organizations involved. From a methodological standpoint, there is also an increasing recognition that our current methodological approaches need to adapt to the rapidly changing complexities of the phenomena we study, and to this end we introduce and illustrate the PMMR approach as a highly suitable and viable approach to studying grand challenges, such as organizational DEI change. In this chapter, we discuss how the PMMR approach can be particularly relevant and suitable for studying grand challenges, such as DEI change in organizations. Notably, we present our arguments on how the guiding values and principles of PMMR are in keeping with those that inform organizational development and change, and that support DEI. Building upon by Israel et al.’s (2013) CBPR framework, we also outline a seven-phase process of conducting PMMR to guide doctoral students and junior scholars (and experienced researchers) toward understanding the theoretical underpinnings of PMMR and applying the practical steps associated with the PMMR approach. We are hopeful that the PMMR approach will help change researchers address today’s grand challenges more successfully and yield valuable research insights that can be translated into sustainable solutions for our organizations and communities tomorrow.

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Using participatory mixed methods to study “grand challenges”  259 Richard, O., Murthi, B., & Ismail, K. (2007). The impact of racial diversity on intermediate and long-term performance: The moderating role of environmental context. Strategic Management Journal, 28(12), 1213–33. Roberson, Q.M. (2019). Diversity in the workplace: A review, synthesis, and future research agenda. Annual Review of Organizational Psychology and Organizational Behavior, 6(1), 69–88. https://​doi​ .org/​10​.1146/​annurev​-orgpsych​-012218​-015243. Roberts, L.M., & Washington, E.F. (2020). U.S. businesses must take meaningful action against racism. Harvard Business Review. https://​hbr​.org/​2020/​06/​u​-s​-businesses​-must​-take​-meaningful​-action​ -against​-racism. Selzer, R., & Foley, T. (2018). Implementing grassroots inclusive change through a cultural audit. Qualitative Research in Organizations & Management, 13(3), 284–302. https://​doi​-org​.proxy​-ub​ .researchport​.umd​.edu/​10​.1108/​QROM​-10​-2016​-1455. Severson, Kim. (2020). Bon Appetit Editor Adam Rapoport resigns. New York Times. https://​www​ .nytimes​.com/​2020/​06/​08/​dining/​bon​-appetit​-adam​-rapoport​.html. Shore, L.M., Chung-Herrera, B.G., Dean, M.A., Ehrhart, K.H., Jung, D.I., Randel, A.E., & Singh, G. (2009). Diversity in organizations: Where are we now and where are we going? Human Resource Management Review, 19(2), 117–33. Shulha, L.M., & Wilson, R.J. (2003). Collaborative mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social & Behavioral Research (pp. 639–70). SAGE. Stanley, C.A., Watson, K.L., Reyes, J.M., & Varela, K.S. (2019). Organizational change and the Chief Diversity Officer: A case study of institutionalizing a diversity plan. Journal of Diversity in Higher Education, 12(3), 255–65. Stevens, F.G., Plaut, V.C., & Sanchez-Burks, J. (2008). Unlocking the benefits of diversity: All-inclusive multiculturalism and positive organizational change. Journal of Applied Behavioral Science, 44(1), 116–33. https://​doi​.org/​10​.1177/​0021886308314460. Stouten, J., Rousseau, D.M., & Cremer, D.D. (2018). Successful organizational change: Integrating the management practice and scholarly literatures. Academy of Management Annals, 12(20), 752–88. https://​doi​.org/​10​.5465/​annals​.2016​.0095. Strauss, J., Sawyer, O., & Oke, A. (2008). Demographics, individual value structures, and diversity attitudes in the United Kingdom. Journal of Change Management, 8(2), 147–70. https://​doi​.org/​10​ .1080/​14697010701799445. Tashakkori, A., & Teddlie, C. (2010). Overview of contemporary issues in mixed methods. In A. Tashakkori & C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research (2nd ed.) (pp. 1–44). SAGE. Van den Brink, M., & Benschop, Y. (2018). Gender interventions in the Dutch police force: Resistance as a tool for change? Journal of Change Management, 18(3), 181–97. https://​doi​-org​.proxy​-ub​ .researchport​.umd​.edu/​10​.1080/​14697017​.2017​.1378695. Wertheimer, M. (1938). Gestalt theory. In W.D. Ellis (Ed.), A Source Book of Gestalt Psychology (pp. 1–11). Kegan Paul, Trench, Trubner & Company. https://​doi​.org/​10​.1037/​11496​-001.

Emerging

12. Conducting phenomenon-driven rapid-response research to explore disruption and its impact on the minority experience Jennifer Y. Kim and Zhida Shang

INTRODUCTION The rapidly changing social, economic, and political landscapes brought on by advances in technology, a changing workforce, and globalization are accelerating the ways through which organizations and individuals within them must adapt and respond (Burnes, 2004; Kotter, 1996). These changes – or jolts to the system – have become more frequent and more palpable, as evidenced by COVID-19, a global pandemic that precipitated social and economic disruptions never before seen (Sohrabi et al., 2020), significantly changing the way we live and work (Spicer, 2020). COVID-19 has necessitated unplanned, rapid, and radical changes within organizations, as organizations reconceptualize the existing paradigms, norms, and views toward how and where employees work (Argyris & Schön, 1978). Within this tumultuous context, existing managerial practices that are premised on planned and deliberate organizational change have become less applicable (Stouten et al., 2018). Instead, the focus has now shifted to understanding how such disruptions are affecting the experiences of employees as more and more practitioners and scholars are calling on organizations to treat the individuals working in organizations as more than just “human resources” (Ferreras et al., 2020). In fact, Porras and Robertson (1992) posited that employees are at the center of organizational change, and consequently their perspectives must not only be explored during these periods of flux but also be used to inform key decisions in how organizations shape and redesign the organizational workplace after a major disruption such as COVID-19 (Christianson & Barton, 2021; Spicer, 2020). Within these new paradigms, organizations are being asked to develop purposeful systems that have meaning for those employed within them, given that successful organizational change rests on promptly harnessing the collective perspective (Amis & Janz, 2020). However, efforts to engage and harness insights from employees can often bypass and ignore minority groups (Nkomo, 2021). This is particularly true for historically marginalized racial minority groups whose experiences can be vastly different from those of the majority members (Holder et al., 2015; Kim et al., 2015), yet whose voices have been notably missing in the organizational literature. For example, though the COVID-19 pandemic affected everyone regardless of their background (Sohrabi et al., 2020), many Asian communities in predominantly White countries have been particularly hard hit due to COVID-19’s association with China, which led to a significant increase in COVID-19-related hate crimes against Asian communities in North America (Kantamneni, 2020). However, amidst the spike in hate crimes and blatant discrimination, which have permeated even the professional workplace, the

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262  Handbook of research methods in organizational change experiences of Asian Americans and Asian Canadians in the U.S. and Canada went largely unacknowledged and underreported (Shang et al., 2021). As organizations strive to redefine and reconstruct the way people work and interact in the post-pandemic workplace, understanding and incorporating the experiences of all employees, including minority voices, is crucial. However, doing so using traditional research methods in the context of the rapidly fluctuating and unstable environment brought about by a disruptive event such as COVID-19 can be challenging, necessitating the need for adapting traditional approaches. This brings us to the crux and focus of our chapter, which aims to delineate adaptive ways of conducting research that can keep up with the constant flux brought on by rapid changes in the modern organizational environment. Specifically, we discuss how we applied rapid-response research on a phenomenon-driven event, focusing on the experiences of Asian voices in organizations in order to capture their experience at a seminal point in time during the onset of the pandemic.

CHALLENGES OF CONDUCTING TIMELY AND RELEVANT RESEARCH IN MANAGEMENT STUDIES Research planning in academia, particularly in the social sciences, which include management science, moves at a glacial pace, often taking years from the inception of an idea to the dissemination of the findings via publication. In fact, the COVID-19 pandemic spotlighted the pain points and lingering concerns about the use of public resources for university and education that yield “questionable social returns with obfuscated outputs, lack of timeliness and accessibility, and fragmentation” (Fainshmidt et al., 2021, p. 1416). For example, a study revealed that the first response time – the duration of the first review round – is, on average, 16–18 weeks for articles in the field of management (Huisman & Smits, 2017). Hence, it typically can take years before an article passes the peer review process and is made available to the public. It is no wonder why the stock of social trust in the science community, particularly for organization and management scholars who are perceived as academics disconnected from the ongoings of the real world, has been eroding (Fainshmidt et al., 2021). In contrast, the medical research community is much more nimble and agile. For example, the average response time for articles in medical and public health journals was about 8 to 9 weeks (Huisman & Smits, 2017). Consequently, the medical community was able to quickly mobilize in order to tackle COVID-19 through streamlined efforts to share important information and key findings for a common purpose, leading to an abundance of scientific findings that eventually led to the rapid development of vaccines (Yong, 2020). Furthermore, this streamlining of research during the COVID-19 pandemic not only accelerated the “hard sciences,” such as vaccine development, but also affected all dimensions of medical science, including sociomedical research (Vindrola-Padros et al., 2020) and research within minority groups (Webber-Ritchey et al., 2021). Many of these sociomedical researchers noted that the reason for their rapid research designs was the need to promptly engage and inform key stakeholders (Vindrola-Padros et al., 2020), a sentiment that was likely mirrored by the journal reviewers and editors. These efforts led to the publication of different forms of qualitative research methods during COVID-19 (Teti et al., 2020), further accelerating the pace of sociomedical research during the pandemic. All these efforts by the medical community, from vaccine development to publicizing qualitative research guidelines, have a common thread

Conducting phenomenon-driven rapid-response research  263 in employing rapid-response research and ultimately benefit healthcare professionals and patients. Unfortunately, despite having many of the same overarching objectives, management science lags behind, nurturing siloes via specialized niches and divisions that preclude effective information sharing and cross-field collaboration. To illustrate, the first author, during a job fair at a management conference, was given the advice by a more experienced academic that “A junior scholar must go deep into their specialization before they can think to expand and go wide,” a message that, for the record, the first author neither agreed with nor took to heart. While there is some benefit to staying focused on a certain topic so that one can develop a certain level of expertise, what is the harm in going deep and wide simultaneously? Who is to say that you cannot do both? The current landscape in management science could greatly benefit from research marked by agility, openness, and rapid dissemination of findings in all fields (Fainshmidt et al., 2021; Wickert et al., 2020) and presents an opportunity for scholars in these fields to look for new and creative ways to achieve this. We argue that this is all the more relevant for research on organizational change, which aims to capture the processual dynamism as an organization shifts from one state to a desired future state.

NEW APPROACHES FOR STUDYING DISRUPTIVE EVENTS AND THEIR IMPACT ON THE MINORITY EXPERIENCE There are different ways that the aforementioned challenges can be tackled within the organization and management research, ranging from changing the types of research questions being asked by scholars, and diversifying the types of research articles published by journals, to creating new outlets for dissemination of study results, and modifying incentives within educational institutions (Fainshmidt et al., 2021). This chapter focuses on the first method – changing the types of research questions asked by scholars. To do so, we propose that one must also delve into new and improved methods for tackling these questions, particularly when exploring the experiences of minority groups whose voice is often missing in the organizational and management literature (Nkomo, 2021). Understanding how individuals navigate and make sense of their identities in the workplace has been a focal interest for management scholars (Weick et al., 2005). This focus has been ever more important for understudied minority groups (Fernando et al., 2019), such as Asian professionals whose experience in the organizational literature remains relatively underexplored (Gee & Peck, 2017; Wong & Halgin, 2006). The need to study such minority groups has become particularly more salient in modern times as organizations must adapt and respond to changes brought on by socio-political events (i.e., Black Lives Matter; Leigh & Melwani, 2019), shifting norms toward work (i.e., the gig economy; Kniffin et al., 2020), and major, global health crises (i.e., COVID-19; Christianson & Barton, 2021; Fainshmidt et al., 2021), which tend to have more pronounced effects on minority groups. As more and more organizations scramble to adapt, change, and respond to the restrictions and limitations placed on them by the realities and aftermath of the pandemic, many professionals have had to learn how to traverse the changing environment and figure out their place in the evolving organizational landscape with or without the help of the organization (Kniffin et al., 2020; Kramer & Kramer, 2020). This process can elicit various reactions,

264  Handbook of research methods in organizational change including emotions, cognitive processes, and behavioral changes among individuals (Leigh & Melwani, 2019; Morgeson et al., 2015). Further, the ensuing sensemaking – the process through which individuals work to understand novel, unexpected, or confusing events (Maitlis & Christianson, 2014) – is also likely to differ for various groups with different social identities (Essers & Benschop, 2009; Fernando et al., 2019). Capturing and understanding these responses are crucial for organizations wishing to create and design an inclusive and psychologically safe environment for all employees (Amis & Janz, 2020; Jun & Wu, 2021). In this chapter, we explore, from a methods perspective, how we chose to study the downstream impact of COVID-19, a life-altering event, through the minority lens. We focus, in particular, on the experience of Asian American and Asian Canadian professionals in the U.S. and Canada (herein referred to as Asians), a group that has been particularly affected and left vulnerable by the COVID-19 pandemic, as illustrated by the steep rise in anti-Asian sentiment and violence across North America (A3PCON, 2021; Asian American Bar Association, 2021; Chan et al., 2020). We detail the various methods we chose to adopt and adapt in order to conduct phenomenon-driven research – research that is driven by and responds to an event or a series of events that occur – rapidly, in real time, in order to understand an evolving topic in an uncertain and dynamic environment as it occurred. Conducting phenomenon-driven rapid-response research on a topic related to COVID-19 during a period characterized by an overwhelming fear of the unknown and uncertainty (Christianson & Barton, 2021; Wu et al., 2020) required constant recalibration on the part of the researchers that influenced everything from recruitment and data collection to data analysis and dissemination. We share key learnings drawn from both the challenges and rewards that were encountered while conducting rapid-response research. The chapter is organized as follows: we begin by offering a brief, general overview of identity work in the context of organizational change brought on by disruptive events, focusing on the importance of capturing the minority experience and arguing why relying on traditional methods may not be the wisest idea. Next, we review phenomenon-driven rapid-response research as a methodology to demonstrate its applicability in organizational change research. We follow this up with a case study focusing on the experience and process of conducting phenomenon-driven rapid-response research that is not traditionally used in management or organizational science (Wickert et al., 2020). We discuss the research methodologies that were undertaken to investigate this phenomenon, detailing the adaptive approach we took to respond to a rapidly evolving and changing event as we sought to investigate and document the experiences of Asians affected by racial discrimination during the pandemic. Note that we focus on the process, not on the content (i.e., findings/results) of the research study.

IDENTITY WORK IN RESPONSE TO DISRUPTIVE EVENTS Though the concept of identity varies across scholars and bodies of research, we adhere to the definition of identity as the subjective knowledge, meanings, and experiences which define an individual, or how they answer the question “Who am I?” (Alvesson et al., 2008; Ramarajan, 2014). How individuals come to define themselves and their identities is mutable, evolving, and changing in response to and feedback from one’s internal collection of identities and cues from the socio-contextual environment. The consensus among identity scholars is that individ-

Conducting phenomenon-driven rapid-response research  265 uals have multiple identities, with some arguing in favor of an organized internal salience hierarchy through which different identities can be invoked given the particular context (Stryker, 1968), while others view the selves as existing in a more chaotic, and at times contradictory collection (Alvesson et al., 2008; Sveningsson & Alvesson, 2003). Despite the varying views of how these identities co-exist within an individual, an individual’s identity is the activated part of one’s overall self-concept that, at that moment, informs the affective and cognitive processes and behavioral responses (Van Knippenberg et al., 2004). For example, outside of work, an Asian American man may define himself as a brother, a husband, and a surfing enthusiast. At work, he may define himself as a subject matter expert on brand management. Identities are influenced by cues and feedback that are elicited from both within and outside the organizational environment (Katz & Kahn, 1978), and this can be particularly pronounced during moments of crisis during which individuals’ routines are interrupted, and they are compelled to assess and make sense of the resulting chaos and change (Maitlis & Sonenshein, 2010). Further, making sense of disruptive events and their ensuing effects often differs for minority employees – members of the organization belonging to social identity groups that have lower levels of power within both the organization and society (Essers & Benschop, 2009; Fernando et al., 2019; Ragins, 1997). Yet, the identity literature and many of its frameworks as it currently stands do not fully speak to the minority experience, owing largely to the fact that these models and frameworks were created predominantly by White scholars who never intended for them to be used for such purposes. This is problematic because the modern workplace is becoming more diverse. Minority employees are going to respond differently than their White counterparts to disruptive events, especially when their group is targeted, attacked, or harmed because of their social identity (Leigh & Melwani, 2019). Examples of such threats that can impact certain minority employees include the separation of families at the U.S./Mexican border by U.S. Immigration, the murder of George Floyd by police officers, and the U.S. Muslim immigration ban, which no doubt negatively impacted many individuals but particularly targeted and affected Latinx, Black, and Muslim employees, respectively. Not being able to capture and understand how different racial groups respond to such disruptions will no doubt lead to blind spots within organizations in how they respond to the unique hardships and needs of their minority employees (Amis et al., 2018; Amis & Janz, 2020). The COVID-19 pandemic, which affected everyone globally, was no exception. The pandemic was hard felt among historically marginalized ethnic minority communities (National Nurses United, 2020; Razai et al., 2021), but two groups that were particularly targeted were the Asian American and Asian Canadian communities due largely to the pandemic’s association with China, where it was first documented. The instigation of anti-Asian sentiment, fueled by racist rhetoric such as “Wuhan virus” and “China flu” used by various politicians, including former president Donald Trump, exacerbated xenophobic sentiments as anti-Asian incidents ranging from insults and avoidance to physical violence sharply increased (A3PCON, 2021; Asian American Bar Association, 2021; Gover et al., 2020). Following the outbreak and use of racist terms, reports showed that racial discrimination tied to COVID-19 targeting Asians manifested in a variety of settings, including businesses and public streets as well as the workplace in the U.S. and Canada (A3PCON, 2021; Pearson, 2021). Naturally, we wanted to understand the experiences of Asian professionals dealing with discrimination in the workplace during COVID-19. We view this as a critical topic that can help organizations create a more inclusive organizational space post disruption.

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USING PHENOMENON-DRIVEN RAPID-RESPONSE RESEARCH To best capture and understand workplace racism experienced by Asian professionals during the pandemic, we utilized phenomenon-driven rapid-response research. Phenomenon-driven research has been around for decades and has been used to capture, document, and conceptualize a particular phenomenon so that relevant and appropriate theorizing can then be developed (Krogh et al., 2012). Despite offering more relevance to studying real-world phenomena compared to theory-driven research, phenomenon-driven research has not been widely adopted in the organization literature (Schwarz & Stensaker, 2014), though it has been used more widely in other fields, particularly in medical science (Hollnagel et al., 2018; Pasanen et al., 1999). While its usage has lagged in mainstream organizational research despite calls from proponents who argue for broader application of phenomenon-driven research (Mathieu, 2016; Schwarz & Stensaker, 2014), the “tide may be turning toward more of a phenomenon-driven approach” (Mathieu, 2016, p. 1138). Indeed, a growing group of scholars has heeded the call for using more nimble research grounded in real-world phenomena and has begun using a phenomenon-driven approach to answer questions directed toward important but unresolved social, ethical, and anthropological concerns, including what scholars have coined “grand challenges” (Doh et al., 2017; Gallardo-Gallardo et al., 2015; Prescott & Filatotchev, 2021). The phenomenon-driven approach differs from most traditional research methods used in management and organization science, in which theory is the primary driver of the research topic and design (Doh, 2005, 2015; Wickert et al., 2020). In contrast, phenomenon-driven research takes real-world problems and challenges that affect the broader community as the primary starting point, after which theory is generated or enhanced. In other words, the primary focus on the actual phenomenon of interest precedes the selection and/or creation of a relevant theory that best informs and illuminates that phenomenon. Thus, the biggest advantage of the phenomenon-driven approach over the traditional, theory-driven approach is the former’s emphasis on beginning with a real-world problem to solve and address, which differs from currently practiced and widely adopted theory-driven research that tends to generate “perpetual academic insights that are further and further removed from organizational contexts to be of any real-world value” (Mathieu, 2016, p. 1138). Moreover, the proximity of phenomenon-driven research to real-world concerns can be demonstrated by the constituents to whom the results will be disseminated. That is, phenomenon-driven research aims primarily to inform practitioners – leaders and managers in businesses who are more able to directly impact levers of change in organizations (Davis, 2015). Phenomenon-driven research can be further enhanced by performing the research in real time or as chronologically close to the event as possible. This rapid-response component is analogous to a news reporter hurrying to the scene of an event to report on the situation as it happens, and signifies conducting research on a phenomenon as it unfolds in a prompt manner. Rapid-response is primarily used in the medical science field, where teams of researchers must come together to quickly tackle an emergency situation (i.e., containing an epidemic such as Ebola or COVID-19) (Finlay et al., 2004; Herzberg, 2020; Vahidy et al., 2021), but has also been used in other fields to inform public policy related to riots (Alexander, 2010), as well as natural disasters (Powell et al., 2011). The rapid-response approach permeates all aspects of the research process, starting with idea generation and data collection and continuing to data analysis and interpretation of the findings (Kuckertz et al., 2020). The time-sensitive nature of this approach lends itself well for informing practical application in that the relevant insights

Conducting phenomenon-driven rapid-response research  267 can be generated quickly and applied to tackle and solve real-world problems and can very well be a refreshing approach that can be more readily adopted in the organizational change literature. Though rapid-response research has not readily been adopted in the management field, events such as advancements in technology, changes in the political and social landscape, and the rise in natural and man-made disasters that can have a notable impact on the workplace and its employees underscore the need for such time-sensitive approaches to conducting research (Wickert et al., 2020). In sum, phenomenon-driven rapid-response research combines the investigation of real-world phenomena that have practical application with the time-sensitive nature of rapid-response research, providing practitioners with a way to make timely and informed decisions on emerging phenomena that can impact individuals in a variety of different settings, including the workplace. See Table 12.1 for a summary of the differences between phenomenon-driven rapid-response research and traditional theory-driven research. Table 12.1

Comparison of phenomenon-driven rapid-response research and theory-driven research

  Primary research goal Research process Role of theory Real-world application Time Primary audience Publication process

Phenomenon-driven rapid-response research Solve a real-world problem in an evidence-based way

Traditional, theory-driven research Contribute to theory

Research begins with a focus on a phenomenon,

Research begins with theory and method

followed by theory and method selection

selection

Theory generation is the means to the end of solving a real-world problem

Theory generation is the primary study endpoint

Real-world application is the primary driver and is

Real-world application is usually explored as an

incorporated into the entire research process

afterthought

Research is conducted in a short amount of time (i.e., Research is conducted over a longer period (i.e., days/weeks/months)

months or years)

Academic and practitioners

Academic

Publication in peer-reviewed journals is judged by relevance and real-world application

Publication in peer-reviewed journals is judged by theoretical depth and contribution to the literature

Using the methodology we just described, we wanted to investigate racial discrimination experienced by Asian professionals in the workplace during the pandemic and its impact on their identity. The aim of unpacking the experience of Asian professionals to inform practical relevance and application for shaping an inclusive work experience undergirded our research motivation (Ahlstrom & Wang, 2020; Amis & Janz, 2020; Spicer, 2020). Conducting phenomenon-driven rapid-response research meant significantly accelerating the initial research planning process, which typically spans the review of the literature and design of the research methodology and implementation, a process that normally can take several months or longer (Breakwell et al., 2006; Yong, 2020). During this phase, we relied heavily on the knowledge gained from previous research exploring discrimination experienced by Asian professionals in the workplace. Knowledge informed by prior research indicated that racial discrimination in the workplace often manifests as racial microaggressions, defined as behaviors and comments that, more often than not, subtly signal denigrating or demeaning messages to the target based on the target’s race (Sue et al., 2008). Racial microaggressions are experienced by Asians, despite the misconception that they do not experience racism due to the “model minority” label, which portrays them as

268  Handbook of research methods in organizational change a minority group that is socially, economically, and professionally well off, and hence not seen as a group that experiences racial oppression (Wong & Halgin, 2006). Consequently, their struggles and the challenges they face related to racial discrimination often go unacknowledged and unaddressed in many organizations (Kim et al., 2019). Based on previous findings, we assumed that workplace racial discrimination against Asian professionals, which is influenced by the COVID-19-related anti-Asian sentiment, would likely not be seen as problematic and hence not addressed by colleagues and leadership. If this were true, it would mean that Asian professionals would be facing additional hardships in the workplace during the pandemic. In other words, they would not only have to navigate the same direct hardships brought on by the pandemic as everyone else but also have to deal with COVID-19-related racial discrimination that would go unchallenged and unaddressed by their leadership and the majority of their colleagues. Thus, instead of waiting for definite confirmation of this type of trend from the literature (i.e., other scholars), we decided to immediately start collecting data during the spring of 2020.

RECRUITMENT AND DATA COLLECTION We began reaching out to our professional network during the first phase of the recruitment effort, which began during the beginning of the pandemic in 2020. Given the similar reports of anti-Asian sentiment that were arising from both Canada and the U.S. (A3PCON, 2021; Zhang et al., 2020), we decided to recruit from both Canada and the U.S. We recruited broadly using the following criteria. Participants had to: (1) self-identify as Asian American or Asian Canadian and (2) have at least one year of full-time work experience in the U.S. or Canada. This initial effort led to a handful of interviews, which provided important data that not only confirmed that COVID-19-related anti-Asian sentiment had permeated the American and Canadian workplace but also informed the design of the semi-structured interview guide, which was amended based on the initial insights gathered from the first several interviews. For example, once we confirmed that experiencing COVID-related microaggressions was a reality faced by the first several participants whom we interviewed and that it was having a negative impact on their mental and psychological well-being, the interview protocol was revised to include additional follow-up questions to gain a deeper understanding into these situations. These follow-up questions asked about the aggressor (the person committing the microaggressions), the participant’s reaction and response to the aggressor, and the situational context (i.e., office environment, company culture, etc.) where these microaggressions occurred. After the initial interviews were conducted, we expanded our recruitment by posting on online professional groups on LinkedIn and Facebook, as well as Reddit. We had used LinkedIn and Facebook to recruit participants for previous studies and had only recently started using Reddit as another avenue for recruitment. We recruited Asian professionals through several different subreddits catering to the Asian and Pan-Asian experience, such as r/AsianAmerican and r/Asian. The effort proved fruitful as we were able to recruit Asian Americans and Asian Canadians from all over the U.S. and Canada, representing a diverse set of professional experience, industries, and perspectives. Both authors agreed to take part in the recruitment and data collection process. Doing so allowed us to significantly increase the speed at which we recruited and conducted interviews. Importantly, conducting the interviews ourselves allowed us to immediately initiate the data

Conducting phenomenon-driven rapid-response research  269 collection process without the need to select, hire, and train interviewers, accelerating the pace at which we collected the data, a key for conducting rapid-response research. The timeliness of our data collection was key for conducting phenomenon-driven rapid-response research, which aims to capture and describe an event of interest (Doh, 2015). Conducting phenomenon-driven rapid-response research as the event unfolded allowed us to more accurately capture the types of racial microaggressions experienced by Asian professionals in the workplace at the start of the pandemic. As a result of our effort, over the span of a few months we interviewed more than 30 Asian American and Asian Canadian professionals from various industries, including consulting, education, healthcare, law, and technology.

INTERVIEW PROTOCOL Given that we were examining a phenomenon that was relatively unknown, the interview protocol was designed using an inductive grounded theory approach. This involved the use of open-ended and probing questions, which were meant to provide us with a descriptive set of rich experiences from which to identify important contextual pieces. We used opening questions such as “Could you tell me a little bit about yourself and your personal and professional background?” to build rapport with the participants before asking them to talk about how COVID-19 affected the types of racial discrimination they experienced in the workplace. This was important as many of the topics that were discussed were quite sensitive and emotional and may not have been as richly revealed had there not been an ice-breaking period. Additionally, after the participant told us about an event that had a significant impact on their psychological or physical well-being, we would change the discussion to a lighter subject, rotating the discussion between lighter and more intense issues to ensure the comfort of the participant. During times when the participant discussed an intense experience, we made sure not to interrupt, and allowed the participant to take short breaks as needed. Importantly, during the initial data collection process, the interview protocol was amended in order to capture the emerging themes and probe deeper into the contextual cues regarding the emerging microaggressions. Follow-up questions included asking about how these microaggressions differed from previous types of microaggressions, as well as asking for specifics regarding the incident, including the relationship of the aggressor to the target and other contextual information that allowed us to get a deeper understanding of the context in which the microaggressions occurred. Examples of contextual information included responses they observed from their colleagues and peers, the leadership at their parent company/organization, and the broader media and political outlets. Since our research question focused on how these microaggressions affected the types of identity work used by Asian professionals, we also asked follow-up questions that focused on the affective, cognitive, and behavioral effects of these interactions using questions such as “What was going through your head when this happened,” “How did this make you feel,” and “How did you respond?” Such questions allowed us to unearth the various ways Asians experienced, responded to, and navigated racial microaggressions during COVID-19.

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RACE OF INTERVIEWERS Both authors who conducted the interviews were of East Asian descent (one Chinese Canadian and one Korean American). There has been a lot of research outlining both the pros and cons of matching the race of the researchers and the participants (Seidman, 2006; Song & Parker, 1995). One of the pros of race-matching is that it can more easily create an effective interviewing relationship due partly to the shared experiences and commonalities and the establishment of psychological safety stemming from the shared racial identity (i.e., the experience of knowing what it is like being an Asian American or Asian Canadian in the U.S. or Canada, respectively). In fact, the research exploring race and its effects on interviewer–interviewee dyads suggests that racial similarity is associated with favorable outcomes, such as more positive evaluations and experiencing less stereotype threat (Davis & Silver, 2003; Goldberg, 2003), facilitating a more free-flowing dialogue between the interviewer and the interviewee. Further, Asians often experience microinvalidations that minimize or deny their experience dealing with racism by non-Asians (Sue et al., 2009) and thus may be less forthcoming when discussing the types of microaggressions and their effects with a non-Asian interviewer (i.e., White). We also acknowledged that they may be processing some of these novel forms of COVID-19-related discrimination for the first time and may feel more at ease to share and unpack these experiences with someone sharing a similar racial background. Hence, we made the conscious choice to mirror the race of the participants interviewed. During the interviews, many of the participants would acknowledge and note the interviewers’ shared racial background, using statements such as “Being Asian, I’m sure you know what that’s like” or “You’re Korean/Chinese. Has this ever happened to you?” Although all the interviews were conducted in English, we observed that some participants chose to animate and describe their experiences using Korean/Mandarin adjectives, which further enrichened the experience. We noted that the shared racial background between the interviewers and interviewees invited a less inhibitive, more candid conversation that allowed us to document the various effects COVID-19-related microaggressions had on the identities of the Asian professionals interviewed.

SAMPLE SIZE We determined our sample size based on the following assumptions. First, the recommended sample size changes depending on whether the sample is heterogenous (i.e., comprised of people from different industries, companies, and levels) or homogeneous (i.e., comprised of people from the same department, same level, etc.). Homogenous samples tend to have smaller Ns (i.e., less than 20), whereas heterogeneous samples tend to have larger Ns (i.e., more than 30), with semi-heterogeneous samples being somewhere between the two (Kindsiko & Poltimäe, 2019). We considered our sample a semi-heterogeneous sample, given that though our participants represented different geographical locations, companies, industries, and positions, they all shared a commonality in that they all identified as Asian American or Asian Canadian. Second, in the past it has previously been recommended that qualitative studies require a minimum sample of at least 12 to reach data saturation (Fugard & Potts, 2015; Guest et al., 2006), at which point additional data gathered from additional interviewees do not yield additional insight or new themes (Glaser & Strauss, 2017). Thus, a study with a sample

Conducting phenomenon-driven rapid-response research  271 of 13 would be considered sufficient (Holder et al., 2015; Sue et al., 2009). However, the recommended saturation point has since been increased. In management and organizational research, for example, the majority of qualitative studies published in high-impact journals (62 percent) were studies with at least 30 participants (Kindsiko & Poltimäe, 2019). We deem this to be a step in the right direction, particularly given that a larger sample size will help capture different perspectives that represent the growing diversity of the general population. Thus, we aimed to recruit well above the recommended original baseline of 12, aiming for at least 20 participants. We continued to interview participants until theoretical saturation was reached. Theoretical saturation was achieved after approximately 28 interviews; however, an additional eight were conducted to confirm saturation (Glaser & Strauss, 2009), leading to a final sample size of 36.

DATA ANALYSIS Transcription In qualitative research using interview data, transcription is the word-for-word or verbatim record/transcript of the interview conversation. Transcription can be done in-house if such a service exists within the research institution or can be outsourced to a third-party transcription services company. Another alternative is using artificial intelligence (AI) or speech recognition technologies to transcribe the interview. Although the generation of the transcript is often instantaneous, it is often prohibitively expensive, and even with paid services, approximately 25 percent of the words can be incorrect (Brewster, 2020), necessitating a human to constantly review the transcription process. Even transcription options such as those offered by Zoom and Microsoft Teams require heavy manual input (i.e., using Zoom’s transcription feature led to the generation of a transcript file; however, about 40 percent of the content had to be manually corrected by a human, which took a considerable amount of time). Even though speech recognition technology is rapidly improving and is bound to become more accurate, it was still largely inefficient at the time of our data analysis, considering the cost and/or need for human involvement. Given that we did not have an in-house transcription service available and that AI transcription can often be unreliable, we were left with the choice of outsourcing the transcription. While outsourcing can be a way to efficiently translate the audio to text, we chose to forego this option and transcribed the interviews ourselves. We did this for three main reasons. First, we wanted to ensure that the information shared with us would be protected. Issues of privacy were particularly important given that participants were discussing highly sensitive and confidential information about their experience dealing with racial discrimination in their organizations. While some participants chose not to name their organizations or specific individuals during the interview, others did. Furthermore, when obtaining consent from the participants, we assured them that the audio recordings and the specific content within would not be shared beyond the research team. Though issues of confidentiality can be addressed when using professional transcription services such that the audio recordings and the transcription files remain within the company, the information may also be released to outside contractors/ freelancers who are typically used by many transcription service companies, increasing the potential for data breaches (Bikman & Whiting, 2007).

272  Handbook of research methods in organizational change Second, doing the transcription ourselves guaranteed the highest level of accuracy, as we had also conducted the interviews. To further ensure transcription accuracy, we completed each transcript within a week of the interview. Furthermore, both authors had previous experience conducting qualitative research in academic and corporate settings and were well versed in transcription procedures. Third, we determined that doing the transcription services ourselves would be faster. If we had chosen to outsource the task to a transcription service company, we would have had to go through several rounds, consisting of bidding processes, interviews, and vetting procedures, to ensure the quality of the final product (Clark et al., 2017). Going through this process would have taken, at minimum, anywhere from a few to several weeks, given that we would have had to take additional time vetting for reputable companies that provided transcription services. By doing the transcription ourselves, we were able to complete the transcription process accurately and within a short period of time. As stated earlier, although AI transcription may seemingly appear to be a near-instantaneous solution, the potential for error and the subsequent time-consuming process of correcting those errors would have likely offset its advantage. Performing the transcription ourselves offered added advantages. For example, doing the transcription ourselves allowed us to note nuances such as body language, facial expressions, and tone of voice during the transcription process, providing richer details that would have been lost with an outsourced transcription service. The process also helped us to become more familiar with the data and the emerging themes during the initial stage of data collection because we transcribed the interviews as they were conducted rather than waiting until the very end. Doing so also helped inform our interview protocol, which was amended a few times to allow for more nuanced follow-up questions based on the emerging themes we identified. Practically, we found that investing in a transcription foot pedal (a piece of hardware that allows the user to control the playing and pausing of the audio file with one’s foot rather than one’s hand) alongside its associated transcription software (not an AI, but rather a program that integrates the foot pedal’s functions into word-processing software) significantly streamlined the transcription process. This allowed us to pause/start and speed up or slow down the audio recording at will, all without leaving the word-processing software. Furthermore, we also believed that having a high (at least 60 words per minute) typing speed is essential when transcribing, in addition to being able to touch type (typing without looking at the keyboard). Lastly, transcription can be mentally and physically demanding; we recommend taking regular breaks and purchasing a wrist rest for the keyboard in order to reduce both mental and physical burnout. Of course, as transcription software becomes more accurate, one can incorporate its usage into the transcription process; however, we still recommend that a human manually check the work. Code Generation In this section, we share our insights into how we managed the coding process, which was highly collaborative in nature, and detail how this style of coding supported our efforts to conduct phenomenon-driven rapid-response research. Collaborative coding The general consensus when it comes to coding is that it should be a collaborative process involving a team of coders (Schreier, 2012). Multiple coders can provide different perspec-

Conducting phenomenon-driven rapid-response research  273 tives and bring different ways of processing and interpreting the data, which is particularly relevant when exploring a new and evolving phenomenon, such as the manifestations of different types of microaggressions against Asians during COVID-19. Different coders enable the raising of stimulating thought questions that can ultimately help generate new and richer codes (Olesen et al., 1994). Collaborative coding, however, necessitates streamlined and centralized coordination, which typically involves the designation of one team member as the codebook editor who manages and maintains the list of codes for the group. This is the approach we adopted, in which both of us analyzed the data to generate codes, with the lead author managing the final list of codes. Though the code generation can be done together via group working sessions, we chose to independently code the data. This approach provided us with the opportunity to go through the data independently and assign meaning to the various examples and situations in a timely manner (i.e., both authors could go through the data at a time convenient for them) while providing a sharpened focus on the codes and examples that needed calibration. Given that we were experienced with the qualitative coding process and the topic of interest, we felt confident that this approach would allow us to process and analyze the data in a timely but rigorous fashion. Moreover, the code generation process occurred in parallel with the data collection. In other words, we did not wait until all the interviews were conducted in order to start the data analysis but instead analyzed the data as we conducted the interviews. This offered us the advantage of approaching and processing the data while the interviews were still fresh in our minds, helping us to note contextual information, such as body language, tone of voice, and facial expressions that were observed during the interviews in the coding process. Doing so helped us generate more accurate codes reflective of the data, given that data were fresh in our minds. Having both authors simultaneously go through all the data and generate codes that were then calibrated through regular meetings or “data sessions” deepened our understanding of the themes generated. This strategy of simultaneous coding while collecting the data necessitated a constant revision of the codes that were previously generated whenever additional data suggested that there should be new or modified codes. Though this method required that we repeatedly go back to previously generated codes in order to compare them to the additional data that was collected, the process kept us close to the data throughout the data collection and analysis process, deepening our understanding of the complexity of the codes generated. Ultimately, this process helped us generate codes that accurately described the evolving phenomenon driven by a major disruptive event in organizations. To support this process, both authors kept detailed memos – research journal entries – where we documented our thoughts and experiences about the participants interviewed, musings about the phenomenon under investigation, and reflections about the data collection and analysis process (Braun & Clarke, 2006; Vogt et al., 2014). Rather than sticking to academic prose with rigorous categorizations (i.e., research question memo, coding memo, theoretical memo, etc.), we chose a more free-flowing memo-keeping style that reflected more of an open-ended journal entry or an email to a friend. We referred to these memos during our regular meetings. One advantage of this more liberal method of memo keeping is the amount of time saved. Given the more time-consuming process of qualitative research (compared to quantitative research), we found that rigorously categorizing these reflections was largely impractical and would hinder our timeline. Lastly, we organized all the material in a secure cloud storage service, with access limited to only the research team.

274  Handbook of research methods in organizational change Through our research effort, we were able to successfully capture the experiences of Asian professionals navigating the steep increase in racism during the beginning of the pandemic. The rapid nature of how we conducted the research allowed us to document a seminal moment in time for many Asian American and Asian Canadian professionals while illustrating the value of adopting new research methods to tackle and investigate disruptive events. Next, we offer our concluding remarks and discuss the implications of our approach.

CONCLUSION The need for more versatile and adaptable research methods has become evident over the past decade as organizations are prompted to respond and adapt to different levers of change, which are happening more and more frequently (Fainshmidt et al., 2021; Wickert et al., 2020). In this chapter, we introduced phenomenon-driven rapid-response research as a way for scholars and practitioners to empirically examine emerging phenomena in order to identify immediate, workable solutions. We detailed the steps we took to perform phenomenon-driven rapid-response research to investigate a quickly emerging phenomenon as it occurred, sharing key learnings and insights generated. Specifically, we detailed each step of our research process, including how we proceeded with recruitment and data collection, designed the protocol, discussed the effects of the interviewers’ race, calculated the sample size, and informed our approach to data analysis, especially with transcription and code generation. By sharing our process for conducting this type of research by adapting existing research methods, we demonstrate how we mobilized to investigate an unplanned and unexpected event in order to conduct research as the event unfolded. The application of this methodology provided several advantages over traditional theory-driven methods. First, it allowed us to promptly delve into the disruptive event as it happened. The flexibility afforded by this method allowed us to focus first on gathering data rather than choosing the right theoretical framework. Though we did not completely let go of the theoretical frameworks during the research planning phase, our research questions were primarily driven and informed by the tangible, real-life events affecting the Asian American and Asian Canadian communities in the U.S. and Canada, respectively. Rather than being guided primarily by theory, we were guided by the real-life stories, anecdotes, and events surrounding the rise of racial discrimination faced by Asian communities in North America. The overarching bull’s eye for our research was clear: to investigate and capture the experiences of Asian professionals navigating racism during the onset of the pandemic. In fact, we promptly began planning our research agenda long before incidents of racially fueled attacks against Asians began gaining mainstream media attention. Both authors, based on their own identities as Asian American and Asian Canadian, anticipated the repercussions of the use of racialized terms used by politicians and leaders (“Kung-flu” or the “China virus”). Consequently, we were able to promptly begin our investigation at the beginning of the pandemic in order to accurately capture the experience of a wide range of Asian professionals. Relatedly, due to the flexibility afforded by the phenomenon-driven rapid-response method and the fact that we were able to begin interviewing participants during the early days of the pandemic, we were able to capitalize on the momentum generated by the immediacy of our research topic. We were able to quickly recruit Asian professionals who were eager to share their experiences and hardships navigating racial discrimination during the pandemic. As

Conducting phenomenon-driven rapid-response research  275 a result, between the two co-authors, we were able to finish data collection in a matter of a few months, which helped to significantly expedite our research process. The research was undergirded by real-world events rather than theory, allowing us to stay close to the phenomenon of interest. Real-world application underpinned not only how we collected the data but also how we analyzed the data and made sense of the findings. Our primary aim was to capture, identify, and understand a problem; in our case, racism experienced by Asian professionals during COVID-19. The findings were to be used to encourage leaders and organizations to take appropriate action by providing necessary acknowledgments and resources for Asian employees navigating the rise in racist encounters in the workplace. And thus, we kept a strong emphasis on the practical application component during the entire research process, which no doubt strengthened the relevance of our findings. Our knowledge and lessons learned can be helpful, particularly for young and emerging scholars looking for ways to enrich their research experience and expand their research repertoire. Throughout this chapter, we shared various practical examples of conducting real-world research, which junior scholars can integrate into their own research processes. We would also like to emphasize that the research process, however daunting it may seem, is not insurmountable and can be done even with a small team of researchers. After all, our study on the experiences of Asian American and Asian Canadian professionals during the pandemic was conducted with a core research team of two people. Lastly, we believe that this research approach worked not only because we chose an appropriate research question/design (i.e., phenomenon-driven rapid-response research) but also because we were driven by a strong personal and community conviction informed by our own experiences as an Asian American and Asian Canadian. As individuals of East Asian descent living in the U.S. and Canada, we witnessed the effects of COVID-19-related anti-Asian racism unfold firsthand. As individuals whose racial identities were under attack, we both felt a personal urge to address this problem. This personally motivated conviction no doubt drove us to dedicate our time and effort to focus on and prioritize this research project, allowing us to conduct a timely and relevant study as one of the first to capture and report on this phenomenon. This speaks to the need for developing and encouraging more researchers of diverse backgrounds to engage in personally driven research or “me-search” to decolonize the largely White-centric management literature by challenging existing theories and frameworks, which do not speak to the lived experiences of people of color (Nkomo, 2021). As we conclude, we invite young scholars to be creative in how they approach their research agenda as there are different methodologies that researchers can choose and adapt to tackle grand challenges and important phenomena of our time that are also personally relevant to them.

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13. Collaborative Developmental Action Inquiry: a new paradigm for leadership and organizational change research William R. Torbert and Sofia-Jeanne Caring

INTRODUCTION All humans (including social scientists!) are practitioners, trying, however explicitly or tacitly and however successfully or unsuccessfully, to act in a timely fashion in a particular setting. In order to do so, we must study ourselves in action (first-person action research), we must also study our interactions with others (second-person action research), and we must study the larger social institutions in which we are embedded (third-person action research). I, Bill Torbert, first undertook to interweave my practice with my research in the fall of 1966, when I became a graduate student in Individual and Organizational Behavior at Yale, as well as founding director of the Yale Upward Bound War on Poverty program. I planned with my advisor, Chris Argyris, to do an action research dissertation on my leadership of Upward Bound, on how the school developed over two seven-week residential summer sessions in 1967 and 1968, and on what difference it made in students’ lives. This would require first-person research on myself, second-person research on my interactions with staff and students, and third-person research on how we interacted with larger institutions like Yale itself, the New Haven school system, and the federal Office of Economic Opportunity that funded the program. In the end, we created a highly collaborative program, we tape-recorded virtually all our staff and community meetings, we cut New Haven’s dropout rate in half, and I wrote a book (Torbert, 1976) that presented an eight-stage theory of organizational development, analogous to Erikson’s (1959) theory of individual development. However, original plans notwithstanding, this research project and book did not serve as my dissertation. Instead, the faculty had become concerned that my study violated a fundamental tenet of modern science: namely that, to assure objectivity, the researcher should not influence the research setting during the study, much less be one of the central objects of study (see chapters 5–7 of Torbert, 2021, for details on these events). Ironically, what makes science science, according to the paradigm of Empirical Positivism, is precisely what makes it impractical for and ungeneralizable to practicing managers—indeed, to everyone in our roles as practitioners. We need to learn, not only general, third-person things, learned in the past about ourselves, about our immediate colleagues, and about our institutional circumstances, but also particular things, learned in the present about what is going on inside us now, what is the quality of our interactions now, and what are the institutional influences now. Moreover, the generalizable theories we need are evolutionary theories of development over time, so we can estimate what action may be timely now.

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282  Handbook of research methods in organizational change This chapter introduces a new paradigm of social science, and of organizational change research in particular, that integrates scientific inquiry and timely action, rather than separating them. This new paradigm is named Collaborative Developmental Action Inquiry (CDAI). In this new paradigm, discovering and validating generalizable (third-person) theories is an indispensable—but secondary—aim. The primary aim is to engage in timely action/inquiry within one’s immediate context and circumstances. In doing so, one is guided in part by third-person theory and data and in part by first- and second-person inquiry and feedback. The chapter is divided into four sections: 1. The underlying philosophy of CDAI; 2. Eight developmental stages of CDAI—the distinct action-logics individuals, organizations, and scientific paradigms can inhabit; 3. Empirical methods and practices for transforming ourselves, our organizations, and our science through CDAI; and 4. Quantitative, empirical findings that test the reliability, the validity, the transformational efficacy, and the limits-to-date of CDAI. Each section provides a brief overview and includes references to 50 years of research amplifying and supporting the various claims. Many, many (billions of) others (friends, colleagues, and strangers) must engage in such collaborative, developmental, action inquiry during the duration of the 21st century, if we are to survive our contemporary unreflective habits. If this chapter is, in a small way, to support so much transformational change, it will have to be presented in a mighty attractive, yet also bitingly provocative, and persuasively sober way. Let us see how we do together in this challenging and paradoxical endeavor.

THE UNDERLYING PHILOSOPHY OF CDAI CDAI departs from modern social science assumptions about the nature of reality. Most social science today is based on either the idea that the externalized material world (e.g., the brain) is all that exists, or on the idea that the internal world (e.g., the mind) is all that exists. These two approaches to social science can also be called Naturalism and Interpretivism (Rosenberg, 2012). In Table 13.2, the paradigms of Behaviorism and Empirical Positivism treat the outside world as the primary reality (i.e., Naturalism). Other early action-logic paradigms, such as Gestalt Psychology, Sociology, and Anthropology or Postmodern Critical Interpretivism, treat thinking/mind/interpretation as the primary reality (i.e., Interpretivism). A third approach treats both mind and matter as fundamentally real. This can be seen in the paradigm of Multi-Method Eclecticism. Sensing the need for a philosophical basis for social science that is different from those listed above, Habermas (1981, 1984) points to a motive beyond the technological and interpretivistic aims: namely, to an emancipatory, action-oriented interest in overcoming dogma, compulsion, and domination. This approach is represented by three paradigms—Action Science Praxis (Torbert, 1976; Argyris, Putnam & Smith, 1985; Alderfer, 2011), Cooperative Ecological Inquiry (Bradbury, 1998; Reason & Bradbury, 2000), and CDAI (Torbert, 2021). In contrast to the assumptions of previous paradigms, CDAI posits that we can each test in our personal experience its claim that our awareness can contact four “territories of experi-

Collaborative Developmental Action Inquiry  283 ence”—the outside world, one’s own embodied action, one’s thinking, and (rarely, unless one intentionally practices) a post-cognitive consciousness that can be aware of the other three at once (Torbert, 1972; 1991, pp. 219–40; 2021). Table 13.1 offers single-word outlines of several different examples of the four territories expressed in different realms. Table 13.1

The four territories in different realms of human activity

First-person awareness

Second-person speech

Third-person organization

First-, second- and third-person science

1. Outside world

Inquiring of others

Outcomes

Data

2. Embodied sensation

Illustrating

Performance

Methods

3. Thinking/feeling

Advocating

Strategy

Theory

4. Consciousness

Framing

Vision

Paradigm

Here is a practical example of the four territories of experience and how they relate to one another. First, a plan to build a wooden back deck (“thinking” territory) can guide one’s choice of when and how to use a saw (“embodied action” territory), and the hand-guided saw can shape the wooden boards (“outside world” territory). By establishing at least occasional contact with all four “territories” of experience, one can estimate whether one’s purpose, strategy, practice, and outcome are congruent with one another or not. If we are missing the nail when hammering the previously sawn boards together, we can try a single-loop change in our hammering action. If, once we hit the nail truly, it bends rather than enters the wood, we probably need a double-loop change in both our strategy and our action—for example, a drill and a screwdriver. If, as we work, we find ourselves daydreaming about Florida and we contemplate this dream’s implications, we may experience a triple-loop change in vision, strategy, and action—moving altogether to Florida’s warmth, rather than continuing to build a New England deck (Torbert & Starr, 1995; Torbert & Erfan, 2020). Thus, the philosophy underlying CDAI calls on us to be aware of both the internal forces that direct our inquiry and the external impact that our actions have on others and the world around us. Unlike formal science, we are not separate from what we study; we are in fact studying the interaction between ourselves, others, and the world. This allows us to understand how our actions, combined with the actions of others, can produce changes we desire in the world.

THE EIGHT DEVELOPMENTAL STAGES OF CDAI How does a student, leader, organization, or science change to become increasingly capable of exercising “four-territory awareness,” timely action, and effective organizational change? This section of the chapter focuses on a theory of sequential stages or action-logics through which leaders, organizations, and the social sciences themselves can develop (Erikson, 1959; Kegan, 1982, 1994; Kohlberg, 1984; Loevinger, 1982; Alexander & Langer, 1990; Wilber, 2000; Edwards, 2009), with special attention to the model first discovered when studying the Yale Upward Bound program (Torbert, 1975, 1976, 1987, 2004, 2021). Each newly birthed action-logic (individually, organizationally, or scientifically) theoretically represents a coherent, enacted worldview that is a qualitative transformation from the previous action-logic, dethroning its assumptions but including its capacities and competencies, and opening a whole

284  Handbook of research methods in organizational change new field of capacities and potential skills to be mastered. These new capacities may, in their own turn, be found inadequate to respond to certain dilemmas, leading to a next action-logic transformation. According to this theory, the periods during which a leader, organization, or social science is transitioning between action-logics are when those systems are most “fragile,” most likely to change leadership, most likely to be sold, most likely to merge, most likely to die, most likely to “drop out” or change careers. This theory is fundamental to the field of organization change management because it points to the most crucial moments in a person’s or an organization’s development and to the transformational qualities of different organizational interventions (Mirvis, 2006, pp. 40–51). This theory of leadership development, organization development, and social scientific development is introduced in outline form as Table 13.2. According to empirical findings described in the fourth section of this chapter, only leadership and consultancy, as well as social science methodology, based on action-logics later than an organization’s current developmental action-logic can reliably support an organization’s transformation (Rooke & Torbert, 1998; Torbert, 2021, pp. 413–30). Reciprocally, only organizations operating at later action-logics than given individuals within them can reliably support those participants to develop to later leadership action-logics (Torbert, 1991). Put more simply, in order to help an organization improve how it operates, we must be thinking at a level beyond the logic that dictates how the organization is operating today. Table 13.2

Leadership, organizational, and scientific developmental action-logics as mapped by CDAI

Leadership

Organization

Scientific Paradigm

1. Opportunist

Investments

Behaviorism

Wins for self, however possible

Champions make spiritual, social,

Researcher has unilateral control of

financial commitments

stimuli/responses

2. Diplomat

Incorporation

Gestalt Psychology, Sociology,

Fits in by avoiding conflict

Services produced; Founder(s) control(s)

Anthropology

norms

Appreciation for “Others” via case studies

3. Expert

Experiments

Empirical Positivism

Focuses on logic and expertise

Alternative strategies/products tried in

Through randomized hypothesis-testing

rapid succession

seeks universal truths

4. Achiever

Systematic Productivity

Multi-Method Eclecticism

Uses goal-oriented feedback for team

Systematic procedures, predefined tasks,

Triangulation among quantitative and

success

quantitative measures

qualitative measures; evidence-based

5. Redefining

Social Network

Postmodern Critical Interpretation

Aware of limits of all action-logics,

Loosely coupled initiatives

Deconstructs dominant narratives

6. Transforming

Collaborative Inquiry

Action Science Praxis

Principled, mutual, open, incongruity

Explicit mission, interpersonal openness,

Action inquirer studies self and others with

resolving

self-amending

feedback to participants and science

practice

seeks shared vision

Collaborative Developmental Action Inquiry  285 Leadership

Organization

Scientific Paradigm

7. Alchemical

Foundational Community

Cooperative Ecological Inquiry

Dancing relaxed in the tension of

Of inquiry, love, and work

Participant-led, justice-seeking, incongruity-confronting play

opposites

Collaborative Developmental Action

8. Ironic

Liberating Disciplines

No longer center stage, behind the

Challenges that expand attention, measure Inquiry

curtain

consequences

Complex, fractal, chaotic order amidst 1st -, 2nd -, and 3rd -person living inquiry

Note: Categories are described in more detail in Torbert (1987, 2004, 2021).

These developmental action-logics represent the path by which humans, organizations, and the social sciences can evolve toward increasing awareness of the interplay among the four “territories of experience.” Here, to give Table 13.2 a little more substance, we will focus first on individual development, next on organization development, and last on scientific development. In the final two sections, we will focus on the interplay among individual, organizational, and scientific development in practice and in quantitative research. Individual Development At the Opportunist action-logic—typically encountered between the ages of 4 and 12—we are primarily establishing a relationship with the outside world territory, learning how to walk and talk and play (literal) games. At the Diplomat action-logic—often engaged between 12 and 20, sometimes for our whole life—we are primarily learning how to manage our own behavior according (or opposed) to family, school, or peer group norms. At the Expert action-logic— sometimes engaged in from about 17 on—we are primarily learning how to think in efficient, systematic ways. At the Achiever action-logic, we are primarily learning how to effectively coordinate the first three territories of experience—planning (strategizing, thinking), doing (embodied action), and outcomes—using single-loop feedback from unintended outcomes to adjust our behavior to become more likely to reach our goal. As we mature, some adults begin to question the very goals and norms our family, school, organization, profession, or broader culture have inculcated in them, recognizing that these goals and norms are socially constructed and are, therefore, potentially reconstructable through double-loop learning. At this Redefining action-logic, we begin to recognize that different people are operating from different action-logics, none of which is “provable” to people at different action-logics; and we begin to have occasional moments of a special kind of presence, or of intimacy with others, or of creative, artistic practice—moments of contact with the post-cognitive consciousness “territory of experience” and of alignment or incongruities among the four. For example, we begin to “see” incongruities between others’ espoused values and their actual behavior (but usually not yet our own incongruities). We become more tolerant of, and more attracted to, human differences—more relativistic—but also less decisive. As we give more attention, thought, and practice to such creative experiences and contributions, we may begin to sense that reality is more dynamic than static and that there is a developmental process of successive transformations in the evolution of the universe, in the evolution of life forms on earth, in the evolution of organizations, in the evolution of the sciences, and in the evolution of consciousness itself. Whereas at the early action-logics power is experienced as externalized and unilateral, as one evolves toward the Transforming action-logic, one becomes increasingly aware that transformation cannot be unilaterally forced

286  Handbook of research methods in organizational change and that there are also forms of mutual power, generated by a dialogue among colleagues between inner motivation and outer circumstance. This dialogue culminates in increasingly frequent fourfold awareness and timely developmental action. Eventually, however, the (heroic and tragic) Transforming action-logic may founder upon the (comic and humbling) trans-egoic Alchemical action-logic. This is no longer a stable structure of meaning and action at all, but rather an ongoing, conscious listening into all four “territories of experience” across temporal dimensions and spatial scales, as they express themselves fractally, interruptedly, and wonder-fully. As we proceed up the action-logic levels, we see new possibilities in how the world around us could operate. At the beginning of our journey, we think mainly about operating within the rules and structures we inhabit. As we grow, we start seeing that the systems we inhabit are not perfect and could stand to change. At some point, if we continue growing, we see ourselves as responsible agents of change in bringing about a better organization, world, and society. Organization Development Unlike the rich history of theorizing individual development that helped me to consolidate my own version, there was no history of theorizing organization development in terms of successive stages when I entered graduate school in the fall of 1967. I will therefore offer a more personal account of the circumstances of its discovery. Argyris conducted studies of his interventions in organizations (e.g., Argyris, 1962, 1970), tape-recording his meetings with senior teams as a primary source of data to analyze before the next meeting, using a reliable behavior-scoring system of his devising for the analysis. In this way, he could show the executives that they were, nearly universally, speaking in dueling advocacies to one another, generating a highly competitive discourse, rather than also engaging in mutual inquiries and sharing their feelings about how their decision processes were evolving—modes of dialogue that could give them more collaborative control over outcomes (Argyris & Schon, 1974). Unlike most social science methods, such data showed patterns of actual practice (including the researcher-leader’s practice), not just espoused attitudes, beliefs, or values gathered by questionnaires or interviews. This emphasis on treating actual, real-life behavior as a key form of data corresponded with the emphasis on attention to one’s own embodied action in the four territories of experience. As previously mentioned, at Upward Bound I followed Argyris’ lead and tape-recorded dozens and dozens of staff meetings and community meetings. I also collected a great deal of archival data and had another graduate student interview staff members (anonymously) about their experience of my leadership. The following four quotes summarize their responses about me: “gentle,” “responsive,” “unrealistically abstract,” and “vacillating when aggressive action needed.” Such second-person research and feedback gains its credibility and significance not because it is statistically tested, but rather because it represents the universe of perspectives with which one is currently working. This second-person research feedback suggested that I was leading primarily from the relativistic Redefining action-logic. This feedback (along with my own first-person efforts to contact a more objective post-cognitive consciousness that could experience my anxiety without getting caught in it) played a key role in helping me gradually become more aware of, and in many cases overcome, my pattern of avoiding rather than confronting conflict in leadership situations. Overall, these different data sources made

Collaborative Developmental Action Inquiry  287 it possible for me afterwards to describe 143 distinct, chronological events that occurred over the 18 months of Upward Bound. The big question that remained was, “Could I learn anything about how organizations develop from the data I had gathered at Upward Bound?” I conducted a comprehensive literature review of theories of group and interpersonal development, exploring to what degree they fit the 143 program events (Erikson’s life cycle theory proved most suggestive). Eventually, I was able to induce a theory of eight sequential stages of organization development (the theory outlined in the middle column of Table 13.2). I applied it, not only to the overall development of the school across the two years, but also to the development of each spring staff recruitment and planning session and each summer residential period, and to the development of the year-round core staff group (Torbert, 1976, pp. 134–66; also Torbert, 2021). Thus, although this was primarily a theory-generating study, it also provided five qualitative mini-tests of the theory. Later, I compared this theory to two other theories of organization development advanced near that time (Lippitt & Schmidt, 1967; Greiner, 1972), both of which I was unaware of when I discovered my theory. It turned out that my theory offered more detailed distinctions than theirs did, in regard to both the earliest and the latest stages of organization development. Much later, Edwards (2009) treated this theory as canonical in his meta-theory. Moreover, its analogousness with the individual development theory and its alternation between more centralized and more decentralized types of organizing gave it conceptual and practical advantages over other organization development theories. In later years, I would flesh out this theory, devoting an entire chapter to each organizational action-logic in one book (Torbert, 1987), relating the different organizational action-logics to different political philosophies in another book (Torbert, 1991), and, in a third book (Torbert, 2004), offering case studies of consulting interventions that, through attention to both leaders’ and organizations’ action-logics, transformed both. In this way, I studied how my own actions as a change leader impacted the actions of others and their cumulative impact on the development of the organization. Moreover, I understood that not all actions are the same; some are based on lower-level logics and are primarily minor improvements within a frame, while others are more advanced in their underlying logic and transformational in nature. I will offer an example of such a case study in the third section on CDAI practices that follows the next subsection about the theory of scientific development. The chapter will then conclude with a fourth section on quantifying and statistically analyzing CDAI empirical results. Scientific Development The reader may wish, at this point, to review the introductory remarks in the first section of this chapter on the underlying philosophy of the four territories of experience and how the developmental theory of scientific paradigms relates to current philosophies of social science. Since, according to developmental theory from Piaget on, each later action-logic both transcends and includes the assumptions and capabilities of all the previous stages, the developmental theories of individual, organizational, and scientific progress displayed in Table 13.2 constitute the full epistemological range of the eighth and most inclusive paradigm of science and action— Collaborative Developmental Action Inquiry (CDAI).

288  Handbook of research methods in organizational change CDAI offers a vivid contrast to earlier scientific action-logics such as Empirical Positivism (Torbert, 2000a, 2000b, 2000c; Reason & Torbert, 2001; Chandler & Torbert, 2003; Erfan & Torbert, 2015; Torbert, 2021). With the objective of maximizing the certainty that reported findings are objectively true, Empirical Positivism works only with third-person, objective data from the past and only with third-person impersonal voice. It seeks data that offer clear single-loop feedback (confirmation or disconfirmation) on specific hypotheses, and it attempts to rigorously exclude scientists from influencing their research so that only the independent variable influences the dependent variable (or not). Empirical Positivism is of course the dominant approach to scientific research today. By way of contrast, CDAI works with first-person, subjective data, as well as second-person, intersubjective data and third-person, objective data, seeking to triangulate among these stances. CDAI also seeks data that offer double-loop (action-logic transforming) and triple-loop (consciousness-awakening) feedback, as well as single-loop (goal-achieving) feedback. Moreover, whereas Empirical Positivism can (but rarely does) offer feedback to participants, that feedback comes only long after the initial inquiry, when the study has been analyzed and published, and is therefore rarely relevant to the original participants. Like Empirical Positivism, CDAI collects third-person before-and-after data; but CDAI also collects first- and second-person data in the present and about future possibilities and can potentially offer single-, double-, or triple-loop feedback to any research participant on the spot or within the following week (Torbert, 2000b; Steckler & Torbert, 2010), with the intent of supporting action-logic transformation toward increasingly timely action by all participants, including the formally-designated researchers. The major advantage that CDAI offers over Empirical Positivism is that it produces opportunities for immediate, sometimes transformational, change, something that should be of great interest to researchers focused on organizational development. Finally, as foreshadowed in the previous sentence, CDAI seeks to include (not exclude) the researchers in the research findings by observing, recording, and measuring their influence and by treating them as (part of) the independent or dependent variable. Because the social sciences have, in general, developed in imitation of the natural sciences, it is not surprising that they have largely adopted Behaviorist and Empirical Positivist approaches and methodologies. However, if one starts with the daily human dilemma of how to inquire so as to gather more data and how to act more effectively “now,” in whatever circumstances one finds oneself, it immediately becomes evident that third-person research is not sufficient. If I am by myself, or in a team meeting, I need some first- and second-person methods of action and inquiry to help me. Through what kind of meditative or prayerful inner inquiry can I find some tranquil distance within myself, so that my strategizing is not controlled by my reactive anxiety, fear, or overconfidence? In other words, how can I become aware of the congruities or incongruities within my experience of the four territories of awareness? I can be taught some guidelines for meditation, but ultimately I must conduct this kind of action inquiry within myself, alone. Through what kind of talking can we generate greater trust, more valid information, and more instances of timely action within a team? In both first- and second-person research, momentary validity is more important than generalizable validity. According to this theory, the more instances of momentary validity one generates, the more one facilitates effective action and, when timely, action-logic transformation in oneself, others, and the organization one is working with. What I learned from the feedback I gathered at Upward Bound about

Collaborative Developmental Action Inquiry  289 my avoidance of conflict led to further instances of practice under conditions of conflict and to a gradual transformation on my part from the Redefining action-logic to the Transforming action-logic and to greater success in future leadership roles (Torbert, 1991, Chapters 4–12.)

RESEARCH METHODS AND ACTUAL PRACTICES CONGRUENT WITH CDAI Research methods and actual practices are often altogether synonymous in first- and second-person CDAI. In this section, I offer four examples of CDAI in practice. The first example involves enacting second-person conversational practice through intentional work with the four parts of speech in Table 13.1—Framing, Advocating, Illustrating, and Inquiring. The second example describes how a consultant engages in meditative first-person inquiry and then chooses to act during a two-day consulting intervention, using both the individual and the organizational developmental theories to guide him toward timely actions. The third example describes the organizational design of an MBA program intended to promote effectiveness of actions. And the fourth example explains how a quantitative measure of individual development was restructured in the spirit of CDAI. Then, the next section presents quantitative tests of how individual and organizational development affect one another. Framing, Advocating, Illustrating, and Inquiring In a given conversation, one may start by “Framing” a possible shared purpose for the conversation, intended to appeal across the multiple action-logics that may be at play. Then, one can “Inquire of others,” to get data from the outside world, about whether the other participants in the meeting are willing to commit to that frame or purpose. If so, different ones in the group may “Advocate” their preferred strategies for proceeding, offering “Illustrations” (specific examples, stories) to clarify and support what they mean. Finally, they can again “Inquire” of one another whether their points are persuasive or not. In most business or family conversations, explicit “Framing” is absent (because “Post-cognitive consciousness” is absent and because different developmental frames are invisible). Also, genuine “Inquiry” is rare in most conversations (because the responses may jeopardize one’s preferred strategy). Helpful “Illustrations” are often no more than occasional (although some conversations consist almost entirely of stories). And dueling “Advocacies” are often ubiquitous (creating a highly competitive environment). An organizational consultant or family therapist can greatly improve the efficacy of such conversations, first by enacting the missing parts of speech when meeting with the clients, and next by imparting these distinctions and skills to the team members, so that they can use them in their own real-time practice. Likewise, a teacher of part-time MBAs can assign a short paper on a recent difficult conversation, when they did not achieve their desired outcomes. The instructor then teaches the four “parts of speech,” and has them try them out first in role plays in small breakout groups in class and then, during the following week, with their real-time “antagonist.” Their next short paper focuses on how they acted differently and whether such differences generated different and improved outcomes (and these experiences are again shared in their breakout groups). The two small group meetings where the students share their experiences and experiments are key to the success of this exercise and exemplify creating

290  Handbook of research methods in organizational change a temporary, second-person “community of inquiry.” (See Torbert, 1991 and 2004, for more detail on such “second-person action inquiry” exercises.) Co-ordinating Circumstances for Individual and Organizational Transformation A second more complex example of first- and second-person action inquiry—this time focusing on the individual and organizational development developmental theories—involves a two-day consultancy at a 35-person computer software company. It illustrates the power of applying both the individual and the organizational developmental theories in a qualitative manner to determine the timing of specific interventions. Note that the consultant/ primary-researcher uses both the individual and the organizational theoretical categories as scaffolding to analyze the situation at the company for himself and to suggest innovative action-proposals. In other words, he is attempting to practice theory; he is not attempting to teach the theory to company members. Also note that his various proposals are, in effect, “action inquiries” (simultaneous interventions and queries about next steps) to which the two partners might or might not have acceded.

CASE STUDY The company has burned through its initial round of venture financing, with net revenues for its products not yet foreseeable on the horizon. The partners are seeking a second round of venture capital, and everybody at the company knows they must achieve a breakthrough in marketing and sales. Yet, this “bottom-line” negative feedback alone, as stark as it is, is not propelling the company into a new operating pattern. The consultant interviews the top management (the president and the three vice presidents for production, marketing, and sales) of the company. The president is a generation older than the three vice presidents, and the company is a partnership between the president and one of the vice presidents. Together, the two of them developed the initial product. In the following three years, the company has produced a number of high-quality products, but they are not selling well. The consultant discovers numerous problems that have remained unresolved for a long time. Neither mission nor market is well defined. Pricing is a subject of acrimonious controversy. Employee morale is fragile because it is unclear whether competence or cronyism is the basis for rewards. Decisions are not driven by any internal sense of mission; they are made only when situations deteriorate into emergencies. The bottleneck in decision making appears to be the relationship between the two partners. They respect one another and attempt to share responsibility as though equals. But they repeatedly fall prey to differences in age, formal role, and managerial style. The president plays the role of optimistic, benign, absent-minded father. The vice president plays the role of pessimistic, sharp, rebellious son. Having interviewed the senior managers individually during the first six hours of his two-day visit, the consultant is next slated to meet with the two partners to set the agenda for the next day’s senior management retreat. But based on what he has heard, the consultant fears that the agenda-setting session may itself fall prey to the partners’ well-intentioned wrangling.

Collaborative Developmental Action Inquiry  291 In his 10-minute walk around the outside of the building prior to the session, the consultant engages in a first-person research/practice of intentionally bringing his attention first to his breathing and then, following that, to the vividness of the outside world, then to his feelings, and—only when he has established an ongoing circulation of attention—to what he now knows about the company. First, he determines that the partners’ pattern of behavior must change before any other productive decisions are likely. Next, he applies developmental theory to the individual partners, to his two-day intervention itself, and to the company as a whole, to help him generate design ideas for his meeting with the partners … only moments away. The consultant estimates that the vice president is in transition from Expert to Achiever, both itching for and resisting the true executive responsibility that a person at the Achiever action-logic relishes. The consultant estimates that the president is in transition from Achiever to Redefining, ready to give up day-to-day executive responsibility in favor of an elder statesman role of mentoring his junior partner and godfathering the company’s research and development function (indeed, the president has spoken wistfully of his preference for the VP R&D position). Applying the developmental theory to the company as a whole, the consultant sees the organization as spread-eagled across the fluid, decentralized Investments and Experiments action-logics, still living off venture capital on the one hand, while on the other hand experimenting with a whole line of products. At the same time, the company is failing to “bite the bullet” and meet the limiting, centralizing, differentiating demands of the Incorporation action-logic—the demand for decisive decision making and net revenues. How can the consultant act decisively and encourage the two partners to act decisively too? In this Incorporation action-logic spirit, the consultant first decides to recommend that the next day’s senior management retreat be limited to the two partners and the consultant, and that whatever decisions the partners reach the next day be put in writing with definite implementation dates. As for the agenda-setting session itself, the consultant’s reasoning leads him to ask how he can reframe the partners’ expectations and pattern of behavior from the very outset. He decides to ask them each to take the other’s role for the agenda-setting session and for the retreat. This will put the vice president into the senior, presidential role of initiating changes and the president into the role of agreeing and disagreeing. The consultant begins his feedback/agenda-setting session with the two partners by proposing that the vice president either resign or become president. This will put the vice president in the action role right away, rather than in his usual role of reacting to the president. Although quiet, the president seems to smile slightly, ready to play whatever this game may be. On the other hand, true to his customary “opposing” role, the vice president initially objects to “rehearsing” as president. “It’s fake,” he says. “Oh, you don’t believe you could be or ought to be president?” asks the consultant. After further probing by the vice president, the two senior officers agree to play this serious game. Now the vice president (in the role of the president) acts decisively rather than reacting combatively. He and the consultant propose various changes, with the president (in the subordinate role) making constructive suggestions and raising questions. The two partners reach written agreement on six major organizational changes the next day. The first of these is implemented at lunch that day. The vice president for sales is invited to join them. The partners discuss the major changes they are considering and ask him to accept a demotion. He agrees, expressing both his disappointment that he has let the company down and his relief that his future duties will be more circumscribed.

292  Handbook of research methods in organizational change A month later, all the changes have been implemented. Two months later, the company completes, six months ahead of schedule, a first-of-its-kind product for a definite and large market. The company fails to get a second round of venture financing, but sales revenues begin to exceed costs for the first time in the company’s history due to the new product. In the meantime, the vice president decides not to become president. The president stipulates that henceforward he will draw a higher salary and exercise the managerial authority of CEO on a day-to-day basis. Another three months later, the vice-presidential partner decides he wishes to become president after all and negotiates the change with the other partner. This case study (condensed from Torbert, 2021, pp. 407–12) illustrates the double-loop (transformational) timely-action-designing capabilities of both first-person CDAI—e.g., the consultant’s 10-minute “meditation” before his meeting with the two partners—and second-person CDAI—e.g., his meeting with the partners. It seems likely that the two-day intervention, along with the partners’ engagement with it, both during those two days and during the following months, catalyzed an organizational transformation to the Incorporation action-logic, as well as personal transformations by the senior partner (to Redefining) and the junior partner (to Achiever). To know with any certainty whether this is true, however, we would need to develop reliability- and validity-tested, third-person quantitative measures of both personal and organizational development. The last section of this chapter describes how such quantitative measures have evolved over several decades, to test and to support the findings and efficacy of the qualitative work, and perhaps to attract younger generations to its promise. Designing a Collaborative Inquiry, Action-Effectiveness MBA Program During the 1980s and 1990s, I took leadership positions of wider time horizons, institutional scale, and responsibility. In 1978, I had taken the position of Graduate Dean at the Boston College School of Management, with the aim of working with my colleagues to transform the MBA program: from a not-especially-distinguished MBA program, not ranked in the top 100 MBA programs in the U.S., operating at the Predefined Productivity action-logic, to the first “Action-Effectiveness” MBA program operating at the Collaborative Inquiry action-logic. (This story is retold in much greater detail in Torbert, 1991, chapter 4). To accomplish this organizational transformation, I recruited the best teacher from each department to form the MBA Core Team, first to plan and then to implement the revised courses and other new features. These new features included a weekly “Integrative Activity” and mid-semester teaching performance assessments, discussed both within the faculty team and then with the students, in order to demonstrate that the faculty were making not just the students but ourselves as well vulnerable to learning from feedback. We also divided the students into an array of six-student collaborative teams that worked on two course projects during the first semester and on a consulting project to an outside organization during the second semester (with the mission of positively changing the organization, not just analyzing it). These organizational change projects culminated in an Oral Presentation Competition. All the student consulting teams participated in the presentations and rated one another’s presentations—the top three each year replayed in front of distinguished business judges. In addition, each student team each semester divided six different leadership roles among them-

Collaborative Developmental Action Inquiry  293 selves and conducted a mid-semester and end-of-semester peer assessment process, the results of which played a role in their grades. Finally, students in the program who so wished competed to be selected to the Second-Year Consultants Team. Those chosen all took a Consulting elective during the summer between the two years, transcribing and analyzing the recording of one of the sessions and writing a paper about it for the whole class. They each also wrote a developmental autobiography and an organizational diagnosis (Torbert & Fisher, 1992). Then, each semester during their second year, they consulted to two first-year teams, with a weekly two-hour clinic with me as well. Shaping a Third-Person Measure of Individual Development into a First-, Second-, and Third-Person Measure Along with all these new “real-time” first- and second-person action/feedback systems at the Boston College MBA program, we obtained a significant grant to do before-and-after, third-person research to determine quantitatively whether the program succeeded in helping our students transform their action-logics. For this, we needed a psychometric measure that was reliable, valid, and practical (i.e., not too expensive, not too long to administer widely, and adaptable for research on leaders and for feedback to them). The only developmental measure that came close to all these criteria was Jane Loevinger’s Washington University Sentence Completion Test (WUSCT) (Loevinger & Wessler, 1970). But the WUSCT had been developed for research on teenage girls; it included a good deal of evaluative and/or academic language inappropriate for feedback purposes; it was weak on the later, post-conventional action-logics; and it had been little tested in field/organizational situations. Over the next 40 years, my primary research colleagues (Peter Reason, Dal Fisher, Susanne Cook-Greuter, Elaine Herdman-Barker, Chuck Palus, John McGuire, Hilary Bradbury, Aftab Erfan, and my other co-authors) and I have addressed these deficiencies in dozens of action research projects, including more than 20,000 administrations of the measure, which is now named the Global Leadership Profile (GLP; Torbert, 2004, Appendix; Torbert, 2016, www​.gla​ .global). These changes have transformed the GLP so that it is now a highly reliable measure of leadership practice and leadership development (with a reliability test, since 2016, on every single protocol). It has also been proven highly valid in numerous field tests. To take just one example, a sample of 283 MBA students completed the GLP and were invited to seek feedback on its findings. The theoretical prediction is that each later action-logic is more receptive to feedback than the previous one; the empirical finding was that at each later action-logic a higher proportion in fact sought out feedback, for a perfect 1.0 correlation (see Torbert, 2017, for other validity studies). In all cases where the GLP is administered, the subjects are asked to estimate their own first-person action-logic before receiving their third-person, psychometric result. They are also offered a second-person coaching session with a GLP-trained-and-accredited coach who can offer their own second-person analysis based on the subject’s behavior during the feedback/coaching session. Thus, the GLP exemplifies a research method that has evolved from a third-person, Empirical Positivist action-logic to the CDAI action-logic that triangulates among first-, second-, and third-person findings. The conversation among the three estimates can help the subject to gain a more confident sense of their current action-logic and of whether they are interested in exploring the next.

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QUANTITATIVE FINDINGS CONSISTENT WITH CDAI PREDICTIONS This final section focuses on two long-term quantitative tests of the individual and organizational developmental theories. The first study explores whether the restructured Boston College MBA program had the power to transform its students’ action-logics. The second study explores whether, as theoretically predicted, company CEOs at the Transforming action-logic were more capable of generating organizational transformation than earlier-action-logic CEOs. With regard to the first study, it is important to acknowledge that we did not yet have a way of quantifying the organizational action-logics, so we cannot say for sure whether the many changes in the design of the program, described earlier, amounted to a full transformation. However, there is good reason to think that the changes did amount to a transformation. First of all, the changes were certainly in the direction of Collaborative Inquiry systems. Secondly, end-of-program questionnaires showed a dramatic rise in satisfaction with the program on the part of students in the Action-Effectiveness version. Third, the program certainly changed in some kind of dramatic way because in the mid-1980s two different MBA ranking surveys rated the Boston College program at #25 and #29, indicating an unusually sharp rise from below the top 100 in public visibility and esteem. Whether or not the MBA program fully attained the Collaborative Inquiry action-logic, we can ask: Did the Boston College Action-Effectiveness MBA program (the independent variable), as predicted, generate systematic developmental transformation among its MBA students (the dependent variable, measured by changes in GLP scores)? A first glance at the before-and-after scores of 192 MBA students suggested that the deflating answer was no. Although there was a slight, positive movement of students’ action-logic scores in the aggregate from pre-program to post-program, it was not widespread and did not achieve a .05 level of significance, much less .01. On more careful analysis, however, we found that 22 of the 24 Second-Year Consultants did transform one action-logic, whereas only three of 168 of the other students did so (Torbert & Fisher, 1992). Thus, the data showed that, with two years’ worth of real-time late action-logic dilemmas and support in resolving them, this MBA program did generate an action-logic transformation for 93 percent of the Second-Year Consultants. Meanwhile, less than 2 percent of the students engaged in action-effectiveness activities for only one year progressively transformed. There was much less than a .01 probability that this was a chance finding. Another factor that may have contributed to the Second-Year Consultants’ success in transforming, beyond sheer additional quantity of action-effectiveness activities, is that the consultants volunteered for their positions, assuring positive motivation for learning the relevant, late action-logic capacities and skills. This element of the design is consistent with the theoretical understanding that late action-logic development cannot be forced, but rather must be generated by the exercise of mutual power. This finding has profound implications for how to organize corporate, governmental, and not-for-profit leadership development programs. We cannot force leaders to transform; they must be willing to transform themselves.

Collaborative Developmental Action Inquiry  295 Quantifying the Measure of Organization Development I resigned from the Graduate Dean role in 1987, in order to devote more time to exploring whether the developmental theory of leaders and organizations would have transformational efficacy in other organizational settings besides a school. During the 1990s, three colleagues of mine and I intervened in and consulted to 10 organizations for an average of three to four years apiece. These organizations had 495 employees on average, half were for-profit and half not-for-profit, and they spanned six different industries. In this study, the GLP measurement of the leadership action-logics of the CEO and the lead consultant, added together in each case, represented the independent variable. Of the lead consultants, three measured at the Transforming action-logic, and one measured as in transition between Transforming and Alchemical. Of the CEOs, five measured at the Transforming action-logic, three at Achiever, one at Expert, and one at Diplomat. In this study, the dependent variable—the degree to which the organizations did or did not transform—was quantified for the first time. Based on case studies of each organization (that did not contain any developmental language), three different raters, working independently, rated: (1) the organization’s starting organizational action-logic; (2) whether the organization remained at that same action-logic at the end of the intervention, regressed, or progressively transformed during the consulting period; and (3) how many action-logics the organizations changed. It turned out that the three raters agreed every time (i.e., with perfect 1.0 reliability) on where the organization started and whether it developmentally remained the same (two cases), regressed (one case), or progressed (seven cases). In addition, the three raters agreed in all but one case (which they then adjudicated) on how many action-logics each organization had transformed (.9 initial reliability) (Rooke & Torbert, 1998). The high reliability of the three organizational raters, combined with the high reliability of the psychometric measure of leaders’ action-logics, resulted in a finding that the “later” the combined action-logics of the organizational CEO and lead consultant, the more the organization transformed progressively, a finding that accounted for an extraordinarily high 59 percent of the variance, with less than a .01 probability that the finding was false … and this despite the small n (10) of the study (Torbert, 2013). These statistical outcomes suggest that the combined action-logics of the CEOs and the lead consultants were the single most significant variable in whether an organization transformed. (This finding may be generalizable to organizations of 1,000 or fewer employees, but seems much less likely to be generalizable to larger, Fortune 500-scale companies.) Net-net, these quantitative findings reinforce the power of CDAI as an approach for both understanding and transforming organizations. If a scholar wishes to produce action in addition to knowledge, CDAI offers a proven approach to do so.

CONCLUSIONS AND LIMITATIONS This chapter begins by proposing an experiential philosophy that brings attention not only to the outside world “territory of experience” and the thinking “territory,” but also to two other “territories”: our own embodied action “territory,” which demands our attention if we are to act in a timely and transforming manner; and the post-cognitive consciousness “territory” which we must practice contacting if we are to become aware of the other three “territories” at

296  Handbook of research methods in organizational change the same time and see whether they function congruently in ourselves or in activities such as those suggested in Table 13.1. The second section of the chapter proposes an eight-action-logic developmental process (Table 13.2) through which persons, organizations, and the social sciences can transform toward fourfold awareness and, thereby, toward genuinely timely and transformational guidance of others. The three analogous developmental theories are presented quite differently. The personal development theory is introduced by very abbreviated, sequential descriptions of the action-logics, in the context of the many related theories. The organization development theory is introduced in terms of how I discovered it. And the social scientific development theory is introduced primarily through contrasting the Empirical Positivism paradigm with the CDAI paradigm. The third section of the chapter illustrates how the personal and organizational action-logics can be used in qualitative, first- and second-person action inquiry in a specific organization (the 35-member computer software company) to break a cycle of ineffective action and to support both personal transformation on the part of the two partners and organizational transformation. The fourth section of the chapter describes how both the personal and the organizational theories have become reliable and valid quantitative measures, permitting third-person, Empirical Positivist-like field experiments that have confirmed the real-world effectiveness and emancipatory power of CDAI. But why, you may ask—despite the strong theoretical and empirical support for this theory of organizational change, as well as the paucity of contending developmental theories—is the overall CDAI framework and the theory of organizational action-logics not (yet?!) well known or much used in the field of organizational change research? I can think of five reasons. First, the research supporting this theory has accumulated over a 50-year time period and has been published in widely divergent journals and books. As a result, few scholars are aware of its comprehensiveness (and I myself have only recently demonstrated this comprehensiveness in a single place [Torbert, 2021]). Second, CDAI research has not been reported in so-called “top tier” journals (except for book reviews of Torbert, 1991, in Academy of Management Review, or AMR, and Administrative Science Quarterly, or ASQ) and thus does not get found in today’s typical literature reviews that concentrate on the most recent five-year period of “top tier” journals. Third, the field of organization development evolved in the late 1950s and 1960s, mostly in relation to large, bureaucratically centralized corporations, operating at the Systematic Productivity action-logic, according to the Torbert theory, and seeking to decentralize and work more collaboratively. Thus, the organization development field tended to focus on a single organization transformation and did not immediately seem to need a theory of multiple organizational transformations, in spite of the fact that, as illustrated by the case of the computer software company, 95 percent of organizations are smaller than 1,000 employees and are operating in any one of several different earlier organizational action-logics. Fourth, to become interested in, and effective at, cultivating late action-logic organization transformations, you must be cultivating late action-logic leadership in yourself through a balancing of first-, second-, and third-person research/practice (action inquiry). If you do not start doing so before you complete graduate work, your professional social science training will be in another paradigm. After attaining their PhD, vanishingly few social scientists re-train

Collaborative Developmental Action Inquiry  297 themselves in a new paradigm and in the theories, methods, and practices that embody that paradigm. Fifth and finally, whereas during the 1960s numerous leading doctoral programs in the U.S. (e.g., MIT, Yale, Columbia, Case, Michigan, UCLA) supported action research and organization development to some degree, the management research field has since veered sharply back toward the Empirical Positivist research paradigm. I know of no contemporary PhD program of top-notch reputation where you can find support for developing high-quality, mutually interwoven first-, second-, and third-person research/practices that lead to more timely action. Nevertheless, as the globe struggles so ineffectively with the Covid-19 pandemic, with climate change, with gender and racial violence, and with rampant media-facilitated disinformation, the demand for kinds of inquiry that lead to mutually vulnerable and mutually powerful timely action has never been more immediate.

REFERENCES Alderfer, C. (2011). The Practice of Organizational Diagnosis: Theory and Methods. Oxford University Press. Alexander, C., & Langer, E. (1990). Higher Stages of Human Development. Oxford University Press. Argyris, C. (1962). Interpersonal Competence and Organizational Effectiveness. Dorsey. Argyris, C. (1970). Intervention theory and method. Addison-Wesley. Argyris, C., & Schon, D. (1974). Theory in Practice: Increasing Professional Effectiveness. Jossey-Bass. Argyris, C., Putnam, R., & Smith, D. (1985). Action Science: Concepts, Methods and Skills for Research and Intervention. Jossey-Bass. Bradbury, H. (1998). Learning with the Natural Step: Cooperative Ecological Inquiry. [Unpublished doctoral dissertation]. Boston College. Chestnut Hill MA. Chandler, D., & Torbert, W. (2003). Transforming inquiry and action: 27 flavors of action research. Action Research, 1(2), 133–52. Edwards, M. (2009). Organizational Transformation for Sustainability: An Integral Meta-Theory. Routledge. Erfan, A., & Torbert, W. (2015). Collaborative Developmental Action Inquiry. In H. Bradbury (Ed.) Handbook of Action Research (3rd ed., pp. 64–75). SAGE. Erikson, E. (1959). Identity and the life cycle: Selected papers by Erik H. Erikson. Psychological Issues, 1(1), Monograph 1. International Universities Press. Greiner, L. (1972). Evolution and revolution as organizations grow. Harvard Business Review, 50(4), 37–46. Habermas, J. (1981, 1984). The Theory of Communicative Action (Vol. 1/2). T. McCarthy (trans.). Beacon Press. Kegan, R. (1982). The Evolving Self. Harvard University Press. Kegan, R. (1994). In Over Our Heads: The Mental Demands of Modern Life. Harvard University Press. Kohlberg, L. (1984) The Psychology of Moral Development, Vol. 2: Essays on Moral Development. Harper & Row. Lippitt, G., & Schmidt, W. (1967, November–December). Non-financial crises in organizational development. Harvard Business Review, 102–12. Loevinger, J. (1982). Ego Development. Jossey-Bass. Loevinger, J., & Wessler, R. (1970). Measuring Ego Development (Vols 1 & 2). Jossey-Bass. Mirvis, P. (2006). Revolutions in OD: The new and the new, new things. In J. Gallos (Ed.) Organization Development (pp. 39–88). Jossey-Bass. Reason, P., & Bradbury, H. (2000). Handbook of Action Research. SAGE. Reason, P., & Torbert, W. (2001). The action turn: Toward a transformational social science. Concepts and Transformation, 6(1), 1–37.

298  Handbook of research methods in organizational change Rooke, D., & Torbert, W. (1998). Organizational transformation as a function of CEOs’ developmental stage. Organization Development Journal, 16, 11–28. Rosenberg, A. (2012). Philosophy of Social Science. Westview Press. Steckler, E., & Torbert, W. (2010). Developing the “Developmental Action Inquiry” approach to teaching and action researching: Through integral first-, second-, and third-person methods in education. In S. Esbjorn-Hargens et al. (Eds) Integral Education (pp. 105–26). SUNY Press. Torbert, W. (1972). Learning from Experience: Toward Consciousness. Columbia University Press. Torbert, W. (1975). Pre-bureaucratic and post-bureaucratic stages of organizational development. Interpersonal Development, 1(5), 1–25. Torbert, W. (1976). Creating a Community of Inquiry: Conflict, Collaboration, Transformation. Wiley Interscience. Torbert, W. (1987). Managing the Corporate Dream: Restructuring for Long-Term Success. Dow Jones-Irwin. Torbert, W. (1991). The Power of Balance: Transforming Self, Society and Scientific Inquiry. SAGE. Torbert, W. (2000a). Transforming social science: Integrating quantitative, qualitative, and action research. In F. Sherman & W. Torbert (Eds), Transforming Social Inquiry, Transforming Social Practice: New Paradigms for Crossing the Theory/Practice Divide (pp. 67–91). Kluwer Academic Publishers. Torbert, W. (2000b). The challenge of creating a community of inquiry among scholar-consultants critiquing one another’s theories-in-practice. In F. Sherman & W. Torbert (Eds) Transforming Social Inquiry, Transforming Social Action: New Paradigms for Crossing the Theory/Practice Divide (pp. 161–88). Kluwer Academic Publishers. Torbert, W. (2000c). A developmental approach to social science: A model for analyzing Charles Alexander’s scientific contributions. Journal of Adult Development, 7(4), 255–68. Torbert, W. (2004). Action Inquiry: The Secret of Timely and Transforming Leadership. Jossey-Bass. Torbert, W. (2013). Listening into the dark: An essay testing the validity and efficacy of Collaborative Developmental Action Inquiry for describing and encouraging transformations of self, society, and scientific inquiry. Integral Review, 9(2), 264–99. Torbert, W. (2016). Brief comparison of five developmental measures: The GLP, the LDP, the MAP, the SOI, and the WUSCT. www​.williamrtorbert​.com. Torbert, W. (2017, November). The pragmatic impact on leaders and organizations of interventions based in the collaborative developmental action inquiry approach. Integral Leadership Review, 17(2). Torbert, W. (2021). Numbskull in the Theatre of Inquiry: Transforming Self, Friends, Organizations, and Social Science. Waterside Productions. Torbert, W., & Erfan, A. (2020). Learning for timely action: An introduction to the cybernetics of Collaborative Developmental Action Inquiry (CDAI). Computing and Human Knowing, 27(2), 81–90. Torbert, W., & Fisher, D. (1992). Autobiographical awareness as a catalyst for managerial and organizational development. Managerial Education and Development, 23(3), 184–98. Torbert, W., & Starr, A. (1995). Timely and transforming leadership inquiry and practice: Toward triple-loop awareness. Integral Review, 1(1), 85–97. Wilber, K. (2000). Integral Psychology. Shambhala.

14. Advancing Strong Structuration Theory in organizational change research David B. Szabla and David A. Jarrett

INTRODUCTION Nothing is more frustrating than to take a photo only to realize that your subject was partly out of the frame. Recent calls to researchers (e.g., Kennedy, Gorman & Lee, 2021) indicate that studies in organizations frequently miss part of the subject in just this way. How can this occur? “Much of the research on organizational outcomes is theorized in terms of either the decisions and actions of (rational) managers, or in terms of institutional and contextual factors that constrain and shape managers” (Kennedy et al., 2021, p. 433). Even change researchers who understand that both agency (decisions and actions) and social structure (institutional and contextual factors) contribute to explaining how change occurs and how resistance to change is overcome must still find a way to account for both. Organizational change is complex, and failure rates are high (Stouten, Rousseau & De Cremer, 2018); for change researchers, especially those seeking to influence practice, much is at stake in the inability to capture the whole scene in the frame. Much of what we believe we know in the field of organization development and change is based on the accounts of actors who interpret events through their own eyes. Often, these accounts fail to point to factors in the setting that may have influenced either their choices of actions or the outcomes achieved. Without this richer understanding, we are given a simple formula for intervening to bring about change that may not be reliable across settings or from one consultant to the next. In the physical sciences, it is safe to assume that experiments conducted in one country can be replicated easily in another. The same is not true of change efforts, since they depend upon the interaction of parties who act based upon beliefs that they have developed from experiences that may differ widely. Without understanding the interaction between the setting and the processes used to carry out change, we are missing critical information that we need to understand change successes and failures. Without such information, we may not understand as much about organizational development and change as we think. Fortunately, good theories consider the interaction of structure and agency. Most prominent among them is structuration theory (Giddens, 1979, 1984), which accounts for two domains: social structure, the historical accumulation of organizational beliefs, norms, and interests generated over time through the practices of individuals; and human agency, the day-to-day practices of actors who are guided by the rules and resources established by social structures (see Figure 14.1). Structuration, or the “duality of structure,” occurs when “social structure is both constituted by human agency and yet is at the same time the very medium of this constitution” (Giddens, 1993, pp. 128–9). Neither structure nor agency is given primacy in the explanation. Human agents draw on social structures to act and, at the same time, their actions produce and reproduce social structure. 299

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Figure 14.1

Duality of structure

Unfortunately, despite high conceptual appeal, structuration theory has proven difficult to apply in empirical research (Stones, 2005). This result is not due to lack of effort; change research using structuration theory early on studied discourse (Heracleous & Barrett, 2001; Heracleous & Hendry, 2000), information technology (Furamo & Melcher, 2006; Orlikowski, 1992), strategy (Jarzabkowski, 2008), accounting (Scapens & Roberts, 1993), and other topics (Barley, 1986; Manning, 1982; Pettigrew, 1985; Ranson, Hinings & Greenwood, 1980; Riley, 1983; Roberts & Scapens, 1985; Smith, 1983; Spybey, 1984; Wilmott, 1987). Yet, scholars have found that structuration theory empirically offers insufficiently well-rounded guidance for conducting structuration research (Pozzebon & Pinsonneault, 2005; Whittington, 1992). Conceptually, critics have called attention to the “looseness” of Giddens’ definition of structures as simply rules and resources. Giddens focuses more on rules than resources, for reasons that are not explained, nor does he provide any clear examples of rules and resources (Thompson, 1989, p. 64). In addition, Giddens’ focus on the duality of structure, “where structures exist as memory traces and in the instantiation of practices” (Stones, 2005, p. 52), means that one cannot tell where structure begins and where agency ends and which occurs first, structure or agency. Finally, structuration theory lacks a useful toolkit for bridging the objectivist and subjectivist paradigms in a way that eliminates the compartmentalization of each approach without also eradicating the autonomous logic of each paradigm (Mouzelis, 2000). Recently, however, scholars have been able to research organizational change with an updated version of structuration theory. With his book Structuration Theory, Rob Stones (2005) developed Strong Structuration Theory (SST) by synthesizing responses to the strongest arguments of structuration critics, incorporating related ideas from other theorists,

Advancing Strong Structuration Theory  301 and focusing on how best to apply the theory empirically. In our view, the result strengthens Giddens’ theory, reintroduces epistemological diversity into structuration research, and provides a clear method theory (Lukka & Vinnari, 2014) originating in sociology for conducting organizational change research. In the last few years, a considerable body of empirical studies with SST has emerged. Enough examples of its application to organizational change research are now available which scholars can synthesize and apply to broaden organizational change research. To offer an introductory example, Makrygiannakis and Jack (2016) used SST to examine facets of management accounting change in a Greek hospitality organization in response to the financial crisis of 2008. With their retrospective study, they studied the particulars of how, why, when, and through whom changes in budgeting and control practices occurred over several years. The use of SST revealed that change is endogenously created even if triggered by contextual factors; in other words, the researchers emphasized that change is created by the conduct of agents (human agency) when responding to the institutional pressures of change (social structures). Early responses to the financial crisis revealed that senior management pressed for more consistent application of the existing norms of budgeting practices. However, as agents interacted with the budgeting and control policies at various levels of the organization, they came to criticize and modify existing norms. The changes resulted in an elaborated and advanced use of budgetary control, where budget revisions became proactive rather than reactive. Most structuration researchers provide institutional accounts of change, which do not address how agents draw and reproduce structures in specific settings (Englund et al., 2011). Strong structuration researchers emphasize issues of research design and the specificities of how, why, when, and through whom change may come about (Coad, Jack & Kholeif, 2015; Parker, 2006). They place the focus on “how and why actors tend to bring certain issues and events into processes of structuration” (Englund et al., 2011, p. 508) – they balance the structural conditions of action with action and interaction. To support organizational change researchers in evaluating SST for their studies and using it effectively, we begin this chapter by providing background on structuration theory (Giddens, 1979, 1984). We then examine key theoretical components of strong structuration (Stones, 2005) and their historical roots. We go on to explain strong structuration in detail as a research tool, including the techniques and methods it provides to the organizational change researcher. Along the way, we will introduce examples of how it has been applied to organizational change studies and illustrate the insights it can generate. We will close with comments on the promise of SST for future organizational change research.

STRUCTURATION THEORY Giddens developed structuration theory in the 1970s and 1980s. It is unique for its notion of the “duality of structure.” With structuration theory, Giddens critically confronted the social traditions of the time, which were based on “dualism.” By dualism, Giddens referred to the dominance of either micro-human agency or macro-social structure in social theory, which led to the perception that these aspects of social existence, the micro (agency) and the macro (structure), were opposed to each other (Giddens, 1979, 1984). Giddens addressed this dichotomy with his notion of “duality” – his innovation was “to conceptualize the relation between

302  Handbook of research methods in organizational change structure and agency as a ‘duality’ that is a relation in which neither of the related terms has any independent existence” (J. Parker, 2000, pp. 58–9). Simply stated, Giddens blended agency and structure, emphasizing their co-dependence (Giddens, 1984). For Giddens, mutual dependence of agency and structure begins with humans as individuals, who during social interaction are what the pragmatist philosopher George Herbert Mead (1934) called “objects to themselves” (J. Parker, 2000). In other words, individuals have experiences, but cannot make sense of them on their own; instead, they rely on the rules and judgments (i.e., the structures) of a collective (J. Parker, 2000). Importantly to Giddens, human activity comes first, with accounts of intention occurring during or after the activity (J. Parker, 2000). Thus, “the hermeneutic dimension of social life entails that social structures enter into the knowledgeability, motivation, and intentions of people” (Stones, 2002, p. 223). In other words, what we know, what inspires us, and what we aim for are the result of our historical interactions within the social, cultural, technical, and political contexts in which we are engaged. Structuration theory has been long recognized for its potential in organizational change research. The theory compels researchers to balance their focus on both micro sociological detail and macro institutional-level structures. More specifically, Giddens’ “duality of structure” underlines the interdependency between structure and agency where “structural properties of social systems are both the medium and outcome of the practices they recursively organize” (Giddens, 1984, p. 25). In their conduct, agents draw upon structures, and this “drawing upon” entails reflexive knowledge of the structural contexts in which they engage (Giddens, 1991). For example, in her study on the interaction between organizations and technology, Orlikowski (1992) found that agents often draw upon their own personal knowledge, as well as their conception of the organization’s resources and norms, while at work. In early technology studies, researchers focused on technology as an objective external force that has deterministic impacts on organizational properties such as structure. In later studies, researchers focused on the human aspect of technology, seeing it as an outcome of agent actions. With her research, Orlikowski (1992) took both views into account (objective institutional properties and subjective agency) and built the structuration model of technology. The model illustrates how technology is both the product of human agency and the medium that constrains and facilitates human action. These reflexive actions and insights become institutionalized at the organizational level; they do not need to be perceived at the conscious level to direct or frame activity (Orlikowski, 1992). Organizational change phenomena are not the product of either structure or agency, but of both (Jones & Karsten, 2008). We believe that structuration theory has unrealized potential to explain the dynamics between macro sociological structures and local micro practices in the field of organizational change – it can move organizational change theorizing from exclusive concerns with either micro or macro practices to an approach that emphasizes the co-dependence of both. For example, if we paid attention only to the macro factors affecting firms in the same industry, we would expect all firms in that industry to develop the same strategies in response to competitive pressures; yet because of the idiosyncrasies of their leadership teams, each firm will develop its own strategy that depends on the sense the team make of the threats and opportunities they are facing. In the literature, Giddens’ structuration theory has been explained and assessed by many sociologists (Alexander, 1985; Archer, 1982, 2004; Bourdieu, 1972; Cohen, 1987; J. Parker, 2000; Sewell, 1992), and these analyses can be helpful in providing additional context for Giddens’ theory. The central argument of this

Advancing Strong Structuration Theory  303 chapter, however, is that it is the “strong” version of structuration theory that will be an effective means to achieve equilibrium between agency (the acts of human beings) and structure (the context within which actions are taken) in organizational change research.

STRONG STRUCTURATION THEORY In some respects, SST is simply a more detailed version of Giddens’ structuration theory. By filling in gaps and adding detail, Stones addressed many of the limitations of using structuration theory as a research method in organizational research. But SST does more: in addition to elaborating Giddens’ theory, Stones also critiqued and synthesized the structuration writings of other sociologists, including Pierre Bourdieu, John B. Thompson, Margaret Archer, Nicolai Mouzelis, and William Sewell (Parker, 2006). The result is a robust and rigorous theoretical tool for use in empirical analysis. Like other powerful and sophisticated research tools, SST can appear to be complex on first impression. Gaining a deep understanding of the different components of the theory and their origins allows researchers to use the theory correctly and efficiently. In this section, we review the fundamental theoretical components of strong structuration and how organizational change researchers can apply them to study organizational change. The section will begin with an in-depth examination of three ideas fundamental to SST: duality of structure, its sophisticated ontology, and its incorporation of the earlier concept of position-practices. The remainder of the section will introduce a four-stage cycle, which Stones (2005) called the quadripartite model of structuration that is the heart of SST. We will examine in detail each of the model’s four elements: external structures, internal structures, agency, and outcomes. Duality of Structure The core of SST is the premise of duality of structure, which blends agency and social structure, emphasizing their co-dependence (Giddens, 1984). With SST, Stones corroborates and remains loyal to this central premise of Giddens’ structuration theory – he emphasizes the interdependency between structure and agency, where “structural properties of social systems are both medium and outcome of the practices they recursively organize” (Giddens, 1984, p. 25). Agents draw upon structures in their conduct, and this “drawing upon” involves the agents’ reflexive knowledge of the structural contexts with which they engage (Giddens, 1991). With their research activities, organizational change researchers engage the duality of structure to balance their focus on both micro detail and macro institutional-level structures surrounding an organizational change. In organizational change studies to date, SST enabled researchers to pay attention to the fine-grained connections between objective, macro-level structures and processes, and the structurally situated interactions and subjectivities of agents at the micro level (e.g., Aristidou & Barrett, 2018; Feeney & Pierce, 2016; Greenhalgh, Stones & Swinglehurst, 2014; Makrygiannakis & Jack, 2016; Moore & McPhail, 2016). We take the position that SST has an inherent capacity to account for and explain the dynamics between macro structures and local micro practices. Researchers who use SST can thus move organizational change theorizing from its concern with either micro practices or macro structures to an

304  Handbook of research methods in organizational change explanation that helps researchers to consider how high-level phenomena press down on micro practices, and how micro practices push back on overlooking configurations. Ontology Researchers of organizational change deal with complex phenomena that can be looked at from different angles that yield varied, often conflicting interpretations. In using SST to study organizational change, researchers can make use of SST’s understanding of how people decide what is real. Following Stones’ assertion that strong structuration is ecumenical (Stones & Jack, 2016), we advocate for a “big tent” perspective that allows divergent views. But we believe the SST research thus far suggests that SST is more amenable to certain views than others on how reality is shaped and what its component parts may be. Given that researchers and research questions work more comfortably with some orientations than others, a clear understanding of SST ontology is essential. Ontology is one’s assumptions about the nature of reality: whether objective and given “out there” in the world or the product of individual cognition in one’s own mind (Burrell & Morgan, 1979). Researchers benefit from a clear ontological position because of ontology’s connections to assumptions in epistemology (the grounds of knowledge), human nature (the relationship between human beings and their environment), and methodology (ways in which one investigates and obtains knowledge) (Burrell & Morgan, 1979; Gioia, 2021). In his early work, Stones (1996) described the ontology informing his views as “rich and complex” (p. 1). It built upon many of Giddens’ source materials, including ideas such as ethnomethodology (i.e., the notion that everyday interactions provide stability and regularity such that they form institutions and structures; Garfinkel, 1967); poststructuralism (i.e., the idea that actors are actively engaged in creating their own reality rather than being controlled by an external underlying reality; Belsey, 2002); and linguistic philosophy (i.e., the view that problems can be solved by paying close attention to the use and meaning of everyday language; Waisman, 1965). Stones also drew on the writings of key sociological thinkers. Inspired by the writings of Goffman, Stones viewed social structures as existing “virtually,” making interactions possible and supporting the idea that the subjective meaning of human behavior is based on how actors interpret and respond to the actions of other actors in a social system (Goffman, 1959). Stones drew on Habermas’ lifeworld to instruct his view that resources and contexts provide the background that enables actors to cooperate based on mutual understandings: shared cultural systems of meaning, institutional orders that stabilize patterns of action, and personality structures acquired in family, church, school, etc. (Habermas, 1985). Finally, with his work, Stones brought into play Marx’s assertion that people make history in circumstances not of their own choosing, but in the context of already existing social structures (Marx, [1867–94] 2018). By the time he introduced SST, Stones had focused all this ontological complexity on a single theme, a concern for relevance to practice: “Strong structuration theory seeks to move beyond the abstract philosophical concepts in which Giddens was particularly interested (the ontology-in-general of ‘structures’ and ‘agents’) and explore empirical applications (the ontology-in-situ of particular structures and agents)” (Greenhalgh & Stones, 2010, p. 1288). The result can be fairly described as subtle and nuanced: “Rob Stones (1996) advocates what he calls a past-modernist realism, which comes somewhere between what he calls defeatist post-modernism and sociological modernism” (O’Reilly, 2012, p. 58). To Stones (1996),

Advancing Strong Structuration Theory  305 defeatist post-modernism expressed hopelessness about the ability to make comparative judgments about the quality of different knowledge claims, while sociological modernism both underestimated the complexity of the social world and overestimated the ability of sociologists to obtain accurate knowledge about that world. In his view, for example, giving a comparatively useful explanation of the actions of an international business executive or government official is possible, but it takes a discriminating focus on a finite number of points on the historical and geographical landscape and offers no assurance of infallibility (Stones, 2005). Rejecting the relativism of post-modernism (Stones & Jack, 2016), Stones (1996) believed that the consequences of his position were that research could and should be “ontologically bold and epistemologically cautious” (p. 64, italics in original). For change research, this phrase means that situation-specific knowledge of actors is a reality to be examined; yet the ability of a researcher to apprehend this knowledge is incomplete. While SST ontology is complicated (Stones & Jack, 2016), it achieves empirical relevance, explaining how a study participant’s reality is shaped and what the component parts of that reality are, through three closely related concepts: the emphasis on ontology-in-situ (making meaning in the moment), a sliding scale of ontological analysis (from abstract to concrete), and a viewpoint at the meso level of analysis (one level above the system being studied) (Parker, 2006). Emphasis on ontology-in-situ Differentiating ontology-in-situ (i.e., the ontology of particular social occurrences at certain moments in time) from ontology-in-general (i.e., the abstract, philosophical level of ontology) allows researchers to consider what is real, not only in a general sense of one’s beliefs about reality, but also in specific situations (Stones, 2005). By definition, ontology-in-situ informs analysis directed toward specific processes involving structures, agents, contexts, and events situated in particular times and places. Referring back to Giddens, Stones (2005) viewed the translation from ontology-in-general to ontology-in-situ to investigate and clarify the relationship between ontology-in-general and the tangible world. To adapt a phrase more often applied to politics, ontology-in-situ helps organizational change researchers account for the fact that “all reality is local.” During research, ontology-in-situ enables scholars to face local variability in a systematic manner (Stones, 2005). Local variability can appear in many forms. Not all actors in the same situation will have the same degree of knowledgeability about that situation. Likewise, actors facing the same situation will experience different position-practice influences in how they respond to the situation. The seven types of in-situ variability appear in Table 14.1, along with their relation to organizational change generally, and their specific appearance in a study of management accounting change by Makrygiannakis and Jack (2016). To use Table 14.1 to best advantage, the researcher developing a study can examine the research site and research question and determine which forms of variability are most likely to be present, and how they might appear. For example, the researcher may determine at an early stage that unusual emotional intensity is present about the change event in scope for the study. In Table 14.1, the second column indicates that emotional intensity in change events is associated with at least three reactions: resistance, ambivalence, and engagement. The researcher will be interested to know which of these three reactions are present and which are absent, which are strongest, and who is experiencing which reaction. Perhaps most importantly, the researcher can examine both the agency of the various actors and the structural influences that may be driving the local variability among these reactions. What Stones (2005) has done in

306  Handbook of research methods in organizational change Table 14.1

Ontology-in-situ in organizational change research

Form of variability

Relation to organizational change

Illustration from study by Makrygiannakis

Degree and type of critical reflection

Willingness (or not) to see a need for

Triggered by the financial crisis, leaders

change based on the evolution of external

in hospitality organizations came to

conditions

criticize the way they practice budgeting

Number of relevant intended and

Whether a change has a single objective

Budgets enabled planning and cost

unintended consequences of action

or more than one that must be balanced in

control in conditions of stability, but

terms of attention and priority

slowed adaptation to new circumstances

and Jack (2016)

until revised Degree of emotional intensity attached to

Resistance or ambivalence (Piderit, 2000)

Initial loyalty to past practices remained

particular consequences

toward the intended change outcome;

until organizations became unprofitable;

engagement from change leaders and

later, actors were willing to simplify

supporters

practices quickly to restore profitability

Extent to which consequences are

Actions or intermediate outcomes not

Loosening of quality and inventory

unexpected

planned in the change initiative that give

control procedures coinciding with

rise to other effects on the organization

a reduction in staff allocated to budgeting

Extent of post hoc ability to understand

Confusion or clarity about why change

Failure to return to profitability after

causes of consequences

projects have unfolded as they did,

a first round of actions was understood

especially in terms of change failure

clearly as a reading of the economic environment as more positive than appropriate

Extent to which consequences enable

Degree to which norms, power, or

Involvement of more people at lower

structural elaboration or preservation

meaning are altered in the aftermath of

levels in the budgeting process increased

a change event

as the depth of analysis expanded

Extent of external structural resistance to

Headwinds created by other initiatives or

Increased need for accuracy to support

an agent’s project

organizational conditions toward change

debt service mitigated against efforts to

project

simplify or cut cost in budgeting activities

Note: The first column is based on Stones (2005), p. 80.

identifying emotional intensity as a form of local variability is to shine a spotlight on this condition – as well as the other six listed in Table 14.1. Knowing what to look for is a considerable advantage for the organizational change researcher. Sliding scale of ontological analysis In introducing SST, Stones (2005) described its ontology as occurring on a sliding scale among three levels: abstract, meso, and ontic. With the image of a sliding scale, Stones (2005) invites change researchers to build a bridge between the most general conditions and the most specific actions, then move smoothly (slide) back and forth across that bridge as studies unfold. The abstract, or philosophical, level transcends organizations to include large forces (Stones, 2005) such as national and international social systems (Feeney & Pierce, 2016) or societal values that inform organizational change situations. For example, Greenhalgh et al. (2014) examined change resistance to a new online booking system in health care by beginning with national-level policy of hospital choice for all patients and the abstract notion of voice, exit, or loyalty (Hirschman, 1970) on which it rested. More generally, Stones (2005) believed that researchers should draw on abstract ontological categories first, then attempt to discern exactly what agents intended and how knowledgeable they were about the circumstances in which

Advancing Strong Structuration Theory  307 they acted. The idea that agents are knowledgeable and purposive is an example of an abstract notion of ontology (Stones, 2005) that is relevant in change research. From this abstract knowledge, researchers can safely assume that organizational participants are not merely carried along in the organization’s flow, but that they influence their own experiences and can articulate how they do it. Especially in change research, scholars typically will be guided by abstract-level concepts in their design of research. One value of researching with SST is that empirical data will then in turn influence concepts, regardless of whether the data is gathered directly from participants as they reflect on their lived or observed experience, or through the researcher’s direct knowledge of the context and direct observation of the research site (see Figure 14.2). In practice, change researchers want to understand the choices that led to change success or failure. Although the same interventions (e.g., survey feedback) may be used in different organizations, the reasons for choosing each intervention and the forces affecting the results achieved may be entirely different.

Figure 14.2

Research perspective on SST ontology in change research

308  Handbook of research methods in organizational change This influence of research data on informing and elaborating concepts begins at the scale’s other end, where the ontic level consists of concrete, deeply contextualized (Jack & Kholeif, 2008), or empirically informed specific realities, including substantive details (Stones, 2005). In research, the ontic is the level of empirical evidence, while the abstract level determines what evidence is admissible or relevant (Stones, 2005). While some researchers describe the ontic level broadly as practice (Mutiganda & Järvinen, 2021) or narrowly as a micro-level focus on individuals (Daff & Jack, 2018), most characterize it as pointing to particular, concrete and/or situated entities having distinctive qualities, relations, and appearance (Kholeif & Jack, 2019). For example, in the Moore & McPhail (2016) study of how organizations adapted to a new carbon pricing regime, abstract ideas of economics theory and climate change placed data about energy avoidance and cost tracking squarely in the frame (see Table 14.2). Specifically, the authors assessed government policy changes and new technologies as the abstract realities Table 14.2 Level

Strong Structuration Theory sliding scale of ontological analysis

Definition

General examples

Examples in organizational

Application in change

change

study: Responses to new carbon pricing in Australia (Moore & McPhail, 2016)

Macro

“Large forces of history”

Society-level structures of

Change in regulatory

Passage of the Clean Energy

(abstract)

(Stones, 2005, p. 190)

health care, employment,

environment, technology

Act, 2011; Parliament’s

such as national and

housing markets, or military breakthrough with broad

Joint Select Committee

international social

escalation

scope

hearings; economics theory

systems (Feeney & Pierce,

and policy; climate change

2016) or cultural values

and government policy responses; carbon-intensive technologies for production

Meso

“Floating” (Stones,

Organization field-level

Merger or acquisition

of drinking water Water industry

1996, p. 77) over the

policy or practice responses

activity as response to

Sustainability Task

organizational field (Jack

to changes in social,

industry consolidation

Group position-practices,

& Kholeif, 2008) “to

economic, or political

carbon trading schemes,

identify relative variations

conditions

development of greenhouse

in the ontic manifestations

emission profiles, the direct

of general ontological

impact on customers of any

concepts” (Stones, 2005,

restrictions, industry-level

Micro

p. 78) Concrete, deeply

Observable details of

Staffing to lead change

emission target setting An individual firm’s

(ontic)

contextualized (Jack

an individual or group

initiatives, action steps

“greenhouse strategy”

& Kholeif, 2008), or

experience of job loss,

during change projects

(p. 1222), including

empirically informed

illness, homelessness, or

measuring its emissions

specific realities, including military service

footprint, energy avoidance

substantive details

and reduction practices,

(Stones, 2005)

employee education and socialization steps, cost tracking actions, and results demonstrations

Note: Higher-level concepts guide lower-level research, which recursively elaborates higher-level concepts (Stones, 2005, pp. 76–8).

Advancing Strong Structuration Theory  309 that led to individual firms’ ontic experiences, such as emissions measurement, employee education, and demonstration projects. In such a study, interviewing organizational participants yields ontic data, especially about the unintended consequences of individual action (Stones, 2005). More generally, the abstract level tells us what forces the organization is responding to, the meso level tells us how the organization typically responds, and the micro level tells us how individual actors or individual actions impact the outcomes of change. At the meso level of analysis Standing between the abstract and ontic levels, the meso level is the apex of the bridge between them and the best vantage point for change researchers wishing to survey the whole landscape. In that sense, it floats (Stones, 1996) over the organizational field (Jack & Kholeif, 2008) such that researchers can identify varied ontic manifestations of abstract ontological concepts while keeping the necessary logical relationships in view (Stones, 2005). Change researchers working at the meso level gain a clear perspective on the context that shapes individual actions and the outcomes that result from them. But for a study to qualify as structurational, in Stones’ view, analysis must include both hermeneutic and structural dimensions. By “hermeneutic,” Stones (2005) refers to agents’ frames of meaning; by “structural,” he means the forces past and present that establish or preserve these frames. Thus, the meso level is where interpretation of participant accounts will occur, largely in a middle zone of temporality where position-practices – positional identities such as qualifications, attributes, and the methods by which their associated prerogatives and obligations become evident – mark the terrain (Stones, 2005). To give examples, Greenhalgh et al. (2016) examined from a socio-material perspective the advantages and limitations of remote video consultations between National Health Service clinicians and patients. In their analysis, the authors recognized patterns of behavior in technology use (“scripts”) as a mediating or meso-level concept expressing the organizational routines and logics influencing the use and acceptance of the video consultations (p. 4). In a Finnish study, Mutiganda and Järvinen (2021) investigated how political accountability might stabilize when agents are faced with changes in external structures such as competition laws and austerity policies. The authors examined accountability mechanisms developed at the meso level as political and managerial processes, such as board meetings and the agenda, motion, and voting processes they entailed. In studying resistance to change, Kholeif and Jack (2019) found that middle managers in Egypt tasked with implementing performance-based budgeting acted with local senior management and lawmakers in government as relevant meso structures. Less typically, Moore and McPhail (2016) positioned the Victorian water industry in Australia at the meso level between carbon measurement policy (macro) and the organizations (micro) comprising the industry (Coad, Jack & Kholeif, 2015). Position-practices Situated within the meso level, or referred to by Stones as the intermediate zone, is another critical element of SST – position-practices (Stones, 1996). Ira J. Cohen, sociologist and historian of social thought, observed that Giddens did not fully develop an institutional link between structure and agency (Cohen, 1989). The closest Giddens came was the concept of social position to encompass the identity, prerogatives, and obligations on the part of individuals with specific roles in organizations, such as the chief executive officer (CEO). Cohen

310  Handbook of research methods in organizational change (1989) argued that Giddens failed to explain how these roles were reproduced in the duality of structure, as Giddens’ notion of social position ignored the capability of individuals to adapt to, modify, or vacate assigned roles. In addition, Giddens never addressed the “ghosts” of networked others that continually inform action (Thrift, 1996, p. 54); i.e., people and practices not present, but influential to the actions of agents. Thus, Cohen put forth the notion that a social position is more accurately viewed “as the institutional link agents encounter as structured practices” and the means by which practices are reproduced to contribute to the perpetuation of structure (Cohen, 1989, p. 210). In his SST, Stones replaced Giddens’ prior notion of social position (Stones, 2005) with the concept of position-practices advanced by Cohen. Position-practices include not only a positional identity as described by Giddens, but a cluster of acknowledged practices through which that identity is made manifest, in addition to a range of other, related practices through which the position is interrelated (Cohen, 1989). Agents-in-focus are conceptualized as being caught up and acting in the flow of position-practices and their relations of networked others who may be present or absent from the interactional conduct (Stones, 2005).

Figure 14.3

Position-practices

The concept of position-practices offers organizational change researchers insight into the complex social relations that comprise an organizational change (see Figure 14.3). The events and practices of an organizational change are understood within a stream of position-practices and their networks of relations – change agents and change recipients are entrenched in a complex web of position-practice relations, both local and historical. Within the interme-

Advancing Strong Structuration Theory  311 diate zone, organizational change researchers can investigate networks and relationships between clusters of agents within the delimited landscape of the change they are studying; i.e., an organizational field, an organization, or a department. By studying position-practices, researchers can build a “theorized contextual frame” relevant to the agent-in-focus (Stones & Tangsupvattana, 2012, p. 223). Researchers using SST examine the degree to which those involved in an organizational change are knowledgeable of their social positions and the social positions and networks of practices surrounding them. By uncovering and understanding this knowledge, researchers can learn how agency is carried out and how structures are reproduced. Thus, position-practices “can serve as a more robust link between structure and institutionalized modes of conduct” (Cohen, 1989, p. 209). Organizational change researchers can “map out” position-practice relations (Cohen, 1989, p. 211) to gain a deep understanding of the intermediate zone of an organizational change. Vertical hierarchical relations among employees and the horizontal relations between clusters of agents in an organization can be traced out to understand how these relations affect an organizational change. As an example, department chairs of universities are engaged in complex relationships – vertically with deans upwards, faculty downwards, and other chairs horizontally. To the extent that these relationships are shaped by expectations of the positions, they comprise position-practice relations. During an organizational change, department chairs account for their unique set of position-practices when participating in an organizational change. In addition, they consider their role as chair, which implies certain responsibilities and norms that are commensurate with how chairs are socially perceived. Social positions emerge over time as previous incumbents establish practices. The duties and conduct that mark the position of department chair are the results of actors who take on the role and reproduce certain conduct associated with the role. By investigating the obligations and power of organizational roles involved in an organizational change, and by mapping those roles to the clusters of position-practices of others engaged in the organizational change, researchers can gain a deep understanding of how positions and practices influence the structuring of an organizational change. The notion of position-practices more clearly sensitizes how agents, situated in time and space, draw upon their knowledge of situated practices when engaged in an organizational change. This kind of analysis can be important in explaining why a change agent might choose an approach that is being followed by others in her role even though she has a sense that the approach will not work in her unit of the organization. Quadripartite Cycle of Structuration If SST offers organizational change researchers “duality of structure” as an organizing principle, ontology-in-situ as a method to examine levels of change, and position-practices to understand complex network relations, then the quadripartite cycle of structuration provides a framework to investigate structuration; i.e., how organizational change is created, reproduced, or changed through the interaction of organizational structures and individual agency. Analytically, it is distinguished by four separate but interlinked elements (see Figure 14.4): (1) external structures (conditions of action), (2) internal structures (dispositional and conjuncturally-specific knowledge within agents), (3) active agency (ways in which agents routinely or strategically draw upon their internal structures and act), and (4) outcomes (changes to or the reproduction and preservation of external structures) (Stones, 2005). The elements work in a temporal sequence with internal and external structures occurring at T1 and agency

312  Handbook of research methods in organizational change occurring at T2 (Parker, 2006). According to Stones, structuration starts when agents engage and begin to interpret the conjuncture between external and internal structures.

Source: Stones (2005), p. 85.

Figure 14.4

Quadripartite nature of structuration

For example, Greenhalgh et al. (2014) examined the introduction of a technology (Choose and Book) intended to help English general practitioners and patients book hospital outpatient appointments. In this situation, the relevant external structures included the economic context, political authority, and what the authors described as medicine’s “internal goods,” or themes of caring, curing, and comforting (p. 213). The participants’ internal structures included the practitioners’ professional identity, values, and morals; administrative staff’s perceptions about the definition of good work; and the skills and techniques for using the technology. Further social structures were inscribed in the Choose and Book technology: the restricted nature of choices, assumptions about the practitioner’s role, and financial influences. The Greenhalgh et al. (2014) study recounted the structuration process that began when agents engaged in the conjuncture between the political and economic pressure to optimize facility use and the values that prioritized patient convenience (booking a facility nearby) and elevated practitioner judgment in selecting a facility well suited to treat the patient. Participants exercised agency in four clinics in varied ways, resulting in both acceptance rates of Choose and

Advancing Strong Structuration Theory  313 Book ranging from 0 percent to 90 percent and new structural influences at each clinic that shaped future usage or non-usage of the technology (Greenhalgh et al., 2014). External structures Stones (2005) described external structures as independent forces and pressuring conditions that limit the freedom of agents to do otherwise. In organizational change, they are the conditions of action, which pose constraints and provide resources and possibilities for action toward the change (Greenhalgh et al., 2014). They exist autonomously from agents-in-focus in two forms. The first form, independent causal influences, includes forces autonomous from agents which agents have no physical capacity to control or resist. They are completely independent of the agents and are constituted, reproduced, or changed apart from the agents-in-focus, without their agreement and irrespective of their needs and desires. They typically exist outside organizations and include, for example, structures of employment, healthcare regulations, housing markets, and military escalation (Jack & Kholeif, 2007; Stones, 2005). Researchers studying organizational change will find that these influences explain the larger context of an organizational change, leading to insights into agent action. Examples during change include health care policy (Greenhalgh et al., 2014), regulations (Kholeif & Jack, 2019), elected and public officials, governance boards (Mutiganda & Järvinen, 2021), and shifts in labor power (Elmassri, Harris & Carter, 2016). The second form of external structure is irresistible causal forces, those forces that agents feel they cannot control or resist, but which they do have the capacity to resist or change in certain circumstances. For example, while a soldier is theoretically capable of disobeying an officer’s unjust order, the solder may not feel free to resist for reasons of training, identity, or previous experience; the officer’s order has become an irresistible causal force to the soldier. Stones (2005) argued that agents can choose to oppose or modify these structures if they possess three properties: ample power to resist, satisfactory knowledge of external structures, and adequate critical reflexive distance from the action. Irresistible causal forces include, as examples, IT systems (Kholeif & Jack, 2019), financial reporting requirements (Coad & Herbert, 2009), and industry practices (Moore & McPhail, 2016), all of which can be changed if agents have power, knowledge, and a sufficient sense of agency. Position-practices and their networked relations are included within external structural conditions as networked others are involved in the duality of structure. Stones (2005) adopted Cohen’s conceptualization of position-practices, which enables researchers to examine the enactment of identities, prerogatives, and obligations as they connect structure and agency. Being a CEO, for example, is not only a positional identity, but also a set of structured practices that position incumbents perform. For organizational change researchers, Stones brings attention to the notion that how agents act during an organizational change depends on the structured position-practices of others in the network; in other words, practices being structured during a current organizational change and those structured during previous organizational changes. Stones refers to these earlier structured practices as “the ghosts of networked others that continually inform action” (Thrift, 1996, p. 54). The notion of position-practices as a structure external to the individual agent is relevant to organizational change research as clusters of actors within the organizational field, the relations between them, and the “ghosts” of past and present impact organizational change outcomes. Thus, position-practices and independent and irresistible forces represent the agent’s perceived context of action and comprise the external influencing forces that hinder or facilitate

314  Handbook of research methods in organizational change organizational change. Although these structures exist autonomously from agents, in SST they are seen through the agent’s eyes, not perceived as simply external to agents. While researchers independently may have partial visibility to systemic forces that influence agents, Stones (2005) insists that these forces are external structures only to the extent that they are on the action-horizon of agents as perceived by the agent and/or the researcher. Naturally, one possible outcome of any organizational change is to modify these external structures and alter their influence on later actions. Internal structures Internal structures reside within agents and represent what individuals know. They are divided diagnostically into two structures: general-dispositional knowledge and conjuncturally-specific knowledge. Agents draw on both structures to act in specific change situations. General-dispositional General dispositions encompass skills, ambitions, attitudes, and personal morals, collectively what Bourdieu (1986) referred to as habitus. According to Stones (2005, p. 88), general dispositions include transposable skills and dispositions, which include generalized worldviews; cultural schemas; classifications; typification of things, people and networks; principles and patterns of action; frameworks of signification; associations related to discourse, and habits of speech and gesture.

In this view, agents adapt their generalized knowledge to a range of practices at specific times and in certain places. Greenhalgh et al. (2014) referred to dispositional knowledge as “durable and deeply socialized aspects of embodied skills, culture, moral values and principles, and so on, built up over time as an actor is exposed to, and interacts with, their social contexts” (p. 213). Here, the emphasis in on aspects of taken-for-granted internal media that agents draw on across specific change situations without thinking (Stones, 2005). In the quadripartite cycle, this stage is where external structures are interpreted in the context of agents’ worldviews, attitudes, and values (Stones, 2009), which were formed through socialization and education (Malsch, Gendron & Grazzini, 2011). Participants experiencing an organizational change draw on their unquestioned habitus when interpreting the external structures relevant to a change, typically without realizing they are basing their reactions on these forces. For example, in the previously mentioned study that explored responses to the Choose and Book system, physicians incorporated their morals, values, and professional identity – but often not consciously – when responding to the new technology (Greenhalgh et al., 2014). In some change situations, these taken-for-granted responses are questioned and disrupted, making conscious dispositions that were previously pre-reflexive open for reflection and discussion. In a study that investigated a change in management accounting practices, the worldview that accountants only see numbers was disrupted as accountants began to see the broader context and work as business partners (Coad & Herbert, 2009). Organizational development and change scholar-practitioners rely on skills such as self-as-instrument, process consultation, coaching, and knowledge of interventions to make decisions about how to tackle organizational change. Often, well-honed skills are applied without conscious thought as change agents rely on their experience to guide them.

Advancing Strong Structuration Theory  315 Examining the application of general-dispositional knowledge can help both scholars and practitioners understand why a change agent chooses one intervention over another. Conjuncturally-specific Conjuncturally-specific knowledge is the agent’s comprehension of the situated context of action that emerges at the conjuncture between external and internal structures. More specifically, the conjuncture is a critical combination of events and circumstances in which human agents draw on both habits (i.e., internal dispositions, beliefs, values, and norms) and knowledge of the here-and-now situation (i.e., assessment of the particular, strategic terrain and how they are expected to act within it) (Greenhalgh et al., 2014). It is knowledge of particular contexts (Stones, 2005) – for example, knowledge among actors of the conditions of an organizational change. While agents make sense of and act on conjuncturally-specific knowledge based on their general-dispositional knowledge, the two are analytically different. In the context of organizational change, conjuncturally-specific knowledge comprises the explicit knowledge of the who, what, where, when, and how of the change: the explicit content of the time-place situated context of the change. For example, in the case of resistance to Choose and Book, conjuncturally-specific knowledge included the perceptions of administrators of the new technology; i.e., whether they had sufficient knowledge of the technology to work effectively (Greenhalgh et al., 2014). Stones (2005) added that conjuncturally-specific knowledge not only is knowledge gained about the conditions and context of the immediate situation, but also may be enduring knowledge of external structures that have been built up over time. Conjuncturally-specific knowledge can be analytically organized into three interrelated aspects of external structures: knowledge of interpretive schemes, power capacities, and the normative expectations and principles of agents within the context. First, knowledge of interpretive schemes comprises understanding of how particular positioned agents in a situation interpret the actions and expressions of others and draw their conclusions and action-informing interpretations (Stones, 2005). Knowledge of interpretive schemes draws attention to the importance of how agents-in-focus not only draw on their own conjuncturally-specific and dispositional knowledge to react to a change, but also consider the behaviors and articulations of others. Second, knowledge of power capacities includes the understanding agents-in-focus have of how agents in the change situation view their own power capacities. Agents-in-focus consider capacities on a relational basis: who they depend on for power resources, and what type and how many power resources other actors in the context command. These questions are answered given the perspective of their interpretative schemes. Third, knowledge of normative expectations is an understanding of how agents within the context are likely to behave in the change situation. According to Stones (2005), agents-in-focus gather this knowledge from their perceptions of the fit or tension between (1) normative beliefs from within their general dispositions about how they should act and (2) pressures to act in certain ways that are specific to the situation. By examining internal structures, organizational change researchers learn how agents draw on their general dispositions – i.e., their habitus of values, beliefs, and norms – and their knowledge of the here-and-now situation; i.e., their assessment of the strategic terrain of the organizational change and how they are expected to act within it (Greenhalgh et al., 2014, p. 214). In their study of political accountability, Mutiganda and Järvinen (2021) found that external structures impact internal structures, and when these structures are seen as irresistible, accountability is low. This outcome then impacts both the internal and external structures of

316  Handbook of research methods in organizational change the organization (the micro, meso, and macro environments) as agents adjust their activities by drawing on structures that could facilitate strategic actions during a continuous process of structuration (Mutiganda & Järvinen, 2021, p. 82). Agency Agency, the third component of the of the quadripartite cycle, is the “active, dynamic moment of structuration” (Stones, 2005, p. 86). In the quadripartite cycle, agency reveals the ways in which agents draw on their internal structures and thus apply their dispositions and knowledge to the situation. The focus here is on observable behavior when “the agent-in-focus actively and more or less reflexively and creatively draws upon internal structures” (Greenhalgh & Stones, 2010, p. 1288) and “chooses to act in order to confront his external structures” (Feeney & Pierce, 2016, p. 1156). Simply stated, this is when an agent engages in purposive action. She consciously or unconsciously draws on her knowledge of interpretive schemes, power capacities, and normative expectations of agents in a particular context and forms “action-informing interpretations” (Stones, 2005, p. 91) upon which to act. Such interpretations involve the interplay between general dispositions that are embedded in the agent’s memories and knowledge of the situated context, including position-practices. In this approach, researchers should conceptualize agents as being caught up in the ebb and flow of position-practice relations (Stones, 2005). Outcomes The final component of the quadripartite model of structuration, outcomes, includes “the effects of actions and interactions on both external and internal structures, as well as all other kinds of outcomes” (Stones, 2005, p. 85). Jack and Kholeif (2007) described the effects of outcomes: “structures may be changed or preserved, consequences may be intended or unintended, and the agent may be facilitated or frustrated” (p. 215). As examples, traditional command and control systems survive (unintended) (Kholeif & Jack, 2019); a new technology is not used, and the change is a failure (unintended) (Greenhalgh et al., 2014); or new financial policies and practices are achieved (intended) (Moore & McPhail, 2016). Coad and Herbert (2009) believed that when structures are affected by outcomes, they are “changed through learning processes, and in turn provide the basis for subsequent action” (p. 180). Stones (2005) indicated that not only are outcomes intended or unintended and agents successful or unsuccessful, but also structures may be sustained or altered. This final component focuses change researchers’ attention on the transformation or reproduction of both the internal and external structures that exist in an organizational change setting. Understanding how alterations or replications of internal structures (i.e., dispositional and conjuncturally-specific knowledge), and external structures (i.e., position-practices, and social, technical, political, and cultural structures) occur can help researchers to bring new insights into the change process. In summary, the quadripartite model of structuration allows change researchers to first isolate and then re-integrate the four crucial components of any participant’s role in organizational change: external structures, internal structures, agency, and outcomes. It recognizes that external and internal structures are both the medium of agents’ conduct and its result, through either reproduction or alteration. The macro structures that each participant draws on, however, are not all alike, and organizing analysis of an agent’s inner world between external structures (whether independent or irresistible causal forces) and internal structures (whether

Advancing Strong Structuration Theory  317 general dispositions or conjuncturally-specific knowledge) offers a comprehensive way to examine both the limits and possibilities on the conduct of actors as they consider the agency they will exercise. In this view, action is intertwined with the other model components. While agency is the dynamic aspect of the framework that is easiest to view, the quadripartite model, as a whole, provides a portal to not only the social practices observed, but also the forces animating those practices. As these structures follow ideological, identity, and cultural schemata, SST allows the researcher to move from the visible to the invisible and thus examine both agency and structure during change.

CONDUCTING RESEARCH USING SST Research based on SST typically produces qualitative studies presented in case study format (Stones, 2005). In general, case studies are appropriate for research questions asking “how” or “why” about a contemporary set of events over which the researcher has little control (Yin, 2014). Researchers study events in natural settings, interpreting them in terms of the meanings people bring to them (Merriam & Tisdell, 2016). Even though case study research emphasizes plausibility and the currency of results over statistical significance or data integrity (Smith, 1990), steps to assure trustworthiness are still essential (Johnson, 1997). Every case study reflects choices along six dimensions: epistemology, defining the case, degree of planning, data collection, data analysis, and reliability or validity (Yazan, 2015). The use of SST implies preferences along the six dimensions. As the earlier epistemology discussion already examined the first dimension, the influence of SST on the other five dimensions will appear in the paragraphs that follow. Defining the Case Most guidance on research case studies emphasizes bounding the case study, which means defining what activity is in scope (e.g., Merriam & Tisdell, 2016; Yin, 2014), a question pertinent to researchers investigating organizational change. Studies with SST tend to feature very broad sets of actors and conditions, as the theory describes internal and external influences with extensive reach. Internal influences, for example, include general dispositions built up over time, requiring examination of each actor’s past to surface the phenomenological perspective by which they frame and perceive events (Stones, 2005). Likewise, external influences affect agents’ wants, desires, and conduct; these too must be examined to discern the interplay of agency and structure. To illustrate, in the case study of a new technology designed to help physicians and patients book appointments, the scope included four sites, computer use in general practice, health care choice policy, socio-material constraints, doctors’ contextual judgments, and changes in social relations (Greenhalgh et al., 2014). In a study of Egyptian investment decisions during extreme change, the scope included investment managers, such non-financial considerations as shifts in labor power, and depressed opportunity for mergers (Elmassri et al., 2016). In a study of a new enterprise resource planning system, the scope included European Union and national government officials, the role of management accountants, and the expectations of other organizational functions (Jack & Kholeif, 2008).

318  Handbook of research methods in organizational change Taken together, these three studies also illustrate another question often faced in defining case study research for organizational change researchers: whether to employ a single-site or multiple-site case strategy. While two of the three studies above featured participants in dispersed locations, the studies all present as single-site studies in terms of integrating data and in the horizon for planning, defined by Stones (2005) as the context of relevance to the agents-in-focus. Degree of Planning Although case studies vary in their degree of design planning (Yazan, 2015), in an SST study one early decision is crucial: whether to use previously collected data or to collect new data. Early SST studies often reanalyzed data acquired by other means (e.g., Coad & Herbert, 2009; Greenhalgh & Stones, 2010). The authors of both cited studies, however, found the practice limiting and recommended incorporating SST into the research design and collecting data with SST to mind. For example, Greenhalgh and Stones (2010) wrote: “Data sources may be multiple and selected pragmatically (e.g., depending on access and availability) and include combinations of documents, ethnographic field notes, semi-structured and other forms of interviews and surveys, and multi-media data such as video or screen capture” (p. 1289). Researchers who take this advice will benefit from a clear interview protocol and initial participant selection. While many approaches to planning and conducting research interviews exist, we have found Seidman’s (1991) three-interview protocol to be an excellent guide to the technique and relationship requirements of SST-informed interviewing. While the three-interview protocol Seidman (1991) recommends is ideal, participants may be less willing to participate in a third interview unless clear value is evident to the participant for a third interview, such as discussion of preliminary findings. Otherwise, a two-interview protocol (no longer than 90 minutes and 60 minutes, respectively) is more practical. Question protocols are used in various ways in planning SST studies. Greenhalgh and Stones (2010) used questions to guide their study of an unfolding technology project, preparing questions in several categories: macro-level, micro-level (focused on specific conjunctures), actors’ internal structures, active agency, outcomes, and policy implications. For example, one macro-level question was: “What is the prevailing political, economic, technological and institutional context within which the technology is being introduced locally or nationally?” (p. 1291). Aristidou and Barrett (2018), on the other hand, planned for questions to be asked verbatim to participants in their study of dynamic service settings. For example, they asked mental health providers: “Can you tell us what you do when you deliver mental health care to your patients?” (p. 692, italics in original). Note that in both cases questions are broad; the plans leave room for the study to evolve with the data and for participants to tell their stories freely and completely. Data Collection For data collected specifically for the study, interviews of participants and researcher observations are common links to physical or virtual data such as e-mails, products, technical documents, and other artifacts (Schein, 2017) that inform the research question. Given the detailed nature of SST data analysis, with participant consent we advise recording interviews, obtaining copies of virtual documents, and collecting photographs of physical objects. Because SST

Advancing Strong Structuration Theory  319 interviews can be reflective, it is also good practice to allow participants to review interview transcripts and clarify their statements, as necessary. The nature of case study research in general and SST research in particular means that the meaning of events and statements may not be clear at the time of data collection. Our experience is that significant meaning making occurs within the individual between the first and second interview, consistent with the Seidman (1991) model expectations. In both first and second interviews, researchers can be alert to who else is identified. To the extent that access to these named individuals is possible, forms of sampling that occur after the study begins, such as snowball sampling (Savin-Baden & Major, 2013), are especially profitable. For example, in their study of new product introduction, Feeney and Pierce (2016) described an emergent approach to data collection. Following exploratory initial interviews and review of extensive internal company documentations, the authors decided on a case study that encompassed two operating units, developing an interview list of finance and operations leaders in each unit. As the interviews progressed, participants provided further archival data; the researchers also decided that interviews with corresponding personnel at the parent company were necessary. Given limited prior studies on the topic, the interview guides built directly on the various elements of the quadripartite model, although the model was not explained to the participants. In conducting interviews, Feeney and Pierce (2016) sought to understand the internal and external structures at play and to explore the role of accounting information – central to their research question – from the participant point of view. Data Analysis A challenge in qualitative studies is to develop an appropriate level of bracketing, in which researcher assumptions are temporarily set aside (Merriam & Tisdell, 2016). When considering structure and agency, an additional layer of bracketing is needed to set one aspect aside while evaluating the other. In SST, bracketing is used to maintain focus on either the conduct or context of agents, by means of agent’s conduct analysis and agent’s context analysis (Stones, 2005). Agent’s conduct analysis focuses on agency, and draws on the agent’s knowledgeability – that is, the ability to understand the likely effects of one’s actions. Agent’s conduct analysis leads back to agents and their self-monitoring and ordering of concerns, motives, and desires, as well as how they act and interact (Stones, 2005). For change researchers, this depth of understanding is necessary to grasp why change (or resistance to change) has unfolded as it did: What has the agent done, and why did she do it? Agent’s context analysis, which focuses on external and internal structures, surveys the terrain that faces agents, especially as it constitutes both possibilities and limitations (Stones, 2005). “In other words, it tells us the agent’s room for maneuver, given prior commitments and degrees of knowledge” (Parker, 2006, p. 134). For change researchers, knowing the terrain is especially important in uncovering the structural factors that influenced actions and responses to change: What did the agent see, and how did she come to see it in that way? It is only by looking at conduct and context at the same time that we can fully understand what happened in a particular change effort and why. Often, we hear only accounts based on conduct – what the change recipient did and why. Without understanding the context, we might replicate the actions in a different setting only to find that they do not work at all. When we consider both conduct and context, we go beyond taking the change recipient’s word for

320  Handbook of research methods in organizational change why they did what they did to look at the situation objectively. This allows us to ask, more intelligently, “Why did that work or not work in this instance?” In developing themes from data analysis, Stones (2005) offered four recurrent steps for researchers: (a) “identify the general-dispositional frames of meaning for the agent-in-focus,” (b) focus “on the ways that the agent perceives her immediate structural terrain from the perspective of her own projects, whether in terms of helplessness or empowerment, or a complex combination of the two,” (c) “identify, as a researcher: relevant external structural clusters; the position-practice relations that routinely constitute them; the authority relations within these and the material resources at the disposal of these hierarchically situated agents” without losing sight of agent’s conduct, and (d) “specify the ‘objective’ possibilities open to, and the constraints upon, the agent(s) in focus” (pp. 123–5). Following these steps allows organizational change researchers to fully grasp both the agency and structural factors at play throughout the life of a change event. Researchers have used these ideas profitably if not in identical ways. For example, in a case study of utility privatization, Coad and Herbert (2009) performed conduct analysis by using evidence of internal dispositions, conjuncturally-specific structures, and practices as they coded agents’ conduct. By contrast, Moore and McPhail (2016) emphasized levels of activity in their study of the change impacts of carbon pricing, analyzing policy effects (macro), industry position-practice responses (meso), and individual firms’ conditions of action (micro). In a still different approach, Greenhalgh et al. (2014) identified the types of change resistance to healthcare technology evident as they concurrently analyzed data and developed theory. These three examples illustrate the adaptability of SST as an analytical approach to varied research questions and settings. Validity and Reliability Qualitative research must achieve three types of validity: descriptive, or factual accuracy; interpretive, or accurate understanding and reporting; and theoretical, or credible explanations that fit the data (Johnson, 1997). Research with SST can meet all three types of validity. For the first two types, given that Stones (1996) writes from a “past-modern realist” perspective, a worthwhile goal is to have a sufficient quality and quantity of data supporting a principal narrative that additional data is unlikely to revise the narrative – a form of saturation (Merriam & Tisdell, 2016). In describing their study of video consultations between clinicians and patients, for example, Greenhalgh et al. (2016) provided a clear look at their reliability planning: micro-level data from viewing 45 video consultations; meso-level data from participant interviews, observation, and document analysis; and macro-level data from policy stakeholder interviews. Researchers using SST often develop the third type of validity, theoretical, as they connect their data analysis to the parts of the quadripartite model. Kholeif and Jack (2019), for example, developed a table in which they connected participant quotations to internal structures, external structures, agency, and outcomes (p. 77); Feeney and Pierce (2016) presented their findings in extended narrative form using the same model categories. In these cases, researchers used the quadripartite model to build case themes, a strategy that imposes an important discipline on the analysis. Coad et al. (2015) cautioned, however, that simply classifying data within the quadripartite model does not make the most of SST; focusing on agency

Advancing Strong Structuration Theory  321 embedded in structural relations and applying agent’s conduct analysis and agent’s context analysis are essential to developing robust findings. Given the sophistication of SST, making good choices in how to present data and findings is also important. We suggest in a first journal submission to provide a complete trail from data segments (example quotations, artifacts, etc.) to sub-themes and themes, explaining how the analysis was derived in light of the quadripartite model. Greenhalgh et al. (2014), for example, provided a four-stage overview of their data structure and analysis (Table 1, p. 215) in their study of appointment booking technology. Reviewers and editors, after becoming convinced of a study’s reliability, may suggest a condensed presentation of the analysis; researchers will likely iterate on this point before acceptance and publication. Because SST is unfamiliar to many readers as theory, displaying results in a familiar format such as the Gioia method (Gehman et al., 2018; Gioia, Corley & Hamilton, 2013) can make the validity and impact of their research findings easier to grasp.

CONCLUDING REMARKS Much organizational change research is theorized in terms of either the actions of agents involved in change or the contextual factors that constrain or enable their actions (Erwin & Garman, 2010). Far less research in organizational development and change, however, has explicitly examined the conjuncture of agency and structure. Likewise, many change researchers have been constrained by prevailing paradigms of objectivism versus subjectivism (Burrell & Morgan, 1979). Researchers either position themselves as objectivists, seeking rational explanations for change phenomena “out there” waiting to be discovered, or as subjectivists, viewing change phenomena as formed by the perceptions of individuals and seeking to uncover the network of individual assumptions that explain the change. For organizational change scholars seeking to balance agency and structure and incorporate both objective and subjective perspectives, research method options have been limited. Through the development of structuration theory, Giddens (1979, 1984) established a conceptual beachhead “to avoid the voluntarism of subjectivism and the reification of objectivism” (Stones, 2005, p. 14). Specifically, Giddens believed that objectivism places emphasis on “impersonal forces” and “subject-less structures” where agents are no more than puppets of reified social systems and that subjectivism “reduces the whole of social life to the actions of individual agents or groups, their actions, interactions, their goals, desires, interpretations and practices” (Stones, 2005, p. 14). Of particular interest to change researchers, subjectivism uproots agents from their social contexts, and objectivism conceptualizes them as having a lack of autonomy to cause even a ripple of change in the social systems in which they are members (Stones, 2005). Critically, Giddens (1984) advanced the notion of structure and agency as a “duality” where each one recursively and continually influences the other. Despite these advances and the publication of studies based on Giddens’ work, researchers found Giddens’ formulation so highly conceptual that rigor in empirical organizational results could not be consistently assured (Stones, 2005). To address this limitation, Rob Stones (2005) developed SST as a support to empirical research. It balances not only agency and structure but also objective and subjective perspectives. Published studies using SST as a methods theory (Lukka & Vinnari, 2014) have demonstrated its value across a sufficient range of research questions that we believe SST

322  Handbook of research methods in organizational change will help organizational change researchers to overcome the dichotomous framings of structure and agency, or objectivism and subjectivism. Through its quadripartite model, focus on in-situ ontology, and attention to epistemological and methodological considerations, SST is robust enough to address the interwoven nature of organizational change; i.e., that the context of an organizational change cannot be separated from the conduct of agents participating in the change – internal structures are comingled with the interpretation of external structures at the conjuncture of agency and structure. Focusing on this conjuncture allows organizational change researchers to balance their efforts on the interdependency between the micro detail and macro-level institutional structures surrounding an organizational change. Within organizational change and development research, we believe that SST will help researchers ask penetrating questions that elicit responses about internal and external agents and structures, context, and perceptions of conduct. Helpfully, SST usage continues to evolve: Kennedy et al. (2021) note that SST is advancing as more organization and management scholars use it in their research. Initially, the theory was applied to existing data sets, but now many scholars integrate the theory into their research designs and data collection. Researchers have used the full quadripartite cycle of structuration to frame their studies (e.g., Moore & McPhail, 2016), emphasized certain components of the theory to address research questions (e.g., external structures foregrounding in Elmassri et al., 2016), and/or paired SST with one or more complementary domain theories (e.g., Greenhalgh & Stones, 2010). Given that SST typically results in research case studies, a flexible form of research in its own right, researchers can experiment with the theory, test it, and advance the discussion of how best to study organizational phenomena (Jack & Kholeif, 2007). SST is a relatively new framework, and its potential is not yet fully realized. Key topics with which SST can be useful to organizational development and change researchers include: How can scholars specify external structures, such as time, process, and history as more vital elements of their studies? What are the series of individual and collective event conjunctures that unfold over the life of a change? How can context be linked with action to expose processes and mechanisms through temporal analysis? How can change capacity and action be linked to independent and irresistible forces to explore organizational performance? How can research intensify the sense of importance of national and regional differences in shaping the success of organizational change? How can key theories such as resistance, momentum, and sustainability be enriched by studying the dynamics of internal and external structures? These questions suggest how SST can address many challenges in organizational development and change research related to the dichotomous framing of objective and subjective perspectives. If organizational change researchers employ SST and thoroughly examine the duality of structure in change situations, their findings may be novel, surprising, and insightful. Finally, there is the intriguing possibility that SST can help to resolve the prevailing separation between scholars and practitioners. By viewing ontology as in-situ and focusing on specific case study situations, scholars can engage with practitioners and use SST to create studies that locate a particular organizational change in its external context while fully respecting the internal mental models of its participants. Change studies of this type will not only provoke interest among researchers, but also have high credibility with practitioners who face similar external structures; for example, those that share an institutional field. By locating particular studies in the concrete experience of organizations and supporting research with a robust conceptual perspective, thoughtful use of SST can provide a closer and deeper engagement between organizational change researchers and practitioners.

Advancing Strong Structuration Theory  323 In summary, with this chapter, we propose strong structuration as a trustworthy, adaptable, and advancing theoretical tradition appropriate for a range of empirical studies in the field of organizational development and change. SST addresses the shortcomings and realizes the promise of Giddens’ structuration theory by providing a methodology that links concepts to practical research know-how. We explain an ontology that challenges the subjectivism– objectivism divide and brings forth a reality for change researchers that helps them see the objective and subjective aspects of an organizational change simultaneously. We illustrate how SST can be integrated into case study research and how the methodological bracketing of agent’s conduct and context analysis is a feasible technique for examining the conjuncture of agency and structure, and how this examination of duality of structure provides change researchers opportunities to explore original theoretical insights from case data. Throughout our discussion, we have presented examples of how SST is being used in the broader organization and management field and establish its credibility for organizational development and change researchers. Finally, we put forth potential research questions that are fitting for SST organizational change research. We hope that by illustrating how the theory can be used in empirical research, we will encourage organization change scholars to apply SST within their case study research to address challenges in the field of organizational development and change and to participate in the evolution of the theory in organizational studies research. While we retain epistemic humility in any researcher’s ability to get the entire organizational change picture in the frame, we believe SST is a powerful wide-angle lens that any change researcher would be glad to have in the camera bag.

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15. Design science for organizational change: how design theory uncovers and shapes generativity logics in organizations Pascal Le Masson, Agathe Gilain, Armand Hatchuel, Caroline Jobin, Maxime Thomas, Chipten Valibhay, and Benoit Weil

INTRODUCTION Design science research has strongly developed in the last few decades with rich contributions to organization science and, more specifically, to organizational change. Design science research today raises great expectations for research on organizational change: intuitively, the reason is that with organizational change being the result of a process of invention, learning, generation, and collective creation, and design science being the science of generativity, invention, and creation, it is reasonable to rely on design science to conduct research on organizational change – and compared to other approaches of organizational change, design science research lets the researcher hope to have better analytical and experimental control of design-based processes of organizational change. Yet, the contributions of design science research to organizational change are not straightforward and cannot be obtained without strong epistemological and theoretical rigor. The expectations raised by design science research also imply strong requirements in terms of research methods and epistemology. Hence this chapter addresses the strengths of and challenges in using design science as an approach to researching change. In the first section of the chapter, we remind readers of the expectations raised by design science research, seen as a way to address the tension between rigor and relevance in management and a way to access original phenomena on collective generativity in organizations. We then show how these expectations actually imply critical requirements and how these requirements can be met by relying on design theory, which enables one to model rationality in design science research on organizational change. We also show that when choosing a design theory framework for research on organizational change, a design science researcher actually chooses a specific analytical framework to take into account the generativity issues in organizational change research. In Section 2 of this chapter, we illustrate this diversity of approaches by analyzing and comparing three families of design science research approaches: (1) empirical studies of how design practices impact organizations, (2) interventional research approaches where researchers design artifacts to develop knowledge on organizational change, and (3) collaborative research where researchers rely on design theory to experiment with design-based organizations. Each of these families relates in a specific way to design theory. We underline for each of them their methodological assumptions, their strengths and challenges, and the type of organizational changes they aim to achieve. We show that these streams account in different ways, and more or less explicitly, for design as a generative process that 327

328  Handbook of research methods in organizational change includes organizational change. We illustrate each of these approaches with specific research cases from the literature. Finally, we conclude with some key insights for organizational change researchers and discuss how design science research could further contribute to the development of organizational change theory.

1.

EPISTEMOLOGICAL ISSUES: THE CRITICAL ROLE OF DESIGN THEORY TO MODEL RATIONALITY IN DESIGN SCIENCE RESEARCH ON ORGANIZATIONAL CHANGE

Traditionally, design science research is defined as the research on design, seen as design action and design artifacts. More specifically, design science research in organizations relates to the collective action of design and is deeply related to the issue of changes, evolutions, and inventions in organizations. Design science research has its roots in Simonian science of the artificial (Simon, 1969), underlining that it aims at studying not only “how things are, but also how they ought to be” – this (often used) quote from Simon being, of course, a clear positioning in favor of a constructivist approach against analytical, positivist ones – and also raising strong issues for research. It means that design science research will not only describe how an organization works but also describe, experiment, and design how it could work differently to get different results – design science is finally a way to have a stronger, more rigorous understanding of what “construction” is (in constructivism). One such example would be examining not only how a company deals with competence management systems but also designing and experimenting with new, “better” competence management systems (see Sein et al., 2011, detailed below in Section 2). Hence, design science research is fundamentally grounded in the theory and practice of change and seems particularly suited to study change and contribute to the field of organizational development and change. Yet, such research promise has been quite hard to reach in practice because it had to meet strong methodological requirements, which actually correspond to critical epistemological issues. In this first section, we describe this growing stream of research in design science. We analyze two main expectations that are at the root of design science research: (1) the expectation of combining relevance and rigor in the study of empirical material, and (2) the expectation of access to original forms of collective action. We underline how these expectations raise critical requirements for solid research. We then show that these requirements correspond to two main epistemological issues – the intervention issue and the theoretical framework issue – and we conclude this section by eliciting how design theory can play a strong role in addressing these issues. A Growing Stream of Research in Design Science Leading to Original Results Today, there are very active academic communities using design science research to study organizations. One can refer to the success of the conferences of DESRIST (Design Science Research in Information Science and Technology); the development of journals, such as the recently launched Design Science Journal; the growing success of the Design Theory SIG (Special Interest Group) of the Design Society; and the development of design science research in specific management areas such as entrepreneurship, the management of information systems, and innovation management.

Design science for organizational change  329 Over time, important developments in the fields of organization science and organizational change research were obtained thanks to a design science research approach. To name a few: Sarasvathy (2003) uncovered the effectuation logic of the entrepreneur based on an analytical framework of entrepreneurship as a science of the artificial; Hatchuel et al. (2006), based on advanced design theory (C-K design theory, with C standing for Concept and K for Knowledge), showed the development of design-oriented organizations with multiple organizational developments; relying on the analysis of the design practice of Frank Gehry, Boland et al. (2008) proposed lessons for organization leaders; relying on Simonian analysis of modularity effectiveness (which shows the impact of modularity in certain classes of actions), Yoo et al. (2010) characterized the new organizing logic of digital innovation; and by studying design thinking methods, Carlgren et al. (2016) have shown that these methods induce only limited changes in organizations. Expectations from design science research and related requirements These research streams share two main expectations. A way to address the tension between rigor and relevance in management Many authors working with the design perspective recalled the long-standing debates on the relevance of knowledge produced in the field of management and organization in the 1980s, 1990s, and 2000s (Avenier, 2010; Berglund et al., 2018; Denyer et al., 2008; Hatchuel, 2005; Romme & Dimov, 2021; Sein et al., 2011; Van Aken, 2005). Romme and Endenburg regret that “over time, the status of design and intervention research in organization studies has changed from a core activity to a relatively minor project outside the mainstream (Baligh et al., 1996) […] largely disconnected from the development toward a generic design science (Warfield, 1996)” (2006, p. 288) and they expect that design science research can precisely reverse this trend and contribute to relevant organization studies. It is widely expected that the design perspective will help combine rigor and relevance. For Van Aken (2005), design science would be Mode 2 knowledge production producing field-tested and grounded technological rules for management science and organization rules. The intuition, explained by Van der Borgh et al. (2020), is that design science research produces empirical material and addresses organizational issues in action and leads to design theory and principles that can be more general than the specific situation where they emerged. Knowledge produced must go beyond idiosyncratic solutions; thus, it is clearly different from consulting and acts as “boundary objects” between academic research and practitioners (Van der Borgh et al., 2020, p. 137). This intuition raises clear requirements: ● Requirement 1: The design problem should be new and original (in comparison with the literature); otherwise, the solution is just an instantiation of already available results (Van Aken & Berends, 2018; Van Aken et al., 2016). ● Requirement 2: Being part of the research process and research object, the design process has to be as controlled as any knowledge production step in scientific research. Hatchuel (2005) went as far as explaining that the relevance gap in management is related to the actionability of the knowledge produced by research; hence, there is a strong link between the epistemology of management and the research method so that a controlled “design” process in research would be a warranty of rigor and relevance of the knowledge produced.

330  Handbook of research methods in organizational change ● Requirement 3: Design research results still have to be expressed in “generic” terms. For example, Denyer et al. (2008) detail how knowledge produced in design science research combines rigor and relevance by leading to propositions that follow the CIMO logic; i.e., always clarifying for any research proposition its Context, the Intervention type it relates to, the generative Mechanism that led to this proposition, and its intended Outcome. Romme and Dimov (2021) proposed to extend this CIMO logic to CAMO logic, replacing Intervention with Agency. Hence, the expectation of bridging relevance and rigor with design science research also raises strong requirements. These requirements do not seem out of reach, but one can already notice that they are related to two strong epistemological issues: on the one hand, the epistemology of intervention and the issue of the validity of knowledge produced in action research; on the other, the epistemology of design, and the issue of the model of rationality for an activity such as design that is inherently related to emergence, the unknown, creativity, invention, discovery … i.e., the many notions that were long considered out of the realm of deductive rationality. This second epistemological issue will be even more acute after studying the second expectation associated to design science – a way to access the original phenomena. A way to access original phenomena on collective generativity in organizations Linked to the relevance-rigor ambition, the researchers in this stream of work also expect to access original knowledge in management science. The intuition is clear: in the design perspective, researchers can follow the design of new practices, new processes, new structures, new values, and new competencies to face contemporary challenges, including sustainable development goals and transitions. Hence, they are on the spot to report the emergence of new forms of collective action and, more precisely, “generative action”; i.e., new capacities to collectively invent, discover, and develop solutions. “Follow the design” appears as a strategy for design science researchers to be able to produce new original results for the management science community. This strategy appeared fruitful in past decades since it enabled researchers to analyze the emergence of new forms of organizing for innovation in companies (e.g., design-oriented organizations (Hatchuel et al., 2006), design-driven laboratories (Dell’Era & Verganti, 2009), and intermediaries as architects of the unknown (Agogué et al., 2013). Today, this strategy might become even more interesting since more and more actors in an organization are now in charge of organizing design (design at the plant level (Harlé et al., in press)), and design in public policy (Pluchinotta et al., 2019), hence leading in the coming years to the emergence of unique, until now unseen, forms of collective action. These scholars who focus on collective design action to be able to identify, as early as possible, the emergence of new forms of collective action face critical requirements. ● Requirement 4: Being able to observe the phenomena. This research strategy requires a capacity to observe design activities, and this is far from self-evident. Design consists in creating objects, services, and knowledge; hence design activity takes place before the artifacts are here. Thus, this is a very abstract activity (compared with production, where the objects and processes can more easily be observed). Of course, there are artifacts in design (drawings, computations, proofs-of-concept, prototypes, etc.), but they are often preceded by important design work. They mirror only one part of the activity, and despite AI (artificial intelligence)-based data treatment, they often remain extremely difficult to

Design science for organizational change  331 analyze. Moreover, in companies and organizations, design is often very strategic and hence confidential, so researchers cannot easily access design action in these places. ● Requirement 5: Being able to analyze, characterize, position, and criticize the phenomena. Researchers, particularly if they are in an action research perspective, are also expected to put observations in perspective, to be able to formulate alternate hypotheses, to keep a critical distance, and to even support the experimentation of alternate forms of action. But these actions require a reference for action – what does it mean to “improve” the quality design action? What can actually be the “quality” of a design process? This requires a reformulation of the underlying theory of design action to be able to characterize its outcomes. One example is given in Elmquist and Le Masson (2009), where the value of a project is discussed in an organization, and design theory enables one to clarify the variety of forms of performance that can be expected from a project (this example is described in more depth below). ● Requirement 6: Being able to control the interaction in a design process. Studying a design process, a researcher can either remain in a pure “observation” position (and the only requirement is the one mentioned above; i.e., how to rigorously observe design without disturbing it), or a researcher can be in an “experimentation” position, being able to design a solution and then to submit this artifact to the target organization to evaluate the impact (and the requirement becomes: why this solution, how to evaluate the reaction of the organization, etc.). This latter solution implicitly considers that the organization is not designing or was not able to design the solution proposed by the researcher. A third solution is to co-design with the organization and make it able to design its own solution. In this latter case, the control of the interaction between the “design” researcher and the “design” organization is more complex and requires a theory of the interaction in a design situation. Fortunately, in recent decades, it has become easier to meet these requirements. On the one hand, advances in design theory provided new capacity for empirical investigations. Based on contemporary design theories, such as C-K theory (Hatchuel et al., 2018; Hatchuel & Weil, 2009), it became easier to observe, analyze, and criticize design processes; easier to propose new testable research hypotheses; and also easier to elaborate new complex experiments where researchers and practitioners co-design new organizations in a controlled way. On the other hand, design action has been more and more digitalized, which enabled new data treatments. In addition, design has also developed in public spaces (e.g., Fablab, open innovation, crowdsourcing) where confidentiality was less of an issue. This contributed to access to new design observations. Hence, contemporary advances in design theory and digitalization contributed to render possible the smart strategy of researchers who focused on collective design action to be able to identify, as early as possible, the emergence of new forms of collective action. Epistemological Issues It is now clear that design science research has great expectations, but these expectations are also related to strong requirements. These requirements correspond to two main epistemological issues: an interventional issue and a theoretical framework issue.

332  Handbook of research methods in organizational change Interventional issue: which scientific control of action research in a design situation? Design science researchers are led to intervene and contribute to collective design processes that involve strong relations with organizational changes. This kind of research can be scientifically fruitful (David & Hatchuel, 2007; Hatchuel & David, 2007; Shani et al., 2007), but it is also recognized as highly demanding – interventional researches need a rigorous observation and understanding of the rich and complex interactions between the researcher and the organizational context. The control of such analysis is a critical issue for research validity (see Coghlan, this volume). It has been claimed that design science is not action research. “Action research is focused on problem-solving through social and organizational change. Design science is focused on problem-solving by creating and positioning an artifact in a natural setting” (Baskerville, 2008, p. 442). Although this distinction appears grounded, it relates to the means of the research (social action vs. artifact design) and not to the scientific control of the research process, be it via social action or via artifact design. In each case, research is expected to have an impact on collective action and hence lead to organizational change, as the process of organizational change is carefully followed and controlled. Thus, design science research must address the epistemological issues related to intervention, just as action research does. Actually, many design science researchers claim to rely on action research principles (see multiple references in design science, action research, and intervention research; e.g., Hatchuel & David, 2007; Radaelli et al., 2014; Sein et al., 2011; Susman & Evered, 1978). The Sein et al. (2011) paper on action design research proposes a design research method that is derived from action research principles but dedicated to design science research. The authors illustrate the action design research method by reinterpreting research conducted with an action research method with a design action; for example, designing a new competence management system at Volvo (Lindgren et al., 2004). According to Sein et al. (2011), action design research identifies four stages: (1) problem formulation (based on principle 1, “practice-inspired research,” and principle 2, “theory-ingrained artifact”); (2) building, intervention, and evaluation (based on principle 3, “reciprocal shaping,” principle 4, “mutually influential roles,” and principle 5, “authentic and concurrent evaluation”); (3) reflection and learning (based on principle 6, “guided emergence”); and (4) formalization of learning (based on principle 7, “generalized outcomes”) (p. 41). Building on action research helps to address several requirements; more specifically, requirement 1 (assure the problem is new and original), requirement 3 (generalized results), requirement 5 (analytical and critical capacity), and requirement 6 (controlled interaction with the design organization). Theoretical framework issue: which theory of the design action rationality? As mentioned above, research on design also has to account for the design process itself: how to observe it, control it, criticize it, improve it, “value” it, and learn from it. Design science research has to rigorously account for what Simon (1969) called the exploration of what “ought to be” (instead of what is). More generally, it has to account for the generativity logic and expandable rationality at the heart of design (Hatchuel et al., 2018). Hence, any design science research must rely (implicitly or explicitly) on assumptions on how such generativity and expandability can be described and modeled. This is what we call the “ design theory” mobilized by the researchers, understood as a theory of the design action rationality (Le Masson et al., 2013).

Design science for organizational change  333 The difficulty has long been recognized – discovery, emergence, unknown, generativity, invention, imagination, and ideation were long considered as beyond the realm of classical epistemology. Some authors even allude to a specific “designerly way of knowing” (Cross, 1982, 2001) that would be inherently separated from science. Several authors mentioned that design reasoning could be linked to Peirce’s “abduction,” but it has also been shown that Peirce’s abduction was, for Peirce, more a research program than a research result; and this research program fell short of accounting for emergence and hypothesis construction, and at best explained the inference to the best explanation (Douven, 2021a, 2021b; Koskela et al., 2018; Kroll et al., in press), which remains far from the issues of generativity. Yet, several streams of research have also developed models of design rationality, showing that at least certain forms of design could be compatible with a rationality model and hence with a scientific approach. Many papers in design science research assume that design action can be modeled as problem-solving (Hevner et al., 2004)). Other authors consider formal design logic, such as Hatchuel and Weil’s (2009) C-K theory. In each case, the assumed design theory has strong implications for the research itself. Such theory (from the Greek θεωρειν, or theorein; i.e., to look at, to observe, to contemplate) acts as a spectacle, an unavoidable filter that conditions research methods, observables, measurements, and analytical frameworks. The assumed design theory will also determine specific types of hypotheses and results. Last but not least, it will strongly impact and eventually generate a research bias on the type of organizational changes that will be explored and observed during a particular design science research. If we follow the requirements listed above, it appears that design theory will play a critical role in requirement 2 (a controlled design process), requirement 4 (capacity to observe design activity), requirement 5 (critique and improvement of the design process and organization), and requirement 6 (controlled interaction with practitioners-designers). Analytical Framework for Research in Design Science and Organizational Change: The Role of Design Theory As a consequence, design science research on organizational change will have to address the two epistemological issues mentioned above, with a specific position towards action research and a (tacit or explicit) design theory. These epistemological choices will consequently enable us to meet the six requirements to fulfill the expectations of design science research, particularly in terms of results on organizational change. In the second section of this chapter, we will describe in more depth three main streams of research in design science, each of these streams being characterized by its choices on the above issues, and hence illustrating how these choices finally induce certain types of results for organizational change. One of the greatest sources of differentiation between the three streams comes from the choice of the design theory that will underline the research. Hence, before digging deeper into these three streams, it is important to underline some of the differences in the approaches of design theory. Many papers in design science build on the Simonian approach of design. A Simonian approach is based on problem-solving, with an (occasionally multicriteria) objective function and several constraints and rules that can be applied to reach the objective of solving the problem. It is largely inspired by the works done by Simon on the General Problem Solver program and variations around branch and bound algorithms (Simon, 1969, 1979). The issue consists in characterizing the way to search for a solution in a knowledge-based, occasionally

334  Handbook of research methods in organizational change highly combinatorial, highly complex problem space.1 With a Simonian approach, design is assimilated into a search in a knowledge-based system. As a consequence, in the literature that builds on Simonian design theory, the authors use the term “design theory” to characterize how specific pieces of knowledge used in a specific design can actually have a broader validity beyond the artifactual single use (Gregor & Jones, 2007); i.e., how they form relevant, generic design rules (Van Aken, 2005). In Table 15.1, we give some examples of how a Simonian approach to design rationality can contribute to meeting the requirements for design science research. In the last few decades, several authors have underlined that a Simonian approach is too limited to account for the generative logic of design (Dorst, 2006; Foerster, 1991; Hatchuel, 2002; Schön, 1990). These authors showed that a Simonian model did not endogenize the “discovery” of new rules and new knowledge during (and because of) the design process. This is the “unfinished program” of Herbert Simon (Hatchuel, 2002). Research on design theory (Braha & Reich, 2003; Hatchuel & Weil, 2009; Shai & Reich, 2004; Tomiyama & Yoshikawa, 1986; Yoshikawa, 1981) precisely progressed by extending this Simonian type of a knowledge-based approach by combining it with models of invention, discoveries, imagination, and partially unknown propositions. This stream of work uses the notion of design theory to characterize the whole design rationality. One example is C-K theory (Hatchuel & Weil, 2009) – a design theory that (a) includes the knowledge of design rules (in K-space); (b) also accounts for the structuration of the unknown before it becomes known, hence the “problems” to be solved, but also chimera, imagination, creativity, etc. (in the C-space); and (c) also accounts for the interaction between the known and the unknown – i.e., how knowledge enables the formulation of new chimera and original paths in the unknown and, vice versa, how these chimera and original paths in the unknown lead to creating new knowledge. These works have contributed to elaborating a design theory comparable in its structure, foundations, and impact to decision theory, optimization, or game theory. Contemporary design theory has led to critical results and opened new possibilities for studying the generativity of design processes. For instance, it enabled researchers to ● characterize the variety of design reasoning (strong C exploration vs. strong K exploration, for instance; or depth-first vs. breadth first in C-space …) (Hatchuel & Weil, 2009), ● measure the variety and originality of C exploration (whereas a problem-solving approach tends to measure the distance to an optimal solution), ● measure fixation in design processes (biases in the generation process; i.e., a generation process that only results in a restricted set of solutions compared to the set of imaginable solutions – these biases are very different from biases in a decision process or a Simonian problem-solving process, which again compare one result to the optimal one) (Duncker, 1945), ● experiment with defixation, ● criticize specific design processes and experiment with less biased ones. 1 Simon is particularly famous for having noticed the fact that in such problem-solving situations, actors (and organizations) show a bounded rationality (to simplify: they tend to reuse well-known routines instead of finding the optimal solution). Simon also showed how complex problems can be solved by identifying critical design rules, in particular rules that modularize the problem – which also relates to clear organizational issues: whether or not an organization relies on modular design knowledge will determine its design performance (Baldwin & Clark, 2000).

Example: to solve the identified problem, rely on top-level expertise

(quality of the state of the art) and explore all combinations (computation

the design process

Example: characterize a solution (e.g., material artifact) and evaluate its fit

with the target

Example: analyze the quality of the fit between problem and solution (and

learn from this)

Example: clear separation between “users” (contribute to set the problem)

and designers (frame the problem, propose solutions)

Requirement 4: observe collective design

action

Requirement 5: analyze, evaluate, criticize

design action

Requirement 6: control complex interaction

in design action

power)

Simonian design theory framework

Requirement 2: factors that might influence

with K standing for Knowledge, C for Concept, and P for Projects)

provision of knowledge and ideas by participants (e.g., KCP process,

Example: multi-actor design processes, with complex (but controlled)

compare generativity of design strategies (breadth vs. depth …)

Example: evaluate defixations (individual or organizational); or:

Example: characterize new “solution paths,” newly created knowledge

independent knowledge, be able to formulate/explore “crazy concepts”

Example: to explore a design concept, discover/create new

Contemporary design theory framework

How the choice of design theory framework impacts on design science research

 

Table 15.1

Design science for organizational change  335

336  Handbook of research methods in organizational change Relying on these advanced design theory formulations opens new ways to address design science research requirements, possibly taking into better account the generativity of the design action. To give some examples (see also Table 15.1, and more detailed cases are in the second section): ● Control specific facets of the design process (requirement 2). C-K design theory suggests paying attention to “independent knowledge” (e.g., through organizational processes) since C-K design theory predicts that access to new independent knowledge can strongly impact the generative capacity (Le Masson et al., 2016). ● Observe new aspects of collective design action actions (requirement 4). Design theory suggests recording not only a solution but also the variety of “solution paths” that were explored since this variety is both a signature of the generative capacity of an organization and also forms a rich ground for re-exploration and continuous development inside a given innovation field (Elmquist & Le Masson, 2009). ● Identify specific quality criteria of design action to enable enriched criticism and to improve collective design action (requirement 5). Design theory suggests characterizing how a collective design process enables one to overcome organization fixation to help generate solutions out of the “easily accessible” ones (Agogué et al., 2014; Jansson & Smith, 1991). ● Enable complex but controlled design interactions between designer-researchers and designer-practitioners (requirement 6). Based on C-K theory, designer-researchers were able to conduct research following complex yet controlled co-design processes (Della Rossa et al., 2022; Elmquist & Segrestin, 2009; Pluchinotta et al., 2019; Vourch et al., in press) to lead to original results in organizational change. These examples illustrate how a design theory framework that takes into account the generativity logic of design leads to specific research methods that enable the control of the factors of the generativity process (requirement 2), observe the generativity phenomena (requirement 4), criticize generativity quality and propose ways to improve it (requirement 5), and elaborate more complex ways to study collective generativity mechanisms that comprise several designers, including researcher-designers and practitioners-designers (requirement 6). Hence, when choosing a design theory, a design science researcher actually chooses a specific analytical framework to observe the generativity issues, and hence chooses one of the very contrasting ways to meet the requirements for design research methods, leading then to different results for research on organizational change and the logic of generativity in organizational change. To illustrate this point, in Section 2 we analyze three main research streams in design science research.

2.

COMPARING DESIGN SCIENCE APPROACHES: THE DEEP CORRESPONDENCE BETWEEN DESIGN THEORY, DESIGN SCIENCE RESEARCH METHODS, AND RESULTS FOR ORGANIZATIONAL CHANGE

Several design science research approaches have unfolded in the last few decades, yet they all share the same logic. They are all (1) addressing a specific organizational issue, (2) based on specific research methods for organizational investigations, (3) framed (implicitly or

Design science for organizational change  337 explicitly) by a specific theory of design rationality (also called design theory), and (4) supported by research results validation. In this section, we apply this framework (organizational issue, method, design theory framework, and results) to analyze three design science research streams in the literature. This calls for three remarks. First, we only focus on design science research that relates to organizational change (there are many other works that deal with design science research but without direct links to organizational change). Second, with these three streams of work, we illustrate the variety of approaches without any claim of exhaustiveness. Third, we selected papers based on empirical research over theoretical papers. The analysis of these three streams shows the deep correspondence between design theory, design science research methods, and results for organizational change. Stream 1: Observe New, Original Design Processes and Organizations without Specific Model of Design Rationality The first stream of design science research focuses on the empirical study of design practices and their impact on organizations (Carlgren et al., 2016; Dell’Era & Verganti, 2009). The authors do not put a particular emphasis on design theory. In this stream, “design” is mainly envisaged by some features (e.g., in design, there is a prototype or there is problem framing; hence the study focuses on prototype or problem framing) or particular outputs (e.g., in design, there is the generation of new meanings; hence the study focuses on meaning) without a specific generativity model; i.e., without specific design theory. The authors try to uncover the organizational forms that lead to these (design-specific) features and outputs; e.g., types of routines, types of resources, and the effects of these features and outputs on the organization. The authors contribute to organizational change by characterizing the organizational impact associated with the emergence of the new design-specific routines: either they characterize the new routines that support these new “design organizations,” or they describe the effect of the new “design organization” on the rest of the organization. In particular, they describe the occasional increased performance of the organization. In Table 15.2, we detail two different examples (Carlgren et al., 2016; Dell’Era & Verganti, 2009). In Carlgren et al. (2016), the authors aim at “increasing our empirical understanding of how design thinking is practiced in organizations” (p. 53); i.e., they characterize, in the language of organizational concepts, a method that has recently emerged in organization studies and is self-defined as “design thinking.” Remarkably, they explain that they avoid relying on any design theory and analyze this self-defined “design thinking” in a general language of organizational concepts – the analytical framework is based on the analysis of similar organizational concepts in other action contexts, namely the analysis of Total Quality Management (TQM) by Dean and Bowen (1994). The authors distinguish different levels of analysis of organizational concepts (principles-mindsets, practices, techniques) in full coherence with the notion of organizational routines (Feldman & Pentland, 2003). Studying six companies that utilize design thinking, the authors uncover five themes that characterize how design thinking is practiced in organizations – user focus, problem framing, visualization, experimentation, and diversity – and they describe each team in terms of principles/mindsets, practices, and techniques. Table 5 in Carlgren et al. (2016, p. 50) shows that there is a variety of practices that combine these themes at different levels. In discussion, they show that these five themes are also present in existing works on design and design theory. The paper makes a clear contribution to the analysis of new organizational concepts (new design-based principles/mindsets,

practice. No evaluation of “generativity” of these practices: good/bad visualization, good/bad

visualization, experimentation, diversity) – a variety of practices combining these five themes

framework related to organizational concepts (Feldman & Pentland: routine in principle vs. in practice)

(principles/mindset, practices, techniques) used by Total Quality Management

design-oriented routine, with

a variety of implementations,

taking soft factors into account

(empathy, comfortable with

ambiguity, enjoyment from

showing that they are different

Verganti, 2009

organizations. generativity process (factors, biases?). Leaves open the question of

“socio-cultural innovation” and “technology innovation”

vs. meaning) (Verganti, 2003)

vs. meaning creation) and

design-driven labs

features (internal actors, external

actors, communication)

improvement and learning in

characterize organizational

languages

development, as opposed to

that do one of the two innovations innovation strategy characterized by two dimensions (function-technology

meaning creation and technology No evaluation of the

organization that combines

from design as brokering

analytical framework, lab organization innovation strategy come (Clark & Fujimoto, 1993), and

change Analyze design-driven Design-driven lab is a new

Case study on nine design-driven labs; The two dimensions of

(technology development

from known organizational forms

Analyze new design-driven labs:

Dell’Era &

organizational learning and

it more difficult to analyze

A static framework makes

diversity? Biases?

facets of a design-based

(user focus, problem framing,

design theory – analytical

based on a routine-based framework

in organizations as a novel

problem-solving)

Analyze organizational

the design thinking concept

No framework linked to

2016

Comments

Results Five themes characterize

Design theory framework

Analyze “design thinking”

Carlgren et al.,

Interview study (36) in six companies,

Organizational issues

 

Method

Two examples of papers that observe new, original design processes and organizations – without a specific model of design rationality

Table 15.2

338  Handbook of research methods in organizational change

Design science for organizational change  339 techniques, practices). However, probably because of the lack of a design theory reference for characterizing the generativity rationality, the paper leaves open for further research the issues of the generativity performance of design thinking themes: is there good/bad visualization, good/bad problem framing, good/bad diversity, and are there biases? Consequently, it also leaves open the question of improvement and learning of the design thinking concept. In Dell’Era and Verganti (2009), the authors aim “to investigate the organizational characteristics of design-driven laboratories and to identify the peculiarities that allow them to adopt different innovation strategies” (p. 16). They study specific, new organizational forms that they call design-driven laboratories (DDL), formal organizational units that manage research and development and that significantly contribute to the generation of new meanings. The analytical framework is twofold: (1) descriptors of the laboratory organization derived from a well-known description of R&D organization – internal organization, internal communication, external organization/coordination of the DDL with external partners (Clark & Wheelwright, 1993); and (2) descriptors of the innovation strategy that contrasts functional innovation vs. meaning innovation, the second one being considered as a specificity of design-driven innovation (Verganti, 2003). Based on nine DDLs, the authors uncover the existence of a specific model for DDLs that (1) organizationally rely on external resources but also conduct internal research and have a development process (clearly separated from research), and (2) strategically combine meaning innovation and technological innovation. The authors also identify three types of DDL (called “technological,” “linguistic,” and “hybrid”) characterized by (1) specific organizational features (in terms of internal organization, communication, and external coordination) and (2) a specific balance of technological/functional innovation that accompanies meaning innovation (a “technological DDL” also makes technological innovation, whereas a “linguistic DDL” makes mainly meaning innovation). This paper makes a clear contribution to the analyses of design-driven organizations. Design theory is used to characterize “meaning creation” as specific to a design-driven organization. Yet, probably because of the lack of a model of generativity rationality, the paper does not discuss the generativity process itself: what are the types of knowledge that lead to original, unique meaning creation? Are there biases in meaning creation? Or how is the organization capable of overcoming certain biases in the generativity process? Consequently, it also leaves open the question of improvement and learning in DDLs. These two examples illustrate a stream of work that manages to describe original, unique design-driven organizational features such as “design thinking” or design-driven labs by relying mainly on “classical”2 organizational descriptors. The analytical framework makes only limited reference to design theory and models of generativity; i.e., only to discuss the coherence of results with known features (see Carlgren et al., 2016) or to characterize a specific organizational performance (such as meaning creation in Dell’Era et al., 2009). From a research method perspective, the great advantages of this approach are: (1) it avoids complex methodological issues (no need to observe new variables; even more, this type of research does not require interventional research), and (2) it uses a well-known organizational language and yet manages to describe an original, unique, new organizational form. The limits are related to the absence of a model of generativity – without such a model, the papers leave open the issue of possible limits of the generativity capacity of the organization (biases,

2



Classical: in the sense of not specific to design action.

340  Handbook of research methods in organizational change fixations, etc.) and its possible improvement by organizational change (via experimentations, learning, etc.) Stream 2: Organizational Change Experimentation by Designing Artifacts In the second stream of design science, researchers design artifacts both to change organizations and to develop scientific knowledge on organizational change processes. This stream, particularly well documented in the information systems and the entrepreneurship literature (e.g., Berglund & Mansoori, in press; Eaton et al., 2015; Yoo et al., 2010), is rooted in a Simonian theory of design, which considers design activities as problem-solving (Gregor and Hevner, 2013; Hevner et al. 2004). In Table 15.3, we detail four papers that follow this approach (Andriessen, 2007; Romme & Endenburg, 2006; Sein et al., 2011; Van der Borgh et al., 2020). This stream of work is deeply linked to research in the management of information systems, where researchers considered, in the early 2000s, that research was often too abstract and not grounded enough. This stream developed highly codified research methods that rely on the design (by the research team) of original solutions that are tested in organizations (Baskerville, 2008; Hevner et al., 2004) and lead to generic results for organization science. In this stream of work, the method is discussed in many papers (e.g., Gregor & Hevner, 2013; Peffers et al., 2007) and books (Dresch et al., 2015) with clear steps and analytical frameworks. The canonical method unfolds as follows: ● Step 1. Each paper clarifies an organizational issue that is not necessarily related to the design activity itself (and most often is not): organizing work councils, reporting intellectual capital, competence management systems, and sales lead black holes. At this step, the research team makes sure that the issue is new in the literature and corresponds to the need of the target organization(s). This diagnostic phase must be very cautious (as described, for instance, in Van der Borgh et al., 2020). Researchers focus on “real problems” instead of “perception problems” (i.e., when a manager has an inaccurate perception of the management process and its performance) or “norm problems” (i.e., unrealistic targets) (Van Aken & Berends, 2018). By focusing on these different problems, researchers avoid “irrelevant academic research” (Van der Borgh et al., 2020, p. 136) and avoid providing practitioners with incorrect recommendations and guidelines. The researchers model the problem by avoiding “dependent variables that do not directly capture the issue” and independent variables that “lack of pragmatic validity” (Van der Borgh et al., 2020). ● Step 2. The research team develops a solution to the problem for the target organization(s). ● Step 3: The solution is implemented, tested, and evaluated. ● Step 4: The research team learns about the organization and the general validity of the proposed solution. The research team learns the “technological rules in management and organization” (Van Aken, 2005) and results appear as propositions that are validated for certain Context, Intervention/Agency, Mechanism, and Outcome (Denyer et al., 2008; Romme & Dimov, 2021). In line with a problem-solving approach, the organization sets the initial problem and evaluates (validates) the satisficing artifacts that solve it. It might seem that there is a limited capacity for the researcher to question the problem and the implicit performance criteria of the organization; hence an organizational change is confined to the designed artifacts. Yet, over

Andriessen, 2007

intellectual capital (IC) tool related generative mechanisms)

implementation steps (diagnosing, context)

the solution adaptation

steps (reflecting, developing K)

activity in the organization

for the design of the (types of problems, of contexts,

then adaptation to specific

theorizing, agenda setting), five

organizational conditions for

and the analysis of the design

No design theory framework intellectual capital method

concept for one generic problem,

three initial steps (designing,

implementation reveals the

evaluating, learning); two last

and organizational change; contra-indications for

problem-solving: solution

action planning, action taking,

Clear results on organization Indications and

Not detailed, described as

action research (six cases):

capital in the organization, the

discuss performance problems)

to current organizational design

principles, (3) design rules for

Reporting on intellectual

evaluate the redesign process

create a “general circle” to

organizational problems related

the art, (2) identify construction

management and workers and processes

design theory framework to

or too poor CEO involvement;

a redesign mode to face complex

organization science state of

genuine consultation between

implementation and testing Design-based research using

with the organization; No

consultancy” (avoid too strong

but a clear design process in

design, in five steps: (1)

of “works council” to provide

(4) organization design, (5)

Describe strong (re)design

implementation of “circular

No explicit design theory –

Endenburg, 2006

Comments

Results New design rules for the

Design theory framework

Science-based organization

Redesign consultative system

Method

Organizational issues

 

Four examples of papers that experiment with organizational change by designing artifacts

Romme &

Table 15.3

Design science for organizational change  341

Several interactions with the

should be more user-controlled), organization, clear learning on CMS – the capacity of the organization to design its own solution is not studied (nor its improvement)

with real-time competence tracking (but should have more feedback loops), with the integration of individual interest to develop new competences (but should incorporate

No explicit design theory, problem-solving approach with diagnostic with the organization, then solution designed by the researchers, then test and learn with the organization

Action design research – the case study is a reinterpretation of published action research (Lindgren et al., 2004): (1) problem formulation, (2) building-intervention-evaluation, (3) reflection and learning, (4) formalization of learning

Develop IT software for

competence management

system (CMS), addressing

three issues (user isolation,

ignoring emerging

competencies, rigid reporting

style)

Improvement trajectory?). The design capacity of the organization is not studied

speed over the optimal assignment, (4) making sure marketing people provide quick feedback

and test bottlenecks in the lead assignment process, (4) develop “artifact”; i.e., experiment to evolve some parameters of the

transformed into sales)

model, (5) testing, learning

the final design (alternatives? is no “sure hit,” (3) prioritizing

and consequences), (3) identify

few marketing leads are

control of the “quality” of

approach (diagnostic, model, find customers, (2) reminding there

exploratory diagnostic (cause

business-to-business, so an optimum inside the model)

emphasizing when there are new sales organization. Limited

rigorous problem-solving

focal field problem, (2)

lead black hole (why, in

2020

organizational perspective) Act on marketing lead by (1)

No explicit design theory but

Find a solution to the sales

Van der Borgh et al.,

Design science: (1) identify

Interesting result on the

Comments

Results A CMS with transparency (but

Design theory framework

Method

Organizational issues

 

Sein et al., 2011

342  Handbook of research methods in organizational change

Design science for organizational change  343 time, this stream of work leads to more applied action research principles (see Sein et al.’s seminal 2011 paper on action design research) precisely to take better account of the issue of the interaction with the target organization both at the problem formulation stage and at the evaluation stage, clarifying the loops that result from this process. These works rely, explicitly or sometimes implicitly, on Simonian problem-solving, used as a design theory analytical framework. This methodological choice is adapted to organizational situations where (a) there is a clear problem to be solved once and for all (or at least for a relatively long time) – even with creativity (Pries-Heje et al., 2019), and (b) the process of the design of the solution can be “externalized” to the research team; i.e., the design process is “isolated” from the target organization, since the organization does not design itself. This methodological choice also implies that: ● Researchers tend to propose one solution, considered as the fittest; i.e., the one that a priori fits best with the target organization. Some authors might propose several solutions – they discuss loops or cycles of trial and learning in their research process. Regardless, this leads the authors to not consider multiple solution paths; to neglect paths with “crazy” ideas, apparently unfeasible, unmarketable ones; and to give few accounts of utopia and imaginary solutions. This standpoint is particularly relevant in the case of organizations that will only implement one solution without redesign, learning, or improvements. Conversely, considering the memory of multiple solution paths, including the craziest one, can be relevant in the case of a repeated game, parallel exploration; i.e., in design situations that require strong generativity, such as cybersecurity or improvement of refugee reception (Amard et al., 2022) or in entrepreneurship (Seckler et al., in press), particularly deep tech entrepreneurship (Agogué et al., 2015). ● Researchers tend to neglect the design capacities of the target organization and, consequently, the capacity of the target organization to change its own design capacities. This clear separation between the researcher-designer and the target organization-user allows the research to only focus on the learning associated with each final solution. The interactions between the research team and the organization during the phase of the design of the solution can be neglected (see, for instance, the very interesting detailed analyses of the interaction in Sein et al., 2011, where interactions are strong during problem setting and solution evaluation, but seem absent during the phase of solution design). In a nutshell, this stream of work provides great results for organizational change (see, for instance, Romme & Dimov, 2021) as long as (1) the design issue does not require too strong generativity and (2) the organization itself is not a generative organization. Stream 3: Uncover a Specific Form of Organizational Change: Generativity Process and Generativity Building in Organizations The third stream of design science research focuses more specifically on the generativity logic in organizations. In this research stream, researchers develop a variety of advanced design theories (for synthesis, see Hatchuel et al., 2011; Le Masson et al., 2013) and mobilize them in order to account for, observe, and participate in a specific class of organizational changes: generative processes and generativity building in organizations. It comprises works that rely on a Simonian approach yet looks for complementary alternative approaches of design ration-

344  Handbook of research methods in organizational change ality to account for generativity logics beyond problem-solving (Dorst, 2006; Hatchuel, 2002; Schön, 1990). Advanced findings have been obtained at the level of leadership (Ezzat, 2017), innovation processes (BenMahmoud-Jouini & Midler, 2020), company strategy (Hooge & Dalmasso, 2015), cross-industry partnerships (Gillier et al., 2010), ecosystems of organizations (Agogué et al., 2013; Agogué et al., 2017), and public policy (Pluchinotta et al., 2019). The organization changes associated with these studies are characterized by the simultaneous generation of new artifacts and of new design capabilities in the organization. In Table 15.4, eight papers illustrate this stream of work, each of them characterized by the organizational issue it addresses, its research method, the design theory it uses as an analytical framework, and its main results. 1. This stream of work is characterized by specific organizational issues. In each paper, the research questions are all related to generativity in an organization and how organizational change could improve this generativity. Hence, in contrast with previous design science research streams, the design issue at hand requires stronger generativity and the organizational change issue consists in increasing the generativity capacity of the organization itself. 2. In this stream of work, researchers use a design theory analytical framework that is at the required level of generativity. In Sarasvathy et al. (2008), the authors introduce a new model of environment design, namely effectuation (Wiltbank et al., 2006), to be able to account for the performance of Starbucks. Many papers explain that they rely on advanced design theories such as C-K design theory to be at the required level of analysis for the generativity phenomena. They observed generativity to overcome fixation (Ezzat et al., 2017), the generativity of several new policy alternatives (Pluchinotta et al., 2019), generativity in technology entrepreneurship (Agogué et al., 2015), generativity of innovative R&D projects (Elmquist & Le Masson, 2009), generativity in cross-industry exploratory partnerships (Gillier et al., 2010), generativity of prototyping (BenMahmoud-Jouini & Midler, 2020), and generativity of new generic technologies (Hooge et al., 2016). Note that the authors use a design theory as a canonical framework that is often adapted to specific contexts. 3. Depending on the research question, the researchers adopt a specific method. This stream of work shows a large variety of methods, of which one can find a sample in Table 15.4: Sarasvathy et al. (2008) conducted thought experiments and simulations, Ezzat et al. (2017) and Agogué et al. (2015) conducted laboratory experiments, BenMahmoud-Jouini and Midler (2020) and Hooge et al. (2016) conducted observations, and Pluchinotta et al. (2019), Elmquist and Le Masson (2009), and Gillier et al. (2010) followed intervention research methods. Yet whatever the method that is chosen, it will always rely heavily on the chosen design theory framework: e.g., in the case of an analytical approach, the design theory framework will structure an analytical framework; in the case of an experimental paper, the design theory framework will structure the construction of hypotheses, the construction of the experiments, and the construction of the observations variables and instruments; and in the case of intervention research, the design theory framework will structure the elaboration of the action model (in particular the rationality of the agents in the model) and, hence, the hypotheses in terms of organizational change. 4. The research results consist of enriched models of organizational generativity, often in a dual logic to (a) uncover biases and limits in the generativity of the existing organization

Gillier et al., 2010

Elmquist et al., 2008

that would better explain Starbucks; e.g., explain

explains better than a model interactions between the four logics. Also: What are the contingency criteria and validity domain?

that a model of four logics of three, hence validating the necessity to complete models of environmental design by effectual logic (Starbucks is an anomaly for a model of three and

approaches (planning, adaptive, visionary) to design environment, depending on control and predictability of the environment – and Wiltbank et al. (2006)

logic, “logical options opened to organizational designers” (p. 340): planning/adaptive/ visionary/effectual

simulation

and organizational design

and experimentation of organizational methods

new goal, new measures (financial sustainability, structured set of ideas,

interpretative framework: follow cognitive processes, providing

Collaborative research, describing empirical practices in use and carrying out a revision of the existing theoretical management models

Evaluation of R&D projects

in the context of intensive

innovation (new framework,

(diagnostic, experiment, learning)

a boundary object (flexible enough to adapt, robust enough to maintain a common identity); reveals generic concepts

developing a method (opera) to map the innovation field of each partner and the intersections

to organize “matching” and “building” with cross-industry exploratory partnership

cross-industry exploratory

partnerships with matching

and building

Close-to-action research

actions, and learning The method acts as (but does not prescribe it) C-K theory led to

Experiment with a design-theory-based method

Overcome issues of

information for evaluation competences), new related

observation, development,

of four) New evaluation criteria:

Simon C-K theory as an

beyond independent projects)

Design theory enables

is explained by a model

add effectual, inspired by

show entrepreneurial expertise and mathematical simulation techniques” identifies three “design”

that there is no model

strategic management

Starbucks, inspired from “thought experiment (p. 341), each scenario based on a specific

Of course, it does not prove

can be interpreted as a mix

Literature review on

based on one case study and

that design their environment;

2008

Comments

Results Starbucks organization

Design theory framework

Method Construct alternative (imagined) histories of of four stories – it proves

Organizational issues

Study design organizations

Sarasvathy et al.,

Examples of papers that uncover a specific form of organizational change: generativity process and generativity building in organizations

 

Table 15.4

Design science for organizational change  345

Generativity performance is measured, the model is predictive – yet in a lab

(resp. negatively) influence idea generation

formulate hypotheses (which feedback can be

organizations and methods value structure with the overcome fixation

Analysis and comparison of prototypes (six cases)

Analyze the organizational

role of prototypes

organization)

framework

validators

stimulators, demonstrators,

Proto archetypes:

the set of policy alternatives experimental)

Analytical paper (not

the development of new modifying stakeholders’ and organization) to

organization; study of impact and learning

can be managed through new

C-K as an analytical

Design theory enables mechanism aimed at

design process (method

for stakeholders); experiment with the

Policy alternatives generation

Jouini et al., 2020

generic lesson: a generative meets the requirements.

helps propose a new

of initial decision situation (and its limits

alternatives”

consequential expansion of

mentioned, but the paper one situation plus a more

design processes (method and

Action research is not A method that works in

diagnose design fixation,

follows (implicitly) action research: diagnostic

the generation of policy

2019

Pilot case study (Puglia water management),

“Lack of methodology for

Pluchinotta et al.,

context

experiment with 60 ideators

idea generation processes

experimental)

Feedback can positively

C-K theory helps

defixating, which is not)

Experimental method: hypothesis formulation,

Leadership for defixating in

competences

and also measure fixation C-K theory: helps

Ezzat et al., 2017

organization (not

performance criteria and

Analysis of original

observation C-K as an analytical Identify specific

causality

enabling behavior

framework

hindsight bias towards

for entrepreneurs, also

design

and effectuation. Elicit

can be used as a method

a new technology platform

design of generic tech

into organizations

by combining causation

and effectuation) and

concepts and explore market opportunities for

(avoiding hindsight bias)

Analysis of the organization for generic tech

can not be easily translated

the technological path

the two logics (causation

entrepreneurs develop technology/product

technology entrepreneurship

How to organize for the

Results from lab experiments

entrepreneur deviates from

C-K theory: accounts for

Agogué et al., 2015

Hooge et al., 2016

Comments

Results How technology

Design theory framework

Method Experiment: 13 teams of young tech

Organizational issues

Understand early-stage

 

346  Handbook of research methods in organizational change

Design science for organizational change  347 and organizational processes, and (b) model, experiment, and evaluate new organizations with increased generativity. At this step, the results correspond (explicitly or implicitly) to the CIMO/CAMO framework (Context, Intervention/Agency, Mechanism, and Outcome) in the specific perspective of generativity (generativity of context, of agency, of design mechanism, of outcomes). Generally speaking, this stream of research depends on the generativity of the design theory it uses. Therefore, a regular effort in this research stream is to improve design theory itself and to account for the variety of forms of generativity design theory. This stream of work also depends on the design issues an organization faces and which the organization leans towards in increasing its generativity capacity. Comparing the Three Streams: Design Theory as a Critical Means to Account for Generativity in Organizations and Organizational Change The systematic review of these three research streams in design science research contributes to clarifying the landscape of design science research in relation to organizational change. First, design science research, as the study of design (action or artifact) in organizations, is based on different theories of design rationality. Second, these theories mainly differ in their ability to account for generativity in organizations. Third, this ability is critical for the research methods used and for the contributions they can make to organization change. We have characterized several streams of research in design science. They are contrasted but also contingent and complementary (see the comparison in Table 15.5). Depending on the research issue, the researcher can choose one or another, with specific consequences in terms of research topic, research investigations and methods, generating scholarly contributions, and bringing about change. We briefly summarize these main features below to guide the researcher to a choice that better fits his or her ambitions and circumstances. In terms of research perspective and research topic: describing a new design-driven method or organization will lead to Stream 1. Changing an organization by providing a newly designed solution to an organizational problem corresponds to Stream 2. Focusing on generativity building in organization corresponds more to Stream 3. Research following Stream 1 will contribute to organizational change by shedding light on unique, original organizations that might be imitated, reproduced, and used as a model for other implementations. Research following Stream 2 will contribute to organizational change by bringing changes to the organization through newly designed artifacts, analyzing the real-life experiment impact, and enabling the validation of new organizational principles. Research following Stream 3 favors an approach to organizational change that focuses on building/increasing the generativity capacities of the organization, hence endogenizing the generativity-building capacity inside the organization. Each choice has clear methodological consequences and each stream puts emphasis on specific aspects: research in Stream 1 requires a capacity to identify original and unique design-oriented organizations, and the capacity to investigate these organizations, and finally it requires that the analytical framework will take care of embedding of the organizational description into the well-established organizational descriptors. Research in Stream 2 requires finding an organization that accepts experimenting with a newly designed solution. It then requires that the research team is able to design a relevant solution and, methodologically speaking, it requires carefully following the methodological steps of action design research.

“generativity newcomers”

organizations that are

…)

generativity building in

organizations

under preservation) in

study

intervention research

generativity process and

(e.g., generativity

generativity building in organizations

the phenomena under

experiment, simulation,

(observation, laboratory level of generativity of

organizations

a specific form of

generativity capacities New forms of generativity Organizational/managerial rules for

would increase their

organizational change –

Variety of methods

Focus on generativity building in

Stream 3: uncover

Design theory at the

Development Goals …) with organization that

Nations’ Sustainable

generativity (the United

issues requiring more

researchers)

tested by (researcher-made) artifacts

Organizational/managerial rules induced and Towards design

artifacts designed by

approach

research (solution

capacities)

change by designing

artifacts

problem-solving

a strong link to action

reference to organization generativity

with organizational

Codified approach, with Simonian,

Organizational change (without

Stream 2: experiment

of design rationality

challenges” …)

organizations? (associated

organizations

with “transitions,” “grand

New design-driven

Describe unique design-driven methods/

(meaning creation)

to infer specific

Design theory used

Potential developments

without a specific model

organizations

original design processes

Results

performance indicators

Observation

Characterize design-driven methods/

Stream 1: observe new,

framework

Design theory

and organizations –

Method

Organizational issues

Comparison of three streams of research in design science

 

Table 15.5

348  Handbook of research methods in organizational change

Design science for organizational change  349 Research in Stream 3 will rigorously clarify its underlying design theory, then rely on this theoretical framework to build the relevant empirical approach that can be more or less interventional (see the set of examples analyzed before). Depending on the choice of the empirical approach, research in Stream 3 might also require the identification of an organization that is interesting for the research analytical approach, or ready/willing to experiment with organizational change oriented towards increased generativity capacities. This confirms the introduction: all three research streams are extremely demanding – yet each of them can lead to great results in research on organization change (as shown by the sample of papers that illustrate the three streams). Each stream can unfold in specific research directions, echoing contemporary societal challenges (to keep a relevance–rigor balance). Stream 1 helps one to study newly emerging design-driven organizations, which may be associated with contemporary design issues of “transitions” or “grand challenges” (Zolfagharian et al., 2019). Stream 2 focuses on the study of design issues requiring more generativity with organizations that are expected to solve several design issues without having specific generative capacities. Stream 3 focuses on the study of new organizational forms of generativity associated with “grand challenges” where, paradoxically, it is expected that the creation will contribute to preservation; i.e., preserve resources, preserve biodiversity, preserve society, preserve democracy, preserve mobility, etc., hence generativity that is creative preservation and not creative destruction (Carvajal Pérez et al., 2020; Hatchuel et al., 2019). These new forms of generativity might also require the development of new generative capacities in organizations that were not used to being design experts. For example, new design-oriented teams and processes were recently set up and studied in organizations that were usually not considered to be design-oriented (Harlé et al., 2022). The challenge was to increase the generativity capacity of a plant that was intended to follow the routines of a design organization in public administration (Pluchinotta et al., 2019). The studies explored how public administration can not only choose and control rules for collective action in industry but can also organize complex design process to enable all actors, including the administration itself, to invent new rules for collective action, and to design organization in expert corporations and professions. Hence it appears that all three approaches are particularly relevant to researching contemporary transformations of collective action, and conversely, design science research on these contemporary transformations will also lead to interesting advances for organizational change.

CONCLUSION: DESIGN SCIENCE FOR ORGANIZATIONAL CHANGE: HOW DESIGN THEORY UNCOVERS AND SHAPES GENERATIVITY LOGICS IN ORGANIZATIONS In this chapter, we showed that design science research, defined as the research on design (action and artifacts) in organizations, is deeply related to critical expectations on organizational change, namely the capacity to combine rigor and relevance and be able to address the critical issue of generativity in an organization – but these expectations are hard to meet because of the critical requirements and epistemological issues they raise. Meeting these requirements and addressing these epistemological issues is related to the choice of a design theory as an analytical framework for the generativity phenomena under study. We showed three specific research streams in design science research that are adapted to specific forms

350  Handbook of research methods in organizational change of generativity and hence provide differentiated methodological guidelines to address specific design and organizational issues – each of these streams relates differently to design theory and intervention methods. Each of these streams proposes a coherent research logic (organizational issues that can be addressed, associated methods, design theory analytical framework, type of results) and can lead to unique results on organizational change and specifically on generativity logic in organizational change. Hence design science research appears as a demanding but unique research method to study organizational change. How does it interfere with other organizational change methods? Interestingly many streams of work in design science research depend on rigorous intervention research methods and epistemology – described, for instance, in Coghlan’s chapter on action research in this book. This facet of design science research is not specific and actually builds on well-established research methods. Design science research is more unique in its way of putting emphasis on design and the logic of generativity in an organization – in this perspective, design science research brings a specific, original contribution to the research community of organizational change: specific research techniques associated with generativity observation, analysis, and experimentation. These techniques are largely associated with progress in design theory and the capacity of design theory to account for new forms of generativity: design theory uncovers and shapes generativity logics in organizations that, conversely, can challenge design theory. Undisputedly, such an active feedback loop between design theory and generativity in collective action leads to relevant results for practitioners. But it also leads to fundamental scientific results: favoring this active feedback loop between design theory and generativity in collective action, design science research can be one of the contributors to fundamental scientific advances on generativity processes, a contemporary scientific challenge at the heart of scientific disciplines as diverse as computer science, life science, mathematics, or physics. Studying with full rigor generativity in organized collective action, design science research appears as a relevant method to strengthen management science as one of the contemporary basic sciences.

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Design science for organizational change  353 Le Masson, P., Dorst, K., & Subrahmanian, E. (2013). Design theory: History, state of the arts and advancements. Research in Engineering Design, 24(2), 97–103. Le Masson, P., Hatchuel, A., & Weil, B. (2016). Design theory at Bauhaus: teaching “splitting” knowledge. Research in Engineering Design, 27, 91–115. Lindgren, R., Henfridsson, O., & Schultze, U. (2004). Design principles for competence management systems: A synthesis of an action research study. MIS Quarterly, 28(3), 435–72. Peffers, K., Tuunanen, T., Rothenberger, M.A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. Pluchinotta, I., Kazakçi, A.O., Giordano, R., & Tsoukiàs, A. (2019). Design theory for generating alternatives in public decision making processes. Group Decision and Negotiation, 28, 341–75. Pries-Heje, J., Baskerville, R., Storey, V., & Kaul, M. (2019). Inducing creativity in design science research. In B. Tulu, S. Djamasbi & G. Leroy (Eds.), Extending the Boundaries of Design Science Theory and Practice (pp. 3–17). Springer International. Radaelli, G., Guerci, M., Cirella, S., & Shani, A.B. (2014). Intervention research as management research in practice: Learning from case in the fashion design industry. British Journal of Management, 25(2), 335–51. Romme, A.G.L., & Dimov, D. (2021). Mixing oil with water: Framing and theorizing in management research informed by design science. Designs, 5(1), 1–16. Romme, A.G.L., & Endenburg, G. (2006). Construction principles and design rules in the case of circular design. Organization Science, 17(2), 287–97. Sarasvathy, S.D. (2003). Entrepreneurship as a science of the artificial. Journal of Economic Psychology, 24(2), 203–20. Sarasvathy, S.D., Dew, N., Read, S., & Wiltbank, R. (2008). Designing organizations that design environments: Lessons from entrepreneurial expertise. Organization Studies, 29(3), 331–50. Schön, D.S. (1990). The design process. In V.A. Howard (Ed.), Varieties of Thinking: Essays from Harvard’s Philosophy of Education Research Center (pp. 110–141). Routledge. Seckler, C., Mauer, R., & Vom Brocke, J. (in press). Design science in entrepreneurship: Conceptual foundations and guiding principles. Journal of Business Venturing Design. Sein, M. K, Henfridsson, O., Purao, S., Rossi, M., & Lindgren, R. (2011). Action design research. MIS Quarterly, 35(1), 37–56. Shai, O., & Reich, Y. (2004). Infused design: I theory. Research in Engineering Design, 15(2), 93–107. Shani, A.B., Mohrman, S., Pasmore, W.A., Stymne, B.A., & Adler, N. (Eds.) (2007). Handbook of Collaborative Management Research. SAGE. Simon, H.A. (1969). The Sciences of the Artificial. MIT Press. Simon H.A. (Ed.) (1979). Models of Thought (Vol. 1). Yale University Press. Susman, G.I., & Evered, R.D. (1978). An assessment of the scientific merits of action research. Administrative Science Quarterly, 23(4), 582–603. Tomiyama, T., & Yoshikawa, H. (1986). Extended General Design Theory (Vol. CS-R8604). Centre for Mathematics and Computer Science. Van Aken, J.E. (2005). Management research as a design science: Articulating the research products of Mode 2 knowledge production in management. British Journal of Management, 16, 19–36. Van Aken, J.E., & Berends, H. (2018). Problem Solving in Organizations: A Methodological Handbook for Business and Management Students (3rd ed.).Cambridge University Press. Van Aken, J.E., Chandrasekaran, A., & Halman, J. (2016). Conducting and publishing design science research. Journal of Operations Management, 47(1), 1–8. Van der Borgh, M., Xu, J., & Sikkenk, M. (2020). Identifying, analyzing, and finding solutions to the sales lead black hole: A design science approach. Industrial Marketing Management, 88, 136–51. Verganti, R. (2003). Design as brokering of languages: Innovation strategies in Italian firms. Design Management Journal, 14(3), 34–42. Vourch, G., Brun, J., Ducrot, C., Cosson, J-F., Le Masson, P., & Weil, B. (in press). Using design theory to foster innovative cross-disciplinary research: Lessons learned from a research network focused on antimicrobial use and animal microbes’ resistance to antimicrobials. Veterinary and Animal Science. Warfield, J.N. (1996). A Science of Generic Design: Managing Complexity through Systems Design (2nd ed.). Iowa State University Press.

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16. Longitudinal designs, big data, and social network analysis in organization development and change research Ramkrishnan (Ram) V. Tenkasi, William B. (Bart) Brock, and Donna L. Ogle

INTRODUCTION A common aim of organization development and change (ODC) is to understand the dynamics of change and develop and apply models that enable organizations to implement intentional change to improve overall organizational effectiveness (Cummings & Worley, 2014). Yet with this common aim, there remains major dissatisfaction with our ability as a field to understand the dynamics and impact of change over time rather than restricting ourselves to a specific point in time. For the most part, quantitative studies in ODC rely on broad cross-sectional analyses that only provide a snapshot picture of change dynamics at a point in time at the expense of longitudinal temporal analyses that can provide causally valid insights on change dynamics over time (Huber & Van de Ven, 1995; Van de Ven & Poole, 1995). Less of a discontent and more an opportunity for ODC research is incorporating the technology of Big Data, and social media analytics that can offer ODC a new future in dealing with issues of organizational development and change (Church & Burke, 2017; Church & Dutta, 2013). Longitudinal Studies and ODC Longitudinal studies have found numerous applications in social science-related fields such as social psychology, developmental and clinical psychology, sociology, and consumer research (Menard, 2007; Ployhart & Vandenberg, 2010; Ruspini, 2003). Studies have looked at rapid fluctuations in thought, emotions, and behavior from moment to moment or day to day; developmental trends across the life span of individuals; life events throughout lifetimes; and changes in consumer attitudes and behaviors brought about by advertisement campaigns over time (Gottman, 1995). Longitudinal studies track the same people over time compared to cross-sectional studies in which different individuals are compared at a point in time. Observing changes is more accurate in longitudinal studies. Despite their popularity in related fields, longitudinal studies in ODC have not enjoyed widespread use. Studies are few and far between. Woodman and Sherwood (1980) conducted a field experiment that entailed a pre-test, intervention, and post-test to assess the impact of a team-building intervention on the performance of teams. Romanelli and Tushman (1994) undertook a formal test of the punctuated equilibrium theory. Their longitudinal study examined the life histories of 25 mini-computer producers founded in the United States between 1967 and 1969. Their quantitative results showed that a large majority of organizational transformations result from rapid and discontinuous change. Small changes in strategy, structure, 355

356  Handbook of research methods in organizational change etc., did not accumulate to produce fundamental transformations. Collins (2001) used longitudinal financial analyses of market performance (stock prices) to separate good companies from great ones. Tenkasi and Kamel (2016) used longitudinal survival analysis to assess the efficacy of four turnaround strategies that enabled companies to emerge out of bankruptcy compared to those that were disbanded while under bankruptcy. Brock and Tenkasi (2015) employed Generalized Estimating Equations (GEE) to assess the impact of three interventions (dialogical, diagnostic, and combined diagnostic-dialogical) over a period of several years on organizational outcomes. Although the use of longitudinal analyses is limited in ODC, there have been increasing calls to emphasize longitudinal research in the study of change and provide a stronger understanding of the temporal dynamics of change and draw better causal inferences about organizational change (Abbott, 1990; Avey et al., 2008; Glick et al., 1990; Isabella, 1990; Pettigrew, 1990; Sabherwal & Robey, 1993; Skup, 2010; Van de Ven & Poole, 1995). Longitudinal analysis helps provide and reveal measurements of organizational change over time for predictive, explanatory, and/or descriptive purposes (Ballinger, 2004; Schaie & Hertzog, 1982; Twisk, 2013), and it can reveal changes in the intensity or direction of relationships (Vittinghoff et al., 2012). Longitudinal designs empower change research by utilizing time as a frame of reference, thus allowing researchers to provide temporal context for organizational changes and capture “reality in flight” (Pettigrew, 1990, p. 270). In this chapter, we discuss and compare traditional cross-sectional designs with longitudinal designs, including costs and benefits. Next, we consider two quantitative methodological approaches for studying organizational change dynamics that directly address the felt lacunae in terms of longitudinal change research through an exposition of Accelerated Failure Time (AFT) analysis and GEE. A third quantitative methodological approach focuses on Big Data and social network analysis (SNA) as future opportunities for ODC change-related research. The rest of the chapter progresses as follows: We start with a brief discussion of the differences between quantitative cross-sectional change research and longitudinal change research. We describe in detail two robust methodologies, AFT and GEE, as appropriate methods for quantitative observational studies of longitudinal change. The exposition of each methodology (AFT and GEE) is followed by a case study involving details of the use of the respective analytical methodology. We move on to explain Big Data and SNA as recent developments that can be harnessed for organizational change research. We present a case study from the authors’ research that used Big Data from the social media site Twitter in conjunction with SNA. Our conclusion draws implications of quantitative longitudinal research methods, Big Data, and social network analyses for studying ODC.

CROSS-SECTIONAL AND LONGITUDINAL DESIGNS COMPARED Cross-sectional and longitudinal change research can be primarily qualitative, quantitative, or mixed methods (Albery & Munafò, 2008). In this chapter, however, we focus on quantitative designs for cross-sectional and longitudinal approaches to change.

Longitudinal designs, big data, and social network analysis  357 Cross-Sectional Observational Design The quantitative cross-sectional study design is predominantly a type of observational study design, although the occasional variation is the cross-sectional quasi-experimental design, also referred to as pseudo longitudinal design (Van Buuren, 2018). In contrast to experimental studies, observational studies involve the study of participants without any forced change to their circumstances; that is, without any intervention. Although the participants’ behavior may change under observation, the intent of observational studies is to investigate the “natural” state of risk factors and their association with outcomes (Sedgwick & Marston, 2013). In a cross-sectional study, the investigator measures the outcomes (dependent variable) and the exposures (predictor variables) in the study participants at the same time. Participants are selected based on the inclusion and exclusion criteria set for the study. Once the participants have been selected for the study, the investigator follows the study to assess the exposure (predictors) and the outcomes. The defining feature of a cross-sectional study is that it can compare different population groups at a single point in time. Think of it as taking a snapshot at a moment in time. Cross-sectional designs largely rely on population-based surveys, and these studies can usually be conducted relatively fast and are inexpensive. However, since this is a one-time measurement of exposure (predictors) and outcomes, it is difficult to derive causal relationships from cross-sectional analysis. This type of study is not able to provide a definitive relationship between cause and effect (a cause-and-effect relationship is one where one action [cause] makes another event happen [effect]). This is major because a cross-sectional study offers a snapshot of a single moment in time and does not consider what happened before or what happens after the snapshot moment of observation. Since cross-sectional studies involve data collected on both predictors and outcomes at a defined time, they rely on the assumption that there is really no time lag between cause and effect. They are viewed as occurring simultaneously. However, in the real world, even a simple act such as kicking a soccer ball or firing a gun has a time interval between the cause action and the effect action. Cross-sectional data, therefore, cannot be used to infer causality since they overlook temporal dynamics. At best, they are more suited to assess the prevalence of particular factors or conditions that may be related. But they cannot be used to answer questions about the causes of phenomena or the results of an intervention. The snapshot moment in time associated with cross-sectional designs also makes it difficult to discern which variable is the cause and which is the effect, due to the lack of temporal ordering. Let us take the example of a cross-sectional study that looks at the relation between diet and exercise (predictor variables) and being overweight/obese (outcome variables). Assume a study of 250 individuals, where through a survey we assess their dietary habits, exercise habits, and body mass index at one point in time. However, imagine a very plausible scenario where individuals who are overweight/obese have started to exercise more or altered their feeding habits by eating more salads. Hence, in a cross-sectional survey, we may find that overweight/obese individuals are also more likely to eat salads and exercise more, thus reversing the hypothesized relationship between exercise (predictor variables) and being overweight/obese (outcome variables) – a case of reverse causality. Therefore, we must be careful about interpreting the associations/relationships and direction of associations/relationships from a cross-sectional survey, let alone imputing causality, since these designs lack temporal sequencing.

358  Handbook of research methods in organizational change Cross-sectional studies typically rely on variance theory explanations of organizational phenomena. Variance theory is based on identifying the input factors (independent variables) that statistically explain variations in some outcome criteria (dependent variables) and is not concerned with the temporal sequence of events that unfold as organizational change occurs (Huber & Van de Ven, 1995; Mohr, 1982). Further, cross-sectional designs count on an implicit assumption that the nature of relationships between variables is stable over time. This assumption comes at the cost of requiring caution if the results of one time period are assumed to be valid at some different point in time, a common pitfall of cross-sectional research, particularly in organizational change. Time as a potential confounder is glossed over. A major advantage of cross-sectional studies, as we indicated earlier, is that they can be conducted more quickly and cost-efficiently than longitudinal studies. Therefore, a benefit for researchers is to start with a cross-sectional study to first establish whether there are links or associations between certain variables. The second phase is to set up a longitudinal design to study cause-and-effect dynamics if associations are observed in the cross-sectional phase. Cross-Sectional Quasi-Experimental Design One variant to the predominantly observational nature of cross-sectional studies is the quasi-experimental design, also referred to as the pseudo longitudinal design (Van Buuren, 2018). In this design, typically, outcome data (dependent variable) are collected at a single point in time based on some natural expressed variability in the sample population, such as age, gender, etc., that constitutes the predictor variable. For example, Thomas et al. (2015) compared two groups of primary medical care centers in Sweden. Three primary medical care centers (denoted as the intervention group) were commissioned to implement patient lifestyle management interventions, and three control centers used a traditional model of care. Both groups were compared on questionnaire-based outcome measures of several health status indices. The cross-sectional quasi-experimental design is clearly a better approach than the observational cross-sectional design in that there is some minimal temporal ordering achieved between cause and effect. It is also less resource-intensive than a proper longitudinal design. However, some of the concerns of cross-sectional design, such as overlooking the confounding effects of time due to a lack of repeated time-based measurements and the implicit assumption that the nature of relationships between variables is stable over time, still remain; hence the term “pseudo longitudinal design” (Van Buuren, 2018). Longitudinal Design A quantitative longitudinal study involves repeated observations of variables (i.e., predictors, controls, and outcomes) over short or long periods of time. Sometimes longitudinal studies can last from a few years to even decades, depending on what kind of information needs to be obtained. The defining aspect of longitudinal methods is analyzing data derived from longitudinal and/or repeated measurements of a subject (e.g., people, items, processes, events) over time. This design overrides the confounding effects of time to either confirm or disconfirm the originally proposed hypotheses. It can either establish the potential constancy of causality over time or alternately discover the varying nature of causality over time. Longitudinal studies help one discover the potentially variable patterns over time, leading to more precise causal

Longitudinal designs, big data, and social network analysis  359 relationships and research outcomes. When researching developmental trends, longitudinal studies allow you to discover changes across lifespans and arrive at valid research outcomes. The benefit of a longitudinal study is that researchers can detect developments or changes in the characteristics of the target population at both the group and the individual level. The key here is that longitudinal studies extend beyond a single moment in time. As a result, they can establish sequences of events. Longitudinal process studies are fundamental to our understanding of the temporal dynamics of organizational life and to the development and testing of theories of organizational adaptation, change, innovation, and redesign over time (Huber & Van de Ven, 1995). Experimental Longitudinal Design Quantitative longitudinal research can be both experimental and observational and/or a combination (Albery & Munafò, 2008). Most experimental designs that involve a pre-test, intervention, and a post-test or a post-test-only design after an intervention are considered notionally longitudinal in that they entail some temporal ordering of observations to determine cause and effect (Shadish et al., 2002). However, others have questioned the adequacy of the pre-test, post-test design in truly assessing the form and amount of change (Gottman, 2013; Rogosa, 1995). While it is true that the pre-test, post-test design is the most common design in the study of change, Gottman contends that two repeated observations “a longitudinal study [does] not make” (Rogosa, 1995, p. 6). Two observations can at best only estimate the amount of change, and the amount of change can be deceptive if the rate of growth is not constant. If the rate of change depends on time and is therefore not constant, then the true estimate will depend crucially on the times of measurement. Observations of individuals at a different set of two time points can give contradictory results. Ratings/rankings among individuals can change as a function of the times of measurement, and this will also be true of correlations of other variables with change scores. Thus, a two-observation-based “pre-test, post-test design may be limited for the study of individual change and individual differences in change” (Gottman, 2013, pp. 4–5; Rogosa, 1995). Comprehensive longitudinal experimental designs such as randomized control trials (RCTs) with multiple observations of experimental and control groups allow the greatest reliability and validity of statistical estimates of experimental treatment effects. Randomization involves randomly allocating the experimental units and control units across the treatment groups. Randomization reduces bias by equalizing other factors that have not been explicitly accounted for in the experimental design (Shadish et al., 2002). However, experimental designs based on non-randomization but that are naturally occurring longitudinal field experiments are equally valid as RCTs (Brock & Tenkasi, 2015; Oldham & Brass, 1979). Observational Longitudinal Designs Much like the observational cross-sectional study, in a longitudinal observational study researchers do not interfere with their subjects. Data are gathered from the same sample repeatedly and collected over an extended period of time. There are three different types of observational longitudinal designs: panel study, cohort study, and cross-sequential design. Cohort designs can be prospective or retrospective.

360  Handbook of research methods in organizational change Panel studies are a particular design of longitudinal study in which the unit of analysis is followed at specified intervals over a long period, often many years. The key feature of panel studies is that they collect repeated measures from the same sample at different points in time. Data are collected based on pre-specified and sometimes emergent hypotheses over time. Cohort studies compare outcomes between or among subgroups of participants defined based on whether they are exposed or not exposed to a particular risk or protective factor (an exposure, for example, can be whether companies under bankruptcy protection hire a Chief Turnaround Officer or not). They provide information on how these exposures are associated with changes in the risk of particular downstream outcomes (e.g., whether hiring a Chief Turnaround Officer while under bankruptcy protection is associated with successful emergence from bankruptcy or disbandment under bankruptcy). Cohort studies take individuals with exposures and look for outcomes. Cohort studies are longitudinal; that is, they involve follow-up of a cohort of participants over time. In a retrospective study, the researcher depends on existing information from previous systematic investigations to discover patterns leading to the study outcomes. In other words, a retrospective study looks backward. It examines exposures to suspected risk or protection factors concerning an outcome established at the start of the study. In a prospective longitudinal study researchers will follow and observe a group of subjects over a period of time to gather information and record the development of outcomes, which is typical of panel studies. A cross-sequential design is a method that combines the benefits of both cross-sectional and longitudinal designs. It aims to correct for some of the problems inherent in the cross-sectional and longitudinal designs. Rather than studying particular individuals across a whole period of time (e.g., 60 companies founded in 1996 and studied between 1996 and 2022) as in a longitudinal design, or multiple companies of different starting ages at one time (e.g., 60 companies founded between 1995 to 2005 and observed only in 2020) as in a cross-sectional design, in a cross-sequential design the researcher chooses a fixed study time window (e.g., 20 years, 2010 to 2020) to study multiple companies of different starting ages (e.g., 60 companies founded from 1995 to 2005 and studied each year from 2010 to 2020). Another example from a developmental psychology perspective is selecting a group of 4-, 6-, and 8-year-olds, from different school environments, and observing them every six months for 10 years (2010 to 2020) to assess the development of their reading skills and math skills. Benefits of Longitudinal Designs There are several benefits of longitudinal studies: (1) they help discover variable patterns over time, leading to more precise causal relationships and research outcomes; (2) when researching developmental trends, they allow the discovery of changes across lifespans; (3) they are highly flexible, where the researcher can adjust the study’s focus while it is ongoing; (4) they collect unique, long-term data and highlight relationships that cannot be discovered in a short-term investigation; (5) they enable additional data collection to investigate unexpected findings at any point given the longitudinal nature of the study. Difficulties of Longitudinal Designs There are several difficulties associated with longitudinal designs: (1) it is difficult to predict the results of longitudinal studies because of the extended time frame; (2) it may take several

Longitudinal designs, big data, and social network analysis  361 years before the data begin to produce observable patterns or relationships that can be monitored particularly in prospective studies; (3) it costs lots of money to sustain a research effort for years; (4) longitudinal studies require a large sample size, which might be challenging to achieve; (5) longitudinal studies often experience panel attrition – some will be unable to complete the study due to reasons like changes in contact information, refusal, incapacity, illness, or simply losing the motivation to participate; (6) longitudinal analysis poses thorny challenges in data structure, such as non-normal distributions, missing records, and within-subject correlation, otherwise known as serial correlation; (7) they can require complex analytical methods – particularly when non-experimental quantitative and qualitative data are mixed together, something very prevalent in ODC.

APPROACHES TO LONGITUDINAL ANALYSIS There are several viable approaches to longitudinal analysis, including Repeated Measures ANOVA, Linear Mixed Models, GEE, and AFT. Yet practitioners are often challenged in their implementation. However, before we approach longitudinal analysis, an important consideration for the researcher engaging in such analyses is considering the appropriate data structure that will best enable the type of analyses the investigator is seeking. Data Structure for Longitudinal Analysis Longitudinal data can be coded into “long” and “wide” formats. A wide dataset will have one record for each individual. The observations made at different time points are coded as different columns. In the wide format, every measure that varies in time occupies a set of columns. In the long format, there will be multiple records for each individual. Some variables that do not vary in time are identical in each record, whereas other variables vary across the records. The long format also needs a “time” variable that records the time in each record and an “id” variable that groups the records from the same person. A simple example of the wide format is as follows, as expressed in Table 16.1. Table 16.1

Example of data structure for longitudinal analysis

id age Y1 Y2 1 14 28 22 2 12 34 16 3… In the long format, this dataset looks like the following: id age Y 1 14 28 1 14 22 2 12 34 2 12 16

Both formats have their advantages. If the data are collected at the same time points, the wide format has no redundancy or repetition. Elementary statistical computations like calculating means, change scores, age-to-age correlations between time points, or the t-test are easy to do in this format. The long format is better at handling irregular and missing data. Also, the long

362  Handbook of research methods in organizational change format has an explicit time variable available that can be used for analysis. Graphs and statistical analyses are easier in the long format. This format is preferred for complex longitudinal analyses. Classical analysis of variance and multivariate analysis of variance techniques for repeated measures and structural equation models for longitudinal data assume the wide format. Modern multilevel techniques such as survival analysis, AFT analyses, longitudinal GEE, and time-based statistical graphs, however, work only from the long format. The distinction between the two formats is a first stumbling block for those new to longitudinal analysis. Despite the numerous statistical methodologies available to examine longitudinal data, in this chapter we demonstrate two practical applications of longitudinal analysis in the context of ODC. The first application concerns the employ of AFT in the context of speed of emergence from bankruptcy protection. The emphasis here is to identify the turnaround strategies that accelerate or decelerate the speed of emergence of a company from bankruptcy protection. The second application describes the use of GEE in studying the impact of three different interventions on organizational outcomes (Brock & Tenkasi, 2015; Brock & Tenkasi, 2019).

ACCELERATED FAILURE TIME ANALYSIS (AFT) AS A METHOD TO ANALYZE THE ACCELERATORS AND DECELERATORS OF THE LENGTH OF TIME TO CHANGE AFT is a robust quantitative longitudinal method to assess the association between time and change. As a method, it is particularly useful in statistically understanding what factors speed up or slow down change. ODC has a long history and interest in the relationship between time and change. William Pasmore (Pasmore et al., 2019; Shani, 2021) indicated the need for and developed a model of fast-cycle socio-technical system change. Meyer and Purser (1993) proposed a six-step model to become a fast cycle time competitor. Tenkasi, Mohrman, and Mohrman (1998) focused on faster learning for accelerated organizational transitions. Van de Ven and Poole (1995) examined how and why innovations develop over time. Huy’s (2001) widely recognized article teased out the inter-relationships between time, temporal capability, and planned change. A second article (Kunisch et al., 2017) focused on time in strategic change research. The popular press has also engaged in the merits of fast vs. slow organizational change (Tullman, 2018). Despite the strong recognition of the role of time in ODC and the potential benefits of accelerated or fast change that sticks, most studies involving time and change are prescriptive, conceptual, or involve qualitative longitudinal analyses. To our knowledge, there has been a lack of quantitative studies that can precisely and statistically associate the determinants of fast change or slow change. AFT is a powerful method that can shed light on time’s association with change. AFT Explained AFT is a form of survival analysis that is concerned with the modeling of time to a particular discrete event. Examples of discrete events can be death, disease occurrence, disease recovery, successful change implementation, failure of change implementation, emergence out of bankruptcy, disbandment under bankruptcy, and failure in mechanical systems. Overall, survival

Longitudinal designs, big data, and social network analysis  363 models relate the time that passes before some event occurs to one or more co-variates that may be associated with that quantity or length of time to the event occurrence. Survival analysis is known as reliability analysis in engineering, duration modeling in economics, and event history analysis in sociology. A key metric in assessing the effect of a co-variate on the occurrence of an event is the hazard rate. The hazard rate describes how the hazard of event occurrence varies in response to explanatory co-variates. For example, in medical research, taking a drug (co-variate) may halve a patient’s hazard rate for an event such as a “stroke or heart failure” occurring. In an engineering context, using a certain material to construct a component may double its hazard rate for failure. Co-variates need not be restricted to binary predictors and can also include continuous co-variates. In the case of continuous co-variates, it is assumed that each unit increase in a co-variate is multiplicative in terms of the hazard rate. The hazard rate responds exponentially where one unit increase of the co-variate results in proportional scaling of the hazard rate. Some common terms associated with survival analysis include: 1. Event: Death, disease occurrence, change implementation, disease recovery, emergence from bankruptcy, disbandment under bankruptcy, or other experience of interest. 2. Time: The time from the beginning of an observation period (such as change launch date or date of filing for bankruptcy) to the time of an event, such as change implementation completion date or successful emergence from bankruptcy or sometimes the end of the study by the researcher and/or attrition on the part of a subject. 3. Censoring/Censored observation: Left censoring occurs when we have some relevant information missing about a subject since the information pertains to a time period before the observation period of the study started. Right censoring is that nothing is known about the subject regarding the status of event occurrence after the observation period. The subject is censored in the sense that nothing is observed or known about that subject after the time of censoring. A censored subject may or may not have an event after the end of observation time. AFT analysis is a special form of survival analysis that is different from the more frequently employed proportional hazard model (Kay & Kinnersely, 2002). Whereas the proportional hazard model assumes the effect of a co-variate is to multiply the hazard of the discrete event occurrence by some constant, the AFT model is based on the idea that the effect of a co-variate is to accelerate or decelerate the “time to an event.” The outcome variable is the “time to a discrete event” and not the “occurrence of the discrete event itself” (Kay & Kinnersely, 2002). The goal of AFT is to identify what co-variates (factors) accelerate or decelerate the “length of time” it takes to reach an outcome (event). The hazard ratio in this case indicates, for example, how much a co-variate (factor) hastens or impedes the time taken to reach a discrete outcome (event). AFT focuses on singular outcomes, such as death, recovery, emergence of disbandment, and co-variates (factors) that speed up or slow down the time to the occurrence of the event. AFT is not based on binary outcome assumptions. So, essentially, it tries to answer the question, for example, of what factors accelerate and what factors decelerate the time for a company to emergence from bankruptcy. A second question using the same example of bankruptcy is: what factors accelerate and what factors decelerate the time for a company to disband while under bankruptcy? Each question requires independent AFT analyses. AFT is not a variance-based approach and does not assume that co-variates (factors) that hasten speed

364  Handbook of research methods in organizational change or length of time to emergence also decelerate or slow down the length of time to disbandment. In ODC thought this would reflect best in Herzberg’s theory of motivation. Co-variates that have a potential causal association to the Hygiene factors are not the co-variates that enjoy a causal association with the motivating factors of Herzberg’s model. Running an AFT Analysis Most statistical software programs such as SPSS and STATA have the option to run an AFT analysis. The first step in conducting an AFT is to choose between a wide vs. long data format depending on whether the data, in terms of both outcomes and co-variates/predictors, are non-recurrent or recurrent over time. The wide format is best used for non-recurrent data, while the long format is required for recurrent data that are linked to a time interval stamp. The difference between regular survival analysis data and recurrent data is that the wide regular data format only uses one ID to identify one record. But in the long format recurrent data, the subject id, and time interval ID are necessary for identifying different records. The AFT model supports four survival time distributions: Exponential, Weibull, Log-normal, and Log-logistic. For selecting the better fitting model, three Information criteria can be used: Akaike Information Criteria (AIC), corrected Akaike Information criterion, and the Bayesian Information Criterion (BIC). The better fitting model is selected according to the value of the information criterion. The hazard function/ratio calculated indicates whether a co-variate is significant and whether it accelerates or decelerates “time to event,” and by how much it hastens or impedes “time to event” whether the event may be emergence from bankruptcy, disbandment under bankruptcy, successful change implementation, or failed change implementation.

CASE 1: APPLICATION OF ACCELERATED FAILURE TIME ANALYSIS The application context of AFT described below relies on a prior study conducted by the first author with a colleague (Tenkasi & Kamel, 2016). The larger rationale for the study was to understand poorly performing firms and their strategies for recovery, an understudied topic in ODC. Firms entering and attempting to emerge out of bankruptcy represent the worst cases of poorly performing and financially distressed organizations. A focus on the organizational turnaround strategies they employ to emerge out of bankruptcy and further sustain their survival post-emergence offers a naturally occurring experiment (Oldham & Brass, 1979). The primary research questions focused on understanding the range of turnaround strategies employed by firms to emerge out of bankruptcy, the association of these strategies to firm emergence (vs. disbandment) and long-term survival, and, of specific interest to the application of AFT, what turnaround strategies accelerate or decelerate “time to emergence,” and what turnaround strategies accelerate or decelerate “time to disbandment”? Once a company files for bankruptcy protection under Chapter 11 of the U.S. Bankruptcy Code and is accepted by the court (vs. liquidated under Chapter 7), the company typically has about 18 months or 547 days to prove to the court that it is “worth more alive than dead.” This determination by the court to let the company emerge or be disbanded during the protection

Longitudinal designs, big data, and social network analysis  365 time of 18 months depends on the soundness of the company’s financial and business turnaround strategies. Data Collection Methods for the AFT Analysis We identified over 1800 publicly listed companies that had filed for Chapter 11 protection between 1983 and 2003. From this larger list, we randomly extracted 500 companies (a 28 percent sample) with a view to balance the year of filing, size, and industry groupings. Next, we collected qualitative data on each organization from a variety of sources, including Securities Exchange Commission (SEC) annual and quarterly filings (10-K, 8-K filings), company annual reports, and particularly Section 1125 reorganization plan filings available from US bankruptcy courts with a view to understanding their turnaround strategies. Our final list included 142 companies that attempted bankruptcy-protected reorganization between 1983 and 2003, for which we were able to collect complete information. For each firm, we compiled data on the month and year of Chapter 11 filing, the month and year of emergence from bankruptcy or disbandment by the court, and whether an emerged firm was still a surviving entity on May 1, 2006. Using computer-assisted qualitative analysis (Epstein & Pava, 1994), we inductively developed a typology of strategies that classified the turnaround actions into four major approaches. A latent factor class analysis using Categorical Principal Component Analysis (CATPCA) employed for factor analyses of categorical data confirmed our qualitative classification of the four strategies as distinct latent constructs. Our longitudinal design was a cross-sequential design that involved the study of multiple companies using the time window between 1983 and 2006. The 142 companies had different start dates of filing for bankruptcy and different end dates of emergence from or disbandment during bankruptcy, and different time orderings in the deployment of the various strategies. The four inductively derived turnaround strategies were rationalizing existing resources, developing existing resources, generating new resources, and investing in future resources. Rationalizing involves a set of strategies that focus on streamlining and/or shedding existing operations for cost containment. Downsizing the workforce, pay cuts, organizational restructuring such as consolidating facilities and product groups, and dropping/selling unprofitable product lines and services are examples. Developing, in contrast to shedding, emphasizes further development of key firm resources such as markets, products, services, and processes, particularly those with successful track records and whose development can ensure future revenue flows. Generating new strategic resources can have an impact on the immediate viability of the firm. Two key thrusts in this regard are leadership changes (e.g., Chief Turnaround Officer) and performance improvement programs (TQM, Kaizen). Investing in new resources that have the potential for future payoffs is key to this approach and could include initiating novel R&D projects, developing new product and service lines, investing in new technologies, and acquiring new businesses. In terms of our first two research questions, firms that emerged compared to those that did not developed existing resources, generated new resources, and rationalized existing resources. A combination of strategies versus any one strategy explained the most success in emerging from Chapter 11. Firms that survived long-term post-emergence had a significant differentiating factor. They invested in future resources in addition to developing and rationalizing existing resources and generating new resources.

366  Handbook of research methods in organizational change ATF Analysis: What Turnaround Strategies Accelerate or Decelerate “Time to Emergence”? The protected period of time of Chapter 11 can offer a new lease on life to a financially distressed firm. However, time spent in Chapter 11 has several direct and indirect costs. The longer the bankruptcy duration, the more expensive the costs. Direct costs encompass the legal and administrative fees, such as the cost of lawyers, accountants, and other professionals involved in the bankruptcy filing. Of higher concern to the firm, however, are the indirect costs that include a wide range of opportunity costs resulting from the stigma of bankruptcy filing. For example, studies have indicated a drop in stock prices (Rosenzweig & Bradley, 1992), reduced earnings per share, and loss of market share to competitors (Opler & Titman, 1994) immediately after filing for Chapter 11. Lost sales, increased operating costs, and key employees abandoning a sinking ship are common issues negatively affecting the profitability and competitiveness of the firm. The sooner the firm can emerge out of bankruptcy, the better its current and future prospects. An additional motivation to speed up emergence from Chapter 11 is that typically firms emerging from bankruptcy almost immediately register an abnormal positive increase in stock prices (Eberhart et al., 1999). The average time spent in Chapter 11 protection for the 86 firms out of the 142 that emerged was 16.5 months (528 days). For the firms at the 25th percentile and below that spent the shortest time in Chapter 11, the average was five months (153 days), while for the firms at the 75th percentile and above that spent the longest time under Chapter 11, the average time was 23 months (699 days). These firms in the upper percentile were given special extensions by a judge over the normal 547 days. Given the wide variability between the upper percentile and lower percentile groups in terms of “time to emerge” (153 days versus 699 days), our interest was in understanding which of these three turnaround strategies – rationalizing existing resources, developing existing resources, and generating new resources that significantly influenced emergence – also accelerated their time to emerge from Chapter 11 or decelerated their attempts to emerge quickly. We were interested in determining the relative importance of the three turnaround strategies in facilitating faster or slower “time to emerge” of a firm from Chapter 11. The AFT model, as explained previously, has found much use in medical research and not as much in ODC research. This technique is used to identify the explanatory factors (i.e., exercise, healthy diet) that can speed up or slow down time to an event, typical events being “time to cure” or “time to death” of a patient. In the context of this chapter, we are examining AFT’s use in the “time to emerge” of firms that have filed for bankruptcy protection – a topic closer to ODC that is concerned about the turnaround of organizations. In addition, we computed hazard ratios, which is a relative measure of risk that not only indicates the patient’s chances of healing but also the relative speed of healing when exposed to a certain treatment versus not being exposed to it. The hazard ratio is a comparison between the probability of events in a treatment group (in this case, a firm that used a particular turnaround strategy) compared to the probability of events in a control group (a firm that did not use the particular turnaround strategy). It is used to see if firms receiving a treatment (turnaround strategy) progress faster (or slower) than those not receiving treatment (absence of a treatment strategy). A hazard ratio of 3 for the use of a particular strategy means that the firm using it will cause the firm to progress three times faster toward emergence compared to firms in the control group not using that strategy. Any ratio above 1 generally means that the treatment

Longitudinal designs, big data, and social network analysis  367 group (or the one using the strategy) moved faster in terms of “time to the event” A ratio of less than 1 indicates the particular strategy was a decelerator and slowed down time to the event. A hazard ratio of exactly 1 means that both groups (with strategy use and without strategy use) are experiencing the same hazard in terms of time to an event – meaning the turnaround strategy does not matter in influencing time to an event. A hazard ratio of 0.333 tells you that the hazard rate in the group with the strategy is one-third slower than the group not using the strategy in terms of “time to event” (see Table 16.2). Table 16.2

Accelerated Failure Time analysis of turnaround strategies on firm speed of “time to emergence”

Predictors

Beta coeff.

Hazard ratio

Controls

 

 

Size

0.24+

0.74+

Industry

0.20

0.15

Time period

-0.03

1.11

Strategies

 

 

Rationalizing existing resources

-0.03

1.02

Developing existing resources

-0.66*

2.06*

Generating new resources

-0.44+

1.52+

Investing in future resources

-0.16

1.17

Developing x generating resources

-0.38**

3.36**

Likelihood ratio chi-square = 14.78* Subjects N = 142 Failures (emerged firms) N = 86

Note: *** p