Organized Complexity in Business: Understanding, Concepts and Tools 3031252365, 9783031252365

Citation preview

Future of Business and Finance

Johannes Strikwerda

Organized Complexity in Business Understanding, Concepts and Tools

Future of Business and Finance

The Future of Business and Finance book series features professional works aimed at defining, analyzing, and charting the future trends in these fields. The focus is mainly on strategic directions, technological advances, challenges and solutions which may affect the way we do business tomorrow, including the future of sustainability and governance practices. Mainly written by practitioners, consultants and academic thinkers, the books are intended to spark and inform further discussions and developments.

Johannes Strikwerda

Organized Complexity in Business Understanding, Concepts and Tools

Johannes Strikwerda Amsterdam Business School University of Amsterdam Eindhoven, The Netherlands

ISSN 2662-2467 ISSN 2662-2475 (electronic) Future of Business and Finance ISBN 978-3-031-25236-5 ISBN 978-3-031-25237-2 (eBook) https://doi.org/10.1007/978-3-031-25237-2 # The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book is dedicated to my granddaughter, Nina, and her generation.

Preface

I enjoyed writing this book. Not because it was an easy process, but because the perspective and the idea of complexity helped me, as it will the reader, to understand in a new and fruitful way essential relations between familiar, concrete issues as knowledge work, combinatorial innovation, exponential organizations, selfmanaging teams, how information outmaneuvers structure, effective tactics to deal with uncertainty, agility in relation how to be in-control, regnorance, adaptation efficiency, CEO-effectiveness, bottom-up initiatives, etc. All this is achieved without losing oneself, and hopefully the reader, in obscure mathematical models or academic theories, whilst maintaining consistency with contemporary economic developments and insights. I also experienced a paradoxical nature of complexity. On the one hand, the complexity in business, as needed for economic growth, is much more complex as traditional theories on complexity assume. Compared to the complexity in business, there is even a kind of naïve simplicity in traditional complexity theories. On the other hand, in business a number of pragmatic concepts and practices exist to deal effectively with the growth of complexity. This is possible because of a growth of knowledge and because of the growth of information. The caveat however is that not everyone in society delights in dealing with complexity in everyday life. For some we need to organize simplicity implying that others need to absorb complexity to maintain the requisite variety. One of my interests is information. We live in an information society, but what is information? To my joy in this book, I managed to integrate the rich cybernetic information theory with complexity theory as a result of which it becomes clear why concepts like mission, values, and abstract thinking are so effective to deal with complexity. Knowing one’s purpose or mission and values also helps to avoid information overload. This also avoids the trap of trying to cope with complexity by brute calculations and using large amounts of data, which seems too trivial in an age of Big Data. To calculate is not the same as to think. The weakness of the ancient Greeks was that they lacked a system for numerical calculations, whereas the Egyptians did have a method for numerical calculations. The Greeks therefore were forced to think in order to understand, resulting in a superior philosophical understanding, whereas the Egyptians did not progress. That is what good managers do, they think and budget time for thinking and reading. Another insight I learned doing the research for this book, the complexity of a problem or situation is not so much an intrinsic property of that situation, vii

viii

Preface

but is in the first place defined by our own knowledge. Complexity in many cases is subjective. You want to reduce an experienced complexity? Grow your knowledge and the world becomes simpler. That indeed is my personal experience. Actually, one of the things helping me making complexity lighter to deal with is the insight that different types of complexity exist and knowing how to deal with different types of complexity in different ways makes life easier. The origin of this book lays in an invitation by Dr. Peter Kinne to speak on complexity in business for a small audience in Düsseldorf in January 2016. The idea, so was concluded at that seminar, is to write a basic text on complexity, from which to distill more accessible presentations to be used in workshops with managers, a task accomplished by Dr. Kinne in separate publications. My task got trapped in an emergence type of process, so characteristic for complex systems, making complexity even more complex by acknowledging that the elements in a complex system, especially social systems, themselves are complex and that even the relations between the elements are complex. But that brings complexity closer to daily life. The writing of a book on complexity is a complex process. This book is in remembrance of my spouse, Nora van Delft, who after a companionship of 45 years passed away just before the book was finished. Eindhoven, The Netherlands

Johannes Strikwerda

Contents

Part I

Understanding Complexity and the Economy

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Newness of this Book . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 What this Book Is About . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Objective of this Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 3 6 7 7

2

Complexity 3.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 The Usefulness of Complexity . . . . . . . . . . . . . . . . . . . . . . . 2.2 Different Types of Complexity . . . . . . . . . . . . . . . . . . . . . . . 2.3 Why Complexity Theory? . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Obsolete Assumptions . . . . . . . . . . . . . . . . . . . . . 2.3.2 Institutionally Induced Complexity . . . . . . . . . . . . 2.3.3 Complexity of Objectives . . . . . . . . . . . . . . . . . . . 2.3.4 Control Without Being Controlled . . . . . . . . . . . . . 2.3.5 Complexity as Provenance . . . . . . . . . . . . . . . . . . 2.4 Complexity Theory and the Changing Nature of the Firm . . . 2.5 New Options to Organize . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 A First Understanding of Organizational Complexity . . . . . . . 2.7 From Modern Complexity to Post-modern Complexity . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 9 11 13 13 14 15 17 18 19 21 23 25 28

3

Simplicity and Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Our Need for Simplicity . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Two Types of Simplicity . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Simplicity and Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Simplicity Versus Limited Complexity . . . . . . . . . . . . . . . . . 3.5 Dominant Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Complexity and Language . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 The Paradox of Traditional System Thinking . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31 31 34 36 38 39 41 45 47

4

Definitions of Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 The Complexity of Complexity . . . . . . . . . . . . . . . . . . . . . .

51 51 ix

x

Contents

4.2 4.3 4.4

Complexity and Systems Thinking . . . . . . . . . . . . . . . . . . . . Complexity, Cybernetics, and Control . . . . . . . . . . . . . . . . . Types of Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Detail Complexity Versus Dynamic Complexity . . . 4.4.2 Objective Complexity and Subjective Complexity . 4.4.3 Disorganized Complexity and Organized Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.4 Overview of Types of Complexities and Their Handling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Organic Organizations as Intelligent Complex Adaptive Systems (ICAS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65 68 70 79

. . . . . .

83 83 88 89 91 93

6

Information and Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 The Paradox of the Information Society . . . . . . . . . . . . . . . . 6.2 Discursive Information and Disinformation . . . . . . . . . . . . . . 6.3 The Cybernetic Concept of Information . . . . . . . . . . . . . . . . 6.3.1 Goal-Information . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Motivation or Axiological Information . . . . . . . . . . 6.3.3 Material Information . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Eidetic Information . . . . . . . . . . . . . . . . . . . . . . . . 6.3.5 Accountability Information . . . . . . . . . . . . . . . . . . 6.3.6 Allelopathic Information . . . . . . . . . . . . . . . . . . . . 6.3.7 Interface Information . . . . . . . . . . . . . . . . . . . . . . 6.3.8 Causal Information and Conceptual Information . . . 6.3.9 Pragmatic Information . . . . . . . . . . . . . . . . . . . . . 6.3.10 Transaction Information (Transaction Data) . . . . . . 6.3.11 Reproductive Information . . . . . . . . . . . . . . . . . . . 6.4 Complexity and Information . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97 97 101 106 106 108 110 113 117 118 119 119 127 129 133 134 135

7

Complex Decision-Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 What Is a Decision? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 What Is a Decision-Problem? . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Well-Structured Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Why By and Large Is Decision-Making Successful? . . . . . . .

139 139 139 140 142 156

5

Economic Growth, Complexity, and Institutional Conflicts . . . . . 5.1 How Complexity Contributes to Economic Growth . . . . . . . 5.2 Moderating Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 The Limitation of Intuitive Management Books . . . . . . . . . 5.4 A Conceptual Model for Economic Complexity . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

52 54 60 61 63

Part II

Organizational Complexity

Contents

xi

7.6

What Is a Complex Decision-Problem? . . . . . . . . . . . . . . . . 7.6.1 Ill-Defined Complexity . . . . . . . . . . . . . . . . . . . . . 7.7 Different Types of Complex Decision-Problems . . . . . . . . . . 7.7.1 A Perspective on Complex Decision-Problems . . . . 7.7.2 Professional Induced Complex Decision-Problems . . 7.7.3 Reflexivity Complex Decisions . . . . . . . . . . . . . . . 7.7.4 Decision-Rights Complexity . . . . . . . . . . . . . . . . . 7.7.5 Epistemological Complex Decision-Problems . . . . . 7.7.6 Discovery Versus Justification Complexity . . . . . . . 7.7.7 Temporality of Complex Decision-Problems . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

158 158 161 161 164 165 166 168 169 170 170

Complexity and Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Complexity and Coordination . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Does Complexity Substitute for Coordination? . . . . 8.2 How Is Coordination Achieved? . . . . . . . . . . . . . . . . . . . . . 8.3 Coordination in Complex Organizations . . . . . . . . . . . . . . . . 8.4 Coordination at the Fifth Level of Complexity: Stigmergic Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.1 Is Coordination Possible in High Complex Organizations? . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4.2 A Kind of Fluidity . . . . . . . . . . . . . . . . . . . . . . . . 8.4.3 The Complexity of Interaction . . . . . . . . . . . . . . . . 8.4.4 Stigmergic Coordination . . . . . . . . . . . . . . . . . . . . 8.4.5 The Upside and the Downside of Free Interaction . . 8.4.6 Managing the Risks in Free Interaction . . . . . . . . . 8.5 Complexity of Markets, Products, and Consumers . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

175 175 175 178 186

8

Part III

190 190 191 195 199 202 205 206 211

Complexity in Practice

9

Examples of Mastering Complexity . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Complexity and Learning from Successful Cases . . . . . . . . . 9.2 The Case IBM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 The Case Procter & Gamble . . . . . . . . . . . . . . . . . . . . . . . . 9.4 The GIOCA Expert Centre in Amsterdam . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

219 219 221 222 223 224

10

How CEOs Cope with Complexity . . . . . . . . . . . . . . . . . . . . . . . . 10.1 CEOs and Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 The Power and Risks of Abstract Thinking . . . . . . . . . . . . . . 10.3 CEO Turnover and Complexity . . . . . . . . . . . . . . . . . . . . . . 10.4 Simplicity Beyond the (New) Complexity . . . . . . . . . . . . . . . 10.5 They Wade Into Complexity . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Some Lessons from Successful CEOs . . . . . . . . . . . . . . . . . . 10.7 Complexity Leadership Versus Transactional Leadership . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

225 225 227 234 235 236 238 238 239

xii

11

12

Contents

Tools Executives Use to Deal with Uncertainty and Complexity . . 11.1 What Connects a Variety of Tools? . . . . . . . . . . . . . . . . . . . 11.2 Mission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 A Hierarchy of Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Reconceptualizing, Reframing . . . . . . . . . . . . . . . . . . . . . . . 11.5 Holistic or System Thinking . . . . . . . . . . . . . . . . . . . . . . . . 11.6 Scenario Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.7 Preparedness and Rolling Forecasts . . . . . . . . . . . . . . . . . . . 11.8 The Organic Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 11.9 Multidimensional Information . . . . . . . . . . . . . . . . . . . . . . . 11.10 Information-Based Empowerment . . . . . . . . . . . . . . . . . . . . 11.11 Loose Control and Loose Programming . . . . . . . . . . . . . . . . 11.12 Management Development and HR-Policy . . . . . . . . . . . . . . 11.13 Architecture and Modularity . . . . . . . . . . . . . . . . . . . . . . . . 11.14 The Resource Allocation Process . . . . . . . . . . . . . . . . . . . . . 11.15 Open Innovation and Open Business Models . . . . . . . . . . . . 11.16 The Real Option Method, Phased Funding, and Discovery-Driven Planning . . . . . . . . . . . . . . . . . . . . . . 11.17 Using Mathematical Models for Risk Management and Managing Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.18 The Concept of the Platform Organization . . . . . . . . . . . . . . 11.19 Fast Feedback Information to Deal with Complexity? . . . . . . 11.19.1 Feedback as a Defining Element in Complexity? . . 11.19.2 The Cognitive Role of Feedback . . . . . . . . . . . . . . 11.19.3 Types of Feedback Loops . . . . . . . . . . . . . . . . . . . 11.19.4 The Context of Feedback: Control . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

243 243 244 245 246 248 250 251 253 256 257 259 261 262 264 265

Organization Design and Complexity . . . . . . . . . . . . . . . . . . . . . . 12.1 Introduction: Is Complexity a Design Principle? . . . . . . . . . . 12.2 How to Factor Complexity into Organization Design? . . . . . . 12.3 Kanter’s Concept of the Modern Organization . . . . . . . . . . . 12.4 Complexity and the Design of Functions . . . . . . . . . . . . . . . 12.4.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.2 The Design of the Customer Value Proposition and Operational Processes . . . . . . . . . . . . . . . . . . . 12.4.3 The Governance System . . . . . . . . . . . . . . . . . . . . 12.4.4 Complexity and the (Strategic) Guidance System . . 12.4.5 Complexity and the Design of the Organization of Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.6 Complexity and the Design of the Support Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

287 287 296 297 298 298

266 267 269 270 270 272 272 279 281

299 300 311 313 315 326 327

Contents

xiii

13

Complexity and Management of Change . . . . . . . . . . . . . . . . . . . . 13.1 The Butterfly-Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Planned Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Organization Development . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 The Emergent School for Change . . . . . . . . . . . . . . . . . . . . . 13.5 The General Management View on Change . . . . . . . . . . . . . 13.6 Systemic Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

331 331 332 332 332 334 335 336

14

A Final Word . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

337

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

339

Part I Understanding Complexity and the Economy

1

Introduction

1.1

The Newness of this Book

New in this book is that it integrates the somewhat esoteric and marginal theories on complexity in the mainstream of thinking and action on governance, management, and organization. Certainly, complexity played a core role in the theories of main management thinkers like e.g. Herbert Simon and Russell Ackoff. Many CEOs use the concepts of complexity theory and system thinking to remain aware of the limitations of the mainstream but reductionist and simplified management models. The latter we need for reasons of simplicity in comprehension and communication. But, as James O’Toole observed in the early nineties, successful executives look for simplicity beyond complexity, or as Martin observes, “they [the executives] wade into complexity.” Despite this insight and practice, complexity remained somewhat disconnected from the mainstream of management concepts, including corporate governance. This is no longer tenable in view of the growing complexity in our economy and technology, and it does not need to be so. It is possible to apply complexity theory to solve effectively core issues in modern business, like how to organize for knowledge work, how to organize complex supply chains, how to be in control in complex markets and industries, without losing the linkages with institutional requirements or getting lost in abstract academic concepts that do not inform executives what decisions to make. The difference with traditional complexity theory is that we now have a better understanding of the positive role of complexity in economic growth and in innovation, especially combinatorial innovation. Traditionally complexity thinking in business was used to explain the need of requisite variety and the need for loose programming and loose control in order to be in control in the sense of survival. A second difference is that due to the declining costs of information we now have technology that preserves and coordinates complexity in an economic way, lifting the restrictions of the old logic of simplification and linear hierarchies, opening new

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_1

3

4

1 Introduction

options for growth and development.1 A third difference with traditional complexity theory is that we now have a better understanding of different types of complexity, amongst others organized complexity versus subjective complexity, that enable us to exploit complexity in an efficient way, especially by enabling the knowledge economy. Until now the paradox of complexity theory was that it tried to master the growing complexity of our business world through the application of reductionist mathematical tools, a contradictio in terminis. In this book, complexity theory is being integrated in the realities of life like the implications of the dominance of intangible assets, the information economy, the systems of corporate governance, the dynamics of markets, etc. It does so by using a far richer theory of information, the cybernetic information theory, compared to the commonly used mathematical theory of information attributed to Shannon. The cybernetic information theory differentiates between different types of information beyond the usual management information and integrates the role of mission, values, and causal relations in our information spaces. This opens up the concept of complexity to include the relation between (complexity reducing) institutions and economic growth, the role of abstract thinking (conceptually, not in the mathematical reductionist way), and the role of knowledge in dealing with complexity. Cybernetic information theory operationalizes Herbert Simon’s insight that in the twenty-first century no longer structure is the primary tool of business administration, but it is the combination of factoring of decision-making and the organization of information. To Simon’s insight, this book adds the role of reconceptualizing as a tool for business success, a way of thinking beyond heuristics, interpretation, and intuition. A question to be answered is the relation between complexity theory and conventional management and organization theories and models. Does complexity replace conventional management and organization theory or does it add an additional dimension to it. Some argue that complexity theory opposes the Cartesian god-view of traditional management and organization theories which assume an observer (a manager, a consultant) standing outside the organization as a thing-like observable entity, as opposed to complexity theory emphasizing a participative perspective of this manager being part of the entity he intends and thinks to observe, resulting in unpredictable emergent behavior.2 In this second school of thinking about complexity theory, understanding of complex systems results from participating in it. This book takes a different stance. Complexity thinking or complexity science is not denying the validity of reductionist science-type models, nor is it opposed to these; complexity science is a state of awareness of the limitations of reductionistscience-type models, including heuristics, in terms of the nature of causality, assumptions, situations, period, duration, transferability, comprehensiveness of situations, etc. Especially in new developing situations complexity thinking is

1 2

Zuboff and Maxmin (2002, p. 292). Stacey (2001).

1.1 The Newness of this Book

5

being aware that situations can be new in the sense that familiar management models do not have the capability to provide an understanding as a reliable basis for decision-making. In addition to this, at a lower level one might say, complexity thinking is the acknowledgment that management models are simplifications, which both are needed and are to be distrusted in terms of aspects of reality being left out. This is not the same as “things that didn’t teach you in business schools” as that attitude denies that executives making such statements often are the prisoner of the language of the MBA. Complexity thinking is an awareness and acknowledgment that language itself may have limitations to deal with new complexities. Complexity science, especially as elaborated in this book offers more specific insights in types of complexity, each with different remedies to master these and it provides a more differentiating insight in different types of information to be deployed in the organization; IT-governance only sees to one of the multiple types of information and information processing: data. The cybernetic information theory offers a richer information space as does the information theory of Shannon and allows an understanding of the complexity of the space beyond the same-level interacting agents in conventional complexity theory. Human beings are not only utility-maximizing economic men, but also have a capability of consciousness, morality, altruism, of system responsibility, the capability to create and maintain institutions, to build society and organizations, to be accountable to others, all this in dynamic complex relations. That is, we need to allow for both upward and downward types of causality in addition to same-level interaction or (non-linear) causality to develop an understanding of our complex world. In that way, it is also possible to understand that in terms of wisdom an implicit awareness of complexity was present with leaders and others in business, but due to the growth of complexity and dynamics in relations, especially due to cyberspace, the role of complexity needs to be more explicit in our conversations, deliberations, and decisions. Successful CEOs demonstrate the capability to reconceptualize industries, markets, business, and their organizations and with that are master of complexity. It is possible to define and communicate simplicity, but it is simplicity beyond the new complexity, facilitated by technology, especially the concept of the platform organization, that preserves complexity as needed for growth, innovation, development, and to be in control in a dynamic complex environment. The concept of the platform organization also illustrates another new phenomenon, to create a simple environment for, e.g., knowledge workers or for the patient in a hospital, whereby the requisite complexity is being organized elsewhere in the organization. What this book adds to the idea of complexity is that whereas traditional complexity theory sees the elements of a complex system as local in terms of information and knowledge, local in responding to influences by other elements, which is basically cell biology, in this book human beings are taken as the elements of complex systems. Human beings have the capability to have local information and global or system level information at the same time. Human beings have the capability to respond to local events and to system-level or global events. Whereas traditional complexity theory assumes that the behavior of the elements of a complex system results from the—lateral—interaction between the elements, not from the

6

1 Introduction

attributes of the elements themselves, in this book it is assumed, based on the interactive perspective model of organizational behavior, that behavior of individuals results from the interaction between the context of an individual and personal attributes, like personality.

1.2

What this Book Is About

This book is about a constructive approach to mastering the new complexities as we create these ourselves in our markets and in our organizations in order to innovate and to grow. This book explains that and how it is possible to be successful in an uncertain world by using the new options created by the declining costs of information, and what new conceptual models are and can be developed and applied for the governance, management, and organization of the firm of the twenty-first century. What will be presented is not all de novo; as so often apparently new organization forms, tools, concepts, etc. have long historical roots. It is more a developing change in the ranking of administrative tools, e.g. whereas in the twentieth-century structure dominated process, now process dominates structure. This shift in ranking reflects a shift from the dominance of tangible assets to intangible assets. We will see new organization forms that have been intuited for a long time, e.g., the informationbased organization. The information-based organization is economically a necessity, it is technically possible today, and it solves the conflict between the nature of the modern firm and compliance requirements based on obsolete institutional requirements, working examples exists, but this concept, still faces strong opposition simply because it is in conflict with traditional concepts of governance, management, and organization. Although organizations as social systems have the capability of organic development, and thus handling diversity as needed in the knowledge economy, based on human understanding and needs, orthodox institutions still tend to impose binary requirements on firms and institutions. The risk society has created itself a regulatory state in an attempt to restore the certainties of a past modern society.3 As managers in times of crisis are tempted to regress to old proven methods, instead of jumping forward, the institutions forming the regulatory state applying obsolete concepts, have created a context of regnorance.4 With that the regulatory state itself runs the risk to be a source of unproductive complexity, slowing down the growth of productivity and being itself a source of risks. Wading into complexity requires understanding the different types of complexity, the different sources of complexity and the different uses of complexity. Ashby’s Law of Requisite Variety not only applies to systems and organization, it also applies to our thinking; only a complex mind can destroy complexity. We need to avoid subjective complexity as results from too simple mental models. Organized

3 4

Glaeser and Shleifer (2001). Gilder (2013, p. 135).

Bibliography

7

complexity needs to be created, as this is necessary in a knowledge economy to support knowledge workers.

1.3

Objective of this Book

This book aims to provide the executive, the manager, the staff professional, the knowledge worker, MBA faculty and MBA-students and others facing the need to deal with complexity, with ideas, examples, concepts, and language to de develop a capability to deal with complexity in a productive and non-stressful way. Different from traditional management books this book is neither simply prescriptive nor apodictic, because that assumes that simple and clear models can be accepted in favor of weak implicit models, as was the case in the first half of the Second Industrial Revolution. This time more sophisticated models and concepts, based partly on conventional theories of organizational behavior, partly based on new economic theories; will be added to and partly replace the simple, clear, and once strongly effective models of the Second Industrial Revolution. Due to the success of the latter the managerial and organizational concepts developed and applied within its context have become so obvious that many of the present generations, academics and consultants included, are not aware of the assumptions underlying these concepts and models and with that are not aware of its limitations. At least not in a cognitive-analytical way, often there is a strong intuition about the limitations of these concepts and models. The gap between a cognitive-analytical understanding of the limitations of concepts and models, and the intuition with respect to these limitations expresses itself in an urge that a change of culture is needed, spontaneous cooperation, an emphasis on values, that people need to connect, etc. This intuition is correct, but without a cognitive analytical understanding of its source in combination with a lack of understanding modern administrative tools, such intuitions do not create progress. We need to understand what needs to be done. Culture programs and leadership programs may create satisfaction with its participants, but it is not doing. Only through doing, we can understand our motives and worldview.

Bibliography Gilder, G. F. (2013). Knowledge and power: The information theory of capitalism and how it is revolutionizing our world. Regnery Publishing. Glaeser, E. L., & Shleifer, A. (2001). The rise of the regulatory state. Harvard University. http:// ssrn.com/abstract=290287 Stacey, R. D. (2001). Complex responsive processes in organizations: Learning and knowledge creation. Routledge. Zuboff, S., & Maxmin, J. (2002). The support economy: Why corporations are failing individuals and the next episode of capitalism. Viking.

2

Complexity 3.0

2.1

The Usefulness of Complexity

The surprising thing about complexity is that if well understood, if the right type of complexity is created and is managed properly, it will bring your organization innovation, adaptive capability (agility), a happy, healthy, productive and innovative workforce, profitability and an organization that will be in control in a dynamic complex economy. Most people hate complexity, and often there are good reasons for this, but living systems, that includes organizations, cannot survive without a requisite level of the right type of complexity. Most managers acknowledge that the world, and thus their business, becomes more complex, but few understand how to profit from this growing complexity. Many executives understand a need for simplicity with their workers but are aware that at the same time a minimum level of complexity in the organization is needed for successful continuity. Decennia of MBA training emphasized simplicity in organizing and in management. This has been successful to be judged by the growth of wealth in the twentieth century and the acknowledged contribution to that growth by management and organization, after technology.1 But the traditional management tools wear off because the assumptions these are based on no longer hold in an economy in which intangible assets are dominant for value creation. Complexity for many was, and often still is, seen as something to be avoided. Complexity often is perceived as a source of costs. Parallel to the emphasis on simplicity there always has been an interest in complexity theory as an alternative way of thinking on management and organization. Related to complexity a second alternative exists to the science type of reductionist thinking, system thinking. Most likely the difference between linear reductionist type of thinking on the one hand and complexity thinking and system thinking on the other hand will continue to exist. There will remain numerous situations that will best be served by linear thinking. As

1

OECD (1987).

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_2

9

10

2 Complexity 3.0

a consequence of the knowledge economy and the consequential changing nature of the firm, in which the dominance of tangible assets has shifted into a dominance of intangible assets and subsequently a shift from transactions to interaction, it is now understood that firms need a form of organized complexity for innovation, for responsiveness, for agility in order to have successful continuity. In the knowledge economy an understanding has developed that a positive relation exists between growth of complexity and the growth of the economy.2 This growth of complexity in the economy describes in the first place the growing complexity in markets. This is to be seen in an increase in specialization of firms, the organization of supply chains, outsourcing, specialization of knowledge, phenomena like open innovation, etc. The question is whether an increase in the complexity in markets implies an increase in the complexity of the organization of firms. One argument might be that if firms focus on producing on basis of specialization on one component of a complex product, e.g., the engine of a car, some other firms specialize on assembly of, e.g., a car, then complicated products do not imply complex organizations. However, most activities, especially in the field of engineering, despite the Internet and digital communication, are not coordinated by the price mechanism, but by hierarchy, project management, product architecture and other non-price coordination mechanisms, even between firms. The growth of complexity not only is in the supply side of the market, it is also on the demand side. On the demand side complexity can be seen that more consumers have a more complex preference set, e.g., including altruistic values in addition to use value and or exchange value. Also, an increasing percentage of consumers display multiple preference sets, even multiple life styles, dependent on context, moment and activities. For a single firm, e.g., a retailer, to serve customers with especially multiple preference sets, in combination with economic criteria like economies of scale, purchasing power, requires a more complex internal organization. Firms that want to appropriate more value from the market through integrated solutions usually in combination with corporate account management cannot achieve this with a simple one-dimensional unit organization; a multidimensional organization is needed. Beyond these developments is even more important that the knowledge economy and the learning economy imply that firms organize for an optimal combination of knowledge elements available within the firm (and attainable outside the firm), across the structures of departments and divisions. Because a material part of that knowledge is personal, non-codified knowledge, combinatorial innovation requires that a free interaction of knowledge workers is being facilitated. At the same time, the firm is supposed to know where it spends its money, that is hours of knowledge workers, and what returns result from this. A reconciliation between these two requirements can be achieved with techniques to be explained in this book, but those involved need to acknowledge that this results in an organization which is simpler for the knowledge worker, but more complex for the executive. This is so because the old structures of business units and departments for reasons of resource management and task specialization do

2

Hidalgo and Hausmann (2009).

2.2 Different Types of Complexity

11

not go away and an additional dimension, that of projects and processes, is added to the old structure which, to add insult to injury for an older generation of managers, will have a less powerful role in the organization. Not only the new organization is more complex, the change processes to be managed will be more complex in terms of new roles, new identities, changes in power relations, a new setup of the resource allocation process, a new infrastructure for performance evaluation, organizing available information, educating sufficient members of the organization to understand the new organization, etc. At first sight, one may wonder about the business case of such a change. Macro-economic economic data and a number of individual cases demonstrate that the business case is positive, but understandably many leaders find it difficult to make these steps.

2.2

Different Types of Complexity

To organize and to exploit complexity in order to achieve innovation, adaptation, and growth of the firm different types of complexity need to be understood as not all types of complexity contribute to growth. Unproductive types of complexity exist and some types of complexity may even impair making sound decisions. The various types of complexity are elaborated in Chap. 5, to understand the nature of this book the main types of complexity are summarized here. The traditional complexity theory focuses on forms of objective complexity, on systems we work with but are not part of. Some authors project this type of complexity on organizations, but organizations do not exist of simple elements as assumed in conventional system thinking, but of people, in themselves complex entities by being, thinking, and behavior. The concept of objective complexity nevertheless is relevant for business because of the growing multidimensionality of markets, consumer behavior and the subsequent need for multidimensional organizations. More common in daily life is subjective complexity. This type of complexity does not exist in reality, but in our minds and in our communications. This type of complexity is a perception that results from trying to understand (new) situations through the lens of old, reductionist and thus simplistic models, concepts, and language. Like other types of perceptions, these may be real in their consequences because subjective complexities will guide the thinking and decision-making of individuals, often trying to simplify situations even where a degree of (new) complexity is needed resulting in failure.3 The changing reading habits, the effects of screen-reading and social media on cognitive development,4 and e.g. the echo chamber effect5 in combination with psychological mechanisms as belief conservation tends to an increase in subjective complexity.

3

Miller (1993). Hayles (2012). 5 Pentland (2014). 4

12

2 Complexity 3.0

A third type of complexity is organized complexity. In this type of complexity, an organization design is such that unplanned interactions and interactions beyond traditional structures between knowledge workers are productive. This concept of organized complexity is not new. It is to be found in Herbert Simon’s concept of complexity, expressed in loose control and in loose programming as needed for adaptation efficiency. It can also be seen in the concept of organic organization (as opposed to mechanic organization) identified in the 1960s by Burns & Stalker. The concept of organized complexity becomes more urgent in a knowledge economy with tacit, uncodified knowledge carried by professionals. However, the concept of organized complexity is to be realized in a context which, e.g., through governance rules, emphasizes risk management, to be in control and compliance. This is a challenge for many executives, but as we will see, doable. Complexity often is perceived as opposite to or conflicting with control, hierarchy, order and predictability, the characteristics of modernity. Some argue that in an era of postmodernism or second modernism, with an erosion of complexity reducing institutions and a blurring of dichotomies, e.g., the dichotomy market-hierarchy, perfect control, linear hierarchies, stable order, and predictability no longer are possible.6 Dependent on personality, some managers have a tolerance for complexity and the unexpected, whereas others prefer order and predictability. Related to that exists a kind of conflict in our society, a tension between the need for development, innovation, a hope for surprises that solve problems. At the same time, we long for ontological security, being protected from physical disasters, immoderate behavior of executives, fraudulent behavior and the breakdown of essential infrastructures and systems. With deregulation of markets and with innovation we increase complexity in society to achieve a higher growth and a richer life. However, we fail to increase our knowledge to deal with this higher complexity in society.7 As a result society sees itself confronted with openness, uncertainty, and obstruction of a selfcreated future that is no longer defined by religion, tradition or utopian thinking.8 In this world complexity, the basic mechanism for survival, paradoxically becomes associated with risk. To achieve or maintain ontological security society responds to the unforeseen consequences of the new complexity with risk management, based on modernist control mechanisms however based on a bipolar world that no longer exists. We need to master complexity, not to reduce it, to create ourselves a new level of ontological security. To balance complexity and restrictive requirements is in itself not new as society always, through regulations either by kings, governments, guilds, religion and professions was based on a number of complexity reducing institutions. What is new this time is that the needed reduction of complexity reducing institutions now includes the concepts and tools of business administration itself, including the

6

Kelly (1994). Beck (1999). 8 Beck (2009). 7

2.3 Why Complexity Theory?

13

concepts, models and tools of HR, organization design, strategy, management control (especially including strategy execution), IT-governance, etc. That is, the traditional tools used by managers to manage complexity are now subject to new levels of complexity. In order to foster economic growth, especially after the trente glorieuses, the high growth period of 1945–1975, neo-liberalism managed to achieve deregulation, that is the reduction of a number of complexity reducing institutions in society. But we still have difficulty at many levels, including that of the organization, to cope with the so-created new options. Because management models and organization forms, as developed in the context of the Second Industrial Revolution, themselves are also a kind of complexity-reducing institutions, it is questionable whether the conventional method of prescribing those management models and organizational forms to cope with the issues of the present economy will work. Therefore, a first objective of this book is to provide an understanding of the new complexities as is necessary, so are the lessons to be learned from successful CEOs, before defining new complexities. A second objective of this book is to explain to managers and workers in organizations how to steer course in the complicatedness of complexity to achieve a productive level of organized complexity in their organizations and avoiding the pitfalls. This will be done not by taking complexity as an isolated panacea for all organizational ills, which would be another example of reductionism, but to see, understand, and apply organized complexity as an administrative tool in relation to other, new, administrative tools.

2.3

Why Complexity Theory?

2.3.1

Obsolete Assumptions

One might ask why not a book on business administration for the twenty-first century straightforward, why this perspective of complexity? There are multiple reasons to use the concept of complexity to bridge the business administration of the Second Industrial Revolution with that of the Fourth Industrial Revolution. This is not to say that no other concepts or perspectives might do the same job. A first reason is that many are not aware of the underlying principles, assumptions, and theories of the traditional tools of business administration, including organization forms. The paradoxical reason for this is that the conventional instruments of business administration, e.g., the divisional organization, have been so successful in the second half of the twentieth century. Success like growth has the risk of being a deceptive teacher, as it does not force us to think through our assumptions, as is failure more inclined to do. To understand and see our assumptions we need to acknowledge that language, concepts, expressions, and communication are part of complexity systems in society. But to what extent is language, e.g. through Pentland’s echo chamber effect, a complexity-reducing institution, creating subjective complexity, and to what extent

14

2 Complexity 3.0

language assists us to escape the limitations of conventional models, concepts and management language? The question to be asked is whether the language of business has the power to question itself. What if the language in which we think and communicate about management and organization itself is part of the problem? This problem befell Jay Galbraith, who due to his book Designing Complex Organizations, published in 1973, for 30 year was the face of the idea of complex organizations, when he tried to describe and to explain the matrix organization in the language implied by the concept of the Weberian hierarchy. The matrix organization does not have the linear hierarchy as does Weber’s hierarchy and the main tool to make a matrix or any dual organization work effectively, is the resource allocation process, not structure. The resource allocation process is not part of Weber’s concept. Complexity science itself does not offer a completely different language but it questions the limitations of the conventional concepts, tools, and methods for management and organization. Complexity theory, as does system thinking, introduces non-linearity, multiple types of causality, spontaneous emergence through combination or mutation (as opposed to planned development), local intelligence that is capable to interpret mission, values and strategy in a productive way without need of hierarchy. Complexity in combination with cybernetic information theory (as opposed to the more common mathematical information theory) also explains that order can be achieved by an increase of information (as opposed to the traditional way of reductionist linear hierarchy). Complexity theory does not so much replace existing management models or theories; it places these in a broader perspective of holistic thinking and of integrative thinking in which purpose and process are emphasized over hierarchy and position, in which local intelligence is emphasized over command. Complexity theory does not offer easy catchwords like “culture” or “teamwork” but invites us to think through new situations. With that complexity theory avoids the trap of going from one type of reductionist thinking to a next type of reductionist thinking as is the case of many management books.

2.3.2

Institutionally Induced Complexity

A second reason to use complexity theory is that a number of the concepts, models, norms of traditional business administration are codified not in the instruments of business administration itself, but in law, regulation, governance codes, etc. It is unlikely that this will change in the next decennia and therefore it is necessary to understand the new developing instruments for business administration not only in contrast to the conventional instruments, but even more in an institutional context which may no longer be congruent with the new types of organizations our economy needs and to an extent already has. At first sight, the most logical academic field to study the issue of institutional incongruence would be that of new institutional economics. In this book that academic field will be used as a source for understanding, but not as a perspective from which to look at the changing nature of the firm. Complexity theory will be used to understand what gaps organizations have to

2.3 Why Complexity Theory?

15

manage and will be able to bridge in order to have a Fourth Industrial Revolution type organization in an regulatory, institutional context that is still dominantly based on the Second Industrial Revolution. An example of this is the system of labor relations in the Netherlands, once heralded as the most harmonious system for industrial relations in the world. Politicians and representatives of employers’ organization and the unions want to continue this on the implicit basis of the separation of labor and capital, and with that on basis of the traditional employment contract. The separation of labor and capital implied that the corporation, respectively its management, has full alienation rights over all of the assets of the firm constituting its capital base, which is the basis of managerial control over the firm. The rise of uncodifiable, personal knowledge as a material part of the capital base of the firm implies that such persons should not be not hired on basis of a labor contract, but on basis of a suppliers contract in which the knowledge-carrying person is selfemployed.9 For the latter type of contract rewards the owner of knowledge as used by the firm for the value of that knowledge (in which needs to be calculated that by applying this knowledge by the firm in many cases new (tacit) knowledge develops, increasing the value of this personal knowledge). Because this underlying principle is not understood, or even denied, in combination with fiscal advantages for selfemployed workers, and a need for a flexible labor market which insufficiently is facilitated by law favoring traditional labor contracts, the system of labor relations has derailed in which real knowledge workers are not properly rewarded for their knowledge, and other workers are forced into self-employment with a net result that investments in human capital are not what the economy needs and institutional conditions as required for a learning economy are not in place. Complexity theory makes clear that such incongruence between operational institutions and basic conditions in the economy is a source of unproductive complexity.

2.3.3

Complexity of Objectives

A third reason to use complexity theory as a tool for understanding ongoing changes is that the conventional tools, concepts, etc. for management and organization are based on the three normative paradigms of neoclassical economics, maximizing efficiency, maximizing utility, and market equilibrium. These three objectives used to be pursued by applying linear models. Efficiency traditionally is achieved through task specialization, linear hierarchy, limited span of control and grouping workers either according to product, process, technology, customers or region.10 Maximizing utility is a bit more complicated because it is conceptual and is difficult to measure. Economists describe behavior of consumers to maximize their utility in terms of

9

Jensen (1998). Simon (1946).

10

16

2 Complexity 3.0

diminishing utility (although for some goods and services increasing utility can be observed), budget constraints, and indifference curves, all simplifying models.11 Both short-term market equilibrium and long-term market equilibrium are expressed in linear models in which the supply curve and the demand curve are the main elements. Maximizing efficiency has developed in economic theory from cost efficiency only (the ration of the value of the output and the costs of resources) to include steps to be taken to achieve this prior to operations, efficiency now include allocation efficiency, as most especially larger firms also have an internal capital market or resource market to acknowledge human capital, and adaptation efficiency, to adapt timely and correctly to changes in the market. Due to the emergence of the multidimensional organization the resource allocation process in many firms needs to be more complex, to be based on multiple planning dimensions as opposed to the one-dimensionality of the traditional unit organization. Maximizing utility, that is understanding and describing consumer behavior is becoming more complex as a result of more complex information spaces in the market and live styles that in a number of cases become more complex.12 Consumer preferences have developed beyond use-value and exchange value. Even profit maximization no longer is one-dimensional. The objective function of the firm now includes externalities, sustainability, the acknowledgment that firms do not create value in splendid isolation and that firms also have a responsibility to maintain the institutions of society on basis of which firms are able to operate. That is, the objective function of the firm has become multidimensional and with that more complex. In itself this is not really new, Peter Drucker observed this already in 1958: success is multidimensional.13 However, as a result of the dominance of the capital market in the 1980s and the 1990s traditional multidimensional performance management was reduced to one-dimensional financial performance management. An implicit assumption underlying one-dimensional performance management is that a monotonous increasing relation exists between an input-variable, e.g., marketing expenses to increase market share, and output, e.g. profit. But in many cases, this relation is concave, which is beyond a certain market share percentage an increase in market share diminishes profit, sometimes even into a loss, due to non-linear relations due to e.g. less profitable customers. In such situations, a more complex, multi-criteria, multi-objective targets need to be formulated. Integrated Reporting was introduced as a tool to have firms observe the various constraints under which to optimize profit, but as introduced by mainly auditors, Integrated Reporting focuses on the reporting, whereas it needs to start with multidimensional, or min-maxconstraint formulated objective functions. Complexity theory does not offer an alternative to economic theory, it even does not tell us, as does neoclassical economic theory, what objectives to pursue. It is no

11

Krugman and Wells (2009, pp. 250–253, 272). Gilboa (2010) and Zinkhan and Braunsberger (2004). 13 Drucker (1958). 12

2.3 Why Complexity Theory?

17

more as a tool to describe and understand situations and the working of some situations, beyond linear reductionist models. Whereas Herbert Simon wrote that workers had to be grouped either according to product, or process, or customers or regions, complexity theory, especially the role of information in the workings of an organization, helps us to understand that these binary either-or choices, now have other workable solutions as well. The profit motive will not disappear. The profit motive has changed in two aspects. The first is that, different from Milton Friedman’s statement, “The social responsibility of business is to increase profits,” the profit motive now is more embedded in a broader responsibility for society for the longer term.14 How precisely differs per firm, industry and jurisdiction and the personal values of executives. The second change is that today there are more alternatives with respect to business models and operational models to pursue the—now-embedded—profit motive.

2.3.4

Control Without Being Controlled

A fourth reason to use complexity theory, as a vehicle for our thoughts, is that it is an ambiguous concept. Complexity for most of us is something to avoid, to live a simple live, to have understanding, to have overview. At the same time, the idea of complexity for many is a carrier of the hope of freedom, of spontaneous organization and order, of self-organization as the antidote to max Weber’s iron cage of the managerial hierarchy and imposed order. In that way complexity theory is also a carrier of human dignity and freedom. In a way, complexity theory reflects the erosion in our societies of trust in governments, in democracy, in experts, in the institutions of society.15 Complexity theory reflects a longing for assuming responsibility, for decentralization, as reflected in the international growth of civil society. The beautiful thing is that complexity theory, provided a proper understanding of different types of complexity in relation to institutional requirements, perhaps in itself is not the solution to this decentralization, but it helps to achieve it. By including in complexity theory, the academic field of cybernetics, especially its rich theory of information, it turns out that organizations with Simon’s concept of complexity, loose control and loose programming, with complexity in the form of a multidimensional organization, enabling information-based empowerment, have a higher degree of in-control as have organizations that are in-control according to the accountant’s standard for in-control, COSO. The latter is associated with restrictive control, taking away responsibility from workers, especially professionals and denying human capital as the most important resource in society to solve problems.

14 15

Friedman (1970). Dahrendorf (2002, 2003).

18

2.3.5

2 Complexity 3.0

Complexity as Provenance

A fifth reason to use complexity theory is that it is not new. This may appear an illogical argument in view of the new type of organizations needed in the Fourth Industrial Revolution. In case of being confronted with new situations, theories or insights we always look for some connection with the past or with the familiar. Complexity theory has a decennia old history, in that way it might be familiar to many. The history of complexity thinking may help us to see that one of the characteristics in the transition of modernism to the second modernism is that the marginal no longer is marginalized. Complexity thinking in modernism used to be secondary to mainstream MBA-type thinking. In the second modernism the two are complementary and of equal value for business. The historical roots of complexity thinking also reminds us of the importance of the provenance of ideas.16 To solve problems or to abandon obsolete practices there is a tendency in management books and in consultancy to introduce new phrases or to use existing concepts as if new. An example is the use of “culture.” Its original meaning, the collective programming of the mind of the members of a group especially through informal socialization processes,17 often is forgotten. Culture has become a non-analytic phrase into which apparently everybody projects his or her own meaning, turning ‘culture’ into a sign that makes invisible the complexity of processes by which individuals are being influenced. Hence the warning of the German philosopher Peter Sloterdijk not to trust people who claim to be authentic by worldview and values, usually these persons are not aware of by whom or by what they have let themselves manipulated.18 The provenance of ideas, in our case complexity thinking is needed to scrutinize our thinking, how our ideas have developed over time, to assist us where possible in the process of development we have overlooked assumptions or aspects or in what way historical roots of an idea still is a source of biases. Or, in what way the development of complexity thinking is influenced by historical economic, social, political and cultural conditions and most likely will be influenced in its future development. Whereas contemporary economic theories emphasize the relation between growth, complexity, and information, and thus objective complexity, the movement under the heading “the complexity turn” is more the result of postmodern thinking and tends to emphasize subjective complexity. Complexity theory itself is part of a complex societal system.

16

Pentland (2014). Hofstede (1980) 18 Sloterdijk (2005). Chapter 5. 17

2.4 Complexity Theory and the Changing Nature of the Firm

2.4

19

Complexity Theory and the Changing Nature of the Firm

The use of complexity theory and of the concept of organized complexity must be understood in the context of the changing nature of the firm and the changing nature of the economy. The core of this change is that whereas in the economy of the Second Industrial Revolution tangible assets, equipment, buildings were dominant in the economic process, in the economy of the Third/Fourth Industrial Revolution intangible assets are, human capital (knowledge), information capital and organization capital. Intangible assets imply a more complex organization of the firm. The economic boundaries of the system of value creation less and less coincide with the legal boundaries of the assets of the firm. The corporation no longer has alienation rights over the complete asset base of the firm, implying that the traditional basis of managerial control is eroding. Intangible assets imply a need for exploiting synergies and in combination with the declining costs of information this implies the end of the one-dimensional unit organization. Knowledge work is more difficult to plan, as is the production of tangible goods. Co-creation with suppliers and customers raises questions of intellectual property rights. That is, the number of parameters and dimensions in organization design and to manage increases. The shift from tangible assets to intangible assets, that is a shift to a knowledge economy with workers with a higher education, eroded the effectiveness of traditional administrative tools and management concepts. The initial response to this was to include culture in the toolbox of managers. This turned out to be a double-edged sword. In itself culture, the collective programming of the mind of the members of a social system through which knowledge with respect to what works and what does not, is an essential aspect of the organization as a social system. So to pay attention to culture helped to solve a number of issues in the changing organization and changing workforce of the seventies and eighties. However, the attention for culture with a focus on people and their values and motives obscured the role of the systemic context in organizations on the behavior of the members of the organization.19 Due to the increasing role of the capital market, with an emphasis on financial performance management and on bonuses, in many cases this systemic context tended to become simpler, where an increase in complexity was needed. The finance dimension within the systemic context to a large extent was driven by the capital market and the field of corporate finance and was growing in power in the organization. In compensation HR-managers in many cases emphasized culture program and such, later competence management, to defend their own power position. To make things more complicated in the eighties and nineties because IT-department developed their own position, based on the business-IT alignment paradigm, which later would turn out to be the business-IT alignment trap as IT systems lacked required complexity for business model innovation. This function-driven movement, based on the traditional reductionist simplification in business studies did not help to think things through, as a result of which many leaders in business, institutions and governments

19

Beer and Nohria (2000).

20

2 Complexity 3.0

have difficulty to transform their organizations into the Fourth Industrial Revolution.20 But is must be noted that in 1993 James O’Toole observed that effective CEOs did not go along with the mainstream of simplification, ‘they looked for simplicity beyond complexity’.21 This looking into a new complexity however, is beyond holistic thinking or system thinking. There is another dimension to this looking into complexity, which is becoming aware of, seeing the changes at the level of institutional foundations underlying the field of business administration and organization. The traditional approach of complexity thinking and system thinking in a way is somewhat one-dimensional; other dimensions need to be seen, acknowledged and to be explored. The reductionism in business administration and management was useful because it helped managers to focus on what to do. This reductionism not only simplified the operational causal relations, but also simplified the world by ignoring or not making explicit underlying assumptions of management models and thus making these vulnerable for basic changes in the economy. That management models have their limitations is well understood by quite some. But non-analytical responses on this intuition in terms of culture or post-modern ways of complexity thinking have not produced alternatives. Figuratively speaking, such responses are horizontal moves of acknowledging complexity, whereas a vertical type of complexity is needed; in depth in terms of understanding critical assumptions and their limitations, and in an upwards move in terms of abstract thinking, identifying principles, etc. But also, a more complex type of complexity is needed compared to that based on biology, one that includes cybernetics and the cybernetic information theory. That moves allows for the roles of the various types of information in the modern firm, including information as a resource, as a product and information capital. To include cybernetic types of information in understanding the modern nature of the firm also helps to understand that the structures of the organization of the firms as much are defined by information patterns as decision rights define these in the Weberian bureaucracy. Whereas in modernist organization structure used to be the first design parameter, in the organization of the firm of the twentieth century the factoring of decision-making is in combination with the organization of information.22 In combination with the increasing role of personal codified knowledge, and the explicit description of the business model of the firm (effect-information) available to all workers, this allows for a new level of complexity in terms of Simon’s loose control and loose programming as needed to be in control in a dynamically complex economy, often expressed in agility, discoverydriven planning, or more formally adaptation efficiency. At the same time a firm wants to be in control as defined in the resource-based view of the firm and as defined in cybernetics, which is to survive in successful way under uncertainty. This is possible provided in the organization more information is being organized and more capacity to process information. This book elaborates that the required

20

Schwab (2016). O’Toole (1993). 22 Simon (1997). 21

2.5 New Options to Organize

21

information is not simply the Shannon-type information to reduce uncertainty, but higher types of information are needed specially to interpret data on changes in the environment. This is not so abstract as it may seem, because this for some time was intuited by the emphasis on mission and values. An information-based concept of complexity helps us to understand that mission and values are specific types of information in the overall information space of an organization, not simply tools for motivation or inspiration, but tools for survival. What makes a difference between traditional organizations and successful organizations for the twenty-first century is how these deal with information beyond the conventional ERP systems. Some examples are IBM, Procter & Gamble, Netflix, but also small organizations like the care organization Buurtzorg in the Netherlands. What we lack however is a paradigmatic description of the organization of the Fourth Industrial Revolution, as was the multidivisional organization of General Motors for the twentieth century, described in Peter Drucker’s book The Concept of the Corporation.23 It might be argued that in a complex economy it is impossible to have one paradigmatic type of organization, also because competing has shifted to innovation of business models and a more precise relation between business models and organization forms, especially processes. We still lack a language with commensurate complexity to communicate with sufficient precision on new ways of organizing.

2.5

New Options to Organize

It is not only that the Fourth Industrial Revolution with its emphasis on knowledge and information requires new organization forms. The new options created by the declining costs of information in combination with the increased level of education and growth of talent, make new organization forms possible, compared to the bureaucratic hierarchies with an emphasis on structure. There is however a deceptive subtlety in studying and understanding these new organization forms. It is not so much that radical new or hereto unknown administrative instruments now are being used; it is more about a shift of roles and importance in the gamut of administrative instruments. Typical for organizations of the Second Industrial Revolution era, structure was the primary administrative tool, because structure included tasks, budget, configuration of resources, attributed decision rights, monitoring and reporting. Structure remains, but reduced to resource configuration, and superseded by tasks, to be accomplished by processes, projects, and strategic themes organized independent of structure. Mission and values in themselves are not new, but become more critical and serious as administrative instruments, and move from the domain of culture into being codified in systems, processes, and procedures. These subtle and for many perhaps even confusing shifts in importance and role of existing administrative instruments does not occur while it is fashionable e.g. as a result for the interest in

23

Drucker (1946).

22

2 Complexity 3.0

behavioral economics. Besides, organizational behavior as an academic field and empirical research dates back to the end of the nineteenth century, including its application in management. This shift reflects a loosening of complexity reducing institutions, structure, fixed tasks, job descriptions, allowing for more freedom, combinations, more flexibility, etc. while maintaining identity, integrity, and productivity. To understand this one might think of describing a glass of beer. If we would want to describe our beer at the level of electrons and nuclei, we would need 1024 to 1025 coordinates. For practical purposes, volume, temperature, percentage of alcohol, and taste will be sufficient. One might argue this is precisely what corporate finance is about, defining a firm in a few financial parameters. But in that way the firm is treated as a black box, corporate finance only describes the correlation between inputted resources and the created value, not how value is being created within that black box. For this reason, Michael Porter, the strategy theorist, did not see corporate strategy as strategy but defined only business strategy as strategy to create value and to emphasize this he introduced his value chain. In a knowledge economy value creation shifts from linear processes into non-linear networks of knowledge workers. We are interested in the question what (new) administrative tools managers can operate to achieve maximum value creation by a firm given the new complexities implied by intangible assets and given the new complexities in the market with a minimum processing of information by management itself, while being in control. The historical example of this question is the introduction of the Dupont- or ROI-tree in combination with the unit organization in 1918. These two tools were an answer by Dupont on how to run a multi-business firm by decentralized decision making and thus information processing, while being in control, given the then high costs of information, communication, low speed of communication and limited channel capacity. The old system was that through labor specialization a certain level of complexity was introduced to achieve productivity. These specialized tasks were coordinated through imposed coordination, which is through structure and planning which at the same time kept the firm in control. Now we need to achieve proactive behavior, initiatives by workers, innovation amongst others through recombination of personal knowledge, a dynamic set of processes, for which structure and year budgets no longer are effective. Imposed coordination by managers simply does not have sufficient information processing capacity to survive the firm in the present complexly dynamic markets in which much is being disrupted. To maximize the information processing capacity of firm it is needed that all of the (knowledge and creative) workers need to understand by themselves mission, the hierarchy of values, the strategy, codes of conducts, to interpret these by themselves what to undertake and to accomplish. The old trick of culture, in the sense of collective programming of the mind, in which experiences of the past, about what worked and what did not, were transferred to new members of the organization especially through socialization, no longer is effective. The past is pushed out by innovation of business models, by abductive thinking, and by design thinking in order to achieve innovation and higher levels of productivity. What did not work in the past, in today’s world may

2.6 A First Understanding of Organizational Complexity

23

work, and what did work in the past, not necessarily will work tomorrow (the fallibility theorem). Traditional types of planning and the related imposed coordination then become like describing a glass of beer at the level of electrons and nuclei. Hence the shift toward administrative tools like mission, values, strategic objectives (but in this era interpreted by knowledge workers, not only top-down translated into budgets), customer value proposition and information (access to and fast feedback). Some argue that where Peter F. Drucker in 1954 replaced management by instruction (MBI) by management by objectives (MBO) we now see a transition to management by values (MBV), or the value-driven organization. Objectives to be achieved remain and well-defined objectives are necessary for self-organizing teams. The issue is that a complex organization implies that more decentralized workers are asked to be more pro-active, to take initiatives and make proposals themselves, to achieve the overall objective of the firm or institution. Preferably as many as possible members of the organization, based on their knowledge of the causal relations in the business model and access to information, can calculate for themselves which of their initiatives and proposals will contribute most to the objective of the firm, including externalities on the various departments. At the same time the mission, the identity, and the integrity of the firm need to be preserved, but these are difficult to express in financial terms and will not be part of such calculations and numerical evaluation of alternatives. The mission and hierarchy of values then serve to evaluate proposals against the mission, values and identity beyond what is calculable; financial performance is not necessarily the ultimate criterion. This all sounds logical and it is if seen from a perspective of the KnowledgeBased View of the Firm, but it is not logical from the perspective of the traditional MBA toolbox. One might argue that in the past shifts of perspective have happened successful, e.g., that from the dominant organization of the First Industrial Revolution (partnership, putting out system, foremen system) to “modern organization” of the Second Industrial Revolution. The shift from the First Industrial Revolution to the Second Industrial Revolution was in a way simple, because practices from the Second Industrial Revolution, partnerships, foremen, were marginalized or eliminated, creating an unambiguous dominant normative set of tools and concepts for management and organization. The shift from the Second Industrial Revolution to the Third/Fourth is a shift from A → A + B, that is in many situations practices of the Second Industrial Revolution will remain effective; there will be more variety and less marginalization of alternative practices. This implies that we not only need to adopt new concept and tools, but also may need to continue to master the old ones, be it with a clear understanding of the limits to the latter. That is, our mental models and cognitive structure also will need to be more complex.

2.6

A First Understanding of Organizational Complexity

In business complexity often is associated with avoidable costs and therefore complexity needs to be reduced if not eliminated, so is the street wisdom. In doing so complexity and complicatedness are mixed up. A certain level of complexity is

24

2 Complexity 3.0

needed by any living system, including firms, for survival. Herbert Simon, the founding father of the field of administrative behavior, observed in the sixties of the twentieth century that for a firm to survive in a changing environment, especially with a degree of unpredictability, the firm needs a complex organization. Herbert Simon defined complexity in a different way as did, e.g., Jay Galbraith in his book Designing Complex Organization and most likely different from how most people in organizations define complexity. Elements in Simon’s concept of complexity are loose control and loose programming. These concepts serve to create discretion for workers in order to respond to changes in the environment by experimentation and so to create a capability for adaptation as needed for survival of the organization. Despite the economic logic of Simon’s concepts and that their effectiveness was confirmed by the concept of the organic organization, loose control and loose programming still do not sit easy with the concept of tight control as a standard in the field of management control. If an organization is complicated, it needs to be cleaned up. If an organization is too complex, it needs to be simplified to a minimal level of complexity as needed for growth and survival in its environment; that minimal level being defined by the complexity of its environment and technology. The manager, the organization, willing to survive successfully in such a complex environment, needs appropriate mental models and subsequent tools, not to simplify the complexity of the environment by reductionist models and concepts, but to master that environment, to exploit its opportunities. As markets, industries, geo-political systems grow more complex as a result of technology, demographic and other causes, and with that creating more opportunities for growth, new mental models, new concepts, new tools of management, new organization forms, are needed to benefit from this growing complexity. The digital technology, the declining costs of information, the Internet, in reflexivity with changing self-images of the consumer, require new (digital) business models, new type of organizations, but also a reconceptualization of e.g. marketing. According to Ashby’s Law of Requisite Variety24 an increase in the complexity of markets and other environments of firms, needs to be answered by a commensurate increase in complexity in the organization of the firm to maintain a higher degree of complexity of the organization as a prerequisite to remain in control. To the criterion of complexity, e.g. in the multidimensional organization, we need to add the dimension of speed of response, speed of decision-making. In order to survive the organization of a firm needs to be at least one degree more complex as its market and to be at least one step faster in responding as in the market.

24

Ashby (1956).

2.7 From Modern Complexity to Post-modern Complexity

2.7

25

From Modern Complexity to Post-modern Complexity

The period of the Second Industrial Revolution more or less coincides with the period of modernism. Modernism, as opposed to its predecessor traditionalism or romanticism, and its possible successors like postmodernism and or second modernism, emphasizes the lack of ambiguity, emphasizes either-or-dualities (market— non-market, family—non-family), individualism based on and limited by strong institutions, a clear geographical demarcated nation-state, science-based rationality, market equilibrium, a clear demarcation of employed work and non-employed work and other dualities.25 Modernism also was characterized by a clear separation between capital and labor and at the same time modernism was based on a delicate balance between capital and labor.26 These dualities simplified the complexity of social life, e.g., labor being a purchased commodity, not being part of the firm as a nexus of contracts. This enabled management to focus on and to pursue efficiency (growth of total factor productivity), based on simplified organization forms in which the benefits of specialization outweighed the costs of coordination. The simplicity of organization forms and management practices in the Second Industrial Revolution had its basis not only in normative simple organization forms, but also in its institutions. The period of the Second Industrial Revolutions (±1875 to ±1975) enjoyed a high congruence between its formative institutions, e.g., corporate law, labor law, property law, and the basic conditions of the economy, especially the salience of tangible assets and the exploitation of codified embodied knowledge through discrete products.27 In addition to these formative institutions the period of modernism was characterized by complexity reducing institutions.28 Complexity reducing institutions reduced the costs of communication between individuals in view of a limited capacity for information processing. A limited set of organization forms, with a limited set of identities and roles in these organization forms, reduced the need of individuals to explain to each other and understand their roles and responsibilities. Corporate law itself not only was formative, creating the corporation and its property rights, but also reduced complexity with respect to ownership and rights to the residual claim by separating capital and labor, suppressing the economic role of tacit personal knowledge carried by its trained craftsmen. With some exceptions this worked well. The development of the knowledge economy, with an increasing role of uncodifiable, personal knowledge, a mobile labor (or better, knowledge) market, a growing exploitation of knowledge that is not embodied in discrete physical goods enabled by digital technology, laid bare the limitations to economic growth imposed by this complexity reducing institutions. The need of

25

Beck and Lau (2005). Hardt and Negri (2012). 27 Strikwerda (2014). 28 Luhmann (1968), North (1990), Strikwerda (2014). 26

26

2 Complexity 3.0

these complexity-reducing institutions in the twenty-first century is being reduced by the increasing information processing capabilities people have today. That is, a tension is growing between the new opportunities created by new technologies and increased education and the restrictive (legal) tools and techniques embodied the traditional, especial complexity reducing institutions. Hence the movement of liberalization of markets and the reduction of the state in the economic process.29 In this way, new and more choices have been created for innovation and to improve allocation efficiency as needed for economic growth, but liberalization also created new dilemmas. The German sociologist Ulrich Beck (1944–2015) observed that the human manufactured increases in choices and opportunities were, and still not are, matched with a commensurate increase in knowledge and understanding needed to make responsible decisions without harming the trust in society, thus creating the risk society. That is, we are creating an increased complexity, for sound economic reasons, but we fail to develop the concepts and tools to harness that complexity, resulting in an experience of being out of control.30 “In fact, the very idea of controllability, certainty or security—which is so fundamental to the first modernity—collapses.31 Clinging to orthodox views and methods for management and organization, society responded to this uncontrollability with risk management. Beck: “Risk represents the perceptual and cognitive schema in accordance with which a society mobilizes itself when it is confronted with the openness, uncertainties and obstructions of a self-created future and which is no longer defined by religion, tradition or the superior power of nature but has even lost its faith in the redemptive power of utopias. . . . The world is not as it is; rather its existence and its future depend on decisions, decisions which play off positive and negative aspects against one another, which connect progress and decline and which, all things human, are bearers of error, ignorance, hubris, the promise of control and ultimately, even the seed of possible self-destruction.”32 The orthodox methods of decision-making were intentional rational, but in reality based on bounded rationality and constrained by strong institutions.33 Decision-making was based on mechanical models, which have linear, direct causality, following the dominance of science or logical positivism. Certainly, there was and is the school of system thinking emphasizing the law of unintended consequences, but this law was to be considered as a second-order effect, not material in a high growth economy, as was the period of the Second Industrial Revolution, in which errors simply were grown away. The Third Industrial Revolution demonstrates lower growth rates compared to the Second Industrial Revolu-

29

Yergin and Stanislaw (1998). Kelly (1994). 31 Beck (1999). 32 Beck (2009). 33 Etzioni (1988). 30

2.7 From Modern Complexity to Post-modern Complexity

27

tion.34 In combination with the fickleness induced by an increasing role of intangible assets, in the Third Industrial Revolution the law of unintended consequences no longer is a second-order effect. Beck’s observation of absence of religion and tradition (which acted as complexity-reducing institutions) has not been compensated, at least not sufficiently, by a growth of knowledge, of new understanding nor by new mental models. Instead, we face a kind of regression to the orthodox, expressed in risk management, but also in a clinging to orthodox ideas, hampering economic growth.35 Risk management, especially in the accounting type version, is a typical example of such an orthodox view and thus induces (more) control, a need for a mechanical type of control (in the form of tight financial control) and controllability. The paradoxical effect of the emphasis of this type of control, as opposed to the cybernetic type of loose control, is that risk management itself has become a source of risk as tight control tends to destroy the capability of adaptation and transformation as is needed for survival in a dynamic complex environment.36 As we will see in Chap. 13 more elegant methods exist to deal with uncertainty and risks. Postmodernism is used by some of authors to develop alternative views on management and organization.37 Postmodernism is used to define an alternative to the iron cage of modernist management and organization concepts. In that way postmodernism and the romanticists in complexity theory, have quite some in common. Postmodernism itself is a serious movement especially in political philosophy in that it describes and partly explains that what was marginalized in modernism, including some groups in society, no longer is marginalized in the present era. To that is related the idea of diversity and with that a greater variety of perspectives and values in debating social and political issues. Also, in business diversity is acknowledged as a source of multiple perspectives and values in addressing issues of whatever kind. In that way, it seems to be logical that management theory and organization theory include postmodernism. Postmodernism emphasizes that the simplification of modernism suppressed or ignored differences and complexities in society. This simplification serves social and political aims. Postmodernism then denies the (complexity reducing) grand narratives in the Western culture, like the Christian redemption, but also Marxism, liberalism and concepts like utilitarianism. In postmodernism the self is not seen as a source of culture, but the self is seen as being made by culture. Whereas postmodernism in its role through feminism, multiculturalism, critical race theory, gender issues etc. was a philosophical movement with major political consequences,38 it did not touch upon the institutions of business, labor law, corporate law, accounting rules, corporate finance, corporate governance, except for sustainability and diversity. As the foundations of

34

Gordon (2016). Stiglitz and Greenwald (2014). 36 Simons (2005). 37 Gergen (1992), Linstead (2004), Boisot and McKelvey (2010), Hatch (1997), Clegg (1990). 38 Based on the writings of prof. Lawrence Cahoon (2010). 35

28

2 Complexity 3.0

management and organization theory are in these fields, postmodernism did not and does not affect the foundations of management and organization theory. Publications on postmodernism, management and organizations therefore are limited to the idea that things should be different, to narratives serving the escapists, it does not lead to action nor change. Diversity and sustainability is accepted by business in the western world at least, because it serves ultimately the value of the firm and thus the interests of its stakeholders. But postmodernism despite its political influence has not touched upon the balance between capital, labor, and knowledge.

Bibliography Ashby, W. R. (1956). An introduction to cybernetics. Chapman & Hall. Beck, U. (1999). World risk society. Polity Press. Beck, U. (2009). World at risk. Polity Press. Beck, U., & Lau, C. (2005). Second modernity as a research agenda: Theoretical and empirical explorations in the ‘meta-change’ of modern society. The British Journal of Sociology, 56(4), 525–557. Beer, M., Nohria, N., & (Eds.). (2000). Breaking the code of change. Harvard Business School Press. Boisot, M., & McKelvey, B. (2010). Integrating modernist and postmodernist perspectives on organizations: A complexity science bridge. Academy of Management Review, 35(3), 415–433. Cahoon, L. (2010). The modern intellectual tradition: From Descartes to Derrida. The Great Courses. Clegg, S. R. (1990). Modern organizations--Organization studies in the postmodern world. Sage. Dahrendorf, R. (2002). Die Krisen der Demokratie: Ein Gespräch met Antonio Polito (R. Seuss, Trans.). Verlag C.H. Beck. Dahrendorf, R. (2003). Auf der Suche nach eine neuen Ordnung: Vorlesungen zur Politik der Freiheit im 21. Jahrhundert. Verlag C.H. Beck. Drucker, P. F. (1946). Concept of the corporation (revised 1993 Transaction Publishers ed.). John Day. Drucker, P. F. (1958). Business objectives and survival needs: Notes on a discipline of business enterprise. The Journal of Business, 31(2), 81–90. Etzioni, A. (1988). The moral dimension: Towards a new economics. The Free Press. Friedman, M. (1970, September 13). The social responsibility of business is to increase profit. New York Times Magazine, 32–33, 122–126. Gergen, K. J. (1992). Organization theory in the postmodern era. In M. Reed & M. Hughes (Eds.), Rethinking organization: New directions in organization theory and analysis. Sage. Gilboa, I., Postlewaite, A., & Schmeidler, D. (2010). The complexity of the consumer problem and mental accounting (Vol. 75, Issue 1, pp. 96–103). Research in economics, 2021. Gordon, R. J. (2016). The rise and fall of American growth: The U.S. standard of living since the Civil War. Princeton University Press. Hardt, M., & Negri, A. (2012). Declaration. Argo Navis Author Services. Hatch, M. J. (1997). Organization theory: Modern, symbolic, and postmodern perspectives. Oxford University Press. Hayles, N. K. (2012). How we think: Digital media and contemporary technogenesis. The University of Chicago Press. Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proc Natl Acad Sci U S A, 106(26), 10570–10575. Hofstede, G. H. (1980). Culture’s consequences: International differences in work-related values. Sage.

Bibliography

29

Jensen, M. C. (1998). Foundations of organizational strategy. Harvard University Press. Kelly, K. (1994). Out of control: The new biology of machines, social systems and the economic world. Addison-Wesley. Krugman, P. R., & Wells, R. (2009). Economics (2nd ed.). Worth Publishers. Linstead, S. (2004). Organization theory and postmodern thought. Sage. Luhmann, N. (1968). Vertrauen: Ein Mechanismus der Reduktion sozialer Komplexität . (4 Auflage ed.). Lucius & Lucius. Miller, D. (1993). The architecture of simplicity. The Academy of Management Review, 18(1), 116–138. North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press. O’Toole, J. (1993). The executive’s compass: Business and the good society. Oxford University Press. OECD. (1987). Structural adjustment and economic performance. Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. The Penguin Press. Schwab, K. (2016). The fourth industrial revolution. World Economic Forum. Simon, H. A. (1946). The proverbs of administration. Public Administration Review, 6(1), 53–67. Simon, H. A. (1997). Administrative behavior: A study of decision-making processes in administrative organizations (4th ed.). Free Press. Simons, R. (2005). Levers of organization design: How managers use accountability systems for greater performance and commitment. Harvard Business School Press. Sloterdijk, P. (2005). Im Weltinnenraum des Kapitals: Für eine philosopische Theorie der Globalisierung. Suhrkamp Verlag. Stiglitz, J. E., & Greenwald, B. C. (2014). Creating a learning society: A new approach to growth, development, and social progress. Columbia University Press. Strikwerda, J. (2014). The paradigms of business administration and the concepts of the balanced scorecard and the strategy map. 76. Retrieved from SSRN website: http://ssrn.com/abstract=24 61789 Yergin, D., & Stanislaw, J. (1998). The commanding heights: The battle between government and the marketplace that is remaking the modern world. Simon & Schuster. Zinkhan, G. M., & Braunsberger, K. (2004). The complexity of consumers’ cognitive structures and its relevance to consumer behavior. Journal of Business Research, 57(6), 575–582. https://doi. org/10.1016/s0148-2963(02)00396-x

3

Simplicity and Complexity

3.1

Our Need for Simplicity

Most of us have an ingrained need for simplicity. In a way, we cannot live without simplicity. For many people, simplicity stands for pureness, authenticity, trust, reliability and for ontological security. Simplicity provides an idea of being in control over one’s life and over one’s environment by comprehensible stories and explanations that reduce uncertainty and make people act in confidence. We want to understand the world, which we in our lucid moments acknowledge to be complex, and we therefore create ourselves images, simple models, explanations with which we feel comfortable. When astronomers succeeded to describe, that is explain and predict, the motions of the sun, the moon and the stars in relatively simple formulas quite accurately, this reinforced the idea that the truth is simple and that underlying a complex world are simple laws and rules. The idea of parsimony preceded simplicity in medieval times in Occam’s Razor, formulated by William of Ockham (1285–1347) pluralitas non es ponenda sine necessitate, the explanation of a phenomenon with fewer assumptions is to be preferred over an explanation with more assumptions, also known as the lex parsimoniae. Understanding for many is that a phenomenon can be explained in let us say half an hour using one blackboard for a not-too-complicated formula and that accurate predictions can be achieved through not-too-complicated calculations with limited data. Understanding also is about coherence, that models explain all phenomena in a consistent way. If we need both simplicity and complexity, a question could be what the relation is between simplicity and complexity. To understand this relation, we need to understand simplicity. A philosopher who wrote about simplicity in the natural sciences was Alfred North Whitehead (1861–1947). In his The Concept of Nature he writes that the aim of science is to seek the simplest explanation of complex facts and events.1 Scientific

1

Whitehead (1920).

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_3

31

32

3

Simplicity and Complexity

research is aimed at saving thought and thinking. This is achieved by neglecting certain details as being not material in calculations or explanations or assuming that specific variables will be constant (ceteris paribus). The neglect of certain variables should be justified and should be documented.2 The process by which science achieves simplicity Whitehead labels the law of the convergence to simplicity by diminution of extent. That is, we abstract from complexity. Hence Albert Einstein’s quip, the simple always is simplified. In view of the successes of science we must acknowledge that the law of the convergence to simplicity is extremely useful. The world may be complex as are its events and facts, but obviously, there are simplifications, or abstractions, that help make us understand, predict, decide and act being right most of the cases. But warns Whitehead, “We are apt to fall into the error of thinking that the facts are simple because simplicity is the goal of our quest.” Simplicity paradoxically is not simple and Bunge, elaborating on different types of simplicity coined the expression “The complexity of simplicity.”3 Bunge distinguishes ontological simplicity and semiotic simplicity. Ontological simplicity is about material and cultural objects (things, events, processes). Semiotic simplicity is about the terms, propositions, proposals, and theories we use to understand and communicate about the world. Ontological simplicity is assumed in science that au fond the complex world can be understood in a set of simple rules. This is the simplicity Whitehead refers to as the subject of science. Bunge elaborates especially on the idea of semiotic simplicity, which is the simplicity of theories, propositions, semantics, etc. An example of this is that the mass of e.g. a car is expressed in a real number with one dimension, kilograms, whereas its velocity, if expressed accurately, needs to be expressed in a complex number (a vector), expressing the velocity in meters per second and the direction of movement, e.g., in degrees on the compass-card. But in daily life we express the speed of a car in kilometers per hour only because we assume the car to follow the axis of the road; we simplify on basis of context. Bunge’s article The Complexity of Simplicity in which he coined the term semiotic complexity, published in 1962, later was elaborated from the perspective of complexity theory as the Kolmogorov complexity.4 Kolmogorov complexity, also labeled algorithmic complexity or descriptive complexity, measures the complexity of a result, a description, or a theory in terms of the minimum number of steps in a computer program to produce that result, respectively to calculate on basis of a given theory that result. In order to understand, to make decision we need to be able to process a model and its data within a certain timeframe. Technical simplicity is the phenomenon that we need to balance the lack of accuracy of a too simple model, e.g., to forecast demand

2

Bunge (1962). Bunge (1962). 4 Lui et al. (2015). 3

3.1 Our Need for Simplicity

33

by the simple formula dn + 1 = 0.8 * dn + 0.2 * edn + 1,5 or a sophisticated algorithm based on machine learning and Big Data. The risk is that the algorithm contains errors, either in its design or codification that data is inaccurate or that operators make mistakes in working with a complex algorithm. Combined with the phenomenon of satisfying behavior we tend to satisfy on accuracy. We tend to prefer the certainty of inaccuracy we understand to the certainty of accuracy we do not understand. Bunge defines epistemological simplicity of a theory or model as the degree of closeness to sense experience in particular to direct observation. Science as we know it today exists by the written word and has as its objects not concrete experience but an abstract unexperienced and often not a direct observed reality.6 We may think of electromagnetic fields and picture these in lines, but we deduce these lines from the pattern of iron particles on a piece of paper with a magnet held beneath it, we do not see this electromagnetic field lines with our own eyes. As a specific type of semiotic simplicity Bunge defines pragmatic simplicity. This is relevant for the fields of business administration as its underlying philosophy is that of pragmatism as developed by American philosophers John Dewey, William James, and C.S. Peirce and later endorsed by philosophers as Richard Rorty and the German philosopher Jürgen Habermas. Pragmatism holds that a theory is true if it works, that is that understanding, decisions, and actions generated by or based on that theory produces results that are useful. Bunge subdivides pragmatic simplicity in psychological simplicity, notational simplicity, algorithmic simplicity, experimental simplicity, and technical simplicity. Psychological simplicity is about obviousness, ease of understanding, familiarity. This type of simplicity is to be found in the way management books are edited, on ease of understanding and familiarity by using cases readers can identify with. Psychological simplicity is culturally and educationally conditioned.7 Management books in Germany are written in a different way as are American management books edited, due to the difference between the Humboldt-type university and the liberal arts college educational system. Notational simplicity is about using mathematics to simplify formulas and to ease symbol manipulation, but at the same time describing economic phenomena in mathematical symbols may obscure what is being said. Mathematical notation is pursued for testing hypothesis, but this type of abstraction is for the inner crowd, it is complicated for those who are supposed to use its insights. Algorithmic simplicity is the reciprocal of Kolmogorov complexity. Experimental simplicity is an issue in the field of business administration because to make research manageable and enable hypothesis-testing experiments, e.g., about decision-making under uncertainty, experiments tend to be oversimplified especially

5 The estimated demand for next month is the sum of 80% of the sales of this month plus 20% of the (subjective) estimated demand for next month. 6 Steven L. Goldman, lectures Great Scientific Ideas That Changed the World. 2007. 7 Bunge (1962).

34

3

Simplicity and Complexity

with respect to the role of context. Experiments, simulation and more recently gamification in business face the dilemma of the need being realistic, but risking becoming too complex beyond comprehension of causal relations as needed for effective learning, or being too simplistic in view of the reality students need to understand to make reliable decisions, especially with respect to non-linear relations. Too much simplification especially of models used in solving problems and situations may result in subjective complexity and thus through wrong decisions into ontological complexity.8 The dilemma of pragmatic simplicity is that on one hand it provides us certainty of understanding and thus to act, we do not hesitate for uncertainty about the validity of (management) models, on the other hand it tempts us to satisfy, not to look further for more valuable options, or even to deny facts that may be foreboding that the days of validity of a model or heuristic are counted due to changes in the environment. Interestingly Bunge asks the question of what types of simplicity are for what reasons in which situations desirable? We might also ask the question what type of simplicity, to what extent, in which situations for what purpose is a responsible or reliable simplicity or simplification? To ask such questions contains the risk that we focus in an instrumental way on questions which heuristics are valid and restrict ourselves to defining critical success factors and other rules of thumb.

3.2

Two Types of Simplicity

We need to be aware that there are two different ways we arrive at simplicity. Bunge states that to achieve simplification is an uphill battle. De Bono states: “True simplicity comes from thorough understanding. Simplicity before understanding is worthless.”9 This is consistent with the observation of O’Toole that successful CEOs “look for simplicity beyond complexity.” This first way to achieve simplicity is an effort, an intellectual stance that starts with the acknowledgment that there is a new situation that cannot be understood nor dealt with by existing rules or models. This intellectual effort includes using or developing new concepts, which relates to what other authors label as the need of reconceptualization and abstract thinking, or cognitive reframing to deal with new complex situations. Without concepts, one remains stuck in the complexity of details and no new understanding can develop.10 Hence, the observation that abstraction is the main tool to deal with complexity to arrive at new, reliable simplicity. In contrast, Miller describes a different process on how in organizations simplicity develops and is maintained.11 We might call this second process simplicity as a downhill slide. This second type of simplicity results from myopia of learning,

8

Cannon et al. (2009). de Bono (1998, p. 283). 10 de Bono (1998, p. 285). 11 Miller (1993). 9

3.2 Two Types of Simplicity

35

which is learning only from one’s own experiences, resulting in overspecialization, routines, with no exploration.12 Such type of learning often initially improves performance and thus reinforces itself. But the lack of variation, exploration, the lack of taking in new knowledge from the outside, isolate the organization from the changes in its environment, impairing the continuity of the organization. In these days of a mobile labor market, temp workers, open innovation, social media and the Internet it may be difficult to imagine that organization have no awareness of changes in its environment. Seeing changes, even being part of it, is not the same as understanding these changes, especially in consequences for the organization. This second type of simplicity is to be avoided. Paradoxically this second type of simplicity may be a cause of complexity in the sense of complicated work methods or processes, because often in an organization there are members for which it is not in their interest to simplify a situation in a positive way, because their legitimacy in the organization is their capability to deal with the complicatedness, solving apparently ad hoc problems at the fly but to solve such a situation in a more systematic way is perceived as a threat.13 The reductionist method of the traditional MBA of course does not intend to, but often results in downhill slide type of simplicity. This is because most people need to be focused in order to be productive, in accordance with the earlier described economic role of specialization. When Zook & Allen published their book Repeatability they stated that ‘successful companies make great efforts to keep their business model as simple as possible.14 The Economist summarized Zook & Allan’s book as that the authors promote simplicity. The title of the book review (April 28, 2012) was “Simplify and Repeat—The best way to deal with growing complexity may be to keep things simple.” In accordance with Ashby’s Law of Requisite Variety, Zook & Allen stated explicitly “as simple as possible”; in order to survive in a complex market, the organization of a firm needs to be at least one degree more complex and needs to make and implement decisions one at least one step faster as the dynamics of the market. Correctly The Economist writes that simplicity is not enough, it may not be an answer to a sudden shift in the market. The Economist should have pointed out the need for a requisite complexity to survive; as both Napoleon and Churchill are reputed to have quipped, “make it is simple as possible, but not simpler.” The article in The Economist illustrates a tendency to be reckoned with in dealing with complexity, a strong human need for simplicity, which has its roots in a deeper need to be able to understand the world, to have a coherent worldview in which one feels comfortable. The Economist could have known better, in 1993 Danny Miller published his article The Architecture of Simplicity, in which he observes: “increasingly simplicity victimizes many once-outstanding

12

Levinthal and March (1993). See for an example of this see Tucker and Edmondson (2003). 14 Zook and Allen (2012). 13

36

3

Simplicity and Complexity

organizations.”15 Earlier Alfred Whitehead wrote and warned: “Seek simplicity, and distrust it.”16

3.3

Simplicity and Perspective

Another aspect of simplicity is perspective. The simplicity management books write about either is without context or it is about the same simplicity for all members of the organization and its external parties. Always the question needs to be asked: simplicity for whom, or simplicity from what (institutional) perspective? Complexity in most cases cannot be reduced according to Asby’s Law of Requisite Variety, but there are options to locate the complexity in a most efficient way in an organization. The traditional organization of a hospital is based on medical specialisms. This creates a simple organization from the perspective of the executive board, a simple linear hierarchy, as it is for medical doctors. Increasingly it acknowledges that care is delivered through teams of medical doctors and that certain groups of patients, especially older patients become more complex. In the case of gastrointestinal oncology patients at the Amsterdam Medical Center, an academic hospital, eight different medical specialists are needed to diagnose the patient and to define a treatment plan. In the old way of working patients needed to make six appointments with six different specialists in six departments in two different divisions of the hospital. This was considered unacceptable by the medical doctors involved and a bottom-up initiative developed resulting in a care path defined from the perspective of the patient, endorsed by the executive board with some facilitation by the board. In the new situation, the patient has one appointment, and apart from a small percentage of patient needing an additional examination, it is a one-day process for the patient.17 In terms of capacity planning and scheduling for the medical staff this simplicity for the patient made the organization more complex. An internal auditor acknowledged the benefits of the new organization for the patient but judged the addition of the multidisciplinary fast-track gastrointestinal oncology care path “a disturbance of the organization model of the hospital.” Hence De Bono’s tenth rule for simplicity “You need to know for whose sake the simplicity is being designed.” To which is to be added: and where the required complexity best can be located and isolated. Creating simplicity as in the example for patient may imply that others in the organization and elsewhere in the organization, a corresponding complexity needs to be absorbed. In the example of gastrointestinal oncology patients, the system and process of capacity planning and scheduling medical staff becomes more complex as is the organization of information. To create simplicity for patients also implies that sufficient members of the staff need some management development to understand the new complexity and to reduce the complexity by increasing

15

Miller (1993). Whitehead (1920). 17 Basta (2017). 16

3.3 Simplicity and Perspective

37

their knowledge. More in general because in mature markets the competition is about the eyeballs and the time of the consumer, the starting point for organization design is the customer value proposition. Derived from this customer value proposition processes are designed and organized to deliver the competitive customer value proposition, across the structure of divisions and departments for reasons of efficient coordination and value creation through complimentary design of intangible assets. In this way, efficiencies are achieved through horizontal and vertical knowledge synergies and through better resource allocation and utilization. The price to be paid for this is an organization with a higher complexity for the management and members of the organization. There is no such thing as a law of conservation of complexity because complexity can be overcome by abstraction and knowledge. But managers need to make a trade off where to organize simplicity and where to absorb complexity in the organization in view of costs and capabilities of people and systems versus a competitive customer value proposition. In this, the sub-unit power principle is to be applied. This principle states that the dimension or aspect in the environment of the organization that is most important for the competitiveness and with that for the survival of an organization should have the most power in the internal organization. In the first three-quarters of the twentieth century manufacturing capability and capacity was most important in an unsaturated market to achieve market share, consequently manufacturing was the most powerful position or function in the internal organization. In the 1960s much power drifted into marketing because marketing began to play a larger role in the success of especially consumer goods firms. In the 1980s the CFO became a new and powerful position because the increasing role of the capital market and active shareholders.18 At the same time, the awareness grew that in maturing markets all focus should be on customers, hence customer first programs. Only few companies took the consequences in the 1990s to interpret “customer first” into the customer being the primary profit center in the accounting system and resource allocation system and giving it priority in resource allocation. In the nineties many firms introduced corporate account management across the divisions, to increase negotiating power and to make doing business simple for corporate accounts by organizing “one point of entry.” However, many firms stuck to their existing divisional structure and assumed that corporate account management could be achieved through cooperation and sharing information through CRM software. That is because its management had difficulty to change the internal power system by making, what was needed to do, the corporate account the primary profit center in the accounting system, and changing the divisions into secondary profit centers. Such a change in system implies a change in the social power system and, at least at that time, many managers for peer group reasons had difficulty to make such decisions. In time, with newer generations managers and workers this problem will solve itself but the process is slow.

18

Zorn (2004).

38

3.4

3

Simplicity and Complexity

Simplicity Versus Limited Complexity

There is a difference between reducing complexity (simplifying) and limited complexity. As North explained in writing about institutions, at least a set of particular institutions served to limit complexity in order to reduce the costs of communications between people in social systems.19 Traditional organization forms as these came into existence with the Second Industrial Revolution, based on functional specialization and a Weberian hierarchy, created a set of limited roles and identities, making interhuman communication easier and more cost-efficient. If a manager explained to someone that his position was that of business unit manager, the other immediately understood his role, responsibilities, and authorities. Related to this is the issue of trust. Trust itself is a complicated concept; various types of trust exist, e.g. identification-based trust, calculus-based trust. The German sociologist Luhmann interpreted trust as a social complexity-reducing mechanism. In this interpretation of trust, it is achieved by limiting the variety of acceptable behaviors by members of a social system, through mechanisms of traditions, norms, rituals, making behavior predictable; trust is not served by unpredictable behavior.20 In such a society each member is supposed to know and to accept its position in society and limit its behavior accordingly. Max Weber, the German sociologist in defining his modern concept of bureaucracy assumed such a society underlying his concept of the legalistic rational bureaucracy.21 As a consequence in the period of the Second Industrial Revolution only a limited set of organization forms were deployed.22 This limited set of organization forms acted—and in many cases still acts—as a complexity-limiting institution in terms of roles, identities, and corresponding behavior.23 A limited set of organization forms simplifies communication and reduces costs of information processing in social communication. At the same time, this cost control of human information processing limited the technical to be achieved efficiency of organizations and with that economic growth. Whereas Weber’s legalistic rational bureaucracy was a breakthrough from the limitations of the personal bureaucracy, the knowledge economy needs a breakthrough from the limitations of Weber’s bureaucracy, enabled by the higher information processing capability of the information society.

19

North (1990). Luhmann (1968). 21 Weber (1947). 22 Grandori and Soda (2006). 23 Strikwerda (2014). 20

3.5 Dominant Logic

3.5

39

Dominant Logic

We cannot work and live without simplifications. But, as Whitehead has instructed us, distrust simplicity. At the same time, we want to trust simplicity in order to act. So, the question is when to trust simplicity and when to reject it or replace it? To be aware of this dilemma and to live with this question in a constructive way, it will be helpful to understand what the source is of simplification and the process by which it comes into being. For this, it is helpful to use the concept of dominant logic, as an expression of cognitive simplification as a tactics to deal with complex problems in real life.24 A dominant logic is a simplified, explicit or implicit, consciousness or unconsciousness, causal model on what to do and what not to do in order to be successful. A dominant logic is rooted in past success, either by own experience, example, or tradition. More specific are heuristics, simple decision rules requiring minimal information processing and allowing for fast decision-making. For some heuristics are fast and frugal and effective tools to deal with complexity.25 For others heuristics stand for biases in (managerial) decision-making, hampering sound decision-making.26 The dominant logic of a CEO in general will develop from a combination of conventional wisdom, including what has been learned at the MBA course, in combination with lessons drawn from successful choices, problem-solving, decision made, resulting in cognitive simplifications. These cognitive simplifications combine with cognitive biases into complex solving behavior, which will be reinforced by success, resulting in sources of dominant logic. That is to say, it results in rules about what works and what not, what to focus on and what to ignore or suppress (Fig. 3.1). A dominant logic is both an individual attribute as it may be a characteristic of an entire organization.27 This dominant logic as cognitive simplification is a kind of dilemma. Dominant logic is needed to an extent for focus, for success. As a cause-and-effect scheme, as a psychological script, its validity is limited in time, due to the reflexive relation between a managerial action model and the reality on which these actions are applied. A too-strong and/or a too long focus on a single dominant logic, on a single success model, has a high risk of turning an asset, the CEO with a successful dominant logic, into a liability, as has been documented by Christensen in his The Innovators Dilemma.28 Also, a dominant logic may develop into an individual and or collective lens through which changes in the environment are interpreted, especially in the way of belief conservation, that is that changes are not interpreted in a way producing new strategies as needed for the survival of the individual or the

24

Prahalad and Bettis (1996), Bettis and Prahalad (1995). Artinger et al. (2015). 26 Bazerman and Moore (2009). 27 Prahalad and Krishnan (2008). 28 Christensen (1997). 25

40 Fig. 3.1 The factors and experiences resulting in a dominant logic (Prahalad and Bettis 1986). This dominant logic is a dilemma because it is needed for focus but risks not seeing changes in the environment pertinent to the survival of the organization

3

Simplicity and Complexity

Sources of Dominant logic Reinforcement Complex problem of a world view solving behavior by market success (operant condioning) Cognive Cognive bias, simplificaons available vs. adequate informaon Convenonal wisdom (paradigms)

Past experience and soluon by analogy (paern recognion in chess games

organization. Some experimentation, some variation is needed at least to discover new modes of success, the degree of which depends on the level and growth of complexity in the market. In the case of more drastic changes in the market complete reconceptualizations will be needed. To this applies what Stafford Beer coined the need of a Meta system.29 Actually, in relation to the firm as an operational system multiple levels of metasystems are needed, varying from MBA concepts, academic theories to philosophies. The traditional MBA-tool box in a way also reflects a kind of dominant logic, of reductionism, to which complexity theory aims to provide an alternative. Which evokes the question what might be the metasystem to the level of the MBA toolbox. To understand what the metasystem of the MBA toolbox might be we need to understand that the MBA toolbox is an operational, managerial simplification of rules and rights from the fields of law, corporate law, labor law, property law, contract law, economic theory and organizational behavior, including some elements of political philosophy.30 These simplifications served us well during the economy of the Second Industrial Revolution, and to a certain extend still serves us, but since about 1980 this simplification is questioned in a steady increasing voice. The language in which this questioning is expressed is in terms of “breaking out of the silos,” an emphasis on talent management, an emphasis on culture (over structure), a quest for integrative management, etc. Only a few authors represent this metasystem, without themselves labeling it this way. One example is Furubotn & Richter who explain that managerial control is based on the corporation having full alienation rights on all of the assets of the firm, based on the partition of Roman property law in the right to use an asset, the right on the economic value created by an asset and the right to alienate, change or destroy and asset.31 To which Michael Jensen consequentially concludes that in the case of uncodifiable personal

29

Beer (1979). Strikwerda (2014). 31 Furubotn and Richter (2000). 30

3.6 Complexity and Language

41

knowledge, the corporation has no alienation rights on this personal knowledge, it is the property of the knowledge worker, not of the corporation, and therefore the shift toward the knowledge economy erodes the traditional basis of managerial control.32 Other authors on this topic are Zingales and Rajan who have explained in which way the changes in the economy are eroding the concept of the firm underlying all of the MBA concepts including the system of corporate governance.33 This is where political philosophy comes in as this touches on the position of shareholders, labor law and through the ideological role of private property for some becomes an issue of political ideology.34 The latter, in combination with the technical problems to adapt in an international context corporate law and labor law, which at the same time is concerned about and concentrated on issues like taxes, fair transfer prices, transparency, disclosure, risk management makes it virtual impossible to have an open debate on the renewal of the MBA toolbox at a true level of metasystems. Therefore, it is to be expected that the discrepancy between the new basic conditions of the economy, as defining the Fourth Industrial Revolution, and the institutional or regulatory context which are still based on the conditions of the Second Industrial Revolution will not be solved for the next 20 years.35 What will facilitate this change is the declining costs of information respectively the availability of big data. The declining costs of information imply a further individualization of ownership rights.36 This is to be seen, e.g., in the debate in the Netherlands on its pension system, originally dominantly a employer or industry-based collective system, in which now individuals are claiming their individual rights, the more since the system changed from benefit based to contribution based. Technically now it is simple to have systems that record these individual contributions. Another example is Duane Morris that has software in place that is capable to measure the contribution of individual lawyers to the overall performance of the firm. This type of economic individualization will play a factor in changing political power relations.

3.6

Complexity and Language

The institutionalization of simplicity is also to be found in language. Language is not a neutral phenomenon but reflects all possible interests, ideologies, power structures, and other aspects of society.37 The question to be asked is whether we have a language available in which we can communicate about complexity and with which we can think effectively about complexity? The concept of linguistics “overcode” suggest differently. “The overcode is the signifier of structuralist theory, 32

Jensen (1998). Rajan and Zingales (2000). 34 Danley (1994). 35 Randers (2012). 36 Lash (2002). 37 Wasson (2004). 33

42

3

Simplicity and Complexity

the abstract unit of language that cuts out standardized concepts and organizes them according to binary oppositions (raw/cooked, man/woman, friend/enemy, etc.).”38 With that language, especial professional language has a tendency to simplify, to act as a complexity-reducing institution. This is especially true for business, where managers learn through their MBA training a specific language with respect to organization and managing, based on business institutions. That language reflects the reductionist nature of the traditional MBA theories. The philosophical, political and cultural movement of postmodernism has as an effect that in the period of modernism marginalized no longer are marginalized weakening the simplifying dualisms of modernity. Postmodernism especially questions the role of language. One of the representatives of postmodernism, Jean-François Lyotard, claims that the context supplies the rules for what the words we use mean and also which word may be used or not. This also used to be based on grand narratives, including dominant economic theory. It may be that postmodernism in political terms has emphasized the positive role of diversity in society and business, it has not marginalized main economic theory. Diversity itself is an increase of complexity as needed for economic growth. For instance, Richard Florida has explained that regional economic success in the US society is determined by talent, technology, and tolerance.39 Postmodernism as a movement in political philosophy has contributed, e.g., through feminism, gender issues, to higher levels of tolerance. Postmodernism also emphasized social constructivism, that is that our social structures, especially organizations are humanly constructed, not God-given.40 For reasons of interests and power man also tends to forget or repress his authorship of the social world. This also is being reflected in language. Baudrillard is even more critical about the use of language. He discerns four uses of signs (words): 1. 2. 3. 4.

As a reflection of a fundamental reality Masking and perverting a fundamental reality Masking the absence of a fundamental reality The sign (word) has no relationship with whatever reality; it is its own simulacrum

Whether Baudrillard’s observation applies to business is to be questioned, but it points to a slightly different phenomenon that easily can be observed. Language should be sufficiently complex to describe adequately (new) complex situations (Fig. 3.2). In addition to value-complexity consumers may display different degrees of cognitive complexity. “Cognitive complexity refers to the structural complexity of an individual’s cognitive system, i.e. it describes the sophistication of the cognitive

38

Holmes (2009). Florida (2004). 40 Berger and Luckmann (1966). 39

3.6 Complexity and Language

43

Fig. 3.2 The relation between the complexity of language of description and organizational reality (Daft and Wiginton 1979)

structure that are used for organizing and storing cognitive content.”41 Often consumers make their decisions what to buy simply by following others, e.g. displaying a specific lifestyle, for reasons of identity. Consumers with an in-depth knowledge in a specific field, e.g. cooking, a reading subject, a hobby not only have detailed knowledge on that field but also know to process, to interpret new information on their field of interests and may be willing to make innovative decisions (early adopters) independently of the experience or choices by others.42 Another variable in the complexity of consumers is that their preference set, as expressed in their price elasticity, may be dependent on the context within which they make decisions. An individual customer may have different price elasticity’s, sets of preferences, on different days, hours and in different contexts as, e.g., in retail.43 When traveling or on holiday a group of customers is willing to pay more for the same product as they would when considering the same product when shopping for weekly groceries. This context dependency or complexity retailers like, e.g., Albert Heijn in the Netherlands have translated in a sub-brand, the AH-ToGo (situated in high traffic areas like railway stations and hospitals), as part of a set of a variety in shop formulas to answer the complexity in consumer preferences and behavior. Emergent complexity in consumer preferences is to be seen when consumers appropriate brand and or products for their own identity and stance in society. This happen in the seventies with the small car Mini as introduced in 1959 by the British carmaker BMC, which was used by a hippie type group in society as an antiestablishment symbol. The attributed image to the Mini was of out of control of the owner of the brand, BMC (this happened not with the second generation of the

41

Zinkhan and Braunsberger (2004). Zinkhan and Braunsberger (2004). 43 Strikwerda (2008). 42

44

3

Simplicity and Complexity

MINI as introduced in 2001 by the new owner, the German carmaker BMW).44 Nutella is a case in which the brand, also through the use of social media more or less is taken out of control of its Italian owners and made into an expression of individuals.45 The increasing complexity of markets also implies that markets may be more difficult to forecast. In combination with that there is never certainty whether (new) actions will produce intended consequences; trial-and-error may be needed to discover appropriate responses to changes in the environment. Hence, Simon’s insight that in order to survive organizations need to be complex, but Simon interpreted this in terms of loose programming, loose control as opposed to tight control, allowing workers in the operations, those working at the edge of the organization with suppliers and customers, discretion to experiment how to respond most appropriately to changes in the environment.46 Simon relates this to complexity because it is about allowing a higher variety of behaviors in the organization as is assumed in the traditional Weberian command and control organization. However, Herbert Simon never elaborated on his type of complexity in terms of organization design.47 We now understand that Simon’s complexity apart from some other requirements requires a free social interaction between members of an organization, across the boundaries of divisions and departments. Simon’s insight never sat (and still not is) easy with especially financial performance management and certainly not with risk management based on audit methods. This despite the fact that Simon’s insight was and to today still is confirmed by the concept of the organic organization (as opposed to the mechanic organization, as identified by Burns & Stalker). The importance of trial and error and that this is needed in order to be in control, more recently is confirmed by. 48 To feel comfortable with trial-and-error also depends on personality, the capability to trust others, to create an organization, e.g., with fast feedback information, that is capable of corrections and learning in the capability to enjoy surprises. This is acknowledged in companies like Google and in concepts like discovery-driven growth.49 Discovery-driven growth acknowledges emergent complexity in markets and acknowledges unintended consequences of decisions. However, trial-and-error needs to be explained in a risk society in which especially politicians may accept trials, but nor errors. It may be that the general public will support trial-and-error, simply because that is how many of us manage daily life, but regulators tend to think differently about it. Trial-and-error as a sign of a vital society needs to be distinguished from scandals, affairs and greed in business. The problem is more that the moment an individual person acts as a representative for a traditional institution that is representing the order of modernity, the individual loses his

44

Mossinkhoff (2012). Cova et al. (2006). 46 Simon (1962). 47 Hatchuel (2002). 48 Manzi (2012). 49 Bock (2015), McGrath and Macmillan (2013). 45

3.7 The Paradox of Traditional System Thinking

45

capability to adapt to the post-modern world and his acceptance of especially the error in trial-and-error.50 What can be observed is that the language in which, based on traditional models and theories, organizations are described, are specified, and by which is being communicated on organizations, lacks variety to express accurate especially new organization forms. For example, in the case of IBM its MNC structure did not change, so many thought the turnaround was not based on a change of structure. But traditional organization theory does not consider the organization of information, nor which is the primary profit center in the accounting system. From the perspective of traditional organization theory with a focus on structure, the IBM transformation does not seem to be based on a change of structure, which in terms of traditional organization theory does only leave a cultural change as an explanation. But the change of the organization of information, disembedding this from the traditional structure and eliminating information asymmetry, and changing the profit center to the customer being the profit center, implies a fundamental change in the system of power structure in the organization of IBM, facilitating a free flow of data, information, and knowledge. But words fail us to describe this type of change, be it that most close is Kanter’s concept of systemic change. Daft & Wiginton label the phenomenon that language fails us to specify accurately new organization forms as the principle of incompatibility. The essence of this principle is that as the complexity of a system increases, our ability to make precise yet significant statements about its behavior diminishes.51 They elaborate on this leading to a paradox with respect to a specific school of system thinking. Daft & Wiginton write: “All models are abstractions, but the simple deterministic model of a complex human system may be an abstraction to such an extent that it fails to satisfy the law of requisite variety. This, we argue, has been recognized intuitively by managers, and may explain why they are unwilling to turn over organizational decision making to mathematical or statistical decision models.”52

3.7

The Paradox of Traditional System Thinking

In the school of system thinking about organizations, e.g., in Stafford Beer’s organizational cybernetics and in his viable system model we can observe a paradox.53 A mathematical language is developed in these concepts to deal with new and increasing complexity. The complexity in business is not only a complexity in terms of processes, structures, choices or decisions to be made, causal relations, or even states of systems, it is also a complexity in terms of property rights, incentives, psychology, motivation, identity, developing knowledge, interests, institutional and

50

Beck (1999). Daft and Wiginton (1979, p. 182). 52 Daft and Wiginton (1979, p. 184). 53 Beer (1985). 51

46

3

Simplicity and Complexity

other external factors, etc. The reductionist language of the mathematical school of systems thinking in its attempt to specify complexity reduces precisely the complexity we want to master. One might think of Schein’s 1985 concept of the complex individual. People do not just have one dominant need, their actions may be determined by needs that vary with their situational factors, such as personality, living situations, work situations, etc. People’s motives may change over time. People are learning, new experiences evoke new emotions. People may have different motives and satisfactions in different types of organizations. People may have a variety of motives to commit themselves to the organization and to perform. People will, dependent on their personalities, preferences, etc. respond in different ways (appreciating—repudiating) to the same style of leadership. This complexity is not sufficiently expressed in the mathematical language of system thinking, other than individuals have objectives of their own. For a manager this is not specific enough. Baudrillard’s observed phenomenon of signs (words) as simulacra is to be recognized in business in a different way. The way the word “culture” is being used in the Netflix slide pack has little to do with the way Margareth Mead defined culture as the collective programming of the mind of the members of an organization, especially from older generations to new generations. In the present economy, we want the younger generation to think in a different way. Instead of increasing the complexity of our language as suggested by Daft & Wiginton, it is to be observed that individuals and groups use words as signs, that is non-analytically and different individuals attach different meanings to the same words as signs. The word “value” has different meanings for different people in different situations, apart from the fact that different types of values exist. For some process is a set of logically related operational activities to deliver a customer solution, for others process is a procedure for compliance. Competence management for some is an alternative description for a complex of skills, knowledge, and attitudes, for others, competence management is a technique for control.54 Pentland has observed that many fail to know the analytical content of words, to which postmodernists will respond that no such thing as a definite or authoritative analytical, on science-based meaning exists, it depends on the context in which the word is used. The point is that multiple contexts exist even within organizations and many people live in different contexts. Combined with a tendency to presentism55 in society, there is a risk that some people fail to see the difference between what is old and what is new. In the Netherlands around 2014 a wave of interest suddenly emerged in a new organization form, existing of accountable entities, especially to be applied in hospitals and in the administrative organization of counties. The consultants promoting this concept, including one of the Big Four, and the executives who showed interest and sympathy, failed to see that the concept of accountable entities is an old concept from the field of management accounting and 54 55

Durand (2004). Rushkoff (2013).

Bibliography

47

failed to see that what was defined as a new organization form, simply was the old unit-organization, going back to 1918. Precisely at that time these organizations needed a more complex organization by adding the dimension of processes and projects to the existing structure to answer the increase of complexity in patients and in local communities. There is a need to increase the complexity of the language we use to specify and understand new organization forms, by number of different words, less so by giving existing word more meanings. This all does not deny that in dealing with complexity, even as needed for economic growth, it must be considered that many people have a strong need for simplicity. This dilemma was already acknowledged in a 1984 HBR article How senior managers think.56 Senior managers both have to manage a network of interrelated problems, an effective manager does not deal with issues as if isolated phenomena, and at the same time this senor manager has to set a simple agenda to the organization, existing of one or two overriding concerns or goals, in order to help people to focus. We will see that successful organizations are a combination of simplicity and complexity, but these are differently organized.

Bibliography Artinger, F., Petersen, M., Gigerenzer, G., & Weibler, J. (2015). Heuristics as adaptive decision strategies in management. Journal of Organizational Behavior, 36(S1), S33–S52. https://doi. org/10.1002/job.1950 Basta, Y. L. (2017). A blueprint for multidisciplinary fast track gastrointestinal oncology care (PhD-thesis). Universiteit van Amsterdam. Bazerman, M. H., & Moore, D. A. (2009). Judgment in managerial decision making (7th ed.). Wiley. Beck, U. (1999). World risk society. Polity Press. Beer, S. (1979). The heart of the enterprise. Wiley. Beer, S. (1985). Diagnosing the system for organizations. Wiley. Berger, P. L., & Luckmann, T. (1966). The social construction of reality: A treatise in the sociology of knowledge. Doubleday. Bettis, R. A., & Prahalad, C. K. (1995). The dominant logic: Retrospective and extension. Strategic Management Journal, 16(1), 5–14. Bock, L. (2015). Work rules! : Insights from inside Google that will transform how you live and lead. Twelve. Bunge, M. (1962). The complexity of simplicity. The Journal of Philosophy, 59(5), 113–135. Cannon, H. M., Friesen, D. P., Lawrence, S. J., & Feinstein, A. H. (2009). The simplicity paradox: Another look at complexity in disign of simulations and experiental exercises. Development in Business Simulation and Experiential Learning, 36, 243–250. Christensen, C. M. (1997). The innovators dilemma: When new technologies cause great firms to fail. Harvard Business School Press.

56

Isenberg (1984).

48

3

Simplicity and Complexity

Cova, B., Tiu Wright, L., & Pace, S. (2006). Brand community of convenience products: New forms of customer empowerment – The case “my Nutella The Community”. European Journal of Marketing, 40(9/10), 1087–1105. https://doi.org/10.1108/03090560610681023 Daft, R. L., & Wiginton, J. C. (1979). Language and organization. The Academy of Management Review, 4(2), 179–191. Danley, J. R. (1994). The role of the modern corporation in a free society. University of Notre Dame Press. de Bono, E. (1998). Simplicity. The Penguin Group. Durand, J.-P. (2004). La Chaîne invisible: Travailler aujourd'hui: flux tendu et servitude volontaire. Éditions du Seuil. Florida, R. (2004). The rise of the creative class. Basic Books. Furubotn, E. G., & Richter, R. (2000). Institutions and economic theory: The contribution of the new institutional economics. The University of Michigan Press. Grandori, A., & Soda, G. (2006). A relational approach to organization design. Industry and Innovation, 13(2), 151–172. Hatchuel, A. (2002). Towards design theory and expandable rationaltiy: The unfinished program of Herbert Simon. Journal of Management and Governance, 5(3–4). Holmes, B. (2009). Escape the overcode: Activist art in the control society. Van Abbemuseum & For Whom/WHW. Isenberg, D. J. (1984). How senior managers think. Harvard Business Review, 62(6), 81–90. Jensen, M. C. (1998). Foundations of organizational strategy. Harvard University Press. Lash, S. (2002). Critique of information. Sage. Levinthal, D. A., & March, J. G. (1993). The myopia of learning. Strategic Management Journal, 14(Winter), 95–112. Luhmann, N. (1968). Vertrauen: Ein Mechanismus der Reduktion sozialer Komplexität . (4 Auflage ed.). Lucius & Lucius. Lui, L. T., Terrazas, G., Zenil, H., Alexander, C., & Krasnogor, N. (2015). Complexity measurement based on information theory and Kolmogorov complexity. Artif Life, 21(2), 205–224. https://doi.org/10.1162/ARTL_a_00157 Manzi, J. (2012). Uncontrolled: The surprising payoff of trial-and-error for business, politics, and society (p. xvii, 300 p.). McGrath, R. G., & Macmillan, I. C. (2013). Discovery-driven growth: A breakthrough process to reduce risk and seize opportunity. Harvard Business Review Press. Miller, D. (1993). The architecture of simplicity. The Academy of Management Review, 18(1), 116–138. Mossinkhoff, M. R. H. (2012). Modern marketing in disguise: Creating value connections between companies and consumers. (Dr. PhD), Univeristy of Amsterdam. North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press. Prahalad, C. K., & Bettis, R. A. (1986). The dominant logic: A new linkage between diversity and performance. Strategic Management Journal, 7(6), 485–501. Prahalad, C. K., & Bettis, R. A. (1996). Dominant logic. In M. Goold & K. S. Luchs (Eds.), Managing the Multibusiness Company (pp. 398–420). Routledge. Prahalad, C. K., & Krishnan, M. S. (2008). The new age of innovation: driving cocreated value through global networks. McGraw-Hill. Rajan, R. G., & Zingales, L. (2000). The governance of the new enterprise. Retrieved from Cambridge, MA. Randers, J. (2012). 2052: A global forecast for the next forty years. Chelsea Green Pub. Rushkoff, D. (2013). Present shock: When everything happens now. In (pp. pages cm). Retrieved from ftp://ppftpuser:[email protected]/Booksellers and Media/Covers/ 2008_2009_New_Covers/9781591844761.jpg Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467–482.

Bibliography

49

Strikwerda, J. (2008). Van unitmanagement naar multidimensionale organisaties. Van GorcumStichting Management Studies. Strikwerda, J. (2014). The paradigms of business administration and the concepts of the balanced scorecard and the strategy map. 76. Retrieved from SSRN website: http://ssrn.com/abstract=24 61789 Tucker, A. L., & Edmondson, A. C. (2003). Why hospitals don’t learn from failures: Organizational and psychological dynamics that inhibit change. California Management Review, 45(2), 55–72. Wasson, C. (2004). The paradoxical language of enterprise. Critical Discourse Studies, 1(2), 175–199. https://doi.org/10.1080/1740590042000302067 Weber, M. (1947). Theory of social and economic organization (A. M. Henderson & T. Parsons, Trans.). Free Press. Whitehead, A. N. (1920). The concept of nature, Tarner lectures delivered in Trinity College, November, 1919. The University Press. Zinkhan, G. M., & Braunsberger, K. (2004). The complexity of consumers’ cognitive structures and its relevance to consumer behavior. Journal of Business Research, 57(6), 575–582. https://doi. org/10.1016/s0148-2963(02)00396-x Zook, C., & Allen, J. (2012). Repeatability: build enduring businesses for a world of constant change. Harvard Business Review Press. Zorn, D. M. (2004). Here a chief, there a chief: The rise of the CFO in the American firm. American Sociological Review, 69(3), 345–364.

4

Definitions of Complexity

4.1

The Complexity of Complexity

What is complexity? A first exploration of definitions of complexity in the literature reveals that many authors define complexity in different ways. Axelrod & Cohen state: “complexity indicates that the system consists of parts which interact in ways that heavily influence the probabilities of later events.”1 The author Holland denies a rigorous definition of complexity, especially in terms of ontology, but focuses on behavior exhibited by complex systems as defining elements: especially emergence (emergence is that the system has phenomena that are not displayed by its elements individually) and the capability of adaptivity.2 A mechanical engineering system like, e.g., a Boeing 787, also has connected subsystems (and millions of parts), but does not display (spontaneous) adaptivity. The capability of adaptivity assumes an element of unpredictability, at least in a deterministic mechanical way. That is not to say that complex systems, e.g., large organizations, cannot be controlled into a certain direction, but this cannot be achieved in a deterministic, planned, command and control-style way. Emergence and adaptivity express also a philosophical element especially with respect to organizations. Whereas Max Weber was aware that his concept of bureaucracy, although needed in a democratic rule of law-based state, might result in an iron cage, conflicting with human dignity and ingenuity. “He was concerned about the psychological and social effects of the proliferation of bureaucracy—the mechanization of human life, the erosion of the human spirit, and the undermining of democracy.”3 Self-organization, adaptivity, as we have seen before in the concept of intelligent complex adaptive systems (ICAS), also refers to the dignity, autonomy, ingenuity, and responsibility of individuals and groups as elements of a complex

1

Axelrod and Cohen (1999). Holland (2002). 3 Capra and Luisi (2014, p. 58). 2

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_4

51

52

4

Definitions of Complexity

system, as it is an expression of hope. Whereas the management thinking and management concepts as these developed in the first quarter of the twentieth century were influenced by the then dominant role and successes of science and its philosophical pendant of logical positivism. The management concepts, e.g., scientific management fitted in the larger movement, described by the Dutch historian of science Eduard Jan Dijksterhuis, of the mechanization of our worldview.4 Complexity assumes at least four aspects, (1) a system that performs a delineated set of functions and defines what is part of the system and what is not; (2) the system as a Gestalt having multiple states, (3) a variety of interacting elements in that system, and (4) non-linear reflexive interacting relations between the elements of the system. That is to say, the behavior of the system cannot be specified in Newton-type linear laws. With that complexity theory aims to develop a richer worldview as the mechanical or Cartesian worldview is able to produce. The description of system as given here most likely lacks sufficient complexity and in a way is reductionist contradicting the intention of system thinking. Some authors suggest that the elements of the system act locally, in interaction with those in their direct environment. In organizations the element of a system are people, with minds and consciousness but for all individuals are members of multiple systems in society, they have multiple inclusions. A talented young professional does not depend on his boss in order to understand the organization, he has access to all kinds of sources, annual reports, SEC reports, press reports, reports published by analysts, to understand the wider context of his job. It is not only that we have a system of systems ordered in a linear way, we are dealing with a system of systems that intersect each other in every possible thinkable way. And the elements of systems are systems themselves, except for some of the material elements like rules of the system. Some elements, information systems, are technical systems that have no consciousness or an agenda of themselves.

4.2

Complexity and Systems Thinking

Complexity is related to two other schools of academic study. A first school is systems thinking. Ackoff c.s. define: “Systems thinking looks at relationships (rather than unrelated objects) connectedness, process (rather than structure), the whole (rather than just its parts), the patterns (rather than the contents) of a system, and context.”5 Systems thinking focuses on multicausality, feedback relations, non-linear interactivity and in terms of traditional academic disciplines respectively functions within the MBA concept, will pursue an interdisciplinary or multidisciplinary view on problems, plans, policies, objectives and decisions to be made. This

4 5

Dijksterhuis (1950). Ackoff et al. (2010).

4.2 Complexity and Systems Thinking

53

multidisciplinary approach of problems is acknowledged by practitioners, but is not reflected in the pedagogical model of MBA courses.6 Complexity thinking and system thinking raised the issue of interdisciplinarity or multidisciplinarity and even transdisciplinarity, especially in business administration but also in economics.7 Some question whether it is possible or even desirable to create one interdisciplinary theory. Von Bertalanffy and Kenneth Boulding attempted to formulate a General Systems Theory “to describe a level of theoretical model-building which lies somewhere between the highly generalized constructions of pure mathematics and the specific theories of the specialized disciplines.”8 In such an abstraction, like those in mathematical economic models, the complexity to be studied is reduced to a way of viewing real problems that is too difficult to translate into practical decisions, also because the General Systems Theory tends to be mathematical. With that, the GST paradoxically is reductionist as well.9 Systems’ thinking is more effective when it helps to make analysts of problems and decision makers to see linkages between multiple mono-disciplinary views to practical issues. Institutional economists that have specified the relation between the discipline of law, e.g. the issue of alienation rights on personal knowledge, and the effectiveness of management control, provide an example of such a linkage. Systems thinking therefore also should facilitate to formulate multiple hypotheses on a problem, whereas practical MBA thinking tends to formulate one hypothesis only. To this adds Martin the concept of the opposable mind, that effective managers are capable to hold two opposing models or theories on a problem and still are able to make effective decision, or even more precise are able to synthesize these opposable views in a new, more successful solution.10 Many quote Michael Porter on having either a low costs strategy or differentiate (or be stuck in the middle) whereas on closer inspection of the underlying assumptions, made explicit by Porter himself, multiple situations, can be identified, e.g., in the case of strong brands, economies of scope, in which these to strategies successfully can be pursued.11 Following the German philosophers Habermas and Sloterdijk, we also need a variety of spheres, of separate academic disciplines, of opposing models, to have variety in our thinking, to have a consciousness on our thinking to deal with complex systems.12

6

Moldoveanu and Martin (2008). Rosser (2010). 8 Boulding (1956). 9 See for a critique on reductionism, e.g., Gilder, p. 104. 10 Martin (2007). 11 Porter (1985), Hill (1988). 12 Sloterdijk (1998, 1999, 2003). 7

54

4.3

4

Definitions of Complexity

Complexity, Cybernetics, and Control

A second school of academic thinking related to complexity theory is cybernetics. Cybernetics explains how living systems, biological, individual, social systems, organizations, different from inorganic physical systems, are organized. The function of this organization is to generate, acquire, store, process and to communicate information in order to control (adapt) the flows of matter (input-output economics), energy (ecology) and information itself, in order that the living system survives in a changing environment by adapting itself to changes in its environment.13 This definition of cybernetics in its effects is comparable with Herbert Simon’s definition of complex organizations. Beniger’s definition of control is perhaps broader as was the original definition by Norbert Wiener in his study of control and communication in biological and engineering systems with feedback (but engineering systems are not complex systems).14 To Beniger’s definition of control must be added that social systems have an extra dimension of control, that is the wish, the hope, the objective to improve living conditions, material, social, intellectual, and mental. The essence of our Western culture is that we want to achieve a state of life that is not generated by the natural cause and flow of nature. To achieve this, we indulge in futuristic thinking, imaging ourselves a state of live, material, social, institutional, intellectual, cultural, religious that not yet exists but is worthwhile to strive for. We do this in various ways, e.g., through utopian thinking as this came en vogue especially during the renaissance, e.g., with Thomas More’s Utopia (1516) and in less political more science fiction style with the books of Jules Verne and the science fiction literature in the twentieth century. This utopian thinking is beyond survival, it is a hope for a better life, and not only a hope, much has been achieved like democracy, the rule of law, education, medical care, transportation, good housing including for the lower incomes.15 To achieve our utopian goals another type of control is needed, a control not only existing of setting goals beyond survival, but also planning specific actions, either based on engineering, design, research and development and political decision-making to get laws accepted that create conditions to achieve a state of living that nature will not provide by its own. In addition to this most people, individual or in religious communities are in whatever style and mode in search of a meaning of life which also may decide the decisions and how they cope with situations. Cybernetics adds an important aspect to complexity theory and to systems thinking. This is the insight that the control of a purposeful system, that is a system striving for continuity and survival, is programmed in all of the elements and in the architecture of the system. Whereas complexity theory assumes interacting elements, each having its own objectives, cybernetics as defined by Beniger adds to this that these interacting elements, be it in different degrees perhaps, have a common interest

13

Beniger (1986). Wiener (1961). 15 Bloch (1959). 14

4.3 Complexity, Cybernetics, and Control

55

or a common value guiding their behavior, apart from individual values and objectives, apart from the awareness that we depend on other people, family, community, for survival and thus are altruistic, not only utility maximizing. These common interests and shared values constitute the unity and shared purpose in intelligent complex adaptive systems. But control being programmed in all of elements of a system, apart from being programmed in its architecture, implies that at least a number of the individuals, groups, or firms, when it is about an industry system, must have an understanding of the system as a whole of which they are a part. This implies that there is a downward causality in social life, working through meaning and symbols in addition to the same-level causality assumed in traditional complexity thinking. The awareness of being part of a larger system with more and less profitable positions may evoke tactical behavior as in the example of Wintel, in which two “elements” in the system of the overall architecture of the personal computer, Intel and Microsoft, one supplying the microprocessor and the other the operating system, controlled the overall system in that they appropriated 80% of the world profits in the personal computer industry for a considerable time.16 Another example is that in the US Defense organization the traditional corporal now is a strategic corporal.17 That is a command no longer is an instruction in the hierarchy, command is the interpretation of the strategic corporal of the mission of the president of the USA in an actual situation what to do to accomplish that mission.18 This of course sets requirements to the formulation of the mission, the training, understanding and decision processes of corporals, available information, etc. But basically, the same is assumed in proactive behavior, in Arrow’s concept of decentralization and in the concept of agility. That is, the assumption in traditional system thinking of elements of the system acting locally, according to parochial interests, is not a valid assumption as the information space of the elements no longer is restricted to the locality of the individuals or groups. This explains why firms embark on training programs on mission, values, often under the title of culture programs or team building, in order to program the members with the mission, the values, and the rules of the game as a replacement for the command-and-control organization. This locks in to the definition of culture as coined by Mead and Hofstede, culture is the collective programming of the mind. That is, culture is one of the mechanisms of control in a social system to achieve continuity. The formal study of control is cybernetics.19 Control is defined as purposive influence toward a predetermined goal.20 In biology and social systems the goal will be survival, in business the goal will be expressed in the mission, or in more specific strategic targets; without goal no control. The founding father of the concept of the

16

Yoffie (1997). Alberts and Hayes (2003). 18 Alberts and Hayes (2006), Alberts and Nissen (2009). 19 Beniger (1986, p. 40). 20 Beniger (1986, p. 35). 17

56

4

Definitions of Complexity

computer, John von Neumann, argued that the difference between order and organization is that the latter “always involves ‘purposive’ or end-directedness.”21 Control and complexity at first sight seem to exclude each other, due to the unpredictability of complex systems and the phenomenon of emergent behavior. But Herbert Simon stated that in order to survive organizations need a degree of complexity, in the sense of loose programming and loose control, as well as complexity in the sense of a variety in behaviors as implied by a complex market. Control is not simply feedback in a defined system (e.g., performance management), adaptivity may be needed on a higher level (new products, moving in new markets) or even at the level of transforming the business. Control therefore has three levels, or distinct problems of control:22 1. Existence or being, the problem of maintaining organization—even in the absence of external change—counter entropy. In business this is the level of management accounting, protecting the values of the firm, compliance and such. 2. Experience or behaving, the problem of adapting goal-directed processes to variation and change in external conditions. In business, this is product-type and volume flexibility and other adaptations mainly within an existing business model and business. 3. Evolution or becoming, the problem of reprogramming less successful goals and procedures while at the same time preserving more successful ones. In business, this is about reconceptualizing industries, markets, business, and transformation of business models. The second level of control as defined by Beniger also is known as first-order learning, the third as second-level learning. The COSO framework for in-control emphasizes the first level of control only, whereas in the resource dependency view on being in-control the emphasis is on the third level of control. Where the coordination of specialized tasks implies the need for information to achieve this coordination, control as defined in cybernetics is the system that processes information and actually defines information beyond Shannon’s mathematical communication theory. In order to process information a program is needed. Cybernetics explains that control is programmed in all aspects of a system. In James Watt’s centrifugal governor, a device used to control the speed of a steam engine, the mechanics of the governor, throttle, shafts, balls, thrust bearing, together are the program that processes information, the actual speed of the steam engine against the set speed, to control the steam throttle valve. In real social life (cybernetic) control also is programmed. This programming can be seen to exist of four levels:23

21

Beniger (1986, p. 35). Beniger (1986, p. 66). 23 Beniger (1986, p. 103). 22

4.3 Complexity, Cybernetics, and Control

57

1. The level of life or molecular programming. At this level, the program is genetically inherited, and information is codified in DNA and partly in the nervous system. DNA itself perhaps is not directly about control; the control in the living system is aimed at preserving the information contained in the DNA and reproducing it. The proteins in a cell duplicate the information in the DNA, write to it, read from it, edit it, transferring it to other media and execute instructions written in the DNA. The instructions for making and regulating these proteins are encoded in the DNA as well.24At this level of programming the first level of control is replication, by protein synthesis, the second level of control is adaptation of genetically processes to changes in the environment, including neuroplasticity, and the third level of control is organic evolution through natural selection. 2. The level of culture. At this level programming exist of learned behavioral programs, implicit through socialization and explicit through educational systems. However, it must be noted that at this level the programming of control also is in artifacts like language, institutions, and such. At the level of cultural programming the first level of control is by programmed behavior stored in the memories of individuals, this may include observance to rituals of a community. The second level of control exists of learned responses to changes in the environment, by some reprogramming. The third level of control in cultural programming is through innovation, cognitive reframing, innovation, by breaking the rules and replacing these with new ones. 3. The level of bureaucratically controlled social systems. At this level programming exists of bureaucratic rules and regulations, and information for control is processed on basis of these rules by formal organizations of individuals. At the level of bureaucratic programming the first level of control exists of managerial control, management accounting, performance management, and other instruments of administrative control. The second level of control is adaptation to changes in the environment through procedural reorganizations as typical in the management of change. The third level of control is business model innovation, reconceptualizing the business, business transformation, and the application of systemic change. 4. The fourth level of programming is technology. Mechanical programming and information processing as in Watt’s regulator, today is overtaken literally by computer programs and computers processing information on basis of these programs. At this fourth level of programming the first level of control is ensuring the integrity of information as input, and preserving the integrity of programs against, e.g., hackers. The second level of control today is pursued through machine learning. The third level of control in the case of technological programming of control is reconceptualization, defining completely new models.

24

Seife (2006, pp. 93–94).

58

4

Definitions of Complexity

These four levels of programming of control are consequential for understanding control in social complex organizations. Earlier we defined intelligent adaptive systems as composed of a large number of self-organizing individuals that seek to maximize their own goals but operate according to rules and in the context of relationships with other individuals and the external world” The cybernetic definition of control implies that the control within an organization depends on dimensions of control that are outside the range of bureaucratic control, DNA and cultural control. Individuals can be selected and deselected, their DNA cannot be changed, although it should be noted that some medicines and some recreational drugs may affect personality, but these are also out of the range of bureaucratic control.25 Bureaucratic control depends on institutions like the rule of law, democracy, a civil society, educational system, religion, corporate law, labor law, and at the end of the nineteenth century some of these institutions were adapted to the needs of business, especially to allow capital accumulation and separating private capital from corporate capital, and separating labor and capital. The cultural programming of individuals in the first half, until about the eighties, was congruent with the needs for bureaucratic control. With the emancipation of workers, increased levels of education, the rise of the media culture, and especially later the Internet and social media the relation between (pre- or ex-organizational) programming and bureaucratic programming become more complicated. Some institutions, like democracy and religion weakened, post-modernism weakened the grand stories like the redemption and the enlightenment and allowed an increasing diversity in identities and selfimages, the rising level of education, especially the professionalization emphasize extra-organizational programming of accountants, lawyers, HR staff, consultants, and at the same time the bureaucratic programming both weakened due to the shift toward intangible assets and even became a liability in view of needed new behavior and thinking. Corporates responded to this change in society by emphasizing corporate culture, programming individuals less by how to work as ordered by a superior, but be acting from self-control, taking initiatives and self-organization to achieve set objectives, not so much on basis of precise information, for this initially was lacking, but on basis of a mission and values. These values themselves implicitly changed from values as expression of what worked in the past and what not, to values expressing initiative, decision-making, taking responsibilities, acting as if based on the co-location principle in economics, unity of ownership, entitlement, decision rights and rights of alienation. Through popular management books, bureaucratic programming moved into the domain of cultural programming, be it an ambiguous way, both normative and cynical. In the 1990s bureaucratic programming partly shifted into technology, when enterprise resource planning software (ERP systems) for reasons earlier explained due to the changing role and nature of the bill of material, were reduced to accounting information systems (AIS). The expression of this was the business-IT alignment paradigm for investments in

25

Fukuyama (2002).

4.3 Complexity, Cybernetics, and Control

59

information technology.26 Whereas this was welcomed from the perspective of accounting information systems as a contribution to be in-control, this paradigm turned out to be a trap, as with the then capabilities of information technology it blocked the third level of control in the cybernetic concept of control.27 This has been solved by reconceptualizing the role of information technology from an extension of accounting information systems, through emphasizing management information over accounting information, to a technology that support knowledge workers, not controlling these. The four levels of programming also question the idea of self-organization in complexity theory. At a level of society, and in a historical perspective, self-organization exists. Does self-organization result from acting individuals based on (local) self-interest as implied by the methodological individualism of economic theory? That is, is to control transactions and do rules result from transactional experience? Or is there some consciousness, resulting in a system of law, that in its turn programs individuals in their interactions?28 In his book The Origins of Political Order, writing on the origins of the rule of law, Fukuyama states: “Human beings have a capacity for abstraction and theory that generates mental models of causality, and a further tendency to posit causation based on invisible or transcendental forces. This is the basis of religious belief, which acts as a critical source of social cohesion.”29 The self-organization of markets as seen by the economic Hayek, the libertarian and father of market liberalization is commented by Fukuyama: “A glance through Hayek’s index shows not a single reference to religion, and yet religion is clearly a critical source of legal rules in Jewish, Christian, Hindu and Muslim societies.”30 Self-organization is not simply order out of chaos, comparable in physics, chemistry, and biology, at the level of local transactions, but depends on a cultural space, which itself historically seen, is the result of human ingenuity, power struggles, brilliant ideas, and sometimes dysfunctional institutional equilibriums. Cultural programming also is labeled as downward causation. This causation is not mechanical, but is based on symbols, signs, and meaning. Language, in relation to what individuals is being taught in terms of support, rewards and punishments in whatever material and non-material forms. This downward causation is not determined but allows for discretion, reinterpretation, exploration, negotiating on various levels, philosophy, the arts, literature, politics, legislation, and the media. DNAbased programming, through variation, and individual ingenuity, discovery, coincidence and mistake-based variety, aka serendipity, is a source of upward causation, through which development and adaptation develops. Whereas traditional complexity science tends to a somewhat flatland-type worldview of horizontal interactions, of perhaps a spacious multidimensional system but with the one-dimensionality of

26

Henderson and Venkatraman (1993). Shpilberg et al. (2007), Davenport (1998). 28 Beniger (1986, p. 93). 29 Fukuyama (2011, p. 43). 30 Fukuyama (2011, p. 256). 27

60

4

Definitions of Complexity

(local) transactions, the historical reality of the various societies in the world demonstrates a far richer, multi-layered process. At the micro-level, this is reflected in the role of the mission and values as administrative tools. A second contribution of cybernetics to complexity theory is that it implies a far richer theory of information compared to the theory of information coined by Shannon & Weaver. This richer theory of information is important to understand modern complexity and specially to understand the concept of organized complexity, and actually modern business administration, so that this cybernetic information theory is elaborated in a separate Chap. 7.

4.4

Types of Complexity

On closer inspection, it must be acknowledged that different types of complexity exist. As these different types of complexity require different tactics to deal with, these different types of complexity need to be carefully distinguished, known, and understood. The concept of complexity itself is complicated. The concept dates back to the 1930s when the French philosopher Gaston Bachelard (1884–1962) used the concept of complexity as an alternative to Cartesian reductionism in science.31 Whereas in Newtonian physics phenomena are expressed in simple formulae, e.g., F = m × a, we experience situations that cannot be expressed adequately in simple formulae or models. The idea of complexity expresses at least two mental states. The first is a willingness to understand phenomena existing of various parts, each having mutual relations or mutual influences which cannot be expressed in a simple formula. The second mental state is that complexity expresses a feeling or experience that we fail to comprehend completely specific situations in terms of its composition and or working. The second aspect of complexity also is called subjective complexity. Bachelard made a distinction between complexity and complicatedness. Complicated refers to, e.g., an organization structure that is not logic by departments, organization of work processes suffering lack of flow, redundant bureaucratic controls, etc. If something is complicated then it needs to be simplified. If we experience an organization as complex but this complexity is needed due to required specialization and based on Ashby’s Law of Requisite Variety, we need to grow our understanding, vocabulary, and conceptual models in order to understand the required complexity. One of the dimensions in this growth of knowledge is the acknowledgment of the existence of different types of complexity.

31

Alhadeff-Jones (2008).

4.4 Types of Complexity

4.4.1

61

Detail Complexity Versus Dynamic Complexity

A first distinction to be made is between detail complexity (or variety) and dynamic complexity. Detail complexity is the situation of a system having many elements, e.g., not only are there different types of customers in terms of spending power, lifestyles, but a certain category of customers also have different sets of preferences dependent on the hour of the day, the day of the week, the context of other activities, e.g., traveling, within which they display and act out their price elasticity. In terms of marketing a customer with multiple preference sets is to be dealt with as multiple customers. As in the last paragraph on the relation between complexity and economics, a society may have a large number of specialist activities or a low number of specialized activities. Regulation may be simple for business or a jurisdiction, e.g., Germany has more regulations for business compared to the Netherlands. There may be many standards in an industry, as was the case around 1900 or there may be uniformed open standards as is the case today. Often businesses are defined as being complex because of a high number of different specialized activities or processes respectively departments, a broad range of products, or products or services with a high variety, without emergence.32 Basically, this is detail-complexity, not the complex organization as defined by Herbert Simon. Different from detail complexity is dynamic complexity. In the case of dynamic complexity various usually non-linear and feedback or reflexive relations exists between the multiple elements or players in a system. For example, the relation between demand for a product, an increase of which will result in higher investments, but through the effect of learning curves, the higher production level will produce learning curves, increasing the yield of existing capacity, resulting in an overcapacity in the industry. The author was involved, working at Philips Electronics in the early nineties; in the business of placement equipment that produce printed circuit boards for all kinds of electronic devices. In forecasting the demand, using Forrester-type mathematical modeling, it was found that the growing demand for circuit boards would not increase the demand for the number of placement machines, because engineers developed better designs, combined more electronic functions in one component, achieved more standardization, etc. as a result of which less machine capacity was needed despite the growth in demand of printed circuits. Dynamic complex models are used in economic science to model macro-economic behavior with multiple factors and players, with many mutual, sometimes contraintuitive relations or non-linear relations (Textbox 4.1).

32

Mocker et al. (2014).

62

4

Definitions of Complexity

Textbox 4.1 Overview of Types of Non-linear Causal Relations (Not Necessarily Limitative) Mechanical linear causality: A → B, then also ØA → ØB; if 8A → B ^ 8ØA → ØB Non-linear causality: Externalities A → B + C, C is outside the system of A and B, C may be positive (standards) or negative (e.g., environmental aspects) Probabilistic relation A → pB (0 ≦ p ≦ 1) Time delay: At = 1 → 0.Bt = 1 + q.Bt = n, n ≪ 1, q≧ 1 Treshold value: nA → 0.B n = 1; nA → q.B n > q > 1 Non-linear relation (convex, concave) Conditional causality A → B dependent on one or multiple third factors Multi-causality A_C → B; ØA^ØC → ØB Effect changes the cause (positive feedback) A → B + pA, 1 ≤ p ≤ 1 Destroying feedback relations An → Bn + 1 → pAn + 2; p ≫ 1. (e.g., successful business models) Effects without causes: situations remain in existence after the cause was taken away (e.g., as the result of a collective memory) Or any combination of the above At a meta-level a reflexive relation between models, decisions by politicians and the reality on which policy-makers, using economic models, apply their policies, thus is changing the empirical basis on which their models are based.33 In business this reflexivity may be even stronger, successful new business models create new competition and change consumer preferences, invalidating the assumptions of the successful business model. This explains why management books fail to identify the holy grail of everlasting critical success factors, in business success cannot be repeated because true success destroys its own foundations. In the case of non-linear relations of dynamic complexity those working with or in such systems have incomplete causal information. This often is expressed in the law of unintended consequences. Seeing unintended consequences may be helped by having a holistic view on a situation, seeing as many as possible elements or players in a system, including e.g. the environment, and asking the question of what effects these might experience of a certain intended decision.

33

Leydesdorff (2000), Beck et al. (1996), Beck (1996), Bourdieu and Wacquant (1992).

4.4 Types of Complexity

4.4.2

63

Objective Complexity and Subjective Complexity

A second distinction in complexity is that between objective complexity and subjective complexity. Objective complexity exists of observable, distinct, countable elements, states, relations, choice opportunities, etc. Objective complexity can be measured.34 Objective complexity has its roots in engineering sciences and sometimes is called ‘algorithmic complexity’ referring that this complexity is to be handled by information as defined in the theory of Shannon & Weaver.35 What here is described as objective complexity Vercelli defines as ontological complexity that refers to the (irreducible) properties of an economic system, e.g. task specialization, the variety in consumer preferences, the variety in sales channels, etc.36 Objective complexity also exists outside the field of engineering; it may exist in regulation, trade techniques, and conventions. A range of products, airplanes, cars, etc. have a detailed complexity of their own (aka construction complexity), implying a necessary complexity in the manufacturing firm. This construction complexity results from task specialization, functional differentiation in products and systems and is necessary to achieve objectives like productivity, product functionality, and such. Although it must be noted that complexity theory does not acknowledge complex engineering or mechanical systems to be complex systems as their elements are lifeless, do not make decisions of their own (although this might change with the advance of robotics). The tactics to deal with objective detail complexity may exist in learning routines, applying heuristics, and especially in applying complexity reducing business institutions, trade conventions, etc. In the case of complex engineering systems, the combination of architecture and modules is another tactic to deal with detail complexity (Sect. 8.3). Subjective complexity results from us observing complex situations through a mental lens or academic lens based on a too-simple model or obsolete models.37 Whereas from the perspective of the knowledge-based view of the firm and the perspective of the information being a capital good the multidimensional organization of, e.g., IBM is a logical thing, from the perspective of the Weberian-M-form and the Business-IT-alignment paradigm the same organization is an incomprehensible complex oxymoron.38 This is an example of the rule “knowledge destroys complexity.” This subjective complexity is important to acknowledge and to understand as a phenomenon because it may impair the capacity to deal with especially new irreducible complexities in markets, industries and in the economy. Subjective complexity may play a complicating factor in creating the necessary organized complexity in and between firms, as may be need for growth, innovation, and adaptation. 34

Adami (2002). Shannon and Weaver (1949). 36 Vercelli (2007). 37 Alhadeff-Jones (2008). 38 Strikwerda and Stoelhorst (2009), Campbell and Strikwerda (2013). 35

64

4

Definitions of Complexity

Subjective complexity has its cognitive psychological roots in the phenomenon of belief conservation. Believe conservation is the phenomenon that any one of us has a tendency to conserve belief, which is to interpret new experiences and information in ways that make them consistent with prior beliefs.39 In a more formal sense subjective complexity manifests itself in epistemic complexity of our concepts and academic models we use to describe and explain the real world or parts thereof. Academic models including mathematical models in economic theory need to be simpler as is the ontological complexity, but too much simplicity or abstraction from reality in such models, especially when these are used in policy decisions do create sincere risks for society as experienced in the Great Crisis of 2008. To let epistemic complexity grow by emphasizing simple models is not without risks, but alike creating more complex models in economics and business by including more dimensions, can be a source of risk itself (labeled risk) either due to errors by human operators or because the dimensions interfere in an unknown way. Subjective complexity as resulting from observing, understanding, working in and working with economically needed new organization forms, but with lack of sufficient complex concepts and language, is a source of work-related stress, mistrust and of suboptimal decisions or worse. An example of this is the concept of shared service centers as these were introduced from about around 1990.40 In the new theory of the firm shared service centers are a concept for (further) decentralization and for exploiting synergies. Even more, shared service centers imply a new organization form compared to the traditional M-form and subsequently a new economic model for the firm.41 But, because shared service centers do not fit into the concept of the M-form, and neither in the concept of line-staff organization, the introduction of shared service centers, of which the economic need was felt intuitively, created confusion and conflicts both with members of organization implementing these concepts and with academics. Initially, it was unclear who should be responsible for defining the output of the shared service center, the manager of the SSC or the managers of the divisions consuming the service. The concept of the shared service center does not fit into the bottom-up resource allocation process as defined by Bower, because Bower’s concept specifically is based on the M-form.42 Consequently, the shared service center was, and in some cases still is, erroneously defined as a central staff department. In some cases, executive boards used the shared service centers as a control instrument to increase their control over the divisions, instead of an administrative instrument to facilitate the business. Some executive boards defined shared service centers as a tool to reduce costs, instead of making a better use of resources. As a result, in many cases the application of the concept of shared service centers was and in some cases is disappointing. Introducing shared service centers in the concept of the M-form introduces an

39

March (1994, p. 183). Strikwerda (2010). 41 Strikwerda (2014). 42 Bower (1986). 40

4.4 Types of Complexity

65

additional dimension, it adds to the complexity of the organization of the firm. This complexity needs to be understood and, e.g., translated in a new designed resource allocation process. The additional complexity implied by the introduction of shared service centers cannot be understood from the concept of the M-form; working with shared service centers through the concept of the M-form creates subjective complexity. The concept that is needed to understand shared service centers and its induced complexity is the infrastructure organization or the platform organization.43 Through that lens the shared service center becomes a logical thing, but because ‘shared’ still refers to the mental model of the M-form, “shared service centers” has been replaced by “platform.” Related to subjective complexity is epistemological complexity.44 Vercelli defines this as the formal properties of economic model representing ontological complexity in the economy. Models by definition and necessity represent reality in a reduced way, as tools to cope with ontological complexity. Consequently, for Vercelli complexity is the difference in degree of ontological complexity, that is the complexity of the economic system we try to understand, to control or to design, and the degree of epistemological complexity, that is the complexity of the theories, models, and concepts we apply to do so. Vercelli’s definition we might say is the academic variant of subjective complexity. The alternative to making concepts or models more complex to narrow the epistemological complexity is conceptual complexity. Conceptual complexity can be defined as the degree to which an individual is able both to discriminate among and is able to integrate multiple perspectives of a situation or a problem to be solved.45 That is such an individual, especially a CEO, is able and willing to see a situation from multiple functional perspectives, multiple levels (operational, tactical, strategic, corporate governance), multiple stakeholders, long term and short term, etc. This may include that such a CEO is capable and willing to assess a situation through multiple conceptual lenses, in the awareness that any single lens will not provide a full understanding. CEOs with conceptual complexity are reported to be more successful in changing contexts.46

4.4.3

Disorganized Complexity and Organized Complexity

A third distinction to be made with respect to complexity is the difference between disorganized complexity and organized complexity.47 In the case of disorganized complexity, the interaction between the entities of the system remain at random and do not create new behavior, new entities or some kind of architecture. In the case of

43

Zuboff and Maxmin (2002). Vercelli (2007). 45 Montuori (2000). 46 Levinson (1994), Montuori (2000). 47 Antonelli (2011b, p. 2), Simon (1996, p. 183). This distinction goes back to W. Weaver (1948). 44

66

4

Definitions of Complexity

organized complexity higher levels of information are present in the system, goal information, axiological information which imply that certain interactions are preferred over others and therefore tend to be reinforced as a result of which a spontaneous order may result, new knowledge, new entities, new types of interactions (new knowledge). There is a stream of interest in a particular type of complexity theory, called chaos theory. Paradoxically the advocates of this stream have an urge to see order in chaos and with that turn chaos theory in a kind of deterministic mechanic science.48 This stream might be seen as an expression of hope, that how incomprehensible the world may be to us, we can trust there will be order. This thinking mainly is based on examples from physics, chemistry, and biology and has as a basis the workings of energy. Order in social systems requires a different dimension. Another stream of thinkers, going back to Von Mises and Hayek, see complexity theory as describing the phenomenon of spontaneous order. This is at the base of Hayek’s liberal market theory, that markets, provided the presence of a number of defining institutions, organize themselves (self-organization) through the interaction, trade, of intelligible actors pursuing their own interest, guided by Adam Smith’s invisible hand. Hayek was even more explicit on this with respect to building academic models: “What we must get rid of is the naïve superstition that the world must be so organized that it is possible by direct observation to discover simple regularities between all phenomena and that this is a necessary presupposition for the application of the scientific method.”49 That is, Hayek understood the need for more complex models to organize decentralized decision-making. Hayek’s self-organization in the economy, however, depends on institutions, in a historical perspective on the role of religion, that is on individuals not only interacting locally on basis of selfinterest but having a broader altruistic or power view on a broader context at a higher level of abstraction. A subsidiary stream to Hayek’s view on the role of complexity is a more romantic view, which emphasizes spontaneous self-organization as applicable to the organization of the firm, as opposed to designed organization. This is also called generative or evolutionary complexity and earned itself a reputation through the concept of autopoiesis coined by Prigogine. The concept of autopoiesis contains two concepts, spontaneous order and self-reproduction of systems. Spontaneous order expresses with respect to organizations the hope for order without an imposing hierarchy, but also it expresses the hope for order where our conventional models begin to fail us in creating new orders. And thus, autopoiesis has an appeal in writings on organization. Self-reproduction of systems applied to organizations implies that self-reproduction is not simply reproduction of what is, but that it has at least an adaptive element, preferably an innovative dimension, and in case of fundamental changes in the environment may include transformation in order to survive. The Greek philosophers Aristotle and Plato defined the role of education in society as the

48 49

Hayles (1991). Von Hayek and Mihnea Moldoveanu (2007).

4.4 Types of Complexity

67

innovative reproduction of society, not simply reproduction. Organization culture as collective programming of the mind, as defined by Margaret Mead (1901–1978), becomes problematic in times of business models innovation and business transformation, hence other authors, notably Deal and Kennedy reduced the meaning of culture to values.50 The view on complexity as spontaneous self-organization and self-reproduction is also inspired by phenomena in physics and biology, especially through the works of Kauffman. In his later book, Kauffman explained that his theory of complexity fails to explain how certain complex phenomena, e.g., the human heart, has developed. There is something outside complexity theory needed, mind, and consciousness, to understand our world.51 The generative type of complexity has become of interest in economics, especially in the knowledge-based view of the firm and in the issue of how to facilitate innovation.52 The idea is that allowing knowledge workers to associate freely, within the organization of the firm and across the boundaries of the firm, spontaneous, unanticipated new combination or insights may develop which most likely will not result from planned innovation. In business, this is acknowledged as the distinction between resource allocation, the traditional methods for strategy execution, and resource mobilization, that knowledge workers, within certain guide lines and criteria (organized complexity) find out for themselves where their knowledge will contribute most to the performance of the firm, and will develop itself best through interaction with other knowledge workers.53 The issue in business and economics is to avoid disorganized complexity and to have organized complexity to deal with a dynamic complex market, through self-discovery, combinatorial innovation and thus adaptation. Antonelli makes a distinction between disorganized complexity and organized complexity.54 Antonelli is somewhat obscure about what precisely disorganized complexity is and defines it in terms of effects; that the interactions between the players smooth each other out. That is, interactions in the case of disorganized complexity do not produce new knowledge, innovation, or performance improvement. In the case of organized complexity variety (or diversity), the interaction between the members of the organization results in new knowledge, innovation of products, services and processes, and improved performance. Specialization, results in new knowledge, new combinations, that is innovation emerges from organized complexity. An example of a firm using this is Google.55 In Chap. 11 it will be explained how firms achieve organized complexity while observing institutional requirements.

50

Deal and Kennedy (1982). Kauffman (2008). 52 Antonelli (2011b). 53 Doz (2005). 54 Antonelli (2011b). 55 Bock (2015). 51

68

4

Definitions of Complexity

In today’s economy, in addition to cost efficiency, allocation efficiency and adaptation efficiency are emphasized. (It should be noted that philosophers like Huma defined efficiency as no waste, no harm). The traditional tool for adaptation was the management of change, focusing on overcoming resistance to change, especially in the case of changing work methods and changing structures. Management of change is by theory based on the concept of punctuated equilibrium in the economy. Change as adaptation to changes in the market is necessary for survival, but as fast as possible the organization should be brought back to a stable state for reasons of efficiency.56 In economic theory the concept of market equilibrium is being questioned and consequentially managers are looking for organization forms with an in-built capacity for continuous adaptation, but being productive and efficient at the same time. Initially this wish and felt need was expressed in terms of flexibility and flexible organizations.57 But the concept of flexibility turned out to be too generic as, with the exception of Ackoff many authors on flexibility focused on structure, flexible structures whereas the solution of flexibility is more in terms of information, processes and projects in addition to (stable) structures formed by business units and divisions). A broader approach is to be found in the book Built to Change.58 As typical for American management books this book emphasizes in an apodictic way the need for organization forms in which adaptation and thus change is built into its design. But it fails to explain the technicalities to be implemented in accounting, IT governance, HR systems needed to achieve this built in capability for adaptation and thus the book lacks practicality. Chapter 12 intends to fill this gap.

4.4.4

Overview of Types of Complexities and Their Handling

The different types of complexity as used in this book might be summarized as in Fig. 4.1.59 Figure 4.1 also suggests that there are different tactics to deal with different types of complexity. Objective detail complexity, for being impossible to understand through a (simplified) model, usually is dealt with through routines, customs, detailed regulation, etc. Objective dynamic complexity is dealt with through

56

Parsons (1962). Ackoff (1977), Galbraith (1994), Volberda (1998). 58 Lawler and Worley (2006). 59 Yolles discerns five types of complexity for social systems Yolles (2006, p. 35): 57

• Computational complexity (number of interacting parts); • Technical complexity (involves a complex of ‘tangle’ of interactive control processes that affects predictability); • Organizational complexity (relating to the administration, governance and rules associated with identifiable parts of a system); • Personal complexity (relating to the complex of subjective views of a situation); • Emotional complexity (involving a complex ‘tangle’ of emotional involvement).

• • • •

Detail complexity

Generative or evolutionary complexity

Subjective Complexity Epistemic Complexity

Organizations: Ashby’s Law of Requisite Variety

Complexity Leadership Organized complexity Emergence System change (Prigogine) Growth in number of system states Self-organization Innovation through interaction

Hidden order in chaos (Chaos Theory) Disorganized complexity

• •

• • • • •

Linear models, reductionism, simplification without understanding

Fig. 4.1 An ordering of different types of complexity. Each of these types need to be dealt with in a different way

Objective complexity

Market complexity Product complexity Training of routines Very detailed regulation (SOX, 3000p)

• Is acknowledge as nonreducible • Is mastered through information systems

Forrester-type mathematical models

Kolmogorov- or algorithmic complexity

Dynamic complexity*

Senge: ‘Systems thinking helps to see underlying structures and patterns of behavior, helps to understand why conventional solutions are failing and where higher leverage actions may be found’

4.4 Types of Complexity 69

70

4

Definitions of Complexity

complicated mathematical modeling, including feedback relations, and sometimes complicated causal relations. This is the field of mathematical economy. Subjective detail complexity usually is “dealt with,” that is tempting to ignore it by simplification, by applying linear models, typical for management books, reductionism, etc. This is countered by authors like Weick and Senge who plead for creating more holistic models, in a more conceptual, non-mathematical way, to become aware of the limitations of reductionist, linear models.60 A more recent development is that of complexity leadership which will be explained in Sect. 10.7. Complexity leadership is about organized complexity to enable adaption and innovation of the firm in order to be in-control in a changing environment. The upper right-hand quadrant is most interesting for organizations and economics. This is the domain of Herbert Simon’s loose programming to foster adaptability of organizations. The dynamic capability view of firms and the concept of the learning organization are alternative expressions of this type of complexity.61 Another practical example of organized complexity, also called combinatorial complexity, is modular or combinatorial innovation.62 The American production system of the nineteenth century, with its standardized interchangeable parts, e.g., guns and machines, has developed into a system of standards, standard components and eventually in modules, e.g. power packs, hard disks and other components for computers, but it also applies to software. In combination with infrastructures like the Internet, GPS and cellular phones this enabled low-investment innovations like Uber and Airbnb. The upper right-hand quadrant in Fig. 4.1 is the domain of emergence, combinatorial innovation, creation of new knowledge, etc. that is organized complexity. Ashby’s Law of Requisite Variety not only applies within a complex system, but it also applies to complexity thinking itself.

4.5

Organic Organizations as Intelligent Complex Adaptive Systems (ICAS)

An illustration of the various types of complexity and how to deal in a variety of ways with types of complexity is implicitly to be found in the concept of complex adaptive systems (CAS). Quite some authors have taken interest in the concept of complex adaptive systems from the perspective of complexity theory. This fits into the pattern of designing organization forms with an inbuilt capacity and mechanisms for adaptation since the period of punctuated equilibrium in the economy is a thing of the past. Because the concept of CAS is popular and economic relevant, the concept of CAS will be reviewed from the perspective of the economy with the objective to move the concept of CAS and its language into the world of management.

60

Weick (1982), Senge (1990). Teece (2007). 62 Ismail et al. (2014), Ryall (2009). 61

4.5 Organic Organizations as Intelligent Complex Adaptive Systems (ICAS)

71

CAS can be defined as: “Complex adaptive systems are composed of a large number of self-organizing components that seek to maximize their own goals but operate according to rules and in the context of relationships with other components and the external world.”63 Bennett & Bennet define intelligent complex adaptive systems (ICAS) to be complex adaptive systems (CAS) in which the components or actors are people. These can be individuals, departments, or other social groups. The characteristic of an ICAS is the ability to operate (to achieve continuity) in a state that is between pure stability and complete instability in a context that also is in between stability and instability. In this state, the system produces innovations, and other adaptations, in an emergent way, while maintaining the identity and the integrity of, in our case, the firm. This relates to concepts as ambidextrous organizations, combining exploitation and exploration, proactive behavior, etc. Herbert Simon presaged ICAS one might say, in his concept of the complex organization, with characteristics like loose programming and loose control allowing for local adaptive behavior.64 Bennet and Bennet define eight emergent properties of their concept of ICAS. These are: 1. 2. 3. 4. 5. 6. 7. 8.

Organizational intelligence Unity and Shared Purpose Optimum complexity Selectivity Knowledge Centricity Flow of Data, Information, and Knowledge Permeable Boundaries Multidimensionality

This list requires some comments, as it may not have a direct appeal to practical managers being too abstract and lacking recognizable business terms. What Bennet and Bennet define as emergent properties, on closer inspection are conditions to be organized by the management of a firm in order that the sought-after innovations and adaptations emerge, without traditional planning and without traditional command and control through bottom-up initiatives and proactive behavior. As we will see the conditions to be created to achieve an ICAS, build upon the concept of the organic organization (as opposed to mechanic organization) as identified by Burns & Stalker in 1963 and documented in their book The Management of Innovation.65 Organic organizations have over time proven to be more successful in innovation and adaptation compared to mechanic organizations, for reasons that we today understand in terms of the knowledge-based view of the firm and complexity. Today the organic organization is also known under the label High

63

Bennet and Bennet (2004, p. 26). Simon (1962). 65 Burns and Stalker (1963). 64

72

4

Definitions of Complexity

Performance Organization.66 New technologies, digitalization, the Internet, social media, higher levels of education, new concepts as open innovation intensify the interactions between people in an organization, thus strengthening the idea of CAS. In the following we will discuss the eight ICAS properties in terms of familiar management tools and administrative tools, as well we will link these to other developing practices and insights to turn the ICAS from the abstract into real-life decisions. Organizational intelligence. Bennet and Bennet quote McMaster who defines organizational intelligence as “the capacity of a corporation as a whole to gather information, to innovate, to generate knowledge, and to act effectively based on the knowledge it has generated.”67 This definition is by structure comparable by that of control in cybernetics. With that its restriction is that McMaster’s definition is too much part of a mechanical worldview, not of an intellectual worldview. In business, in terms of successful survival, the dynamic capabilities view and economic theory suggest that it is the capability to reconceptualize industries, markets, products, organization, including processes like cognitive framing and framing type design thinking that explains successful continuity, growth, and development especially in the context of fundamental (technological) change. The way we use the Internet and the concept of the personal computer (not the technology of the Internet nor the computer) have their origins in books on science fiction.68 This implies that besides data concepts are to be acquired by the organization to redefine industries, markets, products, and consumers, and especially to redefine the existing business models. But as elaborated below in the section on selectivity, it is precisely existing (successful) business models which through the mechanism of dominant logic may obstruct seeing possible relevant data and concepts, or interpretations as needed for the transformation of the firm. Organizational intelligence in the first place is about reconceptualization. This implies that beyond data on changes in the environment (material information) a prerequisite is also that an organization as a social system acquires concepts, insights, visions, on whatever aspect of society to understand or give meaning to new developments, technology, etc. This requires in the first place hiring bright people and hiring diversity to avoid groupthink and to have innovation through combinatorial diverse knowledge. Hiring bright, talented people is not sufficient; to achieve high performance the context within which bright people have to work is critical. This simply follows from the Interactive Perspective Model (IPM) from organizational behavior. The IPM describes that for most people the context within which they work defines stronger their behavior and thus their performance as do their personal attributes. Hence, the rule for managers within Netflix: “Context not Command.” This context is often referred to as culture, but comprises more

66

Waal (2012). McMaster (1996). 68 Wolfe (2016). 67

4.5 Organic Organizations as Intelligent Complex Adaptive Systems (ICAS)

73

elements. The concept of systemic context implies that many more administrative parameters are to be designed and to be decided. These elements are: • • • • • • • • • • • • • • • •

Mission Hierarchy of values Strategy and financial objectives Available and accessible information, including effect information with respect to processes and business models (shared information and shared rules of the game) Reportable dimensions, transparency in issue analysis The system of strategy execution/resource allocation Partition and attribution of decision rights Performance parameters, objective function Perceived career paths Performance assessment criteria and processes Basis of and type of reward system Learning processes, sharing of knowledge Membership of external bodies and societies Psychological climate, degree of socialization Tolerance or not for jerks, lack of integrity, whether trespassers are excommunicated Orientation on society, nature of corporate social responsibility

Examples of firms that deliberately pay attention to the context for talent are Google and Netflix.69 Netflix has published on the Internet a slide pack, Netflix Culture: Freedom & Responsibility.70 In this document, the culture, expressed in seven aspects, is specified as “Values are what we Value, High Performance, Freedom & Responsibility, Context, not Control, Highly Aligned, Loosely Coupled, Pay Top of Market, Promotions & Development.” Under Freedom & Responsibility, the issue of complexity is explicitly addressed. The pack has a slide “Growth Increases Complexity.” The traditional response of firms to growth, in order to stay in-control, is to become more bureaucratic. Bureaucratic procedures and processes drive out talent. Therefore, without a new type of HR policy and organizational policy, growth will drive out talent. So the answer to growth should not be bureaucratic processes for control (not to be confused with innovative operational processes to deliver value) and bureaucratic procedures, but to grow the talent density faster than complexity of the organization grows. To this Pentland explains that to have combinatorial innovation through talent requires creating conditions that facilitate rich interaction patterns between talented people and therefore to focus on talent management should be to focus on creating such conditions and HR policy

69 70

Bock (2015), McCord (2014). http://www.keithrull.com/blog/wp-content/uploads/2013/01/netflix_culture.pdf

74

4

Definitions of Complexity

should not be restricted to the typical individual talent approach to managing organizations.71 Unity and shared purpose refers to the need of a clear mission, a clear hierarchy of values and clear strategic choices. Adaptivity is not a goal in itself it is instrumental to a higher purpose. In biology this is the survival of the species, in business this is the efficient continuity of a firm in changing markets and changing industries. At the level of the economy the purpose is not the continuity of individual firms but the continuity of society, as explained in the theory of organizational ecology.72 To achieve this purpose information needs to be interpreted in terms of actions adapting flows of energy, matter, and information itself. The issue of unity and shared purpose is basically the same phenomena with the same function as Kanter’s Guiding System (Sect. 12.3). There it is expressed in familiar terms like the mission statement, a hierarchy of values and in strategic choices. Different from conventional complexity science, ICAS assumes people to be the elements of a complex system. People as intelligent beings have the capability to identify with the purpose of an organization, its mission, its products and services, its markets, its customers beyond their personal tasks and activities in the organization. This identification is assumed in organization theory for the effective functioning of organizations as this identification fosters self-coordination, proactive behavior, bottom-up initiatives and thus being responsive to changes in the environment at the level of the worker.73 Flow of Data, Information and Knowledge as a characteristic of ICAS more precisely should be free flow of data, information, and knowledge. This characteristic of ICAS is supported by economic theory in terms of improved decision-making, speed of decision-making, combinatorial innovation and the value of information. Data, information, and knowledge increase in value when it (simultaneously) is used for multiple value creating processes, products, and services. This free flow (Permeability), as observed by Michael Porter in 1992, is a requirement for competitiveness of firms.74 Whereas in the period of the Second Industrial Revolution data was within the structure of the internal organization, the modern economic understanding of information as en element of the capital base of the firm implies that the database of the firm should be one, organized disembedded from the internal structure and accessible for all members of the organization. Accessibility of data has replaced the old notion of flow, which still was based on Shannon’s mathematical communication theory, but through the Internet complex flows of data will exist. Flow of information is a more complicated issue, especially when the cybernetic concept of information is being assumed. With respect to flow of information in the context of the concept of CAS at this place it is important to observe that at the level of management information, information is interpreted data. This interpretation in its

71

Pentland (2014). Baum (1996). 73 Simon (1991). 74 Porter and Wayland (1992). 72

4.5 Organic Organizations as Intelligent Complex Adaptive Systems (ICAS)

75

simplest form is through an if-then-else decision rule, usually implied by a business model. Information as interpreted data has far-reaching consequences. It implies that a same set of data, but by different people in an organization, e.g., a marketing manager, an operations manager, an accounting, may have different meanings. Data itself has no meaning, it is purpose and causal relations that turn data into information in the sense of a change of state, a decision or an action. This is where complexity comes in. Simon’s concept of loose programming implies that data being interpreted or processed by programs may have multiple meanings for different actors in the system and that the interpretation of data over time will be different. Also, or even more, the interpretation of data is or should not be monopolized by a hierarchy but complexity is a tolerance for multiple, competing interpretations. From which new, unforeseen options may result. The interpretation of data itself may be a complex system with recursive relations. The free flow of knowledge is dealt with in the section on knowledge centricity. Knowledge centricity. A knowledge-centric organization is defined by Bennet & Bennet as an organization in which knowledge is recognized as a key success factor and is systematically managed through knowledge management best practices.75 Knowledge management as defined by Nonaka & Takeuchi76 and in the wake of that knowledge management practiced on an IT basis, is not effective because this concept does not acknowledge that tacit, uncodified personal knowledge is the property of the knowledge worker, whereas the idea of knowledge management assumes such knowledge to be the property of the corporation.77 Actually, the practice of knowledge management of codifying tacit knowledge and storing this in computer systems of the firm, implies in the case this tacit knowledge is about uncodifiable, personal knowledge a transfer of ownership title from the knowledge worker to the corporation. This never was made explicit but nevertheless felt which explains the failure of the first generation of knowledge management practices. A free flow of knowledge, especially when it is about uncodifiable, personal knowledge, implies a free flow of workers, especially a free interaction of knowledge workers across the structures of the internal organization and across the boundaries of the firm. Tacit knowledge can be inputted in production and innovation processes only by means of personal communications and direct interactions, not through codification, machine storage and retrieval, not through transactions.78 Traditionally workers are assigned unambiguously to one department, business unit or division, according to the concept of the linear chain of command. As the economist Rosen has explained, creative knowledge workers, talented people, want to create for themselves as large as possible market, that is a market for their talents beyond the department they are assigned to. 79 To facilitate interactions between knowledge

75

Bennet and Bennet (2004). Nonaka and Takeuchi (1995). 77 Wilhelm and Downing (2001), Jensen (1998). 78 Antonelli (2011a). 79 Rosen (2004). 76

76

4

Definitions of Complexity

workers beyond existing structures, cross-divisional projects, strategic themes and end-to-end processes are added as governance mechanism in the existing system of internal governance of firms.80 This implies an increase in the complexity of the organization of the firm, not only with respect to the organization design, but also with respect to the system of management control, management accounting, the organization of information, the resource allocation process (or even better, resource mobilization), HR systems, portfolio planning, etc. This is logical from a viewpoint of knowledge governance and the (complex) nature of knowledge and is needed to achieve a highest return on investments in human capital. This economic insight has been translated into the field of management control by Kaplan and Norton through their concepts of the strategy map, the balanced scorecard and their resource allocation system. But the field of management accounting still has difficulty to absorb this economic message. The field of IT governance in a way has absorbed the message of knowledge governance by adopting the criteria that the database of the firm may not be fragmented by de internal structure and is accessible for all. HR policies do pay attention to e.g. self-organization and self-management related to cross-divisional projects but are still wrestling in many cases with the growth of stress and burnout because in many firms the needed adaptation in the system for knowledge work is not completely nor consistently implemented. Another dimension of the knowledgecentric organization, now to be defined as an organization in which the facilitation of knowledge work through cross-divisional projects and themes has priority in the resource allocation process, is that it assumes managers and workers with sufficient knowledge, especially required cognitive complexity with respect to organization to understand the more complex organization. It is not sufficient that workers are good team players and have an attitude to interact with others with non-fixed roles (soft skills). The more complex problems to be solved in engineering, software, finance, consulting, government policy, etc. in the first place require subject knowledge like science, technology, engineering, mathematics (STEM), that is hard skills. This is because it are causal relations in the first place which define the nature of operational processes, which is the basis for horizontal self-organization. Permeability. This has been addressed by cross-divisional processes and projects, disembedded organized data, a free interaction of knowledge workers, including outside the organization, etc. To this can be added open innovation and co-creation, alliances, because knowledge is the most important resource it is in the interest of a firm to have access to as large as possible knowledge space.81 Hence the role of knowledge ecologies is acknowledged in business strategies. Permeability will be controlled by property rights and the identity of the firm. Selectivity in the view of Bennet & Bennet is about filtering incoming information from the outside world and is needed to avoid noise being dominant over signals. With this Bennet & Bennet assume the mathematical information theory of Shannon in their concept of complexity. Complex adaptive systems are about survival of

80 81

Foss and Michailova (2009). Antonelli (2011a).

4.5 Organic Organizations as Intelligent Complex Adaptive Systems (ICAS)

77

living systems, organizations, in changing environments and for such situations Shannon’s information theory, assuming closed well-structured systems of communication, does not apply. Needed is the cybernetic information theory, especially that part of it in which is about interpreting material information (objective facts about changes in the environment) into eidetic information (consequences for the strategy and operations in order to achieve continuity). The transformation or interpretation of material information into eidetic information turns out to be most critical for the survival of the firm. Much goes wrong in this transformation due to psychological and cognitive processes.82 Bennet & Bennet are right about the need of a mission and a hierarchy of values for producing eidetic information as a prerequisite for survival of the firm. In addition to that, the psychology at play in producing good quality eidetic information needs to be understood. First, there is the issue of confirmation bias and availability bias in selecting material information.83 We tend to select that data that confirms our worldviews, our preferences and tend to ignore or even rationalize away that information that contradicts our preferences. Despite the availability of the Internet not all information is alike easy to find, some information may have costs, or may need specific search terms to find. As a result, we tend to work with that information that is easily available, despite that other, more difficultto-find information is more relevant for the continuity of the firm. Complex adaptive systems assume that at all levels enjoy loose control and loose programming to experiment with material information to discover what material information might be material for the organization. Adaptivity is not about selectivity but about discovery. In this, the dominant logic (Fig. 3.1) of the management and that of the organization plays a role. A dominant logic is needed for focus and success. But a dominant logic as a simplified causal model or heuristic is based on a number of usually implicit assumptions. As a model or heuristic, a dominant logic also defines what is relevant material information or not. In a changing world with emerging new causal relations a dominant logic not allowing for variation, exploration, trial-anderror may turn an organization blind for essential changes in the environment and thus becoming the cause of the demise of the firm. Acquiring information from the environment and interpreting this is not about selectivity, but about emergence in the interpretation. In this interpretation, Bennet & Bennet define a role in this decentralized interpretation, as a way of agility, for system thinking, longer timeframes and wider scopes and label such competencies as multidimensionality. This refers to the role of thinking in alternative and new dimensions in cognitive framing. Below we will define another type of multidimensionality. Optimum complexity as a characteristic addresses the question of how much complexity an ICAS needs in order to be successful. Based on Ashby’s Law of Requisite Variety the answer is at least one degree of complexity or variety more as its environment will be needed. To which might be added that the speed of decisionmaking and effectuating actions should be at least one step faster as the market

82 83

Sutcliffe and Weber (2003). Bazerman and Moore (2009).

78

4

Definitions of Complexity

moves. The issue is that this environment is growing in complexity. Markets become multidimensional, consumers have multiple preference sets, and the flows involved in transactions tend to be organized in different ways, especially in the case of e-commerce and mobile telephony. This increasing complexity cannot be answered by traditional Weberian organization structure, but it can be answered by multidimensionality in information and by an agile system of (temporary) processes, teams, and strategic themes. The question is what investments are needed for requisite variety or complexity to balance required investments against being prepared timely for new complexity. Too early investments in too much redundant complexity will reduce return on investments, being too late with a required capacity for complexity will reduce market share and thus the value of the firm. To which is related the question of how to avoid investments that block increases in complexity in terms of required investments. This is an issue with an older generation of ERP systems, which once installed, prove costly to be changed to allow for additional complexity. This however is addressed in new generations. A trade-off needed to be made between the capabilities of software beyond present business requirements and the costs of developing and implementing that software. Due to developments in the costs of hardware and software this trade-off becomes less of an issue. More critical is the required investment in people to be able to handle increasing complexity. This in itself has multiple aspects. Controllers need to be able to design multidimensional management information systems. Also, the professionals in the finance function need to have the capability to support new, but yet unproven business models and their processes and causal relations defined by managers on basis of abductive thinking, that is, without proof of prior experience. It may be expected that next generations managers and knowledge workers, growing up with gaming and social media and more fluid organization forms will suffer less the capital intensity with respect to organization forms as defined by Kenneth Arrow. That is, a high capital density describes that a manager has the capability to work with one type of organization form only. In a context of platform organizations, with multiple temporary projects as main carriers of knowledge creation this is expected to be much more relaxed and flexible. At this point, Pentland introduces a warning under the label of the echo chamber overconfidence effect.84 ICAS is about a free idea flow. Pentland: “Idea flow also depends on the mix of social learning and individual learning, for example, when people see others adopting strategies similar to their own, they often become more confident, and they are then likely to increase their investment in that particular strategy. People’s decision are a blend of personal information and social information, and when the personal information is weak, they will tend to rely more on social information.”85 This however, so continues Pentland, can lead to a type of groupthink, different from the traditional groupthink with its coercion, this type of groupthink can be reinforced by feedback loops in social (media) networks. Especially there is the risk that ideas are circulated in

84 85

Pentland (2014, p. 36). Pentland (2014, p. 35).

Bibliography

79

slightly different versions, without their source, history, or author. The variety of ideas, as assumed in ICAS, is suppressed; collective, fad-type behavior may develop, effectively killing the intention of ICAS. This mechanism may be reinforced by a lack of knowledge of the provenance of concepts, due to the phenomenon of the short argument, a lack of understanding of history and fundamentals, and a lack of consciousness and independent individual thinking. The complexity in the intelligence aspects of ICAS even goes beyond new complexities in separate functions. Increasingly it is necessary at lower levels in the organization, knowledge workers developing new propositions, new processes, that they themselves, at their own level have an integrative view, integrating all aspects, marketing, legal, technical, formational, etc. on their initiative or venture. Also, they are required to understand their new product, service or venture not only at an operational level, but also to think it strategic terms, and even that on multiple levels, positioning, synergies with other products, possible competitors reactions, and the consequences or options with respect to market power. The conclusion might be that the concept of intelligent complex adaptive systems (ICAS) already in early 1962 was acknowledged by the more conventional body of business administration, in Simon’s concept of complex organization and in Burns & Stalker’s concept of organic organization. A further confirmation of the economic importance of ICAS flows from Porter’s observation about the importance of a free flow of data and of knowledge. The declining costs of information enabled IBM to create a multidimensional organization, while observing satisfactory institutional requirements. The ICAS cannot be expressed in traditional Weberian hierarchical concepts of organization. By using the new options provided by the declining costs of information it turns out to be possible to supersede the existing organization structure by a focus on objectives and (temporary) projects. The concept of ICAS cannot be expressed in traditional administrative tools, but it can be expressed in modern administrative tools. Many managers will not feel attracted by the abstract expression of intelligent complex adaptive system, words like agility will have more appeal to them as organization. It turns out that many entrepreneurs intuitively seek after the idea of ICAS; it is solidly supported by economic theory and practice, but ICAS needs translation into the language of managerial actions, managerial concern for people and reconciliation with the language of managerial accountability in view of institutional requirements. With ICAS, as reinterpreted in this section in view of modern economic insights and a richer information theory, a firm stands a better chance to be in-control as it would with the COSO concept of in-control.

Bibliography Ackoff, R. L. (1977). Towards flexible organizations: A multidimensional design. Omega, 5(6), 649–662. Ackoff, R. L., Addison, H. J., & Carey, A. (2010). Systems thinking for curious managers: With 40 new management F-laws. Triarchy Press. Adami, C. (2002). What is complexity? Bioessays, 24(12), 1085–1094. https://doi.org/10.1002/ bies.10192

80

4

Definitions of Complexity

Alberts, D. S., & Hayes, R. E. (2003). Power to the edge: Command, control in the information age. CCRP Publication Series. Alberts, D. S., & Hayes, R. E. (2006). Understanding command and control. CCRP Publications. Alberts, D. S., & Nissen, M. E. (2009). Toward harmonizing command and control with organization and management theory. The International C2 Journal, 3(2), 1–59. Alhadeff-Jones, M. (2008). Three generations of complexity theories: Nuances and ambiguities. Educational Philosophy and Theory, 40(1), 66–82. https://doi.org/10.1111/j.1469-5812.2007. 00411.x Antonelli, C. (2011a). The economic complexity of technological change: Knowledge interaction and path dependence. In C. Antonelli (Ed.), Handbook on the economic complexity of technological change (p. viii, 566 p.). Edward Elgar. Antonelli, C. (2011b). Handbook on the economic complexity of technological change. Edward Elgar. Axelrod, R. M., & Cohen, M. D. (1999). Harnessing complexity: Organizational implications of a scientific frontier. Free Press. Baum, J. A. C. (1996). Organizational ecology. In S. R. Clegg, C. Hardy, & W. R. Nord (Eds.), Handbook of organization studies. Sage. Bazerman, M. H., & Moore, D. A. (2009). Judgment in managerial decision making (7th ed.). Wiley. Beck, U., Giddens, A., & Lash, S. (1996). Reflexive modernisierung: Eine Kontroverse. Suhrkamp. Beniger, J. R. (1986). The control revolution: Technological and economic origins of the information society. Harvard University Press. Bennet, A., & Bennet, D. (2004). The intelligent complex adaptive system. In Organizational survival in the new world (pp. 25–36). Butterworth-Heinemann. Bloch, E. (1959). Das Prinzip Hoffnung (Vol. I, II, III). Suhrkamp. Bock, L. (2015). Work rules! : Insights from inside Google that will transform how you live and lead. Twelve. Boulding, K. E. (1956). General system theory - The skeleton of science. Science, 2(3), 197–208. Bower, J. L. (1986). Managing the resource allocation process. Harvard Business School Press. Burns, T., & Stalker, G. M. (1963). The management of innovation (2nd ed.). Campbell, A., & Strikwerda, H. (2013). The power of one: Towards the new integrated organisation. Journal of Business Strategy, 34(2), 4–12. https://doi.org/10.1108/ 02756661311310404 Capra, F., & Luisi, P. L. (2014). The systems view of life: A unifying vision. Cambridge University Press. Davenport, T. H. (1998). Putting the enterprise into the enterprise system. Harvard Business Review, 76(4), 121–131. Deal, T. E., & Kennedy, A. A. (1982). Corporate cultures: The rites and rituals of corporate life. Addison-Wesley. Dijksterhuis, E. J. (1950). De mechanisering van het wereldbeeld. Meulenhof. Doz, Y. L. (2005). Resource allocation processes in multidimensional organizations: MNCs and alliances. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy. Oxford University Press. Foss, N. J., & Michailova, S. (2009). Knowledge governance: Processes and perspectives. Oxford University Press. Fukuyama, F. (2002). Our posthuman future: Consequences of the biotechnology revolution. Profile Books. Fukuyama, F. (2011). The origins of political order: From prehuman times to the French revolution (1st ed.). Farrar, Straus and Giroux. Galbraith, J. R. (1994). Competing with flexible lateral organizations (2nd ed.). Assison-Wesley. Hayles, N. K. (Ed.). (1991). Chaos and order: Complex dynamics in literature and science. University of Chicago Press.

Bibliography

81

Henderson, J. C., & Venkatraman, N. (1993). Strategic alignment: Leveraging information technology for transforming organizations. IBM Systems Journal, 32(1), 472–484. Hill, C. W. L. (1988). Differentiation versus low cost or differentiation and low cost: a contingency framework. Academy of Management Review, 13(3), 410–412. Holland, J. B. (2002). Financial institutions governance of the internal governance and value creation chain of companies - A grounded theory model. SSRN eLibrary. Ismail, S., Malone, M. S., & van Geest, Y. (2014). Exponential organizations: Why new organization are ten times better, faster, and cheaper than yours. Diversion Books. Jensen, M. C. (1998). Foundations of organizational strategy. Harvard University Press. Kauffman, S. A. (2008). Reinventing the sacred: A new view of science, reason and religion. Basic Books. Lawler, E. E., & Worley, C. G. (2006). Built to change: How to achieve sustained organizational effectiveness. Jossey-Bass. Levinson, H. (1994). Why the behemoths fell: Psychological roots of corporate failure. Leydesdorff, L. (2000). Luhmann, Habermas and the theory of communication. Systems Research and Behavioral Science, 17(3), 273–288. https://doi.org/10.1002/(sici)1099-1743(200005/06) 17:33.0.co;2-r March, J. G. (1994). A primer on decision making: How decisions happen. The Free Press. Martin, R. L. (2007). The opposable mind: How successful leaders win through integrative thinking. Harvard Business School Press. McCord, P. (2014). How netflix reinvented HR. Harvard Business Review (January–February). McMaster, M. D. (1996). The intelligence advantage: Organizing for complexity. ButterworthHeinemann. Mocker, M., Weill, P., & Woerner, S. L. (2014). Revisiting complexity in the digital age. MIT Sloan Management Review. Moldoveanu, M. C., & Martin, R. L. (2008). The future of the MBA: Designing the thinker of the future. Oxford University Press. Montuori, L. A. (2000). Organizational longevity - Integrating systems thinking, learning and conceptual complexity. Journal of Organizational Change Management, 13, 61–73. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation (2nd print. ed.). Oxford University Press. Parsons, T. (1962). The problem of the theory of change. In W. G. Bennis, K. D. Benne, & R. Chin (Eds.), The planning of change. Holt, Rinehart and Winston. Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. The Penguin Press. Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. The Free Press. Porter, M. E., & Wayland, R. (1992). Capital disadvantage: America’s failing capital investment system. Harvard Business Review, 70(5), 65–82. Rosen, S. (2004). Markets and diversity. Harvard University Press. Rosser, J. B. (2010). Is a transdisciplinary perspective on economic complexity possible? Journal of Economic Behavior & Organization, 75(1), 3–11. https://doi.org/10.1016/j.jebo.2010.03.012 Ryall, M. D. (2009). Causal ambiguity, complexity, and capability-based advantage. Management Science, 55(3), 389–403. https://doi.org/10.1287/mnsc.1080.0938 Seife, C. (2006). Decoding the universe: How the new science of information is explaining everything in the cosmos, from our brains to black holes. Viking. Senge, P. M. (1990). Fifth discipline: The art & practice of the learning organization. Double Day. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press. Shpilberg, D., Berez, S., Puryear, R., & Shah, S. (2007). Avoiding the alignment trap in information technology. MIT Sloan Management Review, 49(1), 51–58. Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467–482.

82

4

Definitions of Complexity

Simon, H. A. (1991). Organizations and markets. Journal of Economic Perspectives, 5(2), 25–44. Simon, H. A. (1996). The sciences of the artificial (3rd ed.). The MIT Press. Sloterdijk, P. (1998). Blasen. Suhrkamp. Sloterdijk, P. (1999). Globen (1. Aufl. ed.). Suhrkamp. Sloterdijk, P. (2003). Sferen (H. Driessen, Trans.). Boom. Strikwerda, J. (2010). Shared Service Centers II: Van kostenbesparing naar waardecreatie. Van Gorcum/Stichting Management Studies. Strikwerda, J. (2014). Shared service centers: From cost savings to new ways of value creation and business administration. In T. Bondarouk (Ed.), Shared services as a new organizational form (Vol. 13, p. 15). Emerald. Strikwerda, J., & Stoelhorst, J. W. (2009). The emergence and evolution of the multidimensional organization. California Management Review, 51(4), 11–31. Sutcliffe, K. M., & Weber, K. (2003). The high cost of accurate knowledge. Harvard Business Review, 81(5), 74–82. Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. Vercelli, A. (2007). Rationality, learning and complexity: From the Homo economicus to the Homo sapiens. In M. Salzano & D. Colander (Eds.), Complexity hints for economic policy. Springer Verlag Italia. Volberda, H. W. (1998). Building the flexible firm: How to remain competitive. Oxford University Press. Von Hayek, F. A., & Mihnea Moldoveanu, R. (2007). The theory of complex phenomena. Emergence: Complexity and Organization, 9(1/2), 143–165. Waal, A. A., & d. (2012). Characteristics of high performance organisations. Business Management and Strategy, 3(1), 14–31. Weick, K. E. (1982). The social psychology of organizing (2nd ed.). Addison-Wesley. Wiener, N. (1961). Cybernetics or control and communication in the animal and the machine (2nd ed.). The MIT Press. Wilhelm, W. J., & Downing, J. D. (2001). Information markets: What business can learn from financial innovation. Harvard Business School Press. Wolfe, G. K. (2016). How great science fiction works. The Great Courses. Yoffie, D. B. (1997). Introduction: CHESS and competing in the age of digital convergence. In D. B. Yoffie (Ed.), Competing in the age of digital convergence. Harvard Business School Press. Yolles, M. (2006). Organizations as complex systems: An introduction to knowledge cybernetics. IAP - Information Age Pub. Inc. Zuboff, S., & Maxmin, J. (2002). The support economy: Why corporations are failing individuals and the next episode of capitalism. Viking.

5

Economic Growth, Complexity, and Institutional Conflicts

5.1

How Complexity Contributes to Economic Growth

Wealth and economic development are positively related to the complexity in an economy.1 This is not to be interpreted that complexity is a to-be-manipulated parameter and that to increase complexity will result in economic growth. Complexity is a descriptor of the growth of complexity as this result from e.g. technological innovations. What are mechanisms in the economy that make the complexity of an economy grow? A multiplicity of, not necessarily independent, factors can be identified as the cause of complexity in an economy. 1. Specialization. Specialization of tasks, e.g., in Adam Smith’s example of pin making is a first source of growth of complexity and it is the main source of growth of labor productivity and thus wealth. Specialization is a fundamental characteristic of our economy and is to be found at multiple levels in the economy. Most obvious is specialization at the shop floor level with a variety of technical specialists, with next a layer of functional specialists. Specialization between firms exists, which goes back to the medieval ages, but has increased strongly as a result of improved communications, transportation, product and process specification, resulting in sometimes bewildering complicated supply chains for products like the iPhone, across multiple countries. Specializations of firms, e.g., the different components of a home entertainment system, due to standardization of interfaces and software imply the emergence of network industries in which the consumer assembles in his home, or the builder at a construction site, components purchased from multiple independent suppliers, into the system that represents value for the consumer. With that, the value creation no longer is situated within the firm but has shifted into the market.2

1 2

Hidalgo and Hausmann (2009). Shy (2001).

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_5

83

84

5

Economic Growth, Complexity, and Institutional Conflicts

2. Information space. Adam Smith’s invisible hand of the market was primarily a local market, although since the early medieval age long distance trade existed, but was slow to our standards. Related to the latter a capital market existed, especially in the seventeenth-century Amsterdam, which innovated the capital market by changing the year-frequency of clearing in southern Europe into a daily clearance. The local nature of markets changed especially in de United States by the invention of the telegraph, allowing to communicated information on quantities and prices over a long distance at the speed of light, and the construction of a railroad network, allowing for cheap transportation of merchandise over a long distance, thus creating a large market. This in its turn fostered specialization. The Internet of today allows fast, detailed information on products and services to a virtual global market (although not every inhabitant of the world is connected to the Internet) at the speed of light at virtual no cost. Information generated by capital markets has become as frequent and as important for entrepreneurs as information on prices, supply, and demand. The information position between competitors and between suppliers and customers has dramatically changed and information itself is part of the customer value proposition, is a resource and a product. Because players in the market can combine data from multiple sources to develop strategies, marketing tactics, products, and services free from traditional contexts, a type of emergence of not foreseen behavior is markets develops, as defined in complex systems. Firms respond to this amongst others by agility, discovery-driven planning and trial-and-error. 3. Technological innovation. Technological innovation is itself a source of complexity, through an increase in products, production methods, new possibilities, creating more choices, etc. In combination with the large and more intense information space the phenomenon of innovation, be this at the level of the firm or at the level of markets, e.g., through open innovation, itself is a complex system showing emergence.3 Technological innovation may impact the complexity in society beyond technology itself as in the case of property rights with the invention of radio waves and the launching of satellites.4 Social media increased the complexity of messages between citizens beyond that of the traditional media effecting marketing and political processes. 4. Self-discovery. Economic market theory assumes self-discovery by entrepreneurs of prices, new market opportunities, new technologies, new organization forms etc. This self-discovery is unplanned and related to Schumpeter’s Neukombinationen and creative destruction as a source of economic growth. Self-discovery can be compared with the emergence in complexity theory. 5. Education, emancipation social contexts. The increased level of education, in combination with emancipatory movements in society, in relation with the Internet and social media and a changing cultural context creates more complex consumers. This is consequential for product development, marketing,

3 4

Antonelli (2011). Spar (2001).

5.1 How Complexity Contributes to Economic Growth

85

co-creation, and other interactions between suppliers and consumers. Also, the workforce, especially the match between workers knowledge, skills and attitude and firm requirements becomes more complex, as is motivation and rewards. 6. Market liberalization and deregulation. By creating more freedom for entrepreneurs by eliminating complexity, reducing laws and regulations, more efficiency, increasing innovations will result and a higher degree of matching supply and demand, resulting in a higher economic growth. This implies more choices for entrepreneurs but also more unanticipated moves by competitors, new players in the industry and changes of rules. 7. The role of knowledge. Knowledge, codified and non-codified, personal knowledge is a major resource in the economy and a major asset for firms. Knowledge itself is a complex phenomenon (knowledge complexity5) in terms of emergence and knowledge systems in society are complex systems in terms of property rights, knowledge spill-over, combinatorial innovation, paradigm shifts, economic value, etc.6 As explained before, knowledge as a major resource grows the complexity of the firm and its organization, of resource markets, the interrelatedness of firms, as in the case of open innovation, the labor market, etc. 8. Adaptation. A dynamic market implies that according to the economist Hayek, a first task of the management of a firm is to adapt the firm and its organization to changes in the economy.7 Although economists initially neglected the issue of management of change, assuming that competition would wield out non-adaptive, non-efficient firms and that the economic process would create new firms with new products and new, efficient organization forms, it turns out that markets do not clear and shareholders prefer stable dividend over a longer period from the same firm, whilst at the same time there is value in preserving social capital contained in firms. So, there is value in the adaptation capability and adaptation efficiency of firms. Preferably firms adapt and restructure themselves prior to being forced to do so by either the capital market or the product market.8 This requires amongst others a complex organization as defined by Herbert Simon. So, the economic concept of adaptability can be related to the concept of complex adaptive systems (CAS) from complexity science. Complexity in the economy has its roots in the specialization of tasks to produce goods more efficiently. A famous example is that of pin making described by Adam Smith.9 Instead of one worker performing all the subtasks needed to produce a pin, workers specializing each in one of the subtasks can achieve a far higher output per worker or per workday compared to absence of specialization. This specialization of labor introduces a complexity, as these tasks, by timing, quantities, and types, need

5

Grandori and Kogut (2002). Saviotti (2011). 7 Hayek (1945). 8 Donaldson (1994). 9 Smith (1776). 6

86

5

Economic Growth, Complexity, and Institutional Conflicts

to be coordinated, a task not defined by Adam Smith by the way. The example of pin making is a simple example. The specialization of tasks, including the production of—standardized—components and subsystems (the essence of the American system of manufacturing as this emerged in the ninetieth century) is, in addition to technology and increasing education, an important source of wealth and economic growth.10 With the specialization of tasks also tools have become strongly specialized. Specialized tools are more efficient compared to all-purpose tools, at least for very specific tasks, making the workshop both more efficient and more complex.11 Specialization of tasks and the production of components and subsystems not only occur at the level of products, it is especially important on the level of national and international economies. The contribution of international trade to economic growth is based on countries being specialized in specific economic activities. Specialization also is to be seen in outsourcing, that firms outsource parts of the production and in some cases even the assemblage of products to third parties for reasons of specialization, thus using better knowledge for a process and using economies of scale and enjoy thus lower costs.12 Outsourcing itself may display complex patterns, as the design of a product, e.g., the iPhone, is in a different country (California) from the country where the iPhone is assembled (China) whereas some of the components may be sourced again from other countries. This complexity of the organization of the manufacturing of a product, compared to the vertical integrated firms of pre-WWII and in the fifties and sixties, is not only for reasons of cost efficiency but also for reasons of innovation. A car is a complicated product and its production process is capital intensive. To reduce the risks induced by the capital intensity of manufacturing systems needed to develop and produce cars, the capital market forced car manufacturers to spread the risk into the market, not only for the manufacturing of subsystems, e.g., the engine and dashboards, but also de engineering and development of those subcomponents. The European car industry does so by defining and designing the architecture of the car, which in its turn defines the modules and their interfaces so that when assembled, these modules constitute a working car. These modules then are outsourced for their development and their manufacturing, within the specifications defined by the architecture.13 In this way cost efficiencies are achieved, an access to better knowledge is achieved and risks are mitigated. Vice versa, engineers developing a subsystem will scan the market for subcomponents offered by manufacturers, thus saving development costs and using embodied knowledge from third parties. The higher the variety of offerings in the market for (sub)components, innovative or standard, the higher the probability that engineers can develop innovative, better products at lower costs. An extreme form of this is modular innovation is to be seen in the developments of apps like Uber and

10

Hidalgo and Hausmann (2009). Puu (2010). 12 Prencipe et al. (2003). 13 Sako (2003), Takeishi and Fujimoto (2003). 11

5.1 How Complexity Contributes to Economic Growth

87

Airbnb.14 If complexity of an economy is defined as the degree of specialization in the offering of components, subsystems, products, and services, it may be argued that this degree of specialization is a positive determinant of productivity and thus welfare in an economy. However, there are two complications. The first is that in general the long-range average costs per unit output will be minimal at a certain quantity of output per time unit, requiring thus a minimum size of the market to achieve maximum efficiency. This explains why car manufacturers use the same type of engine in multiple models and more in general why many non-competitive components, e.g., connectors, power packs, are standardized across products, industries and are sourced from third parties. Another way to expand a market is through bilateral or multilateral free trade agreements. Therefore, a general rule is that the larger a market and the more efficient its transport and communication, the more specialization will develop, and the more complex that market will be. 15 A second complication is that an efficient coordination is needed between demand and supply for specialized respectively standardized (sub)components. The simplest method to coordinate supply and demand for (sub)components is through the price mechanism of the market. For engineering projects, the price is not sufficient information, additional, and often very detailed specific technical specifications need to be matched in order to coordinate supply and demand. Often components need to be engineered to design, requiring sometimes-intensive cooperation between suppliers and manufacturers implying that the supply is not based on an arm’s length complete contracts with a single transaction price, but is based on some form of risk sharing alliance type of contract. A second form of coordination for complex engineering projects simply is through hierarchy, which is by using a vertical integrated firm. In the first half of the twentieth century (manufacturing) firms were vertical integrated because of a combination of idiosyncratic standards and non-modular product designs and low market efficiency. Changing design methods in which product differentiation respectively competitiveness was less dependent on firm-specific components (apart from the increasing role of esthetic design and marketing), the development of industry standards like ISO and DIN, a need for reducing the capital structure in relation to economic profit, and a demand for increasing returns to scale in combination with a more efficient market resulted in de-verticalization of manufacturing firms. This resulted in an increase of the complexity of the market, both at the supply side and the demand side, but this also resulted in a growing complexity of rules of competition in various industries. This complexity is positively correlated with the income in a country.16 This complexity of markets has increased even more due to phenomena like open innovation.17 Open innovation is needed from a viewpoint of the knowledge economy of the learning

14

Downes and Nunes (2014), Burgelman et al. (2009, p. 498). Hidalgo and Hausmann (2009). 16 Hidalgo and Hausmann (2009). 17 Chesbrough et al. (2006). 15

88

5

Economic Growth, Complexity, and Institutional Conflicts

economy in order to make a best possible use of available knowledge and because a high complexity facilitates a high level of new combinations of existing knowledge, resulting in new knowledge.18 Open innovation and open business models require an absorptive capacity by the orchestrating firm, beyond the mechanisms of price. What is required is an (open) architecture of products and services, which also can be viewed as a tool to manage the complexity at the level of modules. There is however a discrepancy between the traditional MBA toolbox and the increasing complexity in the economy. The capability of markets, especially through the Internet, to coordinate supply and demand of specifically defined products in terms of engineering specifications has increased tremendously. Coordination of supply and demand no longer is through the price mechanism only. The coordination mechanisms within firms, however, tend to develop slower, varying from some firms that reconceptualize their organization away from traditional hierarchy, to many others lagging in this. The explanation of this most likely is that some managers still prefer the underlying accountability/authority paradigm19 for organizing, whereas others have the insight cum courage to adopt the knowledge governance paradigm, respectively the concept of information-based empowerment.20 A next question of course is whether it can be explained which type of managers, under what conditions, prefer the accountability/authority paradigm and which type of managers prefer to adopt the knowledge governance paradigm in the organization and administration of the firm, e.g., in the case of Netflix. Respectively what conditions can be identified, at industry level, national level that helps to understand when managers make the leap to the knowledge governance paradigm.

5.2

Moderating Variables

The relation between complexity and economic growth is not straight forward, but is affected by a number of moderating variables. Complexity itself grows as a result of technological innovations and developments. Technological innovation is considered a factor in economic growth irrespective of growth of complexity, making technological innovation a confounding variable, operating on both growth of complexity and on economic growth. Growth of complexity is furthered by liberalization of markets, demographic changes in society, the effect of information technology resulting in lower transaction costs and therefore a de-verticalization of firms (outsourcing), itself a source of increasing complexity in markets. Despite the liberalization of markets in the period of 1980–2000 there are still complexityreducing institutions, as market liberalization is not complete, if this is possible at all. In a response to risks and affairs in society, and to avoid new types of

18

Hidalgo (2015). Huber and McDaniel (1986). 20 Foss (2005), Strikwerda (2012). 19

5.3 The Limitation of Intuitive Management Books

89

monopolies, there is a growth of the regulatory state.21 A problem of the regulatory state is on what concepts, models, theories, etc. to base the re-regulation? Beck suggests that in the re-regulation there is a tendency to regress to old, proven, theories and concepts which therefore in themselves may be sources of risk.22 De practice of regulation in the Netherlands, e.g., with respect to hospitals suggests that regulators tend to work from traditional organization forms, to which hospital administrators tend to respond with corresponding traditional organization forms, especially the obsolete unit-organization based on traditional medical specializations, aka the asset-approach.23 From a viewpoint of value of healthcare there is a need for organization innovation, e.g. by adding care paths to the existing structure, that is, there is a need to increase the organizational complexity in order to deliver higher quality of healthcare against lower costs. That is to say, a care path reduces the complexity of the hospital for the patient because the patient now has one appointment to make, and in a number of cases the medical doctors are physically organized around the patient, eliminating the need for physical movement. From the perspective of the executive board and others involved in the administration of the hospital the introduction of care paths increases the complexity, as there are more planning dimensions requiring a more complex resource allocation process and scheduling. This increased complexity in the organization and administration of the hospital is a logical consequence of the complexity of specific diseases. To make the process of diagnosis and treatment for the patient as simple as possible, while maintaining the advantages of specialized medical departments and absence volumes of patients as assumed in Porter’s Integrated Patient Units, as is the case in the Netherlands, the executive board of the hospital, the administrative system of the hospital needs to absorb a higher level of complexity.

5.3

The Limitation of Intuitive Management Books

A first thing to acknowledge, before we criticize management books, is that the simplified management models and organization models as applied in the period of the Second Industrial Revolution (±1875 - ±1975) served society and business well—judged against the unprecedented growth of the economy and welfare in the western world, as so eloquently described in The Rise and Fall of American Growth, written by the American economist Robert Gordon.24 These successful management models, however, did not attempt to describe reality in an adequate way nor to explain it, these US management models, adequately based on underlying economic theories and societal institutions, were based on the art of making things seem true.25

21

Glaeser and Shleifer (2001), Crew and Kleindorfer (2002). Beck (1999). 23 Bohmer and Romney (2009). 24 Gordon (2016). 25 Boorstin (1969). 22

90

5

Economic Growth, Complexity, and Institutional Conflicts

Management books used to be part of a media culture in which life exists of pseudoevents, and in which everything that is being seen or heard (or read) must immediately be understood, preferably in an unconscious way.26 The success of the reductionist management models of the Second Industrial Revolution, including their foundations and their limitations, resulted in “a Modern Management Tradition that has been concealed by its obviousness.”27 A second phenomenon to acknowledge is that the idea of complexity thinking already for a long period is a second voice in the practice of management and with a number of authors. The concept of the organic organization as identified by Burns and Stalker in 1963 answers in essence elements of complexity thinking. Philips Electronics after the Second World War answered Herbert Simon’s concept of complex organization. Many CEOs knew or felt that MBA models should not be taken too dogmatic, although they often needed to communicate differently from their wisdom. MBA courses are supposed to be a source of new ideas, and e.g. authors like Martin plead for a renewal of the MBAs by emphasizing the need for design thinking and changing the curriculum of the MBA’s.28 To this however must be added that most new ideas for organization emerge from practice, from innovative managers facing the need to solve problems.29 Many are management books that intuit new organization forms, pleading to overcoming the limitations of the traditional tools of management and control in the perspective of the knowledge economy. Practices of alternative and effective organization forms do exist.30 As often with sound intuition, what is intended to do is the right thing. But there are three types of issues with these intuitive management books advocating new practices. The first is that it is not difficult to propose a new idea, e.g., putting people first, the problem is to get the old ideas, paradigms, working rules, out. For example, putting people first implies that no longer physical assets are put first in performance management and in resource allocation. Or, customer first implies that no longer the product in the profit center in the accounting system, but the customer is the primary profit center in the accounting and performance system. The reasons that it is often so difficult to get to old ideas out is simply because many practitioners have no consciousness on the rules they live by. To this is related a second weakness of these intuitive management books. These books propose new paradigms for management and organization, but fail to properly locate the place of the old paradigms. The paradigms of business administration, of the MBA, are not so much in the field of the MBA itself, but are to be found in corporate law, labor law, property law, in economic theory, etc. As a result of which

26

Clark and Greatbatch (2004). Kikoski and Kikoski (2004). 28 Moldoveanu and Martin (2008), Martin (2007). 29 Davenport et al. (2003). 30 Strikwerda (2008). 27

5.4 A Conceptual Model for Economic Complexity

91

new paradigms proposed, e.g., those in post-modern organization theory remain within the context and the restrictions of the traditional guiding paradigms.31 A third issue with many of the intuitive management books is that, often for editorial reasons, these lack sufficient technicality to overcome the conventions that rule the functions like HRM, management control & accounting, IT governance, organization design, etc. Not that it is impossible to overcome this; authors like Kanter provide examples of CEOs that solved this problem.32 Another example of the required technicality to overcome the complexity-reducing effects of business institutions is to be found in Kaplan and Norton.33 But even then there is the problem to address all the relevant behavioral aspects in a coherent way as Bower did in his 1986 Managing the Resource Allocation Process but to his disappointment found that his sound advice was little followed, resulting in many strategies being poorly implemented.34 The effect is, that despite the attention within MBAs for innovation, despite the examples of CEOs breaking out of traditional complexity-reducing concepts, the whole body of MBA tools, unintended reinforced by regulation based on accounting rules and corporate law, still acts as a complexity-reducing institution. That is, in many cases, again unintended, the MBA body to a large extent is becoming a source of subjective complexity. Paradoxically an example of this is the book by Galbraith, Designing complex organizations.35 This book is about matrix organizations, especially organizing cross-division or department projects to build complicated products like airplanes. The complexity in such organizations results from viewing such organizations through the lens of the Weberian hierarchy cum one-dimensional functional or multi-divisional organization.

5.4

A Conceptual Model for Economic Complexity

In order to understand complexity in a more productive way we need to understand the function or usefulness of complexity, especially its relation to economic growth as depicted in Fig. 5.1. The relation between complexity and economic growth is not simple straightforward, but mitigated by complexity-reducing institutions, re-regulation, and conventional concepts. It even can be argued that technology development is a confounding variable, in that it both increases complexity and also directly effects economic growth, which is growth of GNP and Total Factor Productivity. The problem at hand, the relation between complexity and economic growth itself is a complex set of relations of influence. The main conflict to be solved is that between seeing and using the new options to organize, as created by technology and liberalization, and

31

Boje (2001), Hatch (1997), Linstead (2004). Kanter (2009), Kanter (2008). 33 Kaplan and Norton (2004, 2008). 34 Bower (1986), Bower and Gilbert (2005). 35 Galbraith (1973). 32

92

5

Economic Growth, Complexity, and Institutional Conflicts

Fig. 5.1 The conflict between the need and possibility for more complexity to have economic growth, liberalization, re-regulation, and the limitations of conventional MBA concepts, resulting in an inconsistency between the nature of the present economy and the institutions of society. Based on (Antonelli, 2011; Hidalgo & Hausmann, 2009; Puu, 2010)

the lack of knowledge or lack of using new insights on a broader scale in business and in society, especially in regulation. Mastering (new) complexity starts as a mental-intellectual process, of seeing new developments, new situations through a different lens compared to conventional concepts. For this no algorithms or tools exist, it has more to do with an attitude, with the personality of a manager, but may also be influenced by a context of intellectual curiosity, the development of new concepts (reconceptualization) and models and with that of vitality in society (that is an interest in new ideas and the willingness to apply these).36 Seeing new options however is complicated by the fact that the implementation of new organization forms like that of IBM does not involve a change in structures in terms of classical organization theory, but involves parameters that are not part of traditional organization theory. To understand such changes is complicated by editors who, as in the case of Gerstner’s book Who says elephants can’t dance emphasize culture as the tool of transformation, whereas this is only one of the elements in the overall transformation.37 Editors do so because culture has become an anchor in our language when it is about non-structural and about behavioral aspects of organization, but with this use of culture it obscures more as it does help see the things we need to see.

36 37

Phelps (2013). Gerstner (2002).

Bibliography

93

Bibliography Antonelli, C. (2011). Handbook on the economic complexity of technological change. Edward Elgar. Beck, U. (1999). World risk society. Polity Press. Bohmer, R. M. J., & Romney, A. C. (2009). Performance management at intermountain healthcare. Harvard Business School. Boje, D. (2001). What is critical postmodern theory? Retrieved from http://cbae.nmsu.edu/~dboje/ pages/critpomo_page Boorstin, D. J. (1969). Het imago; of: Wat is er met de Amerikaanse droom gebeurd? (Y. Foppema, Trans.). Leopold. Bower, J. L. (1986). Managing the resource allocation process. Harvard Business School Press. Bower, J. L., & Gilbert, C. G. (2005). A revised model of the resource allocation process. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy. Oxford University Press. Burgelman, R. A., Christensen, C. M., & Wheelwright, S. C. (2009). Strategic management of technology and innovation (5th ed.). McGraw-Hill Irwin. Chesbrough, H., Vanhaverbeke, W., & West, J. (Eds.). (2006). Open innovation: Researching a new paradigm. Oxford University Press. Clark, T., & Greatbatch, D. (2004). Management fashion as image-spectacle: The production of best-selling management books. Management Communication Quarterly, 17, 396–424. Crew, M. A., & Kleindorfer, P. R. (2002). Regulatory economics: Twenty years of progress? Journal of Regulatory Economics, 21(1), 5–22. Davenport, T. H., Prusak, L., & Wilson, H. J. (2003). What’s the big idea?: Creating and capitalizing on the best management thinking. Harvard Business School Press. Donaldson, G. (1994). corporate restructuring: managing the change process from within. Harvard Business School Press. Downes, L., & Nunes, P. (2014). Big bang disruption: Strategy in the age of devastating innovation. Penguin Group. Foss, N. J. (2005). The knowledge governance approach. SSRN eLibrary. Galbraith, J. R. (1973). Designing complex organizations. Addison-Wesley. Gerstner, L. V. (2002). Who says elephants can’t dance?: Inside IBM’s historic turnaround. HarperBusiness. Glaeser, E. L., & Shleifer, A. (2001). The rise of the regulatory state. Retrieved from Cambridge, MA, http://ssrn.com/abstract=290287 Gordon, R. J. (2016). The rise and fall of American growth: The U.S. standard of living since the Civil War. Princeton University Press. Grandori, A., & Kogut, B. (2002). Dialogue on organization and knowledge. Organization Science, 13(3), 224–231. Hatch, M. J. (1997). Organization theory: Modern, symbolic, and postmodern perspectives. Oxford University Press. Hayek, F. (1945). The use of knowledge in society. American Economic Review, XXXV(4), 519–530. Hidalgo, C. A. (2015). Why information grows: The evolution of order, from atoms to economies. Basic Books. Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proc Natl Acad Sci U S A, 106(26), 10570–10575. Huber, G. P., & McDaniel, R. R. (1986). The decision-making paradigm of organizational design. Management Science, 32(5), 572–589. Kanter, R. M. (2008). Transforming giants. Harvard Business Review (January), 43–52. Kanter, R. M. (2009). Supercorp: How vanguard companies create innovation, profits, growth, and social good (1st ed.). Crown Business. Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business School Press.

94

5

Economic Growth, Complexity, and Institutional Conflicts

Kaplan, R. S., & Norton, D. P. (2008). Mastering the management system. Harvard Business Review, 86(1), 62–77. Kikoski, C. K., & Kikoski, J. F. (2004). The inquiring organization: Tacit knowledge, conversation, and knowledge creation: Skills for 21st-century organizations. Praeger. Linstead, S. (2004). Organization theory and postmodern thought. Sage. Martin, R. L. (2007). The opposable mind: How successful leaders win through integrative thinking. Harvard Business School Press. Moldoveanu, M. C., & Martin, R. L. (2008). The future of the MBA: Designing the thinker of the future. Oxford University Press. Phelps, E. (2013). Mass flourishing: How grassroots innovation created jobs, challenge and change. Princeton University Press. Prencipe, A., Davies, A., & Hobday, M. (Eds.). (2003). The business of systems integration. Oxford University Press. Puu, T. (2010). On the economics of increasing complexity. Journal of Economic Behavior & Organization, 75(1), 59–68. https://doi.org/10.1016/j.jebo.2010.03.011 Sako, M. (2003). Modularity and outsourcing. In A. Prencipe, A. Davies, & M. Hobday (Eds.), The business of systems integration. Oxford University Press. Saviotti, P. P. (2011). The economic complexity of knowledge. In C. Antonelli (Ed.), Handbook on the economic complexity of technological change (p. viii, 566 p.). Edward Elgar. Shy, O. (2001). The economics of network industries. Cambridge University Press. Smith, A. (1776). An inquiry into the of nature and causes of the wealth of nations. Spar, D. L. (2001). Ruling the waves: Cycles of discovery, chaos, and wealth from the compass to the internet. Hartcourt, Inc. Strikwerda, J. (2008). Van unitmanagement naar multidimensionale organisaties. Van GorcumStichting Management Studies. Strikwerda, J. (2012). Empowerment: Hoe professionele ruimte te combineren met in-control zijn. Holland Management Review, 145, 32–40. Takeishi, A., & Fujimoto, T. (2003). Modularization in the car industry: Interlinked multiple hierarchies of product, production, and supplier systems. In A. Prencipe, A. Davies, & M. Hobday (Eds.), The business of systems integration. Oxford University Press.

Part II Organizational Complexity

6

Information and Complexity

6.1

The Paradox of the Information Society

Complexity is associated with entropy and entropy is associated with information, entropy can be interpreted as a lack of information and vice versa an increase of information can undo entropy, without loss of complexity.1 However, this apparent simple relation between complexity and information is restricted to detailcomplexity and needs elaboration to include the other types of complexity identified in Sect. 5.4. It is said that our present era is the information age.2 The concept of information economy subsequently information society is defined as that the processing of data, signs, information is of more economic, political, cultural, and social value, as is the processing of material and of energy. This dominant role of information can be observed in the strategies of firms like Google, Walmart, Amazon, Apple, in the phenomenon of fake news and misinformation and strategies of information superiority. The dominant role of information in our society suggests that clear theories on information exists, what information is in ontological sense, its nature, its roles and functions, its effects on society, but this turns out to be an issue. The concept of “information” in “information technology” has nothing to do with the semantic concept of information in daily life; information technology is only about data. Data has no meaning by itself; it only has meaning in a defined context, on basis of paradigmatic accepted models and concepts. This also implies that a data set may have different meanings, dependent on different individuals interpreting this data on basis of different concepts, tasks, objectives, and interests. The sales slip generated by a supermarket till will have different meanings for the customer, for the

1 2

Floridi (2010, p. 45), Hidalgo (2015, p. 15). Castells (2010).

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_6

97

98

6

Information and Complexity

merchandise manager, for the shop manager, for the accountant and for the supply chain manager, each attributed meaning is valid in itself. In making organized complexity productive indeed information is needed, in this section it will be explained that different types of information need to be discerned to achieve a proper organization of information. To understand and define information, especially in relation to complexity, most authors refer to the information theory as defined in the Mathematical Theory of Communication (MTC) by Shannon & Weaver.3 Shannon himself did not use ‘information’ but “signals.” It was his co-author Warren Weaver who renamed Shannon’s MTC into “information theory,” but Shannon himself did not support this. Shannon’s MTC is without question of utmost importance for the computer sciences and ICT. To an extend the MTC also plays a role in the mathematical decision theory in the economic theory, especially in corporate finance. The MTC is mainly concerned with how to transport a maximum of data with a minimum of loss or disturbance given a limited capacity of communication channels and that external noise may disturb signals. Shannon and Weaver’s concept of information is different from the concept of semantic information as used in daily life. Shannon and Weaver’s concept of information is devoid of any meaning, being just a signal informing a receiver which pre-coded message (which has the semantic meaning) a sender has selected and the receiver should act upon (note that to have effective communication in this theory, first the full code book with semantic messages needs to be copied, usually physical, from the sender to the receiver). In Shannon’s concept of information or better data, has use and value only within a defined context of pre-codified messages. Today it is acknowledged that Shannon’s theory (MTC), despite its continuing importance for ICT, is too limited for a theory of information in the present information society, because it does not include semantics.4 Shannon and Weaver’s definition of information is to be understood in the context of WWII encrypted signals, the then limited capacity of communication channels requiring economizing on signal variety and protecting these against noise in the then analog technology, for which Shannon’s theory made a wonderful contribution. The uncertainty of the receiver, which codified message, and thus which alternative decision or action to be carried out the receiver would have to select, would be solved by the received information, hence information was associated with reducing uncertainty. Weaver extended Shannon’s mathematical definition of information into the realm of science and academic thinking in the in that period dominant tradition of logical positivism. It was a period in which it was believed that science could reduce uncertainty, that concepts could be defined unambiguously, that codification of concepts in academic theory was value free. Wiener defined information in terms of the values of “freedom of choice,” “freedom of speech” and thus freedom of

3 4

Shannon and Weaver (1949). Floridi (2010, pp. 43–45).

6.1 The Paradox of the Information Society

99

information.5 “Weaver here conflates ‘freedom’ in the sense of classical subjectivity and liberal political theory with a sense of ‘freedom’ based on statistical measure within closed systems.”6 Shannon’s theory of information on closer inspection is restricted to signals, assuming a finite, closed and well-structured set of pre-coded messages on both the side of the sender and on the side of the receiver. Through this pre-coding, the amount of data to be transferred could immensely be reduced compared to the information complexity of the pre-coded messages. With that Shannon’s information theory is a mathematical theory of communication engineering, as such of immense importance for the computer, communications, and encryption industry.7 In this engineering or mathematical definition information has value if it reduces uncertainty. Economists use elements of the mathematical or engineering definition of information in, e.g., structured decision support (decision trees, game theory). In the context of mathematical models for decision-making it is possible to determine a value for (mathematical) information, as far as information reduces uncertainty with respect to which of the alternatives to choose from will have the highest economic value.8 This in itself at first sight seems logical and without reason for critique. The mathematical definition of information made useful contributions to decision theory (decision-making under uncertainty) and investment theory. In the economic use of mathematical information, the codification giving meaning to data, is implied by economic theory. This application of the mathematical information theory is restricted to well-structured decision problems, whereas most business problems are not. The question to be asked is whether there is not a contradiction in using Shannon’s mathematical definition of information in complexity theory. Are complex systems closed systems and are the possible states of a complex system unambiguously and completely codifiable? There is another aspect in Shannon’s mathematical information theory. The sender will select a message from a predefined finite set of well-coded messages or a combination of these. The communicated signal only tells the receiver which message(s) the sender selected, and the receiver needs to look up in a codebook the content of the message(s). That is, prior to Shannon’s communication of economized signals, the complete content of the codebook needs to be communicated to the receiver, no economizing on that is possible. In the field of anthropology this latter, rich type of communication, is known as programming. This programming takes multiple forms as we have seen in cybernetic theory, varying from machine-type coding to programming as in culture in society. Culture as the programming of the thinking of the new members of a social system is not based on a closed system with unambiguous codification but is a

5

Day (2001, p. 43). Day (2001, p. 43). 7 Hidalgo (2015, p. 15), Floridi (2010, p. 43). 8 Strikwerda (2011). 6

100

6

Information and Complexity

process full of contestation and open development, that is, it demonstrates emergence. Shannon’s MCT applies to non-complex systems only. When Weaver shifts Shannon’s mathematical information theory outside closed, final and well codified systems into engineering, accounting and some parts of economic theory (operations research), the programming consists of an amalgam of scientific definitions, concepts, paradigms, conceptual theories, academic or mathematical models, but also in academic training of engineers, scientists, and academics. With that, according to Scott Lash, the nature of information shifts from mathematical information to discursive information or discursive knowledge.9 Discursive information is based on codification as well, but codification now exists of engineering, economic theory, management accounting, mathematics, physics, biology and as well on parts social and political sciences. Discursive information therefore is based on abstraction, on selection, on simplification, on complexity reduction.10 The complexity reduction discursive information is based on is achieved through formulating abstract theories, which also, as will be explained below, are labeled as conceptual information. In Shannon’s mathematical definition of information, information does not reduce complexity, there is no complexity in Shannon’s system in the first place simply because Shannon assumes a closed, well-defined system of codified messages. In Shannon’s definition information reduces uncertainty. Discursive information is based on the codification of phenomena; selections are being made about what are relevant phenomena and irrelevant phenomena in nature and in social life on basis of academic models. Our brains need codification, of objects, social relations, identities, the phenomenal world, to reduce its myriad manifestations to a limited set of categories we easily recognize, are familiar with, can deal with, to limit the amount of data to be processed in social settings.11 This codification is not simply a neural process; it is as much a process at the level of society, at the level of political processes. North explains that the role of institutions in society is to limit the need for data processing in accordance with the data information processing capabilities of individuals and social groups, and traditional institutions reduce complexity.12 This implies that the process of codification, a multilevel process, is not a clean scientific process, it is a political, an economic, a cultural process, reflecting shifting power relations and interests in society. In such a context there may be an academic quest for a unifying theory of information,13 Especially when it is about the meaning of information, about what is truth, a theory of information will remain subject to political debate, power struggles and identity politics.14

9

Lash (2002, p. 141). Lash (2002, p. 141). 11 Boisot (1995, p. 47). 12 North (1991). 13 Hofkirchner (1999). 14 Castells (2010), McIntyre (2018). 10

6.2 Discursive Information and Disinformation

6.2

101

Discursive Information and Disinformation

In economic and entrepreneurial processes semantic aspects of information play an important role. In real life, information has a wide variety of meanings, facts, hypothetical knowledge, accepted knowledge, fundamental knowledge, heuristics, algorithms, codes, stories, values, interpretations, rumors, gossip, intelligence, etc.15 Lash discerns within semantic information discursive information and disinformation.16 Codification is not only a mathematical topic in encrypted military communication, or the way cable messages were processed in the fifties in the USA through the use of codebooks, codification also is a political-cultural phenomenon as a defining element in the era of modernism. A characteristic of modernism was the definitions of clear dualities, market $ hierarchy, work $ non-work, family $ non-family, scientific knowledge $ non-scientific knowledge, man $ woman, etc.17 These dualities served to reduce complexity, to facilitate science based social research, especially statistics and to define institutions in society as needed by the modern economic processes. With that language itself is an institution facilitating economic progress. The dualities simplified laws and thus contracting and thus economized economic coordination processes. These dualities however suppressed groups and ideas that were considered marginal to the economy or in society. Language through these dualities “cuts out standardized concepts and organizes these in binary oppositions.”18 This implies that codification is a political and ideological process, not only driven by academic motives but as well by political, cultural, and religious motives and existing power relations in society.19 In itself there is nothing wrong with such a situation as long it serves the general interest. The increasing complexity of social life, as expressed in the political philosophy of postmodernism with its related growth of diversity questioned these dualities and today to a large extent these dualities no longer are accepted or at least are contested. This questions thus codification as a basis both for communication and for information. It might seem that the questioning of the dualities as a deeper foundation of societal institutions especially applies to the social sciences and the political sciences and cultures. But accounting, typically a domain of discursive information, has not yet acknowledged the role of intangible assets, declared non-existent in the duality capital $ labor, although corporate finance has and economic theory has, resulting in accounting rules not being capable with the complexity of the dominance of intangible assets. Another issue with codification is that codes, like formal mathematical systems, are subject to Gödel’s Incompleteness Theorem, that is to say, it is impossible to define a complete set of codes for a field that is at the same time consistent; always 15

Birchler and Bütler (2007, p. 15). Lash (2002). 17 Beck and Lau (2005). 18 Holmes (2009, p. 353). 19 Boisot (1995). 16

102

6

Information and Complexity

ambiguities will exist.20 As a consequence coding is context dependent and to be able to use an incomplete and inconsistent system of codes, reference to a (social) context is needed, that answers questions about purpose, but as well answers questions about values, that is an answer to the question what it is all about. Coding is about creating a foundation for defining information as defined by Shannon, but to an extent as well to be found in science and academic theories, and in language and thus human communication. At the same time coding limits the quantity of data to be processed. Coding has a limited capacity to reduce the amount of information to be processed. A second way to economize on data processing and communication is by formulating abstract models, like the difference between thermodynamics and Newtonian mechanics and as in the difference between strategic concepts and operational planning. Abstract models limit the number of categories by defining new concepts, as a consequence less codes are needed and less information needs to be processed. Economists define abstract models to explain the economy, to make forecasts and define public policies, although they went somewhat too far in this to be judged by role of the too-abstract, too-reductionist economic models in the Great Crisis of 2008. Where codification has a factual nature, abstract models tend to be explanatory, tend to define causal relations and thus serve to make sense of the world in terms of understanding and what to do to achieve set objectives. The nature of causality in abstract or conceptual models may vary from symbolic and meaningbased downward causation as in religion, culture, marketing, or science law-based upward causation to same-level causation as in Newtonian mechanics and DNA-RNA mechanisms (biological information). Due to the nature of artificial intelligence and machine learning, there is a tendency to redefine the (Newtonian) causality in terms of statistical correlation including simplified heuristics.21 The art of abstraction is to reduce the number of variables in a model or in an equation and thus to reduce the amount of information to be processed, but to the degree that the model is still reliable as a basis for decisions and actions. In the context of Kuhn’s paradigms, academic theories define what are relevant categories of phenomena and observations and thus information to be processed by that theory.22 Even more is the role of formulating abstract models the development of understanding that is the development of knowledge. It is typical for cases of the Harvard Business School that these are presented at the level of details, at the level of data. Their solution however is usually at the level of reconceptualizing the problem, defining the case through the lens of a new concept, e.g., that a shift in accepting a new type of customer orders creating problems in operations, only can be solved by seeing that implicitly a new business model has been introduced by accepting those orders. It could also be stated that codification is a first-order limitation of the amount of information to be processed, by excluding variations through dualities, and that

20

Boisot (1995, p. 53). Mumford and Anjum (2013). 22 Kuhn (1970). 21

6.2 Discursive Information and Disinformation

103

abstraction or reconceptualization is a second-order reduction of the amount of information to be processed. A conceptual model, a statement of causal relations itself also is a type of information, dependent on the level of abstraction labeled as conceptual information or causal information. Those mastering conceptual information are mastering a tool to select relevant information from the overall information space and turn this information into meaning of a decision what to do or not to do to either to maintain state or achieve an objective, that is, to be in-control as defined in cybernetics, to survive in a changing environment. Illustrative for this is that in intelligence work big data is not analyzed on basis of statistics, which is an analysis on the level of existing codification, but big data is conceptually-driven analyzed.23 We need both data and concepts, as the philosopher Immanuel Kant (1724–1804) wrote: Concepts without percepts are empty, percepts without concepts are blind. Information is physical, but independent from a specific physical carrier, information has transformability across media. Alike signs can exist, be stored, and communicated independent from systems of codification. The French philosopher Baudrillard in this respect writes about signs (words) that have no relation with whatever reality, these are their own simulacra. In a media culture, in an information culture signs become experiences in themselves, even to the extent that especially Fromm’s marketing personalities judge each other on using the right signs (words, phrases, expression, fashion style), irrespective of attached meaning, even to the extent that such personalities, due to an echo chamber effect implied by social media, are not able to distinguish old from new, the provenance of ideas is lost when communicated as signs.24 Whereas the characteristic of Shannon’s information concept is that information once communicated and thus having reduced uncertainty, has no value if sent a second time, signs, and simulacra have value through their repetition. As people do need purpose and meaning in life communication in politics, marketing, news, social media has shifted from the signs without meaning to signifiers aimed at positioning subjects to receive and interpret messages in a specific way, usually a political-cultural agenda. The signifiers now not being Shannon’s 1’s and 0’s, but exist of images, music, fashion, games, news, sitcoms, movies, etc. In this change from sign to signifiers, we see a reversal of Shannon’s mathematical definition of information. The codification of a sign communicated is not-predefined, as assumed in a command-and-control context, the codification is defined by the receiver or those who sense or acquire information. This development also illustrates that information, Shannon’s signals become less and less context dependent, the context of understanding, purpose, knowledge becomes more important as is the information. Information (data) that cannot be interpreted has no value. The movement of postmodernism implies a second, more or less parallel movement of decontextualizing information from a pre-defined codification, a cultural

23 24

Heuer (1999). Pentland (2014).

104

6

Fig. 6.1 The opposing types of information causing an uncontrollable complexity in the information culture (Birchler and Bütler, 2007; Lash, 2002). Social media are in the lower right quadrant

Information and Complexity

Discursive information

Data Signals

Knowledge Understanding Algorithms

Mathematical information

Semantic information

Noise

Images Experiences Perceptions

Disinformation

decontextualization.25 In a world of globalization, global media, the Internet, in which individuals generate information especially through social media, that is to say without the codification (validating, editing) by newspapers and magazine editors, the cultural context within with the flood of information, news, sports, music, movies, games, social media messages, are interpreted diversifies, dominant cultural contexts for the interpretation of information are contested by a range of identities movements in society. The explosion of the communication channels capacity, the end of the monopolizing airwave-based broadcast system by the combination of Internet-based narrowcast and broadcast, implies that the implicit resistance of postmodernism to the complexity-reducing dualities of modernism is not to focus on information as index to predefined codification, but to communicate through images, movies, personal texts, lifestyles, gadgets, fashion, design, etc., the cultural expressions themselves, but codified in images, stills, and movies. As a result of photography, movies and TV, society has shifted from a word-culture to an image culture. In this image culture primarily emotions are addressed, not the analytical level of consciousness. The German art critic Walter Benjamin already in the thirties of the twentieth century warned for the risks of image illiteracy.26 Scott Lash labels this second type of information (the first type is discursive information) disinformation (Fig. 6.1). The paradox is that the complexity reduction of discursive information, through the cultural-political protest of postmodernism and its increasing diversity in society, results in an uncontrollable complexity of the information culture.27 Whereas discursive information is aimed at avoiding information overload, disinformation, the information culture, results for many in information overload. But at the same time disinformation, its daily consumption results in experiences, identities, in a world of 25

Good and Velody (1998), Cillier (1998). Benjamin (1936). 27 Lash (2002, p. 146). 26

6.2 Discursive Information and Disinformation

105

perceptions, and as a result of cultural decontextualization or individual contexts, in words, signs, as simulacra, not referring to any physical or social reality. As individuals cannot cope with information overload they either adopt frames (e.g., brands like Oprah Winfrey, software like iTunes) to find their way in the modern information space.28 In the cybernetic concept of control the function of the organization of a living system is to survive, that is, there is a purpose. In the mechanical variant of cybernetics, e.g. Watt’s regulator or the thermostat in your home, a preset speed or preset temperature needs to be maintained, irrespective of outside influences. That is another type of information is needed, goal information. In the case of an organization, the goal information will be its mission, and at a more operational level it will be elaborated in strategic choices and dependent on the specifics of a firm, financial targets may be formulated. This goal information is needed in order that in Herbert Simon’s concept of complex organization, with loose programming and loose control, allowing for local, decentralized adaptive behavior, individuals and groups need to know what to adapt to. In common parlance, there needs to be a sense of purpose, a sense of direction, in order to make sensible choices in a world with increasing options. Goal information in terms of Kolmogorov complexity may be simple, a mission statement, it may be somewhat more complex in the case of a multi-attribute, multi-criteria objective function, but its complexity should be an order of magnitude simpler as the operations of the firm. This therefore is the simplistic part of managerial communication and it should be so. In most cases, there will be multiple ways to achieve a mission for which choices needs to be made. These choices may be equivalent in terms of effectiveness of achieving the mission, but may differ in terms of ethics, compliance, or fit in its societal context even to the extent that the organization’s license to operate, either legally or in terms of moral support may be impaired and with that the fulfillment of its mission or objectives. That is, there are values to be considered, in terms of cybernetic information theory, axiological information. This axiological information also should be an order of magnitude simpler as is the complexity of operations, and usually they are at the level of values. However, where compliance comes into the equation the detail complexity of regulation may complicate initiatives and decisions seriously. Discursive information is supposed to be used by decision-makers to maximize their utility by selecting the most valuable alternative from available alternatives. This is the classical mathematical decision theory as defined by Herbert Simon. Simon understood the limitations of his decision theory because it was a branch of science, that is to say, it is about what is, not about what should be.29 Lash’s disinformation molds the perception of consumers, it shapes their identities, their self-image, their preferences, it defines social life and for many to life on this disinformation, it is their consumption. Disinformation is about design and about

28 29

Lash and Lury (2007). Simon (1996).

106

6

Information and Complexity

creativity. Disinformation also feeds self-discovery of new states to life by, new choices to be made. Disinformation in a way expresses the complexity of modern life. However, many use the information space available to them not for exploration or development, but for simplifying their world by selecting that information that confirms their worldview and their self-images. In addition to this, reinforced by the social media reflexivity exists between marketing by firms and the response to it by the assumed target groups. Consumer preferences not only have become more complex in terms of variety, especially combining life styles, but also the social mechanisms by which consumer preferences may develop. Internet firms like e.g. nu.nl and booking.com need continuously to adapt their websites and offerings to continuous changes in consumer preferences. The implication of this is that in business, as in life, those who need to make decisions no longer can do so based on discursive information as is assumed in traditional decision theory, but as much need to base their decisions on disinformation. As in the case individual need purpose and frames to cope with the overload of data on the Internet and other media, such a purpose and frames are needed in business beyond the traditional valuation techniques, let it be beyond the mathematical approaches to decision-making.

6.3

The Cybernetic Concept of Information

The mathematical concept of information is not the type of information that is helpful to achieve organization complexity. A more complex understanding of information is needed to deal with complexity, alike a more complex language is needed to describe and understand complex situations. In this section, we will elaborate on the cybernetic concept of information, which includes a hierarchy in different types of information, goal information, axiological information, conceptual information, etc. Following the concept of control as information processing for survival in cybernetics, multiple types of information are implied by cybernetics to explain the processes of survival and adaptation. The types of information implied by cybernetics are beyond Shannon’s mathematical definition of information. Cybernetics does not deny the validity of Shannon’s concept of information but explains that mathematical information is to be seen in the context of a larger system of information and information processes. In the next sections, the various types of information as implied by cybernetics are introduced and explained.30 The types of cybernetic information are summarized in Fig. 6.2.

6.3.1

Goal-Information

In business goal-information is usually expressed in the mission of the firm. The mission usually is a short answer to the question, “to what this organization is on 30

Beniger (1986), Garfinkel (2008), van Peursen et al. (1968).

6.3 The Cybernetic Concept of Information

107

–

Fig. 6.2 The hierarchy of types of information as defined in cybernetics as this replaces the Weberian organizational hierarchy (Strikwerda, 2021). The original pyramid is by Kaplan and Norton, 2004). This cybernetic concept of information relates topics usually dealt with separately in conventional management books in a coherent way

earth?” “What role we want to play in society?” etc.31 A mission provides a sense of purpose and direction. A well-phrased mission enables members of the organization to identify with. But more important a mission is pivotal in the governance of an organization. In situations of ambiguity, in new situations for which existing operation rules do not provide answers, a mission assists in mastering as complex experienced situations to develop an understanding this new situation by taking recourse to the mission, what ultimate it is we want to achieve. A mission is not merely inspirational or motivational, but it is the first instrument of administration, a mission needs to be codified in all systems, processes of the organization. Especially in a society becoming more complex, creating more options, more choices to make, a mission becomes more important in the process of abstract thinking as the first tool to deal with complexity. Goal-information, together with axiological information (the next section) express the goals and values of the “lifeworld,” an expression which with the German philosopher Jürgen Habermas denotes the civil society, a domain separate from that of the market and the state. In this civil society communicative live is about in humanitarian values and goals. To achieve these, certainly in an efficient way to produce the high level of welfare we are used to, systems are needed, formal organizations of companies, the state, infrastructures. These systems, the

31

Abrahams (1999).

108

6

Information and Complexity

organizations this book is about, tend to place economic rationality above the goals and values of civil society, and tend to become ends in themselves.32 Alike in business, the system of the formal organization should serve the mission and values of the corporation. So, according to Habermas these systems, their processes, procedures, decision rules, need to be anchored in the life world. This anchoring in practical sense is achieved through codification of the mission, goals information, and values, axiological information, in all the aspects of the “system.” Intuitively managers and consultants in the early nineties of the last century felt the need that in a society growing more complex, organization, businesses needed a mission to define their course in this growing complexity. The idea of a mission is older than that, it can be found in corporate law in a number of jurisdictions. There is even a formal quality to the mission of a corporation. In the Dutch jurisdiction, the corporation has the power, in case the executive board acts outside the scope of the mission of the corporation, to request the court to annul contracts closed to buy the executive board and hold its members liable for consequential damages for the corporation. The intuition of the increasing importance of mission statements as felt in the 1990s was in itself the right thing, but lacking the understanding of its real nature and function, mission statements were defined as an act of communication and motivation instead of a type of information in the governance of an organization, resulting in many errors and disappointments with mission statements, as cynically expressed in an article “Sex, Lies and Mission Statements.”33 A mission, a purpose as goal-information is needed in order that the members of an intelligent adaptive system to know what to adapt to. Goal information usually is stated as the mission in the statutes of the corporation, foundation or society as a legal body. In addition to that the mission will be communicated to the members of the organization in order to be informed that members of the organization can identify with the mission. To facilitate the observance of the mission in the case of self-organization, self-management, proactive behavior and bottom-up initiatives, that is a decentralized organization, organizations codify the mission in all systems, processes, procedures, and decision rules in the organization.

6.3.2

Motivation or Axiological Information

This type of information expresses what we value, what is of value to us. This type of information will be expressed in the hierarchy of values of the firm. Not every alternative action or initiatives to achieve a set goal are equal from a perspective of ethics, integrity, identity and compliance and thus the cohesion of a social system. Alternatives cannot only be calculated as in mathematical decision-making; also, judgment is needed in terms of non-economic aspects. As with mission statements it

32 33

Habermas (1981, p. 229). Bart (1997).

6.3 The Cybernetic Concept of Information

109

was felt that values matter in an organization. To an extend this was an intuitive response to the diminishing effectiveness of traditional instruments for managerial control, due to further decentralization, a shifting nature of assets, changing values with new generations of workers. Whereas Schein emphasized in his definition of organization culture the role of often unconscious or subconscious past experiences to teach new members on what works and what does not, Deal & Kennedy, sensing the changes in business models and thus the growing irrelevance of past experience, narrowed the definition of organization culture to values. This raises the question of what actually values are. It is not uncommon to learn that in an organization values are, e.g., “teamwork,” “transparency,” “integrity,” etc. Such expressions are more aimed on individual behavior, less at what the values of the corporations are in the context of society. A value tells us what a person or a group wants to be true or not to be true, irrespective of the actual situation. Values are different from beliefs; these are about assumptions about reality. Values have a strong judgmental element, guiding the conduct of the value holder and serves to judge the conducts of others.34 Values defined in this way are about issues like integrity, how we think of co-workers, being hierarchical or not, positively or negatively, being explorative or not, willing to take risks, etc. We need to discern between instrumental values, teamwork will improve innovation, and final values, what is it we value in life and society. Instrumental values basically are simplified heuristics and as such can be useful but may have risks as well. Especially the risk of the Fundamental Attribution Error that such instrumental values intend to change the behavior of individuals, while neglecting the required system changes for teamwork, cooperation, pro-active behavior and such. As explained before writing on value programs, these have the risk to backfire.35 Final values are about questions of whether we value profit over quality of goods and service or vice versa. Final values are about questions like whether we value sustainability in an economic way to include it in the business case calculation, or whether we define sustainability as an ethical value, setting limits to our actions, processes, choice of raw materials, without economic valuations and calculations. Individuals, groups and cultures organize their values in a hierarchy of importance.36 These hierarchies in our society are different by individuals, groups, political parties, ideological movements, etc. Some types of investors emphasize profit maximization in the short run over innovation, others emphasize continuity and thus innovation over the longer term, over profit maximization on the short term. To guide decentralized initiatives, pro-active behavior, design solutions a hierarchy of values is needed in a complex world with increasing options how to achieve objectives. Alike the mission statement the hierarchy of values need to be codified in all systems and processes of the organization, as not all members of the organization will internalize these values. Workers looking for employment opportunities will not only judge opportunities by monetary rewards but dependent on a number of factors

34

Rollinson and Broadfield (2002, p. 135). Cha and Edmondson (2006). 36 Cha and Edmondson (2006). 35

110

6

Information and Complexity

they will try to find a level of matching between their personal values and those of the employer, a level of Wertebalance.37 This implies that an employer needs to publish not only factual information about open positions (type of job, type of contract, remuneration), but as much other levels of information; its mission and its values. The congruence between the hierarchy of individual workers and the hierarchy of values of the organization will not be perfect; a minimum critical overlap is needed. Axiological information usually is formulated in the bylaws of the corporation, foundation or society as a legal body or in a separate document as, e.g., Our Values at Work: On Being an IBMer.38 In addition to that the values will be communicated to the members of the organization in order to be informed that members of the organization can identify with these values. To facilitate the observance of the values in the case of self-organization, self-management, proactive behavior and bottom-up initiatives, that is a decentralized organization, organizations codify the values in all systems, processes, procedures, and decision rules in the organization.

6.3.3

Material Information

Essential in cybernetic control is the exchange of information between a living system and its environment. It is not only that external information is to be acquired, but also to ensure required resources information needs to be communicated to the environment for both offensive and defensive reasons. The resource dependency view on the firm, which describes that for survival firms depend on resources from their environment, implies that firms need to enter an exchange relation with their environment to acquire resources, that is knowledge, information, materials, energy, etc.39 Material information is data about what the world consists of in fact.40 Especially of interest in relation to control are changes in these facts, to which the living system needs to adapt in order to survive. To acquire material information is an issue of sensing by members of the organization, much material information is sensorial founded, either by human sensors or by technology, but can be as well through— implicit—messages from outside. Investors can signal through either investing in a firm or divesting that they see a needed adaptation of the investment strategy of the firm in the context of changed in the economy. Point-of-sale data is sensory information on actual and changing patterns in consumer preferences. Sensing, acquiring material information can be a complicated process. For obvious reasons selections need to be made from the myriad of available facts on the world, what are relevant facts and what facts can be ignored?

37

Kinne (2009). Kanter (2009), http://www.ibmemployee.com/PDFs/IBM-Our_Values_at_Work.pdf 39 Pfeffer and Salancik (1978). 40 Garfinkel (2008, p. 170). 38

6.3 The Cybernetic Concept of Information

111

The term material information suggests that facts simply are—material—facts. This idea has its basis in biology. In the realm of sociology, societies, including the economy facts are not always bare facts, but may be subject to definitions implied by culture, law, ideology, and facts and may be dependent on specific theories. The facts produced by national statistic bureaus are based on specific concepts in economic theory, sociology, political sciences, etc. To measure the welfare of a nation by its gross national product per capita in some countries is contested. In complex societies, there will be more types and definitions of facts. In addition to facts, strategic thinking and decision-making by executives also may be based on intelligence, rumors, art expressions in whatever form, even changes in music preferences by the public. Material information may vary from simple to complex. Complex material information has more relevant parameters and indices related to a higher causal density in de the context of the organization. The natural tendency is to select a number of those parameters tending to simplify the decision-making process. Critical to responding to changes in the environment is not so much the completeness of the observation or collection of material information, as is the process by which the material information is transformed into eidetic information, that is the interpretation of new material information. At first sight, this will depend on the level of conceptual complexity of the individual decision-maker or a team. Conceptual complexity tends to be static, whereas a dynamic environment requires a dynamism in this conceptual complexity. Integrative complexity describes the degree and scope in cognitive complexity and with that the degree in which a decision-maker or decision-making body is capable to deal effectively with high complex material information.41 Integrative complexity as a characteristic in the personality of a decision-maker describes the scope, the tendency to explore and the willingness for interpreting material information other than through conventional lenses. The elements of integrative complexity are the description of a situation in terms of temporal and local, and is expressed in terms of dimensionality of information (aspects), the amount and rate of information and the density of relations between those dimensions, the nature of information, positive, suggesting new opportunities, confirmation (eucity), or containing negative values, in terms of urgency, threats, danger and time pressure (noxity).42 The way material information is perceived in terms of simple versus complex depends on the degree of conceptual complexity of the executive respectively the members of the organization. It may be that an executive respectively the members of the organization actively search for relevant material information or that material information is imposed on an organization, e.g., by active shareholders or analysis. Search behavior also depends on the level of conceptual complexity of those involved, but also whether a management team has blind trust (no search) or explorative trust (active search for new information and insights).43 Integrative

41

Suedfeld (2010). Driver and Streufert (1969). 43 Driver and Streufert (1969), Jensen (1993). 42

112

6

Information and Complexity

complexity is a measure of the cognitive structure underlying information processing and decision-making in a specific situation and time of interest to the researcher or policymaker. As such, it is a state counterpart of conceptual complexity, the trait (trans situationally and trans temporally stable) component of cognitive structure.44 The selection of facts may be subject to psychological mechanisms like confirmation bias, availability bias, the pain-avoidance syndrome, and a dominant logic, or more in general of the level of conceptual complexity. A strong culture or groupthink in an organization may obstruct the acquisition of material information to the effect that an organization therefore is out-of-control. Acquiring material information is to be organized at all levels of an organization, be it that different levels will acquire different types of material information and it will be differentiated by function. Another aspect of material information in relation to complexity, especially emergence of new facts and insights, is the variety of sources and the nature of sources. Christensen observes that firms may tend to concentrate themselves on existing customers for new customer demands.45 Existing customers may be rooted in their existing business and may not be, dependent on their own market, the source of most innovative requirements. Non-customers may have different and more innovative questions and may be non-customers because they have deliberately not selected your firm as an innovative supplier. The same holds for hiring consultants and suppliers of market analysis and industry analysis (or any supplier), also for such sources variety and exploration is needed to be in-control. Routines in acquiring material information can put the firm at risk. Unless the routine is, as with Philips Electronics, always to invite three suppliers for a quotation, two familiar suppliers and one Philips had not worked with before. The nature of sources for material information is another issue. Academics as a result of the scientific methods for research prefer to analyze laboratory experiments and to do research by surveys. Laboratory experiments, e.g. using adolescent students for research psychological aspects of decision-making or problem solutions, are too sterile compared to fiftyplus CEOs with their experience and a much more complex environment in which to operate. Surveys always are based on existing categories of e.g. organization forms and other management practices and on open questions respondents may not know how to describe a new situation that not fits existing categories. In my research in which I rediscovered the multidimensional organization, the interviewees describing what turned out to be a multidimensional organization knew it was different from a matrix organization, but acknowledged not to have a label for it.46 Not all research methods have the capability to detect and report emerging newness and complexities in business life and therefore do not account for the complexity of real life.47

44

Suedfeld (2010). Christensen (1997). 46 Strikwerda (2008), the concept of the multidimensional organization originates from Prahalad and Doz (1979). 47 Pentland, p. 11. 45

6.3 The Cybernetic Concept of Information

113

At the first level of cybernetic control relevant material information will be defined by the existing business model, and such information will be at the operational level of supply chain management, merchandise management, purchasing, etc. At the second level of cybernetic control it needs to be detected which changes in consumer preferences develop existing products cannot answer. Market research, recording consumer requests the existing system cannot answer. Another technique is demand management; this is the analysis of fluctuations in market demand in terms of cyclical, structural and random developments. At the third level of cybernetic control material information is needed about industry-level changes, fundamental changes in technology, society, which may require a new business model or business transformation. In terms of complexity thinking acquiring material information is about sensing emergent new situations that are not defined by or do not seem to be relevant through the lens of an existing business model. Alike in a going business a balance is needed between exploitation and exploration, in the acquisition of material information a balance is needed between obvious relevant facts, and facts that need to be explored for their possible relevance for the continuity of the firm. That is, sensing is about possible new options for the firm, beyond confirmation of the existing business.

6.3.4

Eidetic Information

Eidetic information is about what the world, all the acquired facts, rumors, intelligence, etc. means for a person responsible for or making a contribution to the continuity of an organization.48 Eidetic information is material information interpreted by an individual or a social group in relation to the mission, values, and interests of the firm to survive successfully. Is a change in strategy needed, which products should be phased out, which new market should be entered, is a new business model needed, what should be the R&D program, all questions to be answered to maintain the mission, to achieve set goals and continuity in a changing world. Eidetic information will be input for the strategy of the firm. Generating good quality eidetic information, that is interpreting changes and new opportunities in the environment is most critical for the success of the firm, it defines the quality of the strategy and the effectiveness of adaptive processes. Firms often tend to overinvest in data acquisition and tend to underinvest in people with the capability to interpret data in a successful way. The capability of people, be they leaders or workers, to deal with ambiguity and incomplete data and to define a course of action is more important as is complete or accurate data.49 Generating eidetic information is not only analytically, as much it may require reframing situations, considering additional perspectives of new players, and reconceptualization of industries, markets, that is seeing new rules of the game, new players, new relationships, etc. Eidetic

48 49

Garfinkel (2008, p. 170). Sutcliffe and Weber (2003).

114

6

Information and Complexity

information is beyond feedback information and is beyond learning from experiences. Learning from experiences only results in overspecialization, suboptimal adaptation and consequently sub-marginal performance. Needed are also new concepts, new definitions of situations. Karl Weick makes a distinction between interpretation and sense-making of (new) situations.50 An interpretation of a situation is creating an understanding of that situation in terms of familiar concepts, theories, and or norms, as, e.g., in law. A simple level of interpretation of material information is feeding data on market developments into an algorithm or spreadsheet model, to forecast market demand, for portfolio analysis. Sense-making is creating new concepts, stories, images, frames to give sense to a new situation or development and therefore sense-making may pertain to data but is informed by values. Other authors use the phrase “reconceptualizing,” creating new concepts in most cases builds on older concepts, but often from another field. There is reflexivity between material information and eidetic information, because of the reflexive relation between models and reality. Successful models change reality and thus invalidate the models based on reality. Not only material information is to be acquired as defined by models-on-use, but in order to be in control material information, but also insights, understanding, new models, new interpretations, etc. need to be acquired or developed to monitor the validity of models-in-use and where necessary to develop new models about industries, markets, customers, competition, operating models, business models, etc. In that sense, at least at strategic level, a consciousness is needed in the organization about the reflexive relation between material information and eidetic information. Alike with the acquisition of material information, the generation of eidetic information is subject to a number of psychological and cognitive mechanisms. The most important psychological mechanism is probably belief conservation, our mind tends to maintain the worldview we have in use and tends to interpret or deny new information that is conflicting with that worldview, in such a way that the existing worldview is maintained.51 This may result in not seeing relevant changes, seeing these too late or misinterpreting these. Since March phrased the concept of belief conservation in the early nineties the overall situation in business and financial markets, especially in systems of corporate governance, has changed. So, the question might be asked whether belief conservation still can exist in a time of the Internet, extensive academic publications, active shareholders and social media. To this Pentland observes that especially in the case of Fromm’s marketing personality there is a tendency that individuals do not use the Internet for exploration, but for confirmation and for acceptance by others and are willing to accept any opinion or insight needed for that.52 The generation of eidetic information or sense-making can be an individual process, more often it is a social process and always there is a social context playing a role in sense-making. Dependent on factors like personality, stage

50

Weick (1995, p. 6). March (1994, p. 183). 52 Pentland (2014). 51

6.3 The Cybernetic Concept of Information

115

of moral development, cognitive structure, interests and a position on the axis of rationality versus rule-following, sense-making is more defined by personal information, concepts, and insights versus social information and social concepts and insights. Individual-based diversity will increase the quality of sense-making in view of complexity. When individuals as members of a social system tasked with sensemaking of new material information feel uncertain, are more concerned by being accepted as a person instead of new ideas being accepted, social information and social concepts may become dominant over a variety of individual interpretations, impoverishing the quality of eidetic information.53 The extreme case of this being group think, which causes a social system to be out of control. The generation of eidetic information is not only an individual process or an in-group process a factor of importance also exist of management books, popular articles, concepts produced by consultants, the financial press, etc. Concepts playing a role in generating eidetic information may circulate in networks, social networks, social media to the effect often without history, source or context, but presented in slightly different ways as a result of which it may not be recognized that ideas presented as new and adding to variety, in reality are the same ideas and do not contribute to the variety and originality as needed for dealing with complexity.54 Individuals differ in the way they acquire information by source and channels, in the range of dimensionality of information and how they process information in terms of interpretation and solving problems. This is measured by two metrics, conceptual complexity and integrative complexity.55 Conceptual complexity measures the capability of an individual or group to perceive a larger number of different dimensions in a situation, a problem, a decision to be made, as well a higher number of perspectives (interests) with respect to a situation, an issue or a problem (differentiation). Conceptual complexity also measures the capability to perceive the relations between those dimensions and perspectives (integration). A second aspect measured by the concept of conceptual complexity is how people think. A way to measure this on a scale varying from using only a single rule for decision-making, limited acquisition of information and rejecting other perspectives as implied by the decision rule, via making trade-offs between different perspectives (compromising) to the integration of multiple rules, information dimensions, perspectives under a super-ordinate schema or purpose.56 This is not to say that individuals who score high on conceptual complexity (complex individuals) always address and solve problems in a complex way. To understand this, we might use the concept of the cognitive manager model. This model states that high-level thinking draws upon more resources, cognitive and other, than lower complexity levels of processing problems and information.57 Complex information processing takes more time and

53

Pentland (2014, p. 35). Pentland (2014, p. 37). 55 Suedfeld (2010), Suedfeld et al. (1992). 56 Suedfeld (2010). 57 Suedfeld (2010). 54

116

6

Information and Complexity

effort, it may be that more people need to be involved, more effort, time and money is needed to acquire the more complex information, more intellectual effort is needed to understand problems, redefine these, generate alternatives, evaluate these, etc. Complex information processing requires more physical and emotional energy. As managers need to economize on decision-making as any other activity in business, the cognitive manager will make a trade-off between the gains of complex problemsolving versus its costs. If he perceives the investment not to be worthwhile he will choose for a simple process and concept to solve a process. To this Simon’s satisfying behavior applies, as well March’s dichotomy of rational decision-making (high complexity) versus rule following decision-making (low complexity). Conceptual complexity is considered as an attribute of the individual. As in the interactive perspective model from organizational behavior, in deciding how to process material information, high complex or low complex, is defined by the interaction of the personal attribute (conceptual complexity) and context, which in this case is labeled integrative complexity. Integrative complexity as a characteristic of material information describes the context for interpreting material information, a situation which is temporal and is local and is expressed in terms of dimensionality of information (aspects) and the density of relations between those dimensions, as well characterized integrative complexity a situation in terms of urgency, danger and time pressure.58 Individuals with a high conceptual complexity in general will be better in sensing and sense-making in situations of integrative complexity compared to individuals with a low conceptual complexity. Individuals with a high conceptual complexity will in general produce better eidetic information. The actual level of complex information processing will vary with the level of integrative complexity, but this may be impaired by emotional and physical exhaustion, as complex information processing consumes (emotional) energy. In political studies the concept of integrative complexity is used to study the behavior of political leaders under different situations of integrative complexity like war and international conflicts. It is not only the CEO or his strategy department that is generating eidetic information as input for the strategy of the firm, today there are many eyeballs seeing to this, both internal the organization and external, analysts, academics, active investors, investment banks, journalists, etc. Belief conservation as a psychological mechanism is invariant over time, but it is working will be affected by contextual, social factors, apart from the role of personality and the level and type of education. This explains the importance of a system of corporate governance in judging the strategy of a corporation. Generating eidetic information is as much an individual intellectual activity (leadership) as it is a social process. Generating eidetic information will be guided in the first place by goal information (the mission, goals), by axiological information and the existing worldview 58

It is tempting to draw up a classification of types of complex environments as, e.g., the Cynefin Framework in Snowden and Boone (2007), including a recipe for how to deal with each type of environmental complexity, but the way they do this is denying the nature of complexity. Snowden and Boone for once do not explain the relation between conceptual complexity and integrative complexity, which makes their classification simplistic.

6.3 The Cybernetic Concept of Information

117

(business model) in its reflexivity with reality, but as well by goals and values of society. CEOs are expected to weigh in the interests of stakeholders; one of these being society itself, upon whose institutions the firm operates. American CEOs, are, be it somewhat implicit phrased, expected to contribute to the growth of the US economy.59 Society imposes values as well on an organization. To be compliant with the law is obvious, sustainability is another, requiring more specific policies. Less obvious is the value that corporations by the way they do business should not erode the institutions of society, freedom of speech, democracy, the rule of law, property law, contract law, etc.60 An increase in complexity of an economy or in an industry, in which the number of choices to be made increase, and especially new choices emerge, explains why the role of mission and values have increased in the last 20 years. One tool to deal with complexity is abstraction. This abstraction has two components, first is to abstract to the mission and the values, what is it all about, and second is abstraction in the sense of creating new models, new concepts to make the work intelligible again. Although it must be noted that effective CEOs have the capability to hold two conflicting models, or interpretations in their mind and are still capable to act effectively in terms of making clear strategic choices. Within an organization a common phenomenon is that different people, different departments may interpret (the same) material information in different, often conflicting ways. This may be caused by difference in personal attributes, experience, education, personal interests, as well by position and or function. Even more, following Habermas’s Erkenntnis und Interesse (1973), people may interpret material information through the lens of their personal interests; often this lens already is active in the selection of material information. For reasons of motivation personal interests are not to be denied, it is the task of the CEO to balance or reconcile personal interests and the interests of the corporation. Eidetic information, interpreting the changes in the world in terms of what to do new or different and which activities to abort to achieve the mission of the firm, is the input for the strategy and results in strategic choices and targets, which can be seen as a more temporal operationalization of the goal information.

6.3.5

Accountability Information

Accountability information as required in a democratic society in which executives work with assets that are not their personal property, usually through the annual report, but this in general will also include tax reporting, compliance reporting and especially integrated reporting.61

59

New York Stock Exchange Commission on Corporate Governance (2010). Cadbury (1995). 61 Eccles et al. (2010). 60

118

6

Information and Complexity

Accountability is not a concept from accounting theory and is not restricted to the annual report, accountability is a concept from the field of political philosophy, especially of the open democratic society, “accountability is an intrinsic feature of reciprocity in human relations and a core feature in democratic systems.”62 In a democratic system, we not only expect from executives that they create shareholder value but even more due stewardship and social responsibility, not only profits, but also [care for] people and planet. This implies that executives need to communicate besides accounting information also their mission, their values, but also their eidetic information, how they interpret changes in their environment, what their vision is. The (re)regulation implies that in addition to management information and accounting information a third outgoing type of information needs to be discerned, compliance information. Accountability information used to be the basis of assurance and a basis for investors to make decision to invest in a corporation or not. Due to the emergence of intangible assets and subsequently basing the valuation of a corporation on future cash flows, the annual report, which report accounting value only, has lost its dominant role in investment decisions and in assurance. Fundamental investors now look into the firm, the quality of the strategy, the quality of management, the quality of plans, the quality of the system of strategy execution to assure themselves whether investments now will produce a profit in let us say 5 years.63 As the precise workings of intangible assets to create value are not known, there is a lack of microeconomic theory for this, this problem of lack of causal knowledge has been circumscribed by focusing of future cash flows and on critical inputs, investments in human capital, investments in information capital and investments in organization capital, as expressed in the concept of the balanced scorecard and the strategy map.

6.3.6

Allelopathic Information

Allelopathy in biology is a process in which living organs, plants, algas, bacteria, fungus, spread specific chemical substances that effect, negatively or positively the growth of organisms in their environment, or deter competitors for those resources, to ensure that these allelopathic organs have the resources as needed for their survival.64 In business various forms of signaling exist to influence the resource environment, including lobbying with law makers and regulators, public relations, building a relation with the press, etc., being active on social media, etc. In order to survive a firm needs also sufficient market power. One of the elements of market power is the reputation or the image of a firm with the public, politicians, media, investors, etc. Hence firms have public relations officers and departments in their corporate offices generating images, impressions, stories to influence policy makers,

62

Pellizzoni (2008). Strikwerda (2013), Boot and Thakor (2011). 64 Cheema et al. (2013). 63

6.3 The Cybernetic Concept of Information

119

politicians, the media, in order to influence the social and political environment of the firm, with respect to regulation, laws, contracts, to serve the interests of the firm. Firms attempt to frame people in their environment through Lash’s disinformation to influence their eidetic information. That is the effect-information in the cybernetic model of information is not restricted to the legal, technical or legal boundaries of the firms, dependent on the nature of the firm it may be necessary to see it as a subset of a larger system of (non-linear) causal relations.

6.3.7

Interface Information

A sub-category of material information is interface information. This is information is created by transactions with customers and searches by customers and potential customers on the websites of firms, government agencies, NGOs, etc. This interface information is being used for analyzing trends in consumer preferences and demand, opinions, for political purposes and is used as input data in algorithms to generate targeted advertising. Interface information is being used as an input in the production function of the firm.65 Possibly interface information is the most valuable information for profit in the longer term. Google even owns a number of US-patents with respect to interface information. Another champion of use of interface information is Netflix, which uses the information generated by its customers on which movies and series are more and which are less popular to enhance its negation power with content providers. Also, Walmart uses its point-of-sales data, also a type of interface information, to optimize its product offering to its customers, to optimize the supply chain and to enhance its negotiating power with suppliers.66

6.3.8

Causal Information and Conceptual Information

Human action results from human beings using their mind or brain, consciously or unconsciously. According to psychologist Chris Argyris: “No meaningful action is possible without an internally consistent design, script or scenario that specifies—for a given set of conditions and a given set of governing values—intended outcomes, as well actual behaviors required to produce those outcomes.”67 Whether behaviorist economists assume specific behavior of a consumer or a manager under specific circumstances, or whether an individual decides for an initiative, an action, this is based on an implicit or explicit causal claim. Such a causal claim may be based on tradition, example, experience, rational scientific models (deduction), simplified management models, heuristics, and induction from facts or, especially in the case

65

Cortada (2011, p. 120). Brynjolfsson and McAfee (2014). 67 Argyris (2000, p. 52). 66

120

6

Information and Complexity

of entrepreneurial behavior, abduction. Alike Von Neumann observed that computer programs are information alike the data processed by these programs, causal and conceptual models, including the scripts in our minds, as we need these for our thinking, observation, and guiding our actions, are a form of information as well.68 Human action, individual, in groups, always in some way is based on a script and thus without some idea on causality, but the very nature of complexity is that causality itself may be a complex phenomenon, in terms of non-linear, emerging, multidimensional, based on symbols and meaning (downward causality, social causality, structural causality), undetermined and with unintended consequences. We discussed earlier that reductionist, simplified causal models are needed in life but in social life there is no stable deep simplicity as in physics.69 Causal- or effect information specifies causal relations usually in the form of formal models, less formal management models or even frameworks like those of Porter and are needed to create an actionable worldview, define relevant facts, define initiatives and actions, analyze deviances in performance against targets and thus are needed to define decision rules to interpret data (pragmatic information) into information in the sense of a defined state or action. Causal information in a way can be compared to a computer program, feed data into it and it produces a result. The generation of Big Data has stimulated the growth of writing algorithms for decision-making, prediction of consumer behavior, to predict crime as with the New York Police Department, to decide on preventive maintenance, to support decisions in the financial markets, even to be used in US courtrooms. Most of these algorithms are learning, machine learning. Therefore, it seems that algorithms are the way forward to deal with detail complexity but the question is whether algorithms are able to deal with changing causalities and emerging new classifications. Machine learning algorithms consist of three components.70 The first is a set of classifications. A classification is a mathematical function that maps a vector of input variables (data) on a single value, the class. This set is the hypothesis space. Classifications not defined in this hypothesis state are not part of machine learning. The second component is an evaluation function or objective function. The evaluation functions values classifiers in terms of good and bad. The third component is a method for optimization the search for the highest-scoring classifier. Especially the classifier is a causal assumption or claim, be it that these classifiers have a basic structure of IF THEN ELSE , in which stands for a Boolean value (TRUE or FALSE). The classifiers themselves are not generated by data, for this the human programmer needs a priori knowledge about the world or assumptions on causality or correlation in the real world.71

68

Casson (1997, p. 172). Gribbin (2005). 70 Domingos (2012). 71 Domingos (2012). 69

6.3 The Cybernetic Concept of Information

121

Perhaps even more interesting is that learning algorithms suffer the curse of dimensionality. Algorithms may work well with a limited number of parameters, e.g., to predict consumer behavior, but the resolution of different outcomes declines exponentially with the number of parameters, irrespective of the training set inputted in the algorithm.72 This is not unlike human processing of data and formulating models and suggests that algorithms have limits to cope with complexity. These limits are also suggested by the limits to machine learning. Machine learning goes well in the case of a number of independent input parameters correlate well with outcomes (class), comparable to Newtonian causality, but if the class is a complex function of the input parameters machine learning may not be reliable.73 In that way learning machines are alike human beings, being it with a different order of magnitude in number of parameters. The simplest form of causal information is Newton’s formula F = m × a.74 But typical for complex systems is, as dealt with in section Sect. 4.4.1 that causal relations are non-linear, have no deep simplicity, are changing, are not only samelevel but as well include downward causality and upward causality. So, the question becomes what techniques are available to simplify the complexity of causality as needed for making decisions and defining actions, while avoiding the error of oversimplification, an erroneous simplification or using a simplification tool long or in conditions for which it was not designed. A tactic is to discern three levels of causal information, conceptual information, effect-information, and decision rules. Conceptual models define the scope of a phenomenon, its categories of causes and outcomes. Examples of this category are the frameworks of Michael Porter, his five forces framework and his diamond framework.75 These frameworks do not define deterministic causal relations, but tell us to understand the profit potential of a business to look into negotiating power, threat of substitutes, entry and exit barriers, new entrants and the nature of rivalry in an industry. With that, such a framework tells us what material data to look for.76 This framework does not tell us precisely how to process the defined data, for this additional conceptual information is needed from fields like industrial organization and business analysis. Michael Porter’s framework do not even assume a priori defined causal relations based on either induction or deduction, but in an entrepreneurial spirit deliberately allow the discovery or assumption of new possible causal relations (abductive) thinking, by defining the function of these frameworks, not only to define categories but to be used for testing the plausibility of innovative entrepreneurial ideas. The latter acknowledges the emergence of new causal relations in complex systems be it within defined categories. Entrepreneurship is seeing the limits of such a framework and defining

72

Domingos (2012). Domingos (2012). 74 Note that Newton’s formula only is valid in case m is a point mass or the direction of F precisely passes through the center of gravity in case m is not a point mass. 75 Porter (1985, 1990). 76 Marion (2008). 73

122

6

Information and Complexity

new ones, this is the art of reconceptualization. The dilemma is, as explained in the case of the phenomenon of dominant logic, that causal models both make us see relevant material information and making us blind to other material information that might be relevant for survival. The three levels of control as defined in cybernetics imply that causal models need to be questioned for their validity continuously; the simplicity of causal models always needs to be distrusted. This is precisely what successful CEOs do: always searching for better models and valuing peripheral vision, paying attention to weak signals. Whereas causal models are operational, conceptual models are about questioning validity of assumed causal relations.77 Conceptual information therefore is information about the nature of reality, about the assumptions underlying operational models, e.g., business models. Conceptual information serves as the meta-control in business. That is, conceptual information teaches the observer to look for different types of material information, different from what operational causal models do. Because the nature of discursive information, of which causal models are part of, is to exclude certain categories of phenomena in order to simplify. With respect to downward causality conceptual information is usually taken from the field of organizational behavior, with concepts like identification, values congruence, inspiration, rewards, the interactive perspective model, resulting in systemic change. There is a tendency to address behavior of workers as resulting from downward causality through insights and findings from neurology, but what is needed more is complexity leadership, that is creating conditions in which localized instances of adaptive behavior of the organization can emerge in response to changes, as needed to accomplish the mission of the firm. Below the level of conceptual information is the level of effect-information, more operational causal models, e.g. to increase profits, increase market share. Causality can be of engineering nature as discussed in the concept of the bill of materials. For various functions in the firm, an array of causal models exists, but for the firm as a whole, no micro-economic causal model exists. This explains the existence of heuristics and other simplifications. Causal information is programmed in a large variety of ways, scripts in the mind of people, tacit knowledge, culture, process descriptions, engineering specifications, software, etc. Causal information not only applies to mechanical, chemical or other engineering types of effects, but as much to social systems, behavior, etc. To increase decentralization, proactive behavior, bottom-up initiatives, in line with Hayek’s theory, firms are now in a process to make their business models as statements of cause-and-effect models explicit, that is, moving it out of the minds of middle managers.78 Due to the dominance of Newtonian causality, we tend predominantly to think in terms of same-level causality. An extensive theory has been developed on biological information.79 In academic studies on behavior and on decision-making in business

77

Casson (1997, p. 173). Osterwalder (2004). 79 Floridi (2010), Walker et al. (2017). 78

6.3 The Cybernetic Concept of Information

123

there is a growing interest in neurological processes in human thinking. We need to be aware of such processes, e.g., the fight-or-flight syndrome and the pain-avoidance syndrome, but in a moral society, we cannot excuse ourselves for such processes. Nevertheless, the theory of biological information has introduced the concepts of upward causality, how DNA and RNA, etc. do influence our behavior, but not in a deterministic, Newtonian way, and can be a source of adaptation and innovation, as well as downward causality, which we see reflected in the role of mission and the hierarchy of values, which is neither a causality in a deterministic way. Simon’s concept of loose programming as a characteristic of complex organization, so we might say, is confirmed by the concepts of downward causality and upward causality from biological information theory, but we see it as well in bottom-up problemsolving and top-down guidance in business. It must be noted that it is impossible to formulate complete and accurate causal models, especially in the case of complex systems. James March wrote on causal complexity: “The systems being modeled and analyzed are substantially more complex than can be comprehended either by the analytical tools or the understanding of analysts. As a result, important variables and interactions among them are invariably overlooked or incorrectly specified.”80 The level of causal information in a complex world is not so much about a priori knowing about causal relations, but about discovering (new, emerging) causal relations. This is reflected in the approach of trial-and-error and in discovery-driven planning.81 Trial-and-error assumed a clear strategic direction and fast feedback information. Discovery-driven planning differs from traditional mile-stone planning in projects that at each phase the question is asked, what new options now are available not known a phase earlier, what did we learn, and what about our assumptions-to-knowledge ratio? The latter illustrates that causal information is not just a given, it is as much explorative, imaginative and something to be invented. The operational management techniques of TQM and Lean Six Sigma have opened up many processes as black boxes in the firm by specifying processes and defining non-financial process parameters. The combination of the shift toward intangible assets, which need to be organized complementary and the declining costs of information, and thus the declining costs of coordination, has triggered a move away from emphasis on structure toward the emphasis on (end-to-end) processes to run a company. Processes are with that the container of effect-information, causal relations, what to do in terms of initiatives in order to deliver a promised customer solution. Related to that is a shift in business from budget-driven resource allocation to cause-and-effect based resource allocation, which is more precise and transparent. In the modern generation ERP systems not only accounting data and other basic facts are recorded, but as well the processes are specified to facilitate selfcoordination and self-organization, as well the reengineering and adaptation of processes to changes in the environment. The older generation ERP systems tended

80 81

March (2006). Manzi (2012), McGrath and Macmillan (2013).

124

6

Information and Complexity

to be based on processes as defined, implicit programmed in the software, structuring the data, which in the late 1990s, when business models needed to be changed, caused problems in business. However, to run a business through processes, which from a viewpoint of the nature of intangible assets is a most logical thing to do, with processes that are adaptable to changes of whatever nature and therefore should rely on data that is recorded by structure and retrievability independent of the processes and independent of the configuration of resources, creates a more complex organization from a viewpoint of the executives and some staff departments. That is to say, more complex viewed through the lens of the old unit organization. To achieve higher levels of efficiency, and to free TQM and LSS from the limitations of departmentorganized processes, this move to process management is needed.82 This level of processes is also the level of knowledge discovery in databases (KDD). That is knowledge in the sense of effect-information and which actually is more about correlation as it is about causation.83 Within the context of effect-information, a world of (partial) decision rules exists of varying degree in terms of heuristics to conditional statements and calculation models. A number these (structured decisions) can be codified into computer programs or algorithms. The applicability of a decision rules and is validity will be defined by its context of processes and conceptual models. No decision rule has eternal or universal validity, the art of decision-making is to see when a proved rule does not apply. Conceptual information and causal information basically are about knowledge. This raises four questions. The first question is about complexity of knowledge, is knowledge simple or is knowledge complex and what is the relation between the complexity of a system (organization) and the nature of knowledge on a scale of simple versus complex? The second question is what is the source of knowledge? The third question is how is knowledge absorbed by social complex systems? The fourth question is how is knowledge organized, where does knowledge reside in a social complex system? Knowledge may be simple, in terms of the expression of relations between variables as in Newton’s law of gravitation, or complex in the sense of Kolmogorov-complexity, that relations cannot be expressed in simple formulas or short algorithms. Business models can be simple or complex as in the case of Ryanair.84 In the case of a high construction complexity, e.g., an airplane, multiple domains of interacting knowledge need to be integrated, mechanical engineering, electrical engineering, materials science, software engineering, etc., which implies

82

Hammer (2002). Aiden and Michel (2013, p. 19). 84 Casadesus-Masanell and Ricart (2011). 83

6.3 The Cybernetic Concept of Information

125

both at the level of architecture and modules a complexity of integrating these domains of knowledge. This integration requires higher levels of abstraction, as in defining architecture, to coordinate the work knowledge workers by facilitating their interaction to integrate especially tacit knowledge.85 One might say that the architecture of a product, as a hierarchy of levels of functional abstractions, replaces the Weberian hierarchy that in business originally also served to leverage scarce knowledge by concentrating it in the top of the organization. The higher the construction complexity of products and services, the more conceptual information is needed compared to causal information to coordination product innovation, product development, and product manufacturing. This brings us to the second question, what is the source of conceptual information and causal information, which is knowledge of various types, a corporation depends on for success and survival? The development of knowledge in our societies is itself a complex process existing of different schools and ideologies as analyzed in the field of ‘sociology of knowledge’. Until the establishment of research & development departments by companies like General Electric, BASF, and Philips Electronics, the development of science and engineering knowledge was exogenous to firms. Especially in high-volume manufacturing, steel, cars, steep learning curves developed in engineering manufacturing processes resulting in a decline of costs per unit output by doubling of the cumulative output against constant prices in the range of 50–70%. Hence the strategy of highest market shares to beat the competitor. With that, the firm became a system of knowledge creation, be it that it was to a large degree firm-process specific knowledge, partially codified and not always fit for knowledge spillover to other firms. Knowledge spillover between firms is an important source of economic growth because it leverages investments in knowledge creation. With the increase of knowledge intensity of products, which went along with a shift from codified knowledge to uncodified knowledge and a shift toward design as opposed to mass produced goods, knowledge creation became less concentrated in the R&D department, but also became a part of manufacturing processes itself. The nature of knowledge is that when existing knowledge is applied to especially new problems, e.g., posed by customers, knowledge is not consumed in creating and delivering a new customer value proposition, by even new knowledge is created, reversing the classical law of diminishing returns. This is not to say that knowledge creation is endogenous to the firm only, the knowledge-creating firm is an element in the overall system in society, universities, research institutions, and other firms that generates new knowledge in a field of politics, policies, ideologies, institutions, social developments, etc. as described in the endogenous growth theory in economics.86 This has two implications. The first is that to survive an organization not only

85

Grant (1996). Mentions as mechanisms for integrating knowledge direction and organizational routines, this is in view of the practice of architecture and modularity incomprehensible, as modules are not about routines. 86 Aghion and Howitt (1999).

126

6

Information and Complexity

needs the acquisition of the material factual information about changes in its environment, it needs as much to exchange different types of knowledge, engineering level up to abstract conceptual knowledge in order to adapt its body of conceptual and causal information to developments in its industry and markets. This knowledge exchange is operationalized through open innovation, open business models, co-creation, and architecture-based outsourcing with modules defined on basis of build-to-performance. Knowledge spillover as a source of economic growth, growth of the firm, implies that the beneficiary is not the firm that burdens the investment, a fair exchange is needed to maintain the system of knowledge spillover. The complicating factor is that shareholders expect that a cash flow not distributed as dividends but invested in the creation of new knowledge be turned into property rights for the shareholders in the form of patents. The second implication of knowledge creation in operations is that a decision needs to be made between spending time by knowledge workers on the short term, earning money, and spending time on the long term, investing in new knowledge to income on a longer term. The latter exists in participating in projects and teams. This requires as discussed in Sect. 11.14 a somewhat more complex system for resource allocation or resource mobilization, but in line with the earlier discussion of the shift of budget-driven strategy execution to cause-and-effect-based strategy execution, including its further development into discovery-driven growth and trial-and-error type strategy execution. To develop and acquire new knowledge is not the same as absorbing new knowledge by and in a social system. Absorbing is to say that new knowledge is applied in systems, processes, in the business model, in the system of internal governance, and thus integrated in the scripts of a critical mass of managers and workers, the new knowledge has become a new standard. Absorbing new insights is different from integrating new knowledge. The latter is usually at the level of product development, absorbing new knowledge is usually at the level of internal governance. The managers of the American car industry initially had difficulty to adopt them in Japan successful methods for TQM and Lean Six Sigma, although originating from the USA, because the concepts did not fit into their administrative view of command and control, and Toyota system did not fit in their image of shop floor levels workers.87 Especially when new technologies, new practices imply a shift in existing power relations, in existing interests, examples are abundant that firms, even economies, get stuck in a suboptimal equilibrium, until forced to adopt new technologies and practices. This even is an issue with introducing project management, process management and account management. This raises the question of what makes an organization more receptive in absorbing new knowledge beyond just integrating new knowledge. No systematic studies on this are available, but cases like IBM, Publicis and Procter & Gamble suggest that it is a type of leadership that senses changes at industry level, especially in changing rules of the

87

Butman (1997).

6.3 The Cybernetic Concept of Information

127

game and changing power relations, the need for a fundamental transformation and the willingness to change the existing social system of power relations, especially consisting of position oriented senior managers, to answer the sub-unit power principles, if power in the industry, in the market changes to a new dimension, so it must in the internal organization. This implies a change in the internal systems of the organization, task structure, the organization of information, allocation of resources, planning & reportable dimensions, usually toward a higher complexity in the system of internal governance. The fourth question to be answered is how is knowledge organized. Knowledge is a semi-public good; application of knowledge in the production of one product does not exclude it from application in a second product. The value of knowledge is highest if used by as many as possible individuals and firms. This implies building libraries, firm specific and access to public libraries, but this applies only to codifiable knowledge. The value of an element of knowledge increases if it is part of combinatorial innovation; its value is leverage by other knowledge elements as it increases the value of other knowledge elements. But knowledge is also a social phenomenon, individual workers are carriers of tacit knowledge, validation of knowledge is through a redundant system and in a world in which conventional index systems cannot keep up with the growth in variety in types of knowledge, indexing or tagging becomes folksonomy.88 Two factors then are at play in the flow of knowledge and thus combinatorial innovation. The first is the degree of complexity. Highly complex knowledge is transferred difficult and requires the movement of experts across geography, but this may be global. The flow of simple knowledge is through the Internet. Knowledge of moderate complexity is found in circles of social proximity, which may be either geographic, around modules in a product architecture or processes. The flow of knowledge of moderate complexity exists of a combination of social relations between workers and high-fidelity transmission; drawings and HDTV-conferencing. Organizing knowledge is complex organizations is facilitating a free as possible flow of information in terms of combining elements of knowledge from whatever source, but in its outcome guided, through processes and projects, by the strategic choices of the firm.

6.3.9

Pragmatic Information

Pragmatic information is also known as choice information or management information, that is information to make decisions. To equate pragmatic information with management information is problematic; it has it origin in the historical development of business models not being explicit and changing only at a low pace. Management information is defined as all the information managers and workers in an organization need to define actions and make decisions in order to achieve set

88

Panke and Gaiser (2009).

128

6

Information and Complexity

goals. This is not simply about structured decisions, in which decision can be made on basis of data and induction, but in a complex economy even decisions in the operations may include novel initiatives, sense-making and decentralized decisionmaking as described by Arrow that workers and teams can calculate by themselves which alternative decisions will contribute most to the overall performance of the firm. To achieve this not only data is needed, but as much context is needed, mission, a hierarchy of values, a strategy, an understanding of the causal nature of the business model, etc. This implies that the modern conception of management information comprises all the types of information as defined in this chapter. Management information, different from accountability information and different from accounting information, is future oriented and non-financial in the first place, but a relation with financial information exists because goals often are formulated financially. Management information often is presented in dashboards and other visuals, presenting sales, back orders, market share, customer satisfaction, perceived product quality, costs, etc., the choice of parameters depending on the nature of the business. Under the influence of the capital market in the 1970s and 1980s, in which financial performance was emphasized, firms allocated more power to the finance function in the organization, e.g., by appointing a CFO, and performance management in many cases was reduced to financial performance management.89 As a result in systems for management accounting information the emphasis was on accounting information to serve the accountability to the capital market. But accounting information by nature is historical and financial, whereas management information, that is information needed to achieve set goals, is dominantly future oriented and non-financial of nature.90 In 1987 Johnson & Kaplan warned that management information cannot and should not be based on or generated by systems designed for accounting information. “Rather than attempt to extract such information from a system designed primarily to satisfy external reporting and auditing requirements, we should design systems consistent with the technology of the organization, its product strategy, and its organizational structure.”91 Pragmatic information therefore can be defined as the (operational) data on which daily, operational decisions are made to achieve set goals within the context of an existing, routine-like business model. These decisions are about parameters in the production process that can be controlled by operators, like input parameters (quantity of raw materials) and process parameters (e.g., temperature, pressure). Other examples of parameters are market share, sales per day of a product, stock levels, but it also may include customer complaints, product returns, warranty claims, etc. Often causal information on the precise working of a business model is not available to define input and performance parameters validated through statistical regression and experienced based critical success factors (CFS) are chosen to manage a business. The development of Big Data implies that increasingly

89

Zorn (2004). Kaplan and Norton (1996). 91 Johnson and Kaplan (1987, p. 261). 90

6.3 The Cybernetic Concept of Information

129

statistically validated input and performance parameters are being chosen to manage a business, but this may conflict with the future orientation of management information. The latter is important because the value of a firm is defined by its future cash flows, not by its accounting value, to incorporate the contribution of intangible assets, and investors therefore look for assurance based on the quality of plans, the quality of management and inputs in processes and projects, the organization of processes and projects, that is, they try to assess the quality of management information to make investment decisions. Often in organization workers complain about information overload with respect to management information. But alike are the complaints about information anxiety, about information that is not accessible, incomplete, inconsistent, etc. Information overload with respect to management information results not from the amount of information but from a lack of reference frames, business models, decision models, causal models that inform the worker about what is relevant information and how to process that management information. Alike Herbert Simon observed that the complexity of a problem is not defined by the nature of the problem but by the knowledge or lack thereof mastered by the problem owner, information overload results from lack of clarity or lack of availability of a clear mission statement, hierarchy of values, strategic choices, and lack of decision rules based on a business modal as a causal model. The person who has purpose and knows his or her business therefore also knows what is relevant pragmatic information and understands how to process it. Management information typical is data which in itself has no meaning, but only will have a meaning when it is interpreted by and in the context of a business model. The simplest rule by which to turn an element of pragmatic information or data into information, that is a state of choice, decision, or action, is by applying an IF THEN ELSE —statement to that element of data. In this, we are back at Shannon’s definition of information, now with the difference that the coding of messages is not static as in a codebook, but is dynamic by an algorithm. Algorithmic interpretation of data to make decisions, however, only applies to structured decisions.

6.3.10 Transaction Information (Transaction Data) Transaction information is primarily the recorded data on financial transactions between a firm and its customers and suppliers. Transaction information also includes firm-internal transactions, e.g., the number of hours a worker has spent on a specific product or service and the transfer of goods and services between departments, divisions, etc. Transaction data also includes the recording of the stock of goods, be it raw materials, semi-finished products and finished products and mutations in these stocks. Transaction data includes (cash) payments, bank transfers, inventory records, invoices, outstanding bills, bills to be paid, purchasing orders, accounts payable, shipping documents, work-in-progress, and any other type

130

6

Information and Complexity

of assets and liabilities.92 This transactional data is recorded in the general ledger, to this recording specific accounting rules apply. Historically transaction data is the typical accounting information as needed for the financial control of the firm and to generate its annual reports and other external reports. Transaction data is the oldest type of explicit information in business, going back to the invention of the double-entry bookkeeping as early as the year 1340.93 So it is understandable that the first generation of the application of information technology in business was based mainly on the concept of accounting information. A second, limited basis was the field of operations research. Today we have a better understanding of the various types of information, as well there is a growing variety in business models as well innovation of business models and consequentially it is acknowledged that accounting information is also part of management information, but management information includes more types of information. As a consequence, systems for management information cannot be based on systems designed on basis of the concept of accounting information. Another illustration of the limitations of accounting information, which is not to deny the quintessential role of accounting information for the firm, is the insight that the business-IT-alignment paradigm, especially in the case of a dynamic, innovative industry, turns out to be the BusinessIT-alignment trap. The quintessential role of transaction data implies, precisely in the context of the growing complexity in the economy and in business, that this type of data needs to satisfy a number of criteria, a number of which are traditional and some new criteria. It goes without saying that a first criterion applying to transaction data is that all transactions of the firm need to be recorded timely, without errors, based on an unambiguously set of definitions and record structure, completely and be accessible for whatever purpose this data is needed. Increasingly transactions, due to digital technology, are recorded real time by digital devices, e.g., electronic payments. What defines “all transactions” not only is defined by accounting rules, but may also be implied by the business model, e.g., a project business in which a project consumes resources from various divisions or operating companies, and this includes as well all internal transactions. The completeness of the recording of a transaction at a first level will be defined by accounting rules and legal requirements, at a second level the attributes of a recorded transaction will be defined by the complexity of a business model and by the complexity of the market. Corporate account management implies that the transactions of a customer purchasing goods and services from multiple division of the same firm, are recorded so that the computer system generates automatically the total sales, etc. per period on that customer directly, not via those divisions. Cross-business projects imply that internal transactions are recorded, how much time a worker from a specific department has spent on that project, so that the project manager is reported such information over

92 93

Vaassen et al. (2009). Gleeson-White (2012).

6.3 The Cybernetic Concept of Information

131

all workers from all departments independent from the departments. To the number and nature of attributes with which to record a transaction applies Ashby’s Law of Requisite Variety; the role transaction data plays in the firm being in-control, implies that the complexity of a recorded transaction, that is the number of attributes of a transaction, needs to be higher as is the complexity of the business model, respectively the complexity of the market or the industry. An important requirement of transaction data is that its recording is based on a rigorous system of corporate-wide standardized semantic definitions, what is a product, what is a customer, etc. This seems trivial but the fragmentation of applications in business and with that the fragmentation of databases in numerous cases has resulted in a lack of standardized definitions, making consolidation of information difficult and unreliable, as well as analysis, impairing the value of transaction data and raising the costs of finance. Part of this standardization is also that data elements, e.g., the postcode of a customer across the firm, across all transactions is recorded in the same field of the record. The foregoing implies also that the transaction database of a firm, should logically be one entity at corporate level, not fragmented by applications nor by the internal structure of the firm, not even its legal structure. Transactions themselves are universal, in their core not dependent on jurisdiction, a transaction database with sufficient attributes allows for generating annual report in all jurisdictions. The requirement of accessibility of transaction data at a first level is trivial for reporting, monitoring, corrective actions, etc. External transaction information also constitutes interface information (Sect. 6.3.7). This implies that this part of the transaction database will be used for purposes of planning, analysis, optimizing processes (the supply chain), purchasing power. This use of transaction data may be by different functions, HR for staff planning, purchasing, merchandising, pricing policy, marketing, production planning, supply chain management, etc.94 Examples of this can be found in companies like Walmart, Tesco, Albert Heijn, IBM, and Nestlé. It also illustrates the principle that information, especially the transaction database, needs to be organized outside the structure of the organization of the firm.95 Initially, this was achieved through shared service centers, today this information is organized in the support platform as element in the organization of the firm. To this it must be noted that analysis of the interface information domain of transaction information only is not sufficient for a reliable planning of the activities of the firm, nor will it be sufficient to create a learning organization, as learning from own activities only creates learning myopia and subsequent suboptimal performance. The multiple uses of transaction information illustrates the nature of mathematical information theory, data itself has no meaning, although transactions have a meaning based on legally defined property rights, data is transformed into meaning, choices, decisions, actions, by a (functional) concept; the same database may be attributed different logical valid meanings.

94 95

De Kuijper (2009). Lash (2002).

132

6

Information and Complexity

Due to the historical precedence of transaction information, under the name of accounting information, it is understandable that systems for management information (MIS) initially tended to be based on systems for accounting information (AIS). Management information is all the information needed by the management of a firm and others in that firm to accomplish objectives and ensure that the firm is in-control, as defined in the resource dependency view of the firm. From the oversight of different types of information in this chapter it follows that management information comprises other types of information beyond accounting information. As a consequence, as explained before, it was concluded in the eighties that systems for management information never should be based on or extended from systems for accounting information.96 The basis of a management information system is a combination of (future) business concepts and dynamics (complexity) at the level of the industry, absorbing external data, allowing for all three levels of control as defined in cybernetics. The higher order of management information over accounting information, or accounting information being a subset of management information, does not deny a number of crucial roles of transaction information, defined in a broad way as before, in organized complexity. The modern database of transaction information, satisfying the criteria as defined before, enables are a number of options in the modern firm. First it, it creates the foundation for the multidimensional firm, that is a firm for which the performance can be planned, executed, monitored, and assessed on multiple dimensions, like product, divisions, geography, customer, etc. To achieve this multidimensionality some other measures will be needed like a redesigned resource allocation process, priority setting on dimensions, training of the workforce and some specific decisions rules, e.g., that the customer is the first profit center in the business system and thus in the accounting system. This multidimensionality enables the firm to respond better to changes in dominant structures in the market and thus to changes in market power.97 This multidimensionality of the organization of the firm is critical to be in-control in a dynamic complex market. A second aspect of the modern transaction database is that it eliminates the use of internal transfer prices to coordinated activities between, e.g., a manufacturing department and a sales department. This coordination now can be achieved on basis of causal relations of activities, e.g., as defined in an end-to-end process, using cost allocation, even across borders. This solves the problem of suboptimal performance as a result of double marginalization resulting from internal transfer pricing.98 However, the international (OECD) rules for fiscal transfer pricing require that market-based fiscal transfer prices need to be reported to the tax authorities, requiring a separate accounting system. A third consequence of the modern transaction database is that this eliminates the distinction between profit centers and cost centers, because such a database allows

96

Johnson and Kaplan (1987, p. 261). Strikwerda and Stoelhorst (2009). 98 Brickley et al. (2001, pp. 483–485). 97

6.3 The Cybernetic Concept of Information

133

for measuring the contribution of cost centers to the overall performance of the firm.99 A fine-grained transaction recording and database system also allows for measuring the contribution of knowledge workers across multiple projects and cross-business activities, thus enabling a better use of human capital, self-organization and resource mobilization in the firm.100 This implies that a multidimensional transaction system, in combination with an appropriately designed resource allocation process, facilitates cross-departmental projects and thus combinatorial innovation based on human capital. With that, such a transaction database is an element in absorbing the complexity in organizations using cross-unit projects, strategic themes, etc. With that, it facilitates the execution of specific types of strategies, including those based on modularity of products and processes. A fourth aspect or consequence of multidimensional reporting enabled by this modern transaction database is that reporting and analysis of root causes of problems becomes the same action, allowing for almost real-time analyzing the cause of problems in performance. Thus enabling faster corrective actions. Usually, transaction data is recorded in ERP systems, like those of software suppliers like SAP and Oracle. Such systems have a life span beyond that of the existing business. Until around 2000 it was thought that the functionality of such ERP systems should be based on the Business-IT-Alignment paradigm, that turned out to be a trap in the dynamics of industries.101 Transaction data should be recorded as neutral as possible with respect to the existing business model, allowing to facilitate business model innovation and next generations of business models.

6.3.11 Reproductive Information The planning and coordination of the production and assembly of mass-produced complex products like cars, smartphones, computers, airplanes, etc. is achieved through the tool of the bill of material. The bill of material organizes all the information on such products, their parts, sub-assemblies and production processes through a hierarchical architecture of the product, relating all the modules, including information for time-phased planning and coordination. The amount of information in a bill of material can be very large, e.g., a Boeing 787 counts approx. 3.5 million parts, and subsequently these bills of materials and thus production, assembly and logistic processes are managed through ERP computer systems. A bill of material can be compared with DNA as the information in biology to reproduce plants, animals, and human beings.102 The bill of material, earlier discussed under coordination, is dependent on the nature of products less stable, subject to innovation, etc. Levels of stability can be

99

Kaplan (2007). Bower (2003), Doz (2005). 101 Shpilberg et al. (2007). 102 Battail (2014). 100

134

6

Information and Complexity

discerned. In case software development is based on libraries or objects, or products are based on modules, databases recording such information will be more stable. The concept of bill of material does not apply to all situations. In contexts of knowledge work for products development and design some firms use transactive memory systems. Transactive memory system is a concept for specifying and capturing the cognitive structure and the functioning of teams. Transaction memory systems are defined as a system for the shared division of cognitive labor with respect to encoding, storing, and retrieving knowledge from individual areas of expertise.103 The difference with knowledge management systems is that transactive memory systems facilitate specialization and coordination in knowledge work, apart from facilitating combinatorial innovation through the storing and retrieving of individual knowledge.

6.4

Complexity and Information

The types of information defined by cybernetics clarify a relation between complexity and simplicity, different from the claim in the mathematical definition of information, that information reduces entropy related to complexity. The cybernetic concept of information changes the implicit same-level causality in traditional complexity thinking in a multidimensional space of causality and of processing information. Cybernetic information theory explains why abstract thinking is a first tool for dealing with complexity, as is a completely defined information space in terms of types of information beyond data. Cybernetic information theory also helps us to understand a constructive relationship between complexity and simplicity. The simplicity is in the mission, short, to the point, memorable, and communicative. A mission does not include details, nor does a mission exclude details, a well-defined mission is inclusive. Detail complexity is in the level of pragmatic information. These details can be mastered by well-defined management models with the nature of conditional statements, but awareness of the difference between causal information and conceptual information limits the risks that simple conditional rules, heuristics, are overused, used too long or beyond their scope of validity, because conceptual information constitutes a level of meta-control. Also, conceptual information avoids the risks of myopia of learning and provides a basis for knowledge creation organizations, not by stealing personal knowledge from workers by attempting to codify that knowledge in corporate systems, but be developing new insights, new understanding, creating new levels of conceptual simplicity. At the same time, the phenomena of the modern multidimensional general ledger and the bill of material show how a different type of information preserves complexity while reducing entropy and how this type of complexity is made manageable by discursive concepts and tools in combination with the simple goal information and axiological information. The information space defined by cybernetics enables

103

Heavey and Simsek (2015).

Bibliography

135

individual and groups to have an orientation on both complexity and simplicity. The cybernetic information space also explains why trail-and-error works, provided there is a clear purpose and strategic guidance, downward causality, and fast feedback information, to relate experience and results to the mission and strategy (and trialand-error assumes upward causality through the genius of workers.104 The cybernetic information categories also explain Simon’s concept of complex organization, loose programming and loose control; these concepts fit into the concept of downward causality. The cybernetic information theory also clarifies the relatedness of knowledge ecologies, of which firms are part of and information spaces. With that, it clarifies Drucker’s concepts of the information-based organization and his concept of knowledge work. The information-based organization is not simply data; it is precisely the higher levels of information, not captured by data management, that define the information-based organization and how to organize knowledge work. The combination of knowledge ecologies as complex systems with emergence and information spaces also implies a shift from scientific management to intellectual management. The traditional bureaucratic hierarchy is contextualized by the hierarchies of abstract thinking and architectural hierarchies.

Bibliography Abrahams, J. (1999). The mission statement book: 301 corporate mission statements from America’s top companies. Ten Speed Press. Aghion, P., & Howitt, P. (1999). Endogenous growth theory. The MIT Press. Aiden, E., & Michel, J.-B. (2013). Uncharted: Big data as a lens on human culture. Riverhead Books. Argyris, C. (2000). Flawed advice and the management trap: How managers can know when they’re getting good advice and when they’re not. Oxford University Press. Bart, C. K. (1997). Sex, lies and mission statements. Business Horizons (November–December), 9–19. Battail, G. (2014). Information and life. Springer. Beck, U., & Lau, C. (2005). Second modernity as a research agenda: Theoretical and empirical explorations in the ‘meta-change’ of modern society. The British Journal of Sociology, 56(4), 525–557. Beniger, J. R. (1986). The control revolution: Technological and economic origins of the information society. Harvard University Press. Benjamin, W. (1936). Das Kunstwerk im Zeitalter seiner technischen Reproduzierbarkeit. Zeitschrift für Socialforschung. Birchler, U. W., & Bütler, M. (2007). Information economics. In Routledge advanced texts in economics and finance (p. xxiii, 462 p.). Retrieved from http://www.loc.gov/catdir/toc/ecip0 77/2006102041.html Boisot, M. H. (1995). Information space: A framework for learning in organizations, institutions and culture. Routledge.

104

Manzi (2012).

136

6

Information and Complexity

Boot, A. W. A., & Thakor, A. V. (2011). Managerial autonomy, allocation of control rights, and optimal capital structure. Review of Financial Studies, 24(10), 3434–3485. https://doi.org/10. 1093/rfs/hhr045 Bower, J. L. (2003). Building the Velcro organization: Creating value through integration and maintaining organization-wide efficiency. Ivey Business Journal, 68(2), 1–10. Brickley, J. A., Smith, C. W., & Zimmerman, J. L. (2001). Managerial economics and organizational architecture (2nd ed.). McGraw-Hill. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies (1st ed.). W.W. Norton & Co.. Butman, J. (1997). Juran: A lifetime of influence. Wiley. Cadbury, A. (1995). The company chairman (2nd ed.). Director Books. Casadesus-Masanell, R., & Ricart, J. E. (2011). How to design a winning business model. Harvard Business Review, 89(1/2), 100–107. Casson, M. (1997). Information and organization: A new perspective on the theory of the firm. Clarendon Press. Castells, M. (2010). The power of identity (2nd ed.). Blackwell. Cha, S. E., & Edmondson, A. C. (2006). When values backfire: Leadership, attribution, and disenchantment in a values-driven organization. The Leadership Quarterly, 17(1), 57–78. Cheema, Z. A., Farooq, M., & Wahid, A. (Eds.). (2013). Allelopathy: Current trends and future applications. Springer. Christensen, C. M. (1997). The innovators dilemma: When new technologies cause great firms to fail. Harvard Business School Press. Cillier, P. (1998). Complexity and postmodernism: Understanding complex systems. Routledge. Cortada, J. W. (2011). Information and the modern corporation. MIT Press. Day, R. E. (2001). The modern invention of information: Discourse, history, and power. Southern Illinois University Press. De Kuijper, M. (2009). Profit power economics: A new competitive strategy for creating sustainable wealth. Oxford University Press. Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10), 78–87. Doz, Y. L. (2005). Resource allocation processes in multidimensional organizations: MNCs and alliances. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy. Oxford University Press. Driver, M. J., & Streufert, S. (1969). Integrative complexity: An approach to individuals and groups as information-processing systems. Administrative Science Quarterly, 14(2), 272–285. Eccles, R. G., Cheng, B., & Saltzman, D. (2010). The landscape of integrated reporting: Reflections and next steps. Harvard Business School. Floridi, L. (2010). Information: A very short introduction. Oxford University Press. Garfinkel, H. (2008). Toward a sociological theory of information. Paradigm Publishers. Gleeson-White, J. (2012). Double entry: How the merchants of Venice created modern finance (1st American ed.). W.W. Norton & Co. Good, J., & Velody, I. (1998). The politics of postmodernity. Cambridge University Press. Grant, R. M. (1996). Prospering in dynamically-competitive environments: Organizational capability as knowledge integration. Organization Science, 7(4), 375–387. Gribbin, J. (2005). Deep simplicity: Chaos, complexity and the emergence of life (1st U.S. ed.). Penguin Books. Habermas, J. (1973). Erkenntnis und Interesse. Suhrkamp. Habermas, J. (1981). Theorie des kommunikativen Handelns, Band 2: Zur Kritik der funktionalistischen Vernunft (Vol. 2, 4th ed.). Frankfurt am Main. Hammer, M. (2002). Process management and the future of Six Sigma. MIT Sloan Management Review, 43(2), 26–32.

Bibliography

137

Heavey, C., & Simsek, Z. (2015). Transactive memory systems and firm performance: An upper echelons perspective. Organization Science, 26(4), 941–959. https://doi.org/10.1287/orsc.2015. 0979 Heuer, R. J. (1999). Psychology of intelligence analysis. Central Intelligence Agency Hidalgo, C. A. (2015). Why information grows: The evolution of order, from atoms to economies. Basic Books. Hofkirchner, W. (1999). The quest for a unified theory of information: Proceedings of the Second International Conference on the Foundations of Information Science. Gordon and Breach. Holmes, B. (2009). Escape the overcode: Activist art in the control society. Van Abbemuseum & For Whom/WHW. Jensen, M. C. (1993). Modern industrial revolution, exit, and the failure of internal control systems. SSRN. Johnson, H. T., & Kaplan, R. S. (1987). Relevance lost: The rise and fall of management accounting. Harvard Business School Press. Kanter, R. M. (2009). IBM’s values and corporate citizenship. In: Harvard Business School. Kaplan, R. S. (2007). The demise of cost and profit centers. Retrieved from Boston, MA.: Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard: Translating strategy into action. Harvard Business School Press. Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business School Press. Kinne, P. (2009). Integratives Wertemanagement: Eine Methodik zur Steuerungsoptimierung immaterieller Ressourcen in mittelständischen Unternehmen. (PhD), Universiteit van Leiden. Kuhn, T. S. (1970). The structure of scientific revolutions (2nd ed.). University of Chicago Press. Lash, S. (2002). Critique of information. Sage. Lash, S., & Lury, C. (2007). Global culture industry: The mediation of things. Polity. Manzi, J. (2012). Uncontrolled: The surprising payoff of trial-and-error for business, politics, and society (p. xvii, 300 p.). March, J. G. (1994). A primer on decision making: How decisions happen. The Free Press. March, J. G. (2006). Rationality, foolishness, and adaptive intelligence. Strategic Management Journal, 27, 201–214. Marion, R. (2008). Complexity theory for organizations and organizational leadership. In M. Uhl-Bien & R. Marion (Eds.), Complexity leadership - Part 1: Conceptual foundations (pp. 1–15). Information Age Publishing. McGrath, R. G., & Macmillan, I. C. (2013). Discovery-driven growth: A breakthrough process to reduce risk and seize opportunity. Harvard Business Review Press. McIntyre, L. C. (2018). Post-truth. MIT Press. Mumford, S., & Anjum, R. L. (2013). Causation: A very short introduction (1st ed.). Oxford University Press. New York Stock Exchange Commission on Corporate Governance. (2010). Report of the New York stock exchange commission on corporate governance. Retrieved from New York: North, D. C. (1991). Institutions. The Journal of Economic Perspectives, 5(1), 97–112. Osterwalder, A. (2004). The business model ontology: A proposition in a design science approach. (Docteur en Informatique de Gestion). Université de Lausanne. Panke, S., & Gaiser, B. (2009). With my head up in the clouds. Journal of Business and Technical Communication, 23(3), 318–349. https://doi.org/10.1177/1050651909333275 Pellizzoni, L. (2008). The antinomy of accountability. In M. Boström & C. Garsten (Eds.), Organizing transnational accountability. Edward Elger. Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. The Penguin Press. Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource dependence perspective. Stanford Business Books. Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. The Free Press.

138

6

Information and Complexity

Porter, M. E. (1990). The competitive advantage of nations. The McMillan Press. Prahalad, C. K., & Doz, Y. L. (1979). Strategic Reorientation in the multidimensional organization (195). Rollinson, D., & Broadfield, A. (2002). Organisational behaviour and analysis: An integrated approach (2nd ed.). Prentice Hall Financial Times. Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. University of Illinois Press. Shpilberg, D., Berez, S., Puryear, R., & Shah, S. (2007). Avoiding the alignment trap in information technology. MIT Sloan Management Review, 49(1), 51–58. Simon, H. A. (1996). The sciences of the artificial (3rd ed.). The MIT Press. Snowden, D. J., & Boone, M. E. (2007). A leader’s framework for decision making. (cover story). Harvard Business Review, 85(11), 68–76. Strikwerda, J. (2008). Van unitmanagement naar multidimensionale organisaties. Van GorcumStichting Management Studies. Strikwerda, J. (2011). Competing on information: An exploration of concepts. SSRN eLibrary. Strikwerda, J. (2013). Sense making in corporate governance: A multilayered model for information asymmetries between investors and executives. SSRN eLibrary. http://ssrn.com/abstract=23 70304. https://doi.org/10.2139/ssrn.2370304 Strikwerda, J. (2021). Wanneer is er sprake van verantwoorde bestuurlijke informatie? MAB, 95(3/4), 127–135. https://doi.org/10.5117/mab.95.57679 Strikwerda, J., & Stoelhorst, J. W. (2009). The emergence and evolution of the multidimensional organization. California Management Review, 51(4), 11–31. Suedfeld, P. (2010). The cognitive processing of politics and politicians: Archival studies of conceptual and integrative complexity. Journal of Personality, 78(6), 1669–1702. https://doi. org/10.1111/j.1467-6494.2010.00666.x Suedfeld, P., Tetlock, P. E., & Streufert, S. (1992). Conceptual/integrative complexity. In C. P. Smith (Ed.), Motivation and personality: Handbook of thematic content analysis. Cambridge University Press. Sutcliffe, K. M., & Weber, K. (2003). The high cost of accurate knowledge. Harvard Business Review, 81(5), 74–82. Vaassen, E. H. J., Meuwissen, R., & Schelleman, C. (2009). Accounting information systems and internal control (2nd ed.). Wiley. van Peursen, C. A., Bertels, C. P., & Nauta, D. (1968). Informatie - Een interdisciplinaire studie. Aula-boeken. Walker, S. I., Davies, P. C. W., & Ellis, G. F. R. (2017). From matter to life: Information and causality. Cambridge University Press. Weick, K. E. (1995). Sensemaking in organizations. Sage. Zorn, D. M. (2004). Here a chief, there a chief: The rise of the CFO in the American firm. American Sociological Review, 69(3), 345–364.

7

Complex Decision-Making

7.1

Introduction

What does the increasing complexity imply for making decisions? To answer this question is important because a popular belief is that complex decision-problems are difficult to solve in comparison with non-complex or so-called well-structured decision-problems. In this chapter, we will explain that complex decision-problems indeed are different from ‘standard’ decision-problems, but quite different as often thought. We will also demonstrate that, dependent on the type of complex decision-problem, a variety of methods, processes and tools exist to solve complex problems satisfactorily. But first we need to understand what the increase in complexity implies for the nature of decision-problems. Will there be a shift from well-structured problems to more decision-problems having a higher complexity? Will decision-making procedures be more complex? Will there be an increase in techniques, tools, and methods for decision-making? What implications follow from a more complex context for decision-making, institutional, industry-level, market, the system of corporate governance and the organization of the firm itself? What are the implications of organized complexity for the organization of decision-making in the organization of the firm, by style of decisionmaking and processes? What may be or will be effects of the growth of information on decision-making?

7.2

What Is a Decision?

In discussing complex decision-making, we need to ask the question what actually is a decision? In the traditional management theory and management books, based on Herbert Simon, decision-making is defined as identifying and selecting that alternative from available options that has the highest value for the firm to solve a problem or achieve an objective. Usually, the alternative options are taken to be investment # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_7

139

140

7 Complex Decision-Making

options (a new product, a new piece of equipment, an acquisition, etc.), thus decision-making is committing resources to a specific course of action, excluding other courses of action or opportunities. Some define a decision not as a commitment to a specific action, but a commitment of resources (setting a budget for an investment), not necessarily defining specific actions.1 The question to be raised is whether decisions so defined are not themselves a source of especially subjective and epistemological complexity. Decisions as committing resources to an investment always must be made under uncertainty. Hence an emphasis on the role of information to reduce this uncertainty, respectively tactics like the real option theory to phase decisions in time to reduce uncertainty. To define decision-making as making a choice from alternatives also implies a notion of linearity in decision-making, of a number of steps to go through as explained further on, and with that decision-making defined as making a choice from existing alternatives has a notion of one-dimensionality. This one-dimensionality expresses itself in the mathematical decision-theory with an emphasis on refining mathematical tools as opposed to reconceptualizing a problem, to see a problem at a higher level of abstraction and or from different perspectives. A well familiar example of this is the phenomenon of market myopia. How to define one’s business, producing the best-encrypted phone or smart phones to connect people, is decisive for the development and success of a firm, as was demonstrated in the case of Blackberry. Such a decision is not a choice between alternatives but is about an understanding of our social world and how it develops. Alike is defining a mission, being a pivotal element in the governance of a firm, an important act. Even more, to decide the hierarchy of values of an organization is an expression of personal values in relations to the values of a community. As will be explained in this chapter, one of the sources of the experience of complex problems is that in the management literature on decision-making, it is reduced to an act or process of choice, whereas decision-making is a much broader and richer process.

7.3

What Is a Decision-Problem?

A decision-problem can be defined as a state of mind or consciousness in which the management of a firm acknowledges that existing routines, procedures, heuristics or algorithms no longer produce the results or will not bring the results the management is after. Thinking will be needed, the acquisition and or development of new knowledge, new insights, a new understanding is needed to identify a new course of action that is likely to bring the success management is after. Many decisions, if not most, in an organization are programmed decisions, these are choices to be made, but can be made in an effortless way, based on intuition, simple heuristics or simple calculations.

1

Langley et al. (1995).

7.3 What Is a Decision-Problem? Observe Unfolding Circumstances

Orient

Implicit Guidance & Control

Feed Observations Forward

141 Decide Implicit Guidance & Control

Cultural Traditions

Genetic Heritage

New Information

Outside Information Unfolding Interaction With Environment

Act

Analyses & Synthesis

Previous Experience

Feedback

Feed Forward

Decision (Hypothesis)

Feed Forward

Action (Test) Unfolding Interaction With Environment

Feedback

Fig. 7.1 Boyd’s OODA-loop, depicting fast and slow thinking in relation to situational decisionmaking (Alberts 2011). This loop is by far superior to the PDCA cycle in its application in administration and organization

To understand what the usefulness is of complex problems as temporary problems we use the distinction between System 1 thinking, intuition and heuristics (fast thinking), and System 2 thinking that is about reasoning (slow thinking).2 Bazerman defines as one of the characteristics of system 2 thinking that it is rule governed, but heuristics are—informal—rules. System 1 thinking is stronger rule governed, implicit as in intuition and explicit through the application of heuristics. Bazerman’s concept of system 2 thinking is better to be characterized by explorative thinking, exploring new causal relations, through research, by experimenting, by trail-and error, by acquiring from outside new knowledge, etc. Decision-making might be described as the art to see, to understand when intuition or heuristics can be relied on, and where and when not. The need to be able to make this distinction can be expressed in the OODA-loop formulated by Boyd (Fig. 7.1). Boyd’s OODA-loop acknowledges that through the role of genetic heritage, personality, cultural tradition, previous personal experiences, personal interests and motives, we may have a tendency to observe (new) problem situations through lenses, models, concepts developed in the past, that were successful in the past and thus we tend to cling to these old models, including applying these to new situations with the result that these obsolete models make us either not to see the problem, seeing the problem to be complex (subjective complexity), or defining the problem in a wrong way, all resulting in making wrong decisions or not making a decision where a decision is needed. To acknowledge a situation as a decision-problem is not that easy as it touches on the reputation of the CEO, the expectations of shareholders and possible at the mentality of the workers in the organization, who expect leaders to lead, to show direction, to know, not to be at ends. Therefore, such an acknowledgment by a CEO

2

Bazerman and Moore (2009).

142

7 Complex Decision-Making

will most likely be private, and to be shared with some confidents, friends perhaps, an external consultant. If the decision-problem results from a manifest situation, financial distress, a natural disaster or other events known to members of the organization and stakeholders, double talk may be necessary, providing assurance to others that the situation is in-control, but privately that a reconceptualization of the situation is necessary. If the decision-problem follows from the canonical tasks of the CEO to scrutinize the future and issues are identified that most likely are not known to the stakeholders, the CEO needs a private space for exploration of the issue to develop a new vision. Three different responses can be observed to happen to a problem situation. The first is wait-and-see, hoping that the problem will solve itself. This response is partly due the working of the human brain which tends to interpret the need to make a decision as a threat. This relates to the error of omission. If we make a decision that turns out to be wrong, we need to account for having made that decision. If we do not do anything and things turn out badly, we will defend ourselves by emphasizing that I cannot be blamed for things going wrong because I did not do anything; no cause, no consequences. Of course, this argument does not hold in court because the duty of care may imply that a decision needed to be made, not making a decision, including postponing it, is also a decision. A second response to a decision-problem that the newness of the situation is ignored or repressed and that the problem is redefined in such a way that at least an illusion is created that available knowledge, heuristics, and insights can be applied to make a decision. This need not necessarily be a bad decision, dependent on the way the decision is being dealt with in terms of implementation and the nature of the decision. Not making a decision easily results in a state of apathy. Making a bad decision provides the opportunity to manage the bad decision into a non-bad or even a good decision as decisions are always changed in the process of implementation. Alike this may result in muddling through. Applying old heuristics to new situation can be explained as a kind of wisdom, but usually results in a marginal existence of the firm. The third response may be that a process of exploration is started, by hiring consulting, talking to other CEOs, interviewing experts, studying literature, visiting other firms, debates on basis of the style of constructive friction, with which the organization starts a learning process. This approach may lead to breakthroughs in terms of higher performance, a better strategic development, more options to choose from, etc.

7.4

Well-Structured Problems

To understand the nature of complex decision-making we need first to understand the nature of non-complex decision-making. The neoclassical economy assumes that managers of firms are rational in setting objectives, which is maximizing utility or profits and are rational in the way they achieve those objectives, that is with a maximum of efficiency. This is known as the

7.4 Well-Structured Problems

143

rational-economic model or the rational-deductive ideal of decision-making.3 In this model it is assumed that decision-making exists of a number of discrete steps to be taken in a linear order by the manager, labeled by Herbert Simon as the General Problem Solver:4 1. The manager acknowledges the need for taking a decision and takes a decision the moment the situation asks for it (this is also called the intelligence phase of decision-making); 2. The manager knows how to define the problem, scoping the problem so that it fits into a familiar category, e.g., an investment decision, a marketing decision (there exists a theory to describe and formalize the problem and to organize the knowledge with respect to the problem); 3. The manager understands how much time is available to make a decision and when the decision should be made (timing and setting the agenda); 4. The information needed to make the decision can be identified and is available or can be acquired within the time-frame without undue costs or effort; 5. The manager knows and decides the process of decision-making in terms of who should be involved, in what way, and to what end; 6. The manager knows the objective function or preference criteria against which to evaluate the outcomes or values of the alternative decisions; 7. The manager can identify the decision variables, that is the variables under control of the manager respectively those who have to execute the decision and their causal relation to outcomes, as the basis of the design activity to invent, design or identify alternative courses of actions and decisions; 8. The manager can identify the constraints which apply to the alternative solutions, available resources, regulations, business ethics, physical constraints, etc.; 9. The manager has all the information and the right evaluation tools (e.g., DCF, real options) to evaluate the value of all of the alternative solutions within the time constraint; 10. The manager knows how to manage the decision into a success. A decision that satisfies these criteria is said to be a well-structured problem (WSP).5 A decision that is difficult or impossible to be structured according to the criteria of a WSP is called an ill-structured problem (ISP). According to Simon the boundary between well-structured problems and ill-structured problems is not welldefined and is not susceptible to formalization. Especially the definition of a problem has not so much to do with information in the sense of data, but depends more on concepts, models and holistic thinking, that is the cognitive base of the problem

3

Huczynski and Buchanan (2007, p. 739) and Simon (1973). Simon (1973). 5 Simon (1973). 4

144

7 Complex Decision-Making

solver and his or her style of thinking, reductionistic or integrative and transdisciplinary. Simon’s criterion to distinguish well-structured problems from ill-structured problems, in the day-to-day practice of business, may therefore depend on the knowledge base of the problem-owner or those tasked to solve the problem.6 The delineation between well-structured and ill-structured problems shifts with the growth of knowledge. Initially, Simon suggested that with the growth of knowledge more ill-structured problems become well-structured problems and therefore can be solved with the same methods used to solve well-structured problems. This may be true for operational-type problems related to the growth of available information (Big Data). As we will explain in Sect. 11.11 there is a type of problem that requires different methods like abstraction and reframing in order to solve these problems. Most well-structured problems are so because, as Einstein quipped, the simple is always the simplified, that is, a well-structured problem is a structured problem because the problem-owner or problem solver has defined it so, by either ignoring or deliberately leaving out many factors. Simon’s definition of a well-structured problem, however, leaves out a number of important aspects that need to be considered before we discuss complex decisionmaking. A decision is always made in a (socio-economic-institutional) context. A decision in business affects direct or indirect other people. To what extent a decision needs to conform to the existing context, or needs to push that context to new limits or relations? To what extent a decision is framed or structured by its context and should the decision-maker accept this induced structuring of a problem or resist it? Simon does not ask the question of why the decision-maker is the decision-maker for a problem in the first place. Usually it is assumed that a decision-maker is a manager. A manager typically operates in a formal organization, either a corporation or an institution. In such a setting formal rules are at play, implied by, e.g., corporate law and a system of corporate governance or the system of internal governance. There is a system of budgets, attributed decision rights, reserved powers, performance management, a reward system, accountability, oversight, etc. A manager has to deal with other people, which raises the issue of procedural justice in decision-making. Can a manager or should a manager make a decision on his own, or should other people be involved in the decision-making process? Are decisions made or do they emerge in a social setting? So, the context of a decision to be made may be either simple or complex. To understand the nature of decision-making in relation to complexity, we need to enrich the concept of well-structured problems which its contexts by becoming aware that Herbert Simon left out a number of aspects material to decision-making in its full. A first aspect that is left out of the definition of a well-structured problem is the issue of decision rights. The concept of the WSP assumes that the problem to be solved has an identified and uncontested problem owner, who is responsible for the

6

Simon (1973) and Dorst (2006).

7.4 Well-Structured Problems

145

Fig. 7.2 The system of decision control in the case of separation of ownership and control as between the supervisory board and the executive board. This schema is to be found in most corporate governance codes. Fama and Jensen (1998) (org. 1983)

decision to be made and it is implicitly assumed that this problem owner is also the decision-maker, and either is the owner of the necessary decision rights because the decision-maker is the owner of all of the resources involved, or in the case of separation of ownership and control the decision-maker is attributed all the necessary decision rights to make the decision. A second aspect is, based on the co-location principle from economics, that the decision-maker will be confronted unmitigated with all the economic consequences, positive and negative, of the decision made in his personal wealth and income, either by being the owner of the assets involved in the decision, or in case of separation of ownership and control, by a proper reward system, absent agency costs. This should include all externalities of a decision made. In case of lack of such a feedback on the personal income the decision-maker may base decisions on a different objective function as that of the firm, a personal objective function, as described in the agency theory of corporate finance. The separation of ownership and control in public companies implies that executives are not the residual claimants to the economic effects of their decisions; the shareholders are. The fact that some executives are also shareholders and whether agency costs are attempted to be curbed through bonus systems, are not effective compensation for this absence of shareholders, and not the decision-maker, being the residual claimant to the economic effects of decisions. Hence a system of decision control is needed, in which a distinction is being made between management rights and control rights (Fig. 7.2). The management rights, vested in the executive board or the CEOs, consist of: (1) The right and duty of initiation, foreseeing, making plans, setting objectives, defining investments; and (3) The right and duty of the execution of the strategy and actions defined. The control rights, vested in the (supervisory) board consist of: (2) Ratification, including, depending on jurisdiction and statutes, testing a proposed decision against legal requirements like the duty of care and the duty of loyalty; and

146

7 Complex Decision-Making

(4) Monitoring, this is the duty to oversee in an active way the execution of approved decisions. Depending on jurisdiction some decisions may need the approval of the general meeting of shareholders, e.g., in the Dutch jurisdiction a proposal to change the identity of the corporation, respectively a proposal that implies a change in the identity of the corporation needs the prior approval of the general meeting of shareholders. Below the level of the executive board a comparable system of decision control exists in the system of internal governance of the firm. The basic rule is that an approved budget empowers the budget holder, e.g., the manager of a division or business unit, to whatever is necessary as needed to achieve the objectives set out in the budget, but strictly within the framework of the budget, which usually will include non-financial targets, and financial and non-financial constraints, including compliance, but still a number of reserved powers need to be defined, as implicated by duty of executive action of the executive board respectively the CEO. The duty of executive action states that the executive board needs to be involved in those decisions by lower-level managers, e.g., material contracts, which may affect the interests of the corporation as a whole.7 That is to say in case a division manager intends to close a material contract with a supplier or a customer, that is a contract with a value of over 15% of the budgeted turnover, approval of the executive board is needed, in some cases even the supervisory board, in view of risks implied by such a contract, usually treasury and financing risks. The partition and attribution of decision rights to lower levels in the organization itself is a design decision with respect to the organization of a firm as a system for decision-making. Most decisions in an organization are pre-structured by the structure of the organization. This partition is partly based on the organization form, the F-form or M-form and partly on information asymmetries (Fig. 7.3). The business unit and the division usually will be profit centers, based on productmarket combination. A sales department will be a revenue center, whereas a manufacturing department will be a cost center, as will be shared service centers. The decisions by managers are programmed by both the functional specialization in the organization, an imposed objective function and imposed constraints. In the traditional tangible assets-based firm, programming of decisions is narrower at the operational level. At the operational level problems to be solved in general will be structured problems, contrary to what Simon assumes, more defined by the context of the organization and technology and less by the problem solver. This is not to say that decision-making at lower level, therefore, is simpler compared to a higher level where more trade-offs need to be made, at lower level there will be a higher degree of engineering-complexity in decision-making. The categories and criteria in Fig. 7.3 are based on the M-form. The question is whether the rather simple categories and criteria in Fig. 7.3 apply to more complex organization forms, through the deployment of shared service centers, corporate account management, cross-division

7

Tricker (1984, p. 7).

7.4 Well-Structured Problems

147

Fig. 7.3 The relationship between decision rights, information asymmetry and performance measures (Brickley et al., 2001, p. 433). It illustrates the importance of assumptions underlying concepts and conventions

projects and processes, using strategic themes for strategy execution, etc. It turns out that the categories in Fig. 7.3 apply in more complex organization forms, but the issue is that a reassignment of categories is needed. In case a firm transforms its business model into a project business the consequence and requirement for success is that the projects are assigned the status of profit centers and the old business units, now supplying to these projects, become cost centers. That affects the status and the power relations in the organization, apart from the fact that more complex management information is needed. This induces a complexity in perceptions, perceptions of threat of status, loss of power, loss of identity, a perception of a more complex organization, a loss of control for some (but more for the project manager), which will not be expressed as such, but in terms of whether the new strategy will work, whether the new organization will work, needed system decisions are evaded by shifting the debate to the need of a change in culture, etc. This may result in a situation in which a decided strategy is implemented half-heartedly, resulting in an organization that is experienced as complex. As stated before, in a principal-agent relation there may be a divergence between the interests and values of the lower-level manager (the agent) and the higher-level management (the principal) resulting in a decision approval process of which the nature is as much psychological as it is economic. The principal tasked to ratify a decision proposed by the agent will apply different criteria in the ratification procedure as the criteria by which the agent has drawn up a proposal, e.g., an investment. Principal and agent may have different perceptions on the proposal in terms of simplicity and complexity. The players involved in the process of the ratification of a decision may perceive a well-structured problem as an ill-defined problem because of divergent interests, values, and information asymmetry.

148

7 Complex Decision-Making

The phenomenon of decision control is the standard operating procedure in business for many years, so basically all kinds of routines exist to deal with this, including court procedures and court rulings, but the separation of decision rights introduces additional dimensions to the decision-making process. Behaviors like decision framing is being applied in the dichotomy of initiating and ratification, ambiguity in objectives, budget gaming, evading monitoring, manipulating reports, splitting large decision in smaller decisions to evade the ratification process, etc. Techniques exist and are applied to curb the agency costs implied by the described behaviors, especially a switch is going from budget-driven strategy execution to cause-and-effect-based strategy execution. The categories mentioned in Fig. 7.3 are based on the traditional organization forms, the F-form and the M-form and with that on tangible assets and high costs of information. The new business models, with, e.g., multiplier profit models, imply that projects, strategic themes, end-to-end processes, customers, etc. need to be included as elements for the partitioning and attribution of decision rights. A new strategy of the firm, e.g. that of IBM in the 1990s of global key account management and integrated solutions, required that the customer was defined in the system to be the primary profit center, no longer the countries or products (although the information on the profitability on these dimensions remains for analysis purposes). The design variable “information asymmetry” changes with the declining costs of information and with the nature of products and services, e.g., the transition to information products. This implies that the organization of the firm as a decision-making system, especially the factoring of decision-making best can be seen as a dynamic system, itself subject to design decisions. New developments in technology, industries, markets, etc. may imply that in the process of especially initiatives and ratification an issue may arise that executives develop proposals, e.g., new businesses or market strategies, which implicitly are based on new concepts, e.g., an integrated firm, whereas those tasked with ratification judge, unconsciously, the proposal through the concept of the multi-business firm and make a judgment based on break-up value instead of synergies to be exploited. In the system of internal governance new situations may arise which are not covered by the existing system of decision rights. In the 1990s some banks in the Netherlands, but also other firms found themselves in internal fights about the turnover and profit generated by e-banking and e-commerce. In the case of banking local subsidiaries used to be defined as the lowest level of profit centers, but implicitly this was a proxy for the customer as profit center. E-banking did not fit into this system and the managers of the subsidiaries required that the turnover and profit of customers generated through e-banking be deconsolidated in their books and performance reporting (that is the basis of their remuneration). This has been solved by redefining the system of profit centers into the products being the primary and lowest level of profit center in the system, changing the status of the subsidiaries into customer service infrastructures facilitating the sales of products. Alike in retail chains, e-commerce conflicts with the local shop being a profit center, as customer may decide to fit garments in the shop, but order these from home (which in the

7.4 Well-Structured Problems

149

1990s especially was the case with teenagers in London). In this case, the solution is to define an article group as profit center. These examples illustrate another, second shortcoming of the definition of wellstructured problems, often a problem cannot be solved at the level on which the decision-problem is being defined, e.g., a conflict between the managers of subsidiaries and the e-commerce manager, but requires a redefinition o a higher level. It can be argued that such a redefinition is covered by step two in the defined steps for well-structured decisions, and certainly the description of step two does not excluded a more thorough redefinition of the problem, but most, especially mathematical-oriented literature on decision-making, but in a way also the psychological oriented literature on decision-making at least is weak in emphasizing the need of reconceptualizing problem situations to find a solution, although it is part of the Harvard case-method. Decision-making is more than making a choice from alternative options; at least it also includes the judgment to see when reconceptualization is needed. With that, an implicit assumption underlying the concept of wellstructured problems is that these can be defined and be solved within one discipline and at the level at which the problem presents itself.8 A third shortcoming in the concept of the well-structured problem is that it assumes perfect rationality, in terms of complete information, rational valuation of all alternatives and that economic rationality is preferred over non-economic rationalities, e.g., March’s rule following decision-making as opposed to rational decision-making. Herbert Simon was aware of this issue of rationality and introduced the concept of bounded-rationality. Even in non-complex situations the assumptions of the rational-inductive ideal, or rational economic model do not hold as explained in Fig. 7.4. The rational-inductive ideal is a prescriptive model of decision-making. Because its assumptions do not hold decision-making is more adequately described by the behavioral theory of decision-making or the administrative model.9 The latter are descriptive, non-prescriptive models of decision-making and take into account the role of individual behavior in decision-making, including notions of bounded rationality and satisficing behavior. Virtual decisions are made on basis of imperfect information and on basis of bounded rationality, often through the use of intuition and or by applying heuristics (rules of thumb).10 Opposed to the rational-deductive ideal the practice is that decisions only can be made on basis of imperfect, inconsistent, incomplete and often contradictory information. This not only applies to management or choice information (data) but as much, and with the growth of new business models, increasing with respect to knowledge about causal relations, we do not have perfect models, it is impossible to have a perfect understanding of causality.11

8

Beers et al. (2006). Simon (1965), Cyert and March (1963/1992), and March (1991). 10 Simon (1987), March (1991), Artinger et al. (2015). 11 March (2006). 9

150

7 Complex Decision-Making

Fig. 7.4 The Rational-economic Model Assumptions (rational-deductive ideal) and how these differ from reality (Huczynski & Buchanan, 2007, p. 739). In the concept of administrative man the reality is acknowledged, contrary to economic rationalism

According to Jensen there is more to what makes a good decision than that it maximizes the value of the firm. Jensen defines as characteristics of a good decision:12 • It serves the value-maximizing objective, without side effects or externalities that are harmful to the material or immaterial value of the firm. • It is actionable. • It is explicable to those who have to act on it, and the decision has an authority and logic of its own. • Within available time and resource constraints a best use has been made of available information and insights with respect to cause and effect, the decision is based on understanding the situation. • It does not fix details which better can be left to the experience and judgment of local managers. • It motivates those who have to execute the decision, even if the decision is contrary to their own direct interests. • Both making the decision and executing it provides a learning opportunity. • The decision is arrived on basis of a properly chosen process or due process. These characteristics place a decision and especially the decision process in the context of an organization, this time in a more social organization compared to the more formal context implied by the system of corporate governance. A decision needs to take into account those who have to execute the decision. 12

Jensen and Wruck (1998).

7.4 Well-Structured Problems

151

Style Label

Autocratic AI

Autocratic AII

Consultative CI

Consultative CII

Participants

Leader

Leader and subordinate(s)

Leader and subordinate(s)

Leader and group

Leader uses information available at the time

Leader obtains necessary information from subordinates

Leader obtains necessary suggestions and ideas from subordinates

Leader obtains the collective ideas of subordinates

Information/ suggestions/ideas

Alternatives

Negotiation GI Leader and single subordinate Leader obtains subordinates’ ideas

Group GII Leader and group Leader and subordinates together consider alternatives

Delegation D Leader and single subordinate Leader supplies subordinates with any information possessed

Joint problem solvers

None Role of subordinate(s)

Leader explains problem to subordinate(s)? Who makes the decision/ solves the problem?

Consulted individually

Consulted individually

Consulted as a group Joint problem solvers

Sole problem solver

Subordinates, collectively with leader, generate and evaluate alternatives to reach a agreement (consensus) on a solution. Leader acts as neutral chairperson

Leader may or may not request subordinate to inform him/her of the decision made

Leader generates and evaluates solution alone

Subordinate(s) provide only information

Subordinate(s) provide ideas and suggestions

Subordinates provide ideas and suggestions

No

Perhaps

Yes

Yes

Yes

Yes

Yes

Leader alone

Leader alone (perhaps reflecting group inputs)

Leader and subordinate together arrive at a mutually agreeable solution

Leader accepts and implements any solution that has the support of the entire group

Leader delegates problem to subordinate

Leader alone

Leader alone

Fig. 7.5 The Vroom–Yetton styles of decision-making. These are not to be confused with styles of leadership, although there is some correlation (Huczynski & Buchanan, 2007, p. 735). Precisely in an era in which some erroneously think that algorithms can make decisions, the social dimension of decision-making requires awareness of these styles and when to apply

To turn a decision-making process into a learning process in the organization assumes that a decision is not made on basis of an algorithm nor by applying a heuristic, but that the decision is made by exploring and acquiring new insights and understanding. This implies that a decision-maker is not so much a decision as assumed in the definition of Simon, but that the decision-maker in the context of an organization needs to make a decision with respect to how a decision best can be made in view of the efficiency of the decision-making process, as described in the Vroom–Yetton scheme of styles of decision-making (Fig. 7.5). A decision-making process itself needs to be efficient as any operation in a firm. Using too few resources, that is knowledge, will not produce the best decision, bothering the organization with decisions to which members cannot contribute is not efficient either. Different types of decisions, different situations require different styles of decision-making. Vroom and Yetton defined a number of criteria and subsequent decision trees to decide in which situation best which decision styles should be applied:13

13

Huczynski and Buchanan (2007, p. 736).

152

7 Complex Decision-Making

(a) Is there a quality requirement such that one solution is likely to be more rational than another? (b) Does the decision-maker have sufficient information to make a high-quality decision? (c) Is the problem well-structured in the sense of (human) sources of information and proposals for solution, can the decision be partitioned to different individuals or groups? Does the decision-maker know what assistance can be required from whom and how to obtain it? (d) Is acceptance of the decision by subordinates critical to effective implementation? (e) If the decision-maker were to make the decision by himself, is it reasonably certain it would be accepted by his subordinates? Or does procedural justice play a material role in accepting and executing the decision? (f) Do subordinates share the organizational goals to be attained involving this problem? (g) Is conflict between subordinates likely over preferred solutions? The questions above are related to, but are different from the definition of a wellstructured problem and refer to the social process of decision-making and the degree a decision affects especially the members of the organization in their personal work and interests. Decisions with respect to financing the firm usually are seen not to affect the organization as a social system and therefore are made in the isolation of the CEO-CFO. The relation with complexity is defined by Simon’s hypothesis of simplicity: “we use the simplicity of process to deal with complexity of state” (Sect. 11.1). The Vroom–Yetton typology of styles of decision-making seems to be somewhat sterile in view of both the wide scope of problems in an organization and the role of psychology. There is a wide array of types of problems in an organization (Fig. 7.6). There may be a tendency to define problems limited to only one of the business dimensions in Fig. 7.6. Such an approach induces the risk of oversimplifying problems and creating coordination problems or overlooking better solutions. For instance, a large diary firm asked for assistance to design a cost-savings program for its purchasing department. At first, approach was to analyze the operational costs of the purchasing department. A second thought into the problem was that the market prices for the raw material to be purchased, milk, cacao, showed a strong volatility in prices. So, to control costs of purchased goods hedging was required, that is the scope of the problem now included de finance department. That department asked for better quality information on future needs of raw materials, resulting in an extension of the problem to improve the quality of demand management at product management. In this way, the original problem was solved, not by too fast reducing the problem, but by redefining the problem at the system level. The idea of styles in decision-making also depends on the nature of the problem, is it a strategic problem or an operational problem, a structured problem (to be solved as a programmed decision) or an unstructured problem, is the scope of the problem narrow of is it wide? (Fig. 7.7).

7.4 Well-Structured Problems

153

Fig. 7.6 A possible overview of types of problems by function as often implicit used by managers to “delegate” a problem to one department, whereas often business problems touch on multiple departments

Unstructred

Systemic thinking e.g. Becoming a learning organization

Qualitative / Conceptual

e.g. Scenario Planning e.g. Internal governance

Structured

Character of the of the problem

Functional thinking

e.g. Efficiency of HRtransactions

Narrow

e.g. IT-workplaces

Quantitative / Algorithmic

Wide

Scope of the problem in terms of functions Fig. 7.7 An example of how to compare different types of problems. Traditional decisions literature assumes well-structured problems, whereas most problems are difficult to structure

154

7 Complex Decision-Making Directive

• • • •

Prefer simple, clear solutions Make decisions rapidly Do not consider many alternatives Rely on existing rules (heuristics)

Analytical • • • •

Prefer complex problems Carefully analyze alternatives Enjoy solving problems Willing to use innovative methods

Conceptual • • • •

Socially oriented Humanistic and artistic approach Solve problems creatively Enjoy new ideas

Behavioral • • • •

Concern for the organization Interest in helping others Open to suggestions Rely on meetings

Fig. 7.8 Styles of problem-solving in the management literature from the modern era. Styles needed for contemporary complex situation need to be developed in addition to these styles. Mote the preference for complex problems in this overview is followed by reductionist-analysis, not by abstract thinking—reconceptualization (Greenberg & Baron, 2003, p. 366).

The way problems are defined in nature and scope, not only depends on the nature of the problem to the judgment of an objective viewer from the universal perspective of management science, more often this definition is determined by the personality of the person and especially the position a person has in the organization. Different personalities prefer different styles to solve problems and to make decisions, including the valuation of alternatives. Four styles can be identified, directive, analytical, conceptual, and behavioral, these are summarized in Fig. 7.8. Managers with a directive style will be less inclined to broaden the scope of a decision-problem even if this may result in better solutions. The analytical style, although no good research exists on this, will be more likely to be found with staff workers. It follows from research on dynamic capabilities that in today’s dynamic economy, with the convergence of industries, change at industry level; successful CEOs demonstrate a capability to reconceptualize situations. The different styles of problem-solving relate to Herbert Simon’s observation that whether a problem is labeled as ill-structured or complex, mainly is determined by the knowledge base of the executive. But note that the different styles of problem-solving in Fig. 7.8 implicitly are based on the reductionism from the modern era, for the “prefer complex problems” is followed by the traditional decision-making of “carefully analyzing alternatives,” whereas, as will be explained further on, complex problems first require abstract thinking and reconceptualization. How a decision-maker defines a problem or even acknowledges the existence of a problem also may be influenced on his or her position in the organization. The case of Sony Europe, playing in the 1990s, demonstrates that European management clearly saw the implications of the integration of the European market, with pan-European distributors, for the organization of Sony Europe. The country managers, for a part the former owners of the acquired importing wholesalers, simply did not see the consequences of the European integration for the organization of Sony Europe because their personal interests and experiences were national. Another example of contextual influence on decision-making can be found in Bower’s bottom-up resource allocation process.14 The basic idea of this method for resource allocation is that management sets top down a strategic direction and asks 14

Bower (1986).

7.4 Well-Structured Problems

155

bottom-up initiatives that are implemented will perform the strategy and the objectives. Bower noted however that the systemic context, that is the whole of expected career paths, remuneration system, assessment criteria, access to information, values, etc. may be of more influence on the nature of bottom-up initiatives as will be the content of a new strategy.15 Unit managers may perceive the resource allocation process as an opportunity to negotiate as large as possible chunk from the available investment budget for their unit, whether this contributes to the strategy or not. Proposed operational targets may be influenced by the bonus system as managers do not want to risk their bonus, even if this is detrimental to the firm. In case a bonus is based on ROI of a unit, the unit-managers will tend to underestimate market growth to avoid that they have to invest commensurate with that growth, but if the growth turns out to be lower, they forfeit their ROI-based bonus.16 However, Bower observed that in many cases top-managers fail to first translate a new strategy into a corresponding new systemic context as a condition that lower-level managers will generate, that is making a decision which alternative to present, such initiatives as required by the new strategy as opposed to serving parochial interests.17 The phenomenon described by Bower is explained in a different way, more in terms of cognitive structure, by the concept of dominant logic.18 This dominant logic usually develops from success, from how issues and problems successfully have been solved, resulting in a number of heuristics for decision-making. Besides resulting in heuristics, a dominant logic, a phenomenon to be found both in individuals and at the organization level, also results in a lens through which to observe the outer world and specially to interpret signals from the outer world. The effect may be that essential signals for the survival of the firm are not seen or wrongly interpreted, as a result of which needed decisions are not made or wrong decisions are made in terms of the interests of the firm. Another aspect of decision-making is the agenda theory. This theory states that the agenda of the executive board consists of three categories of issues. The first category consists of issues that are actively dealt with in terms of exploration, requiring information, making explicit decisions, etc., the second consists of issues that are monitored, and the third category consists of issues to which the board is passive. The boundaries between these three categories are defined by: (1) The structure of the organization, e.g., the M-form, respectively the costs of the organization form; (2) The costs of information, which may be different between internal information and external information. A general tendency is that when the costs of information decline the categories monitored and passive tend to shrink.19 Even

15

Bower (2000). Jensen (2003). 17 Bower and Gilbert (2005). 18 Prahalad and Bettis (1986, 1996) and Bettis and Prahalad (1995). 19 Arrow (1974). 16

156

7 Complex Decision-Making

more, the organization form, e.g., the M-form, has a more specific effect on the agenda of the executive board and thus decision-making.20 1. Recommendations submitted to the board are defined by the type of operating model, e.g., the multidivisional form. 2. The organization form defines the nature of the options recommended to the board. 3. The organization form defines the criteria on basis of which the executive board makes decisions. 4. The organization form defines what and how the executive board learns from decisions made. This is one of the reasons that in response to Chandler’s rule “structure follows strategy,” Mintzberg quipped “like one foot follows the other,” and an inattentive board may be tempted to let the structure guide decisions, as in the case of March’s rule following decision-making, instead of adapting the structure to a new strategy. A tool to avoid this agenda pitfall is the concept of strategic themes, introduced by Kaplan and Norton, adding a dimension of planning and reporting and thus decisionmaking to the existing structure, independent of that structure.21 This implies that the factoring of decision-making, especially in view of new strategies, new business models may require a great deal from executives. So, the idea of well-structured problems and related to that the rational-economic model of decision-making is a too simple presentation of the reality within which an on which decisions are being made. The administrative model of decision-making and within that the phenomenon of bounded discretion, that is to restrict the definition of problems to be solved and alternatives to choose from to what is conventional and what is familiar with at best incremental new elements, in which satisficing alternatives are decided, is more realistic. However, the economic problem with the administrative model of decision-making is that it results in suboptimal performance.

7.5

Why By and Large Is Decision-Making Successful?

The foregoing raises the question of why then, in terms of economic growth, decision-making in the twentieth century by and large has been so successful. There are multiple reasons to explain the success at the level of the macro-economy, but this has its limits as well. At the level of business itself, there is a high tolerance of wrong decisions and failure, after all, business is a venture, trying new things. The case of the success of Honda’s light motorcycles in the USA, most likely is a rationalization afterward, following an unintended action by some employees of Honda. A second explanation is that many decisions suboptimal themselves, allow

20 21

Hammond (1994). Kaplan and Norton (2004).

7.5 Why By and Large Is Decision-Making Successful?

157

for improving the decision in the execution (but alike good decisions are turned wrong in the implementation). A third explanation is that the context of decisionmaking executives, national economic policy, strong institutions, laws, regulators, industrial relations, the rules of the game in an industry, the capital market, investors, banks, journalist provide or provided a guiding direction for decision-making at executive level. Executives are keen on their reputation, they want to stand out, but they also want to be accepted by the community. Investors like Carnegie forced managers to hire Taylor to improve labor productivity.22 It was the movement of institutional entrepreneurship in the first quarter of the twentieth century, which placed scientific management at the national agenda of the USA and after WWII of that of other countries.23 So by and large decision-making went well but there are three examples that demonstrate that more was needed for good decision-making. The first example is Total Quality Management. Total Quality Management, including Lean and Six Sigma are most important tools for improving labor productivity and capital productivity.24 From that perspective the rational-deductive ideal of neoclassical economy implies that managers will decide to apply TQM-techniques to increase the value of the firm. When Juran and Demming introduced total quality management in the USA in the 1950s, they got no response from US managers; these did not decide to apply it in their firms. Only when Demming and Juran went to Japan and their TQM methods and techniques were adopted and were applied by Japanese firms to the effect that this threatened the competitive position of US firms, TQM became applied in US firms.25 A second example is the correction by the capital market of US executives in the 1980s. Jensen suggests that the stakeholder context of US firms, in combination with an aging population of CEOs being risk averse due to their memories of the 1929 crisis, resulted in errors like the cash flow trap, governance overhang, blind trust, resulting in too little innovation, lack of growth of US firms, with as a consequence that this executive behavior was hurting the US economy.26 This inadequate decision-making needed to be corrected and was corrected by an ideological change, the shift from stakeholder value to shareholder value and an emphasis on maximizing short-term profit, promoted by Friedman, implemented by a capital market, supported by the concepts and tools of corporate finance, through which the US economy effectively was restructured by the capital market forcing firms to restructure, forcing metrics like shareholder value on firms and testing executives for their effectiveness through the concept of break-up value.27 Also, a control revolution was forced upon firms, in which the discretion of executive with respect to the 22

Kanigel (1997). Guillén (1994). 24 Jensen (1998). 25 Pascale and Athos (1981) and Juran (1995). 26 Jensen (1993). 27 Jensen (1993). 23

158

7 Complex Decision-Making

use of free cash flow of the firm was restricted.28 That is to say, the quality of decision-making by executives from a macro-economic viewpoint in the 1970s was substandard and was corrected by a system overarching the CEOs. A third example is the issue of investing in intangible assets. At the end of the 1980s, Michael Porter discovered that the competitiveness of the US economy was suffering, compared to economies of Japan and Germany, because US executives overinvested in tangible assets and underinvested in intangible assets.29 Partly this was due to accounting rules, which do not allow for activating investments in human capital, information capital and organization capital on the balance sheet. Partly this was due to the rhetoric of the capital market to which mergers and acquisitions were easier to sell, as are investments in intangible assets, which by accounting rules need to be taken as expenses detrimental to short-term profit. While at that moment it became clear to economists and analysts that intangible assets had become more important for value creation and the value of the firm as were tangible assets, as reflected in the change adopted within corporate finance to base the value of the firm on future cash flows, not on balance sheet items. So according to the rational deductive ideal of decision-making, US executive did not make the right decisions. Porter, Kaplan & Norton, Harvard Business School and a configuration of other influencers managed to correct the decision-making with respect to investments in intangible assets amongst others through introducing the Balanced Scorecard as a rhetorical tool for managers to explain investments in intangible assets to the capital market.30 Contexts may play a role in decision-making at the executive level. This is relevant in view of the weakening of complexity-reducing institutions. To understand complex decision-making we need to consider that the traditional rational-deductive ideal of decision-making is a too much reduced depiction of decision-making, by scope of factors that may play a role, diversity in the nature of decisions, the process of decision-making, the role of the individual, the factoring of decision-making, even in traditional, non-complex situations.

7.6

What Is a Complex Decision-Problem?

7.6.1

Ill-Defined Complexity

Multiple authors define complex problems as the opposite of well-structured problems that is ill-structured problems. “Ill-structured problems are those lacking a well-defined and complete specification of desired outcome states, input

28

Donaldson (1994). Porter and Wayland (1992). 30 Kaplan (2010). 29

7.6 What Is a Complex Decision-Problem?

159

conditions, problem constraints, and processes involved in transforming input conditions into desired output states.”31 Von Werder lists as characteristics of a complex decision-problem three factors: (1) An unknown number of relevant mutual influencing factors; (2) A difficulty to predict consequences of actions, including unintended consequences, and a difficulty to define the domain of consequences from which there might be feed-back consequences for the organization; (3) A difficulty to define an objective function in view of a multiplicity of stakeholders.32 It is striking that authors on complex decision-making do not provide concrete examples of complex decision-problems, they only present characteristics.33 The question is whether their characteristics of complex decision-problem result from looking at decisions through the lens of the mathematical rational-deductive model of managerial decision-making or whether these characteristics are implied by the nature of the situation. The acknowledgment of possible unknown factors may result from an attitude of the decision-maker to accept the idea that no perfect, comprehensive or universal valid models exist, as described in Martin’s Opposable Mind as a characteristic of successful CEOs, always exploring for better models. This assumes that the problem-owner or decision-maker for some reason acknowledges that existing models of cause-and-effect do not apply to the problem situation. One possibility for such an acknowledgment is that earlier attempts to solve the problem with an available (proven) model did not produce the intended consequences and or produced unacceptable unintended consequences. Implicitly Von Werder assumes that it is difficult or not possible to know the unknown factors of a problem. This is to be questioned. Tactics to deal with a situation of acknowledging that there is a lack of insight and understanding are conducting interviews with experts and players in the field, advanced analysis of available data and trial-and-error, to discover relevant factors. Martin details that successful CEOs always question existing models, use multiple, opposing models, acknowledge that better models exist and try to identify or formulate these.34 Even more, it is well known that each model as a description of causal relation is a simplification, a number of factors have been left out as being not significant in either explanation or prediction, and often assumptions underlying a model are not specified, but can be identified and tested for validity. With that, it is well known that any management- or decision model is not universally applicable or will have validity over a longer period of time due to reflexivity. The fallibility principle states that the fact that a model that has proven to be true until now and applicable is no guarantee that it will be valid tomorrow or for the next problem. With that, the idea of an unknown

31

Qudrat-Ullah et al. (2008, p. 25). Werder (1994, p. 135). 33 Werder (1994), Qudrat-Ullah et al. (2008). 34 Martin (2007). 32

160

7 Complex Decision-Making

number of influencing factors in a decision-problem is not a distinctive criterion to define a problem as a complex problem. With respect to the second criterion, a difficulty to predict consequences of actions, assumes that the problem-owner and decision-maker acknowledge that the available knowledge of causal relations for some reason is inadequate or unreliable and or that needed information is not available. It assumes that the decision-maker does not force the existing causal relations in his script, his experience, or his or her dominant logic, on a situation. There is the acknowledgment of not to know or to understand the problem situation. Tactics to deal with this acknowledgment of not understanding (new) causal relations and hence a difficulty to predict consequences including unintended consequences, are experimenting, trial-and-error, further analyzing the problem-situation by a wider circle of experts, applying methods like e.g. the Delphi-method, Forrester-type modeling, simulations, etc. Other techniques might be to reconceptualize a problem and or to apply abstraction.35 So, the acknowledgment that existing (simple) causal models are inadequate in a decision-problem, may require additional steps in understanding the problem. With respect to the third criterion for a complex decision, to define an objective function in view of a multiplicity of stakeholders, a CEO will understand what political processes and steps to take either to align stakeholders or, as regulated by corporate law, to make a trade-off of interests, to enable the more analytical part of decision-making. In the case of a corporation the system of corporate governance defines the decision rights of various stakeholders. It indeed may be more complex in case stakeholders have power over the corporation, e.g. a major supplier or customer, negotiating power, of NGO who may exert influence through publicity, political action, or legal recourse. Von Werder suggests that one objective function is to be defined to serve all stakeholders in order to have a non-complex problem. In the case of a firm, the concept of objective function appears to be applicable in terms of maximizing stakeholder value, except that stakeholder value cannot be quantified, and certainly not in a single measure. Corporate law in most jurisdictions imply that the CEO or the executive board of an incorporated firm first has to serve the interests of the corporation as legal persona first, and a secondary responsibility is that to the general meeting of shareholders.36 The Supreme Court of the Netherlands is explicit that the executive boards is obliged to serve the interests of the corporation, even if this is contrary to the interests of shareholders. The interest of the corporation is assumed to be the resultant of the interests of all those involved with the corporation as weighted and seen fit by the executive board under supervision and approval of the supervisory board, to which, e.g., the Dutch law states that in weighing the various interests no stakeholder may be unduly prejudiced. That is to say, corporate law structures to a certain level the issue of multiple stakeholders in terms of due process. The legal system provides a voice to employees and shareholders, but not to customers and suppliers nor other stakeholders. Dependent on the nature of

35 36

Lindsey (2012). Cadbury (1995).

7.7 Different Types of Complex Decision-Problems

161

contracts, switching costs and the type of market, suppliers, and customer can take their business elsewhere and thus exert powers over the firm which the executive board will need to consider. Therefore, from an economic perspective, an executive board will strive to create and maintain an efficient firm, that is a firm with an organization form Y that yields outcomes that are satisfactory for each of those involved and no organization form X exists that yields outcomes that are preferred by at least one stakeholder over the outcomes of Y without harming the value of the outcomes for all the other stakeholders. In the case of private equity for example there is a majority shareholder using its power in the shareholder meeting to impose its preference (often a 40% profit in its investment in 4 years) ignoring the interests of other stakeholders to a limit that these will not harm the interests of private equity. So, the third criterion defined by Von Werder to define a decision-problem, a difficulty to define an objective function for multiple stakeholders overlooks how this issue is perhaps not completely regulated in corporate law, but structure to a degree that a CEO or executive board has sufficient powers and guidance how to deal with such situations. Therefore, the criteria suggested by Von Werder to define a complex problem are not satisfactory in view of available methods and procedures to deal adequately with the type of problems suggested by Von Werder. From the comments on Von Werder characterization of complex problems, in which acknowledgment of lack of knowledge plays a role by factors and causality and procedures and legal recourse of structuring multiple interests it may be concluded that a complex problem better can be defined as a—temporary—state of a problem situation for which adequate insights, understanding, modeling, an objective function, etc. not yet have been found, discovered, developed, experimented, etc. A complex problem is a temporary status of learning, of growth, where a wellstructured problem is a process of non-growth.

7.7

Different Types of Complex Decision-Problems

7.7.1

A Perspective on Complex Decision-Problems

Alike different types of complexity exist, different types of complex decisionproblems need to be understood, as these require different strategies to cope with. To understand these and specially to see the available tools, methods, concepts, etc. to effectively deal with complex problems, we need to be aware of the perspective from which we see and contemplate these problems. In Sect. 7.7.4 it was explained that many authors tend to observe complex problems from the perspective of decision-making as making a choice between existing alternatives. But the tasks of management are far broader than making decisions. The tasks of management are defined in what can be called the extended doctrine for business administration formulated by the Frenchman Henri Fayol. In this doctrine the management of the firm has the following seven tasks:

162

7 Complex Decision-Making

1. The constitutional task: setting the mission of the firm (what business the firm is in), its identity (investment profile), its hierarchy of values, establishing its legal organization, its system of corporate governance, its statutes and bylaws, its business principles and to what multiparty codes of conduct the firm will be committed; 2. The tasks to foresee, to scrutinize the future for developments and events relevant for the firm, to define plans and objectives, to set the strategy of the firm; 3. To organize, the acquisition of and control over needed resources, to define the material organization, to develop and maintain the social organization; 4. To provide leadership, that is assisting and facilitating the members of the organization to perform, to develop them, and to provide training; 5. To coordinate the activities of the firm on the market and the activities within the organization of the firm, respectively to establish systems for (self)coordination, including the resource allocation process; 6. To monitor performance against targets set and against assumptions made, to organize learning processes, to monitor compliance; 7. To be accountable to those having a legitimate right to accountability and to those in whose interest of the firm it is to be accountable to. Each of these tasks requires different mental activities beyond the narrow definition of decision-making. Setting a mission, defining a hierarchy of values, identifying business principles is as much a process of awareness, of thinking beyond what is, in relationship with multiple stakeholders, and is more guiding a process of making choices as it is a simple decision. From the cybernetic information theory, different from the mathematical information theory, follows that a welldefined mission and a hierarchy of values are essential tools to understand, at a higher level of abstraction, complex problems. A well-defined governance system may be a help to organize decision-making with multiple stakeholders involved. To scrutinize the future, to ask the question of what might happen and to project one’s own ambitions into it, and with that to be prepared for what might happen or will happen, is a first step to be not surprised by events, but having a mental framework with which such events can be interpreted and organized. To be prepared for future developments, to see these and to be able to interpret these requires “improving concept expandability: learning to manipulate concepts that correspond to non-countable sets or perceptual structures.”37 That is to say, we cannot understand new developments without making a leap to new concepts. The capability of conceptualizing and reconceptualizing existing and new situations is an important tool to master complexity. Another aspect of the task of looking forward is selfdiscovery (see also Sect. 10.2 on abstract thinking). The executive is expected to discover, to see new opportunities, new markets, new products and services, new methods for delivering goods and services, etc.38 Entrepreneurial self-discovery is

37 38

Hatchuel (2002). Hausmann and Rodrik (2003).

7.7 Different Types of Complex Decision-Problems

163

seeing what so far did exist but was not observed and also seeing as a creative act, creating something that was not there until.39 To organize, especially to decide the most efficient organization form of the firm, used to be a matter of selection of one of the conventional organization forms. To grow the economy new organization forms are needed in relation to new business models to break out the restrictions, in terms of production function, of the conventional organization forms. These new organization forms need a creative act, in combination with the creativity to create new customer value propositions and new business models. Creativity in business, like in art, is the capability not only to see the conventional, but especially its implicit rules and limitations and possibilities outside these rules and limitations. A specific issue in organizing is how to organize for creative knowledge workers.40 These creative knowledge workers have different motives, are not only interested in a reward for their contributions, but as much in opportunities to work on that grows their knowledge and personal development. Creative knowledge workers expect both a performance measurement infrastructure through which they can show to others their contributions in the various projects and absence of bureaucratic control. They love the complexity of problems they are to solve, but not the complexity of compliance. A knowledge-based firm implies knowledge governance as opposed to the exploitation of physical resources. One of the consequences of knowledge governance is that mechanisms like projects and processes are organized across existing divisions and departments to serve as governance mechanisms that allow for a more dynamic recombination of knowledge workers with tacit knowledge as changes of structure would be able to do. This implies adding dimensions to the existing structure, which from the perspective of the classical Weberian linear hierarchy makes the organization more complex. Conceptual thinking CEOs solve this by seeing that those projects and processes have priority in the resource allocation process over the traditional departments and thus by changing the internal governance system. The task of coordination traditionally was interpreted in terms of structure and in the resource allocation process. The knowledge economy implies a shift from imposed coordination to decentralize horizontal coordination, from coordination through planning to coordination through proactive behavior and bottom-up initiatives guided by a clear mission and a hierarchy of values. Proactive behavior and bottom-up initiatives also implies that the business model of the firm, as a system of cause-and-effect relations are made explicit and communicated that as many as possible members of the organization can decide for themselves which of their alternative initiatives will contribute most to the overall performance of the firm. This not only requires that as many as possible members of the organization understand the business model but also have access to all relevant information needed for sensing, sense-making and to make calculations and perform simulations.

39 40

Alvarez and Barney (2007). Florida (2004).

164

7 Complex Decision-Making

The implication of the foregoing is that if we think to encounter a complex problem, or a problem that cannot be solve with existing rules and decision rights, that we first need to ask the problem from which perspective, from what responsibility we observe and define a problem.

7.7.2

Professional Induced Complex Decision-Problems

A first type of complex decision-problems results from a lack of knowledge of available strategies, methods, and tools to solve problems with multiple objectives, unknown causalities and multiple stakeholders. The definitions of complex problems in the section here before result from an academic or professional narrowness, and therefore can be labeled professional induced complex decision problems. As explained in Sect. 4.4.2, based on Herbert Simon, the difference between simple problems and complex problems to a large extend are determined by the scope and depth of the cognitive base, to which we add the nature of the knowledge of the problem solver. In this we need to distinguish between the body of knowledge of the decision-maker or problem solver involved, and what knowledge is universally existing. Pfeffer and Sutton have described that managers and professionals tend to regress to known routines and often are reluctant to explore for new knowledge or even ignore this.41 On the other hand, the universally existing knowledge, that is all the knowledge in business, professional firms, academia, with practitioners, is not easily accessible for lack of meta-knowledge or an index system, being recorded in inaccessible ways, or simply being not recorded. All kinds of factors may be at play in shifting the boundary between knowledge ready at hand and knowledge in the domain of to be explored. Available time, budget, the quality of the infrastructure of knowledge institutes and libraries, although improved by the Internet, but also cultural factors may play a role, the nature and level of education, the expectations of the capital market, the nature of the competition, the personalities of the executives involved, etc. Professional-induced complex problems can be solved by tapping the overall body of knowledge in academia and in business, decision modeling, operations research, linear programming, experimenting, exploring through, e.g., scenarioplanning, etc. This may include problems of organizational change. Large-scale change programs are based on the traditional management of change and tend to be limited to behavioral interventions and these behavioral interventions tend to be limited to programmatic change as opposed to task alignment change.42 As a result of which large-scale change programs are very detailed in that these target each of the individual workers in an organization. As individuals differ in learning styles and require different approaches to change minds, such programs are characterized by (detail) complexity. Systemic change acknowledges the working of the Interactive

41 42

Pfeffer and Sutton (2006). Beer et al. (1990, pp. 60–62).

7.7 Different Types of Complex Decision-Problems

165

Perspective Model, that context is a decisive factor in what determines behavior. Changing the few parameters in the systemic context is much more effective to facilitate new change and therefore makes the problem of large-scale change much simpler.

7.7.3

Reflexivity Complex Decisions

A second type of complex decision can be labeled reflexivity complexity decisionproblems. Reflexivity can be understood as the mutual influence between the observer (decision-maker) and the observed, that is the situation of the decisionproblem, respectively the system.43 Contrary to an implicit science-based assumption underlying the rational-deductive ideal of decision-maker, reflexivity acknowledges that what is being observed, the decision-problem in an organization, is influenced by the observer, the decision-maker. The moment the members of an organization learn that the management considers a decision with respect to their organization this will influence their behavior. Vice versa, the decision-maker according to reflexivity, will be influenced by the object of observation, especially when this is about an organizational change to be decided. This is to be seen in March’s rule-following decision-making. It is also reflected in Beer’s concepts of programmatic change as opposed to the task-alignment type of change. Reflexivity implies that the decision-maker, in his or her definition of the problem, valuation criteria (objectives), alternatives, valuation of alternatives, preferred type of finding a solution, preferred style of decision-making is influenced by the decision-problem. Dependent on personality, the nature of the organization, the role of information asymmetries, power relations, etc. this may result in a non-convergent process of difficulty to find a consensus on the definition of the problem, viable alternatives, valuation criteria, etc. As well it may create a situation of innovation and exploration. Another level of reflexivity is that successful business models, because of their success, change the market, attract new competitors, change consumer preferences etc., and create their own demise. When Drucker in his 1946 Concept of the Corporation, announced the end of the success of General Motors strategy of market segmentation as the basis for their multidivisional model, he was expelled from the GM-premises for 30 years, because at that time none of the executive was willing to believe that the success of the strategy and the organization form of GM would come to an end. Drucker proved to be right, when the board in the early 1990s had to oust Stempel, being a product of the GM organization, who could not see necessary changes. In the present digital economy with fast feedback loops some business models are short lived, but there is also reflexivity between management models and concepts and the situation on which these are applied. Hayek’s economic theory implied decentralization of decision-making, which was achieved through a combination of new accounting techniques, behavioral interventions and the

43

Beinhocker (2013), Lash (2003).

166

7 Complex Decision-Making

unit-organization, and this was successful. The growth of uncodified knowledge, and the increasing role of information as an asset, an input and output of the production process implied that a next degree of decentralization required a different organization form, different from the M-form. But it is precisely the success of the M-form, having become a mental model, producing identities and roles, accounting schemes, etc. that makes it for many managers and members of traditional organization forms difficult to see a next-generation organization forms. Reflexivity is with that also about finding new rules, whereas in the era of modernity theses were institutionally defined.44 Reflexivity can also be observed in the endogenic relation between strategic thinking and strategy execution. In itself, it is useful to involve members of the organization in the development of strategies, because in that way a best use is made of available knowledge in the organization and it helps to develop commitment to the strategy, as elaborated in the bottom-up resource allocation process of Bower. Those involved, however, often unconsciously, interpret strategic alternatives in terms what it will bring them as individuals or their departments, especially in terms of the allocation of investment funds. This, of course, is expressed in apparent objective terms and therefore strategy definition, by both process and content is being experienced as a complex process of which those involved find it difficult to understand what is going on.

7.7.4

Decision-Rights Complexity

A third type of a complex problem can be characterized as decision rights complexity. A problem can be labeled as complex in case a number people acknowledge there is a problem to be solved, but that for whatever reason there is a lack of clarity about the required decision rights to solve the problem, respectively these decision rights are distributed among multiple parties with divergent objectives and interests. The cause of complex decision rights complexity can be multiple. There may be sloppiness in the system of internal governance. More often is the case that new technologies create new opportunities, e.g., an e-commerce channel, which implies new decision-problems, multi-channel policies, for which the existing structure of the organization has not defined decision rights. An example of this is in the Netherlands with respect to the organization of the so-called basic databases for public use, e.g., the citizens register. These are legally mandated registration on persons, buildings, infrastructures, etc. By law, the counties are responsible for these registrations. The information in these databases is needed by a number of governance agencies, the internal revenue service, counties, social security agencies, police, etc. Due to the mobility of citizens beyond the boundaries of the counties and agencies operating above and over the level of counties there is a need for a national database for these basic data. To achieve this,

44

Lash (2003).

7.7 Different Types of Complex Decision-Problems

167

the civil servants involved have defined the objective to create a national-level system of these databases, as a problem of cooperation in a chain, the links in the45chain being the state, government agencies, counties, police, etc. Because the ownership of the data is considered to be a given, the counties are by law the owner. This implies the question of who is responsible for investments, who is entitled to the monetary benefits of the database, who is responsible for the input, verification, accuracy, and reliability of information, who will be liable for malfunction or abuse of information, etc. Within this framework, the solution is seen to be that of cooperation between the parties involved. This cooperation is hindered by a system of rights based on a situation in which the parties involved are not mutually dependent for information, dating back to the nineteenth century. New technologies, especially when it is about infrastructures, tend to create new property rights and therefore require a new system of decision rights. The interesting thing is that Denmark facing the same problem has solved this by declaring such databases of national interests, being the new infrastructures of a country in an information society, and has moved the ownership of the data from the counties to the state, including the budgets for these databases and including all decision rights with respect to these databases. That is a change of problem definition and a state assuming responsibility and having the political power to make decisions with respect to the system causing the problem to be a complex problem reduced the complexity of the problem. More in general it must be observed that executives understandably have tendency to ‘solve’ the issues as induced by liberalization of markets, the relaxing of rules, the emergence of intangible assets within existing concepts or by emphasizing culture, values and the new behavior of employees. Ultimately this does not work because such changes also change the system of decision rights: “The breakdown of the either/or logic of first modernity cannot be accepted in silence, because ultimately it paralyses institutional action and decision-making.”46 The silence the sociologist Beck refers to is the lack of discussion with respect to the needed changes in the system of internal governance and thus the system of decision rights in order to adjust the latter to the new situations created by technology. Related to decision-rights complexity might be defined information complexity that is that needed information to make a good decision is difficult to come by for the decision-maker. There are two elements to this. The first is that it may be difficult to define what is relevant information, for understanding the problem, understanding (new) causal relations, unintended consequences, etc. When it is about basic data, this type of information complexity tends to be less of a problem, as firms tend to eliminate information asymmetry in their internal organization, as e.g. IBM has done. Usually, the role of external consultant in decision-problems is, through interviews and through analysis in the organization, to eliminate decision-blocking information asymmetry. Another issue may be to define what is relevant

45 46

Spar (2001). Beck and Lau (2005).

168

7 Complex Decision-Making

information. For this, a (new) theory or model will be needed. A third type of information-complexity in decision-making may be that the knowledge required for making a good decision is tacit, personal, uncodified knowledge, vested in a number of (knowledge)workers in the organization. The situation resulting from that may be defined in terms of power relations and how to achieve consensus, more effectively this third type of information asymmetry is to be understood as a problem of property rights.47 Tacit knowledge is the property of the individual carrying that knowledge, and property rights imply decision rights. Attempts to control such decision rights by corporations through systems for knowledge management have failed.48 According to Jensen, it will be more effective to define new types of contracts between tacit knowledge carrying workers and their employers.

7.7.5

Epistemological Complex Decision-Problems

A fourth type of complex decision-problems can be labeled epistemological complex decision-problems. In line with epistemological complexity, and different from complexity induced by a too-narrow focus on existing tools, these types of complex decision result from trying to understand or to define a problem with a concept that fails the required variety or does not correspond with the basic conditions underlying the problem to be solved. When Levi, the CEO of the French marketing company Publicis made his first attempt to digitalize Publicis, it failed, because that attempt was defined within the logic of the existing business model. The second attempt, using the transformation acquisition of Digitas, a Boston-based marketing firm specialized in digital marketing, was successful, because it started with and was organized on basis of the new insight of Levi that digital technology would change not only the marketing industry, but it rules of the game, would bring new players like Google, would change the very idea was marketing is in relation to the effects of digitalization on consumers. This is what the economist Phelps writes about when he observes that European CEOs underestimate the capability of US CEOs to reconceptualize their industry and their business to address new opportunities, new options, threads resulting from amongst others technological developments and liberalization. Alike Stiglitz observes that executives sticking to obsolete concepts of industries, markets and organization impair economic growth.49 This reconceptualization requires the capability of abstract thinking informed by knowledge of underlying principles and an understanding of developments in society, in terms of both how these affect the basic needs of consumers and affect the often-implicit rules of the game in an industry or market.50

47

Wilhelm and Downing (2001). Wilhelm and Downing (2001). 49 Stiglitz and Greenwald (2014). 50 Woiceshyn (2009). 48

7.7 Different Types of Complex Decision-Problems

169

That is the epistemological type of complexity also results from weakening the complexity-reducing institutions of modernity, institutions that also regulated decision-making. To respond to new opportunities in an incremental way, and containing risks induced by the weakening of the institutions, is itself a source of risk.51 It results in not making those new type of decision with respect to strategies, organization forms as are possible and needed from an economic viewpoint, but result in a conservatism, often sold as a tactic to deal effectively with the new complexities.

7.7.6

Discovery Versus Justification Complexity

A fifth type of complex decision-problem might be labeled as discovery versus justification complexity. Following the philosophy of science, we may state that a firm has a context of discovery (opportunities in markets, new technologies, operating processes, demographic developments, etc.) and a context of justification, the capital market, shareholders, stakeholders, the system of corporate governance, regulators, the press, labor unions.52 The context of discovery may offer the firms new business models, new operation models, new organization forms, e.g., information-based empowerment, that conflict with the concepts, norms, and expectations in the context of justification. Active investors whose business model is exploiting the break-up value of firms will be reluctant to accept the value of an integrated, synergies exploiting firm. The economical required decentralization through information-based empowerment, and which today is possible through the combination of a higher educated workforce and lower costs of information with its loose programming en loose control to be in-control, conflicts with the concept of tight control as underlying the concept of in-control used by auditors and regulators. In the context of corporate governance and on risk management focused chartered accountants, internal auditors and regulators tend to base themselves on concepts, norms, tools of which they think are proven, but are so for the twentieth century, and increasingly are conflicting with the requirements and available options in the twenty-first century. Zuboff and Maxmin observe that the root cause of problems we try to solve in business is not to be found with managers themselves, but in their context of education and especially first work experience: “The problem wasn’t management, per se, but the institutionalized practices and logic that shaped managers’ work.”53 Part of that institutionalized logic is language. Executives want to implement new organization forms, facilitating their knowledge workers for combinatorial innovation, thus self-organization and self-coordination, proactive behavior, and possible applying trial-and-error to be successful. But in what language managers

51

Beck (2009). Schickore and Steinle (2006). 53 Zuboff and Maxmin (2002, p. xiii). 52

170

7 Complex Decision-Making

should explain such a type of organization to the supervisory board or other parties in the context of justification? We saw this issue earlier with the need, the logic to invest in intangible assets, but a capital market preferring investments in tangible assets. There is a tendency to emphasize elements like culture and values, because explaining the new organization form in required system changes is not quite popular and a supervisory board from their frame of reference might perceive changes in the systems of the organization, like accounting systems and information system as risky. Paradoxically the context of justification may also reside within the organization. Due to the historical dominance of sociologically organization theory over economic organization theory, many members of organization, reinforced by popular management books, expect new organization forms, but expect them to be explained in popular non-technical terms, as a result of which managers show a tendency to respond to this and to fail to implement the material conditions as required for new organization forms. Luhmann already warned for this phenomenon in commenting the sender-receiver relationship of traditional communication theory, because the sender wants to be accepted by the receiver, the sender is sensitive to subtle signals of the receiver to the effect that the receiver determines what the sender is telling and how he or she is telling it.54

7.7.7

Temporality of Complex Decision-Problems

Complex problems do exist, not so much as objective facts in nature, but by the willingness of those responsible for making decisions, that existing routines, existing heuristics do not serve the situation, and that simplifying the new situation to the familiar is irresponsible. Dependent on the nature of a problem to be a complex problem, a variety of techniques, tactics, processes and methods exists to solve the complex problem. Although perhaps in the awareness that any solution of today is the cause of problems tomorrow, final solutions do not exist. A complex decisionproblem in a normal flow of events is a state of mind, individually and collectively of a higher level of awareness of the need and willingness to learn through thinking, exploration, redefining situations, and experimenting. This explains the O’Toole observation that successful CEOs look for simplicity beyond complexity and Martin’s observation “they wade into complexity” and “better models exist.” Solving complex decision-problems is learning at a conceptual level.

Bibliography Alberts, D. S. (2011). The agility advantage: A survival guide for complex enterprises and endeavors (p. 114). CCRP.

54

Luhmann (1984).

Bibliography

171

Alvarez, S. A., & Barney, J. B. (2007). Discovery and creation: Alternative theories of entrepreneurial action. Strategic Entrepreneurship Journal, 1(1–2), 11–26. https://doi.org/10.1002/sej.4 Arrow, K. J. (1974). The limits of organization. Norton. Artinger, F., Petersen, M., Gigerenzer, G., & Weibler, J. (2015). Heuristics as adaptive decision strategies in management. Journal of Organizational Behavior, 36(S1), S33–S52. https://doi. org/10.1002/job.1950 Bazerman, M. H., & Moore, D. A. (2009). Judgment in managerial decision making (7th ed.). Wiley. Beck, U. (2009). World at risk. Polity Press. Beck, U., & Lau, C. (2005). Second modernity as a research agenda: Theoretical and empirical explorations in the ‘meta-change’ of modern society. The British Journal of Sociology, 56(4), 525–557. Beer, M., Eisenstat, R. A., & Spector, B. (1990). The critical path to corporate renewal. Harvard Business School Press. Beers, P. J., Boshuizen, H. P. A., Kirschner, P. A., & Gijselaers, W. H. (2006). Common ground, complex problems and decision making. Group Decision and Negotiation, 15(6), 529–556. https://doi.org/10.1007/s10726-006-9030-1 Beinhocker, E. D. (2013). Reflexivity, complexity, and the nature of social science. Journal of Economic Methodology, 20(4), 330–342. https://doi.org/10.1080/1350178X.2013.859403 Bettis, R. A., & Prahalad, C. K. (1995). The dominant logic: Retrospective and extension. Strategic Management Journal, 16(1), 5–14. Bower, J. L. (1986). Managing the resource allocation process. Harvard Business School Press. Bower, J. L. (2000). A general management view of change. In M. Beer & N. Nohria (Eds.), Breaking the code of change. Harvard Business School Press. Bower, J. L., & Gilbert, C. G. (2005). A revised model of the resource allocation process. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy. Oxford University Press. Brickley, J. A., Smith, C. W., & Zimmerman, J. L. (2001). Managerial economics and organizational architecture (2nd ed.). McGraw-Hill. Cadbury, A. (1995). The company chairman (2nd ed.). Director Books. Cyert, R. M., & March, J. G. (1963/1992). A behavioral theory of the firm 2. Blackwell. Donaldson, G. (1994). corporate restructuring: managing the change process from within. Harvard Business School Press. Dorst, K. (2006). Design problems and design paradoxes. Design Issues, 22(3), 4–17. Fama, E. F., & Jensen, M. C. (1998 (org. 1983)). Separation of ownership and control. In M. C. Jensen (Ed.), Foundations of organizational strategy. Harvard University Press. Florida, R. (2004). The rise of the creative class. Basic Books. Greenberg, J., & Baron, R. A. (2003). Behavior in organizations: Understanding and managing the human side of work (8th ed.). Prentice-Hall. Guillén, M. F. (1994). Models of management: Work, authority and organization in a comparative perspective. The University of Chicago Press. Hammond, T. H. (1994). Structure, strategy, and the agenda of the firm. In R. P. Rumelt, D. E. Schendel, & D. J. Teece (Eds.), Fundamental issues in strategy: A research agenda. Harvard Business School Press. Hatchuel, A. (2002). Towards design theory and expandable rationaltiy: The unfinished program of Herbert Simon. Journal of Management and Governance, 5(3–4). Hausmann, R., & Rodrik, D. (2003). Economic development as self-discovery. Retrieved from Cambridge, MA. Huczynski, A. A., & Buchanan, D. A. (2007). Organizational behaviour (6th ed.). Financial TimesPrentice Hall. Jensen, M. C. (1993). Modern industrial revolution, exit, and the failure of internal control systems. SSRN. Jensen, M. C. (1998). Foundations of organizational strategy. Harvard University Press.

172

7 Complex Decision-Making

Jensen, M. C. (2003). Paying people to lie: The truth about the budgeting process. European Financial Management, 9(3), 379–406. Jensen, M. C., & Wruck, K. H. (1998). Coordination, control, and the management of organizations: Course content and materials (3rd of 4 CCMO documents). SSRN eLibrary. Juran, J. M. (1995). Managerial breakthrough (revised ed.). McGraw-Hill. Kanigel, R. (1997). The one best way: Frederick Winslow Taylor and the enigma of efficiency. Viking. Kaplan, R. S. (2010). Conceptual Foundations of the Balanced Scorecard. SSRN eLibrary.https:// doi.org/10.2139/ssrn.1562586 Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business School Press. Langley, A., Mintzberg, H., Pitcher, P., Posada, E., & Saint-Macary, J. (1995). Opening up decision making: The view from the black stool. Organization Science, 6(3), 260–279. Lash, S. (2003). Reflexivity as non-linearity. Theory, Culture & Society, 20(2), 49–57. Lindsey, B. (2012). Human capitalism: How economic growth has made us smarter-and more unequal. Princeton University Press. Luhmann, N. (1984). Soziale Systeme: Grundriß einer allgemeinen Theorie. Suhrkamp. March, J. G. (1991). How decisions happen in organizations. Human-Computer Interaction, 6, 95–117. March, J. G. (2006). Rationality, foolishness, and adaptive intelligence. Strategic Management Journal, 27, 201–214. Martin, R. L. (2007). The opposable mind: How successful leaders win through integrative thinking. Harvard Business School Press. Pascale, R. T., & Athos, A. G. (1981). The art of Japanese management: Applications for American executives. Simon and Schuster. Pfeffer, J., & Sutton, R. I. (2006). Hard facts, dangerous half-truths, and total nonsense: Profiting from evidence-based management. Harvard Business School Press. Porter, M. E., & Wayland, R. (1992). Capital disadvantage: America’s failing capital investment system. Harvard Business Review, 70(5), 65–82. Prahalad, C. K., & Bettis, R. A. (1986). The dominant logic: A new linkage between diversity and performance. Strategic Management Journal, 7(6), 485–501. Prahalad, C. K., & Bettis, R. A. (1996). Dominant logic. In M. Goold & K. S. Luchs (Eds.), Managing the Multibusiness Company (pp. 398–420). Routledge. Qudrat-Ullah, H., Spector, J. M., & Davidsen, P. I. (2008). Complex decision making: Theory and practice. Springer. Schickore, J., & Steinle, F. (2006). Revisiting discovery and justification: Historical and philosophical perspectives on the context distinction. Springer. Simon, H. (1965). Administrative decision making. Public Administration Review, 25(1), 31–37. Simon, H. A. (1973). The structure of ill structured problems. Artificial Intelligence, 4(3-4), 181–201. Simon, H. A. (1987). Making management decisions: The role of intuition and emotion. The Academy of Management Executive, 1(1), 55–64. Spar, D. L. (2001). Ruling the waves: Cycles of discovery, chaos, and wealth from the compass to the internet. Hartcourt, Inc. Stiglitz, J. E., & Greenwald, B. C. (2014). Creating a learning society: A new approach to growth, development, and social progress. Columbia University Press. Tricker, R. I. (1984). Corporate governance. Gower.

Bibliography

173

Werder, A. V. (1994). Unternehmungsführung und Argumentationsrationalität: Grundlagen einer Theorie der abgestuften Entscheidungsvorbereitung. Schäffer-Poeschel. Wilhelm, W. J., & Downing, J. D. (2001). Information markets: What business can learn from financial innovation. Harvard Business School Press. Woiceshyn, J. (2009). Lessons from “good minds”: How CEOs use intuition, analysis and guiding principles to make strategic decisions. Long Range Planning, 42(3), 298–319. https://doi.org/ 10.1016/j.lrp.2009.05.002 Zuboff, S., & Maxmin, J. (2002). The support economy: Why corporations are failing individuals and the next episode of capitalism. Viking.

8

Complexity and Coordination

8.1

Complexity and Coordination

8.1.1

Does Complexity Substitute for Coordination?

A core function of the organization of the firm, within the context of its function of control as defined in § 5.3, is to coordinate the internal and external activities of the firm.1 Related to that in the international administrative doctrine coordination is defined as one of the core tasks of the executive.2 The need for deliberate coordination is induced by the specialization of tasks as needed to increase productivity and with that to increase economic growth.3 In § 3.1, it was explained that through specialization of tasks a positive relation exists between specialization, economic growth, and complexity. In its turn the task of coordination implies a need for information, more specifically the organization of information and the organization of the processing of information.4 Traditionally the coordination function of the organization was achieved through a managerial hierarchy in a combination of organizational structure, planning, scheduling, and issuing commands (management by instruction) to individual workers. This coordination function of the hierarchy was mixed with other roles of the same hierarchy, achieving leverage on scarce knowledge as levels of vocational training and training in engineering were low in society and high-level training limited to a few, and with the role to maintain standards. Further, these roles were mixed with the role of the hierarchy to represent the ownership of the assets with

1

Fayol (1918/1999), Barnard (1948), Coase (1937), Staehle (1991), p. 11. Barnard (1948), Fayol (1918/1999). 3 Milgrom and Roberts (1992), p. 25. 4 Milgrom and Roberts (1992), p. 26. Casson (1997). Chapter 2, Casson however limits coordination to resource allocation. 2

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_8

175

176

8 Complexity and Coordination

which workers worked and thus to execute the ius utendi, maintaining a balance of power between capital and labor. The system of hierarchical or imposed coordination based on non-price information stood opposed to the coordination of demand and supply in the open market based on prices. Hence, the criterion of Coase that the legitimacy of vertically integrated firms had its base in that this managerial coordination needs to be more efficient as the coordination through a market mechanism of the same specialized activities. The market mechanism has costs as well, called transaction costs. The decline of those transaction costs after WWII resulted in the reduction of the degree of vertical integration and subsequently a growth of outsourcing and (global) supply chains (thus increasing the complexity of markets). There are two issues in comparing managerial coordination and the coordination by the price-based market mechanism. The first issue is that the theory on market mechanisms assumes that in the price of a product all the information about the product is incorporated, subsequently the only three parameters needed for arm’s length market transactions are identification of the commodity, quantity, and price. The reality is that both in the B2B-market and in the B2C-market products have a wide variety, often within the same category and additional, sometimes very detailed information (product fact sheets) are needed, and in the B2B-market also information on availability by time and place. The price-based market coordination is therefore restricted to matching supply and demand. The second issue in comparing managerial coordination with price-based market coordination is that managerial coordination primarily is based on the product-structure to achieve flow production using specialized activities, which is not only about balancing purchasing of raw materials, hiring workers, procuring production equipment with planned production, but as much this coordination is about sequencing, scheduling, logistic planning, to avoid idle time, buffer stocks, redundant capacity, and other costs impairing the benefits of specialized activities. As a consequence, managerial coordination requires a high level of information to be processed to achieve efficient coordination. It was Hayek who observed that this high level of information to be processed cannot be processed in a hierarchy because the information channels in the hierarchy nor those at the top of the hierarchy possess the capacity to process all this information accurately, completely, and timely. With the increasingly differentiation of products and more sophisticated product and production technologies in the eighties, the tactics to deal with coordinating complex organization introduced around 1918 hit their limits and an idea came into existence of complex organizations, different from Herbert Simon’s concept of complex organization, in which complexity is achieving coordination through local adaptive behavior by actors in the system. As we will see below, how sympathetic the idea is in itself, it basically is introducing price-based market coordination in the organization, foregoing that the market coordination is based on the co-location principle: actors are the owner of the product they take to market or purchase, have full decision rights of alienation, and have full rights to the economic value of the transaction. For all kind of good reasons employees in a corporation do not satisfy the co-location principle. This is not to say there is no value in the idea of actors acting local or decentralized to achieve coordination, at the contrary, as Hayek

8.1 Complexity and Coordination

177

explained, this is necessary to maximize the information processing capacity of the organization, needed to maximize the growth capacity of the firm, but to achieve this quite some specific conditions need to be in place. To understand this in the next section, it will be explored in more detail what coordination is and what instruments for coordination are, especially in relation to the phenomenon of complex organizations and organized complexity. From that it will be clear that complexity does not substitute for coordination. To understand what the relation is or can be between complexity and coordination, or more specific, how coordination is achieved in a complex organization, we first need to understand what coordination is by function, objective and how it can be achieved. What is coordination? A puzzling fact is that despite a general acknowledgement that coordination is a core function of the organization and a core task of the executive, a variety of definitions can be found in only a few articles on the theory of coordination.5 There is little explicit theorizing on coordination. This can be explained because in economic theory and with that in economic organization theory, coordination is assumed to be performed by the price-based market mechanism.6 In the managerial theory the function and task of coordination is seen as the essence of management, but is seen to be executed not as a separate function, but executed through a combination of planning, organizational structure, directing, culture, and control.7 The execution of the coordination function through all the other functions and tasks of management certainly is true but, as we will see, it obscures a precise understanding of what coordination is and how it can be achieved in the complex organization. We will start with a definition of coordination that implicitly is based on production, later on we will see that this definition needs a modification to understand coordination in complex organizations. Our “zero”-definition of coordination is: The coordination of specialized and functional activities, to result in a defined customer value proposition (product, service), is the planning (sequencing) and organization of these activities such that the production of the customer value proposition is efficient in terms of avoidance of unproductive waiting times for resources (no waste), and the availability of workers, knowledge, information, materials, sub-assemblies, tools, equipment at the right time, at the right place, in the right quantities or capabilities and according to the required specifications.

This is what Henri Fayol defined in his wording: coordonner, c’est-à-dire relier, unir, harmonizer tous les actes et tous les efforts.8 The proposed definition finds its practical application in Just-in-Time (JIT) production systems, the concept of flow production, in end-to-end processes and in the concept of lean synchronization.9 The

5

Jarzabkowski et al. (2012), Okhuysen and Bechky (2009), Malone and Crowston (1994). Picot, et al. (2005), Milgrom and Roberts (1992). 7 Jones (2013), Weihrich and Koontz (1993/1955), Appleby (1994). 8 Fayol (1918/1999), p. 8. 9 Slack et al. (2012), p. 352. 6

178

8 Complexity and Coordination

proposed definition not only applies to activities within and by the organization of one firm, it as well applies to supply chains across multiple firms. Following Coase, we have included the criterion of efficiency. The managerial coordination needs to be more efficient compared coordination through the price-based market mechanism.10 This criterion rules out imprecise definitions like “coordination is managing dependencies between activities.”11 Note that the efficiency to be achieved by coordination is different from the efficiency of the specialized and functional activities themselves. A third category of efficiency is the efficiency of the coordination process itself. Coordination is to be achieved by multiple instruments, but the proposed definition excludes coordination through redundant capacity and redundant intermediate stocks, as these reduce efficiency. This zero-definition of coordination appears to exclude Simon’s loose programming and loose control and with that the complex organization. In situations of unpredictability, in which processes cannot completely be controlled or, e.g., variations in the composition of raw materials require local improvisation and responsiveness to deviations some discretionary freedom for action will be needed to achieve or efficient coordination. Dependent per product or service, a wellcoordinated supply chain may need to have the capability for product or service adaptation to answer specific or new customer needs.

8.2

How Is Coordination Achieved?

To understand what the relation between complexity and coordination may be, it will be helpful to have a better understanding through which administrative instruments coordination is achieved in organization. To which must be added that the coordination within organization to a large extent depends on the institutional context of the firm: a metric system for lengths, sizes, weights, industry standards (ISO, DIN), a universal time measurement system (UTC),12 language, law, professional education, vocational training, a transport and communication infrastructure, etc. To understand how coordination is achieved, we need to distinguish multiple categories of coordination, as depicted in a kind of three-dimensional space (Fig. 8.1). The zero-definition of coordination is in the quadrant of hierarchical-explicit coordination, although through, e.g., Kanban techniques some degree of self-coordination can be included in lean synchronization. Coordination in intelligent complex adaptive system is assumed to be dominantly in the quadrant of implicit-selfcoordination and it will include stigmergic coordination. Following Hayek (decentralized information processing) the self-coordination needs to be maximized and the hierarchical coordination needs to be minimized. The principle of proactive

10

Coase (1937). Malone and Crowston (1994). 12 Landes (2000). 11

8.2 How Is Coordination Achieved?

179

Hierarchical or imposed coordination Feed-forward coordination

is -ax e Tim

Explicit coordination

Implicit coordination Stigmergic coordination

Feedback coordination Horizontal or selfcoordination

Fig. 8.1 Categories of instruments for coordination (and control) in the organization of the firm can be arranged on three dimensions, showing that coordination itself is a complex function

behavior implies that feedforward coordination should be dominant over feedback coordination. A core question is how in the case of an intelligent complex adaptive system efficient coordination can be achieved whilst maintaining the integrity and the identity of the firm. The claim in management theory that the function of coordination is performed through multiple functions, tasks, and aspects of management and organization is illustrated in Fig. 8.2. To understand how all of these instruments work together an additional ordering is needed to that suggested in Fig. 8.2. With that also an additional dimension is introduced, the role of institutions in the coordination and cooperation between economic actors.13 Formal, formative institutions define property rights, contract law, labor law, corporate law, and patent law as implicit underlying cooperation and coordination.14 Informal institutions define language, money systems, metric systems, and industry standards (ISO, DIN) without which coordination and cooperation would be difficult. Educational institutions define professional fields like engineering, accounting, and management sciences. The formative institutions reduce basic uncertainties in society. During the Middle Ages, European merchants

13 14

Picot et al. (2005), p. 10. Furubotn and Richter (2000), p. 268.

180

8 Complexity and Coordination Im p o s e d c o o rd in a tio n M iss io n s ta te m en t C o m p a ny va lu e s

E xe m p la ry b e h a v io r

R ecru itm en t crite ria

R e se rve d p o w e rs P o lic ie s S ta nd ards

C om m un ica te d S tra te g y

O p e ra tio n a l P la nn in g / B ill o f M a te ria ls

D ecisio n rig h ts

In stru ctio n s A ssessm en t Building R em u n e ra tio n D e cisio n Loyalty crite ria p ro m o tion P re -o rg an iza tio n a l P ro gra m m e d S o cializa tio n d e cision s P h ysica l layou t (vo ca tio na l/ tra in in g ) P ro ce d u re s T ra in in g M a n a g e m e nt In fo rm atio n Im p lic it D e ve lo p m e n t c o o rd in atio n E xp lic it c au s e -e ffe ct d ia g ra m s O rg a niza tio n a l (P ro c e ss e s) so cia liza tio n

C u ltu re

E xp e rie n ce s D om in a n t lo g ic

T a sk-structu re (p e rso n a l, B U ’s)

P ro fessio n al tra in in g

S o cie ta l v a lu e s & n o rm s

S to rie s

T a rg e t se ttin g / R e so u rce a llo ca tio n process P erform a nce re vie w

T rust

R o u tin e s

E x p lic it c o o rd in a tio n

P ro fe ss io n a l sta nd a rd s

(process-) tea m s P e er grou p co ntrol S e lf c o o rd in atio n

Fig. 8.2 Overview of administrative instruments for coordination arranged on the two dimensions of hierarchical versus horizontal coordination and implicit versus explicit coordination. Effective CEOs know these instruments and know how to apply these. This overview also illustrates that different from Fayol, coordination like control is to be programmed in all dimensions of the organization

were eventually able to force the feudal ruling class to acknowledge property rights and contract law, in order to develop a long-distance trading system.15 North defines: “Institutions are the humanly devised constraints that structure political, economic and social interactions. They consist of both informal constraints (sanctions, taboos, customs, traditions, and codes of conduct), and formal rules (constitutions, laws, property rights). The major role of institutions in a society is to reduce uncertainty by establishing a stable (but not necessarily efficient) structure to human interaction.”16 Throughout history institutions have been devised by human beings to create order and reduce uncertainty in exchange by limiting the variety of behaviors and relations, and thus reducing complexity. To an extent, this was also necessary to limit the amount of information to be processed in relationships and thus the coordination of activities in view of the then limited capacity in societies to process information.17 The concepts for management and organization of the first half of the twentieth century also can be understood as complexity reducing institutions in view of the then limited capacity to process information, to avoid that the efficiency gains through economies of scale would be lost by high costs of (centralized) coordination. 15

Landes et al. (2010), p. 176. North (1990), p. 6. 17 North (1991). 16

8.2 How Is Coordination Achieved? Fig. 8.3 Levels of coordination in the conventional firm. Much of the efficiency in coordination results from indirect conditions (institutions) and instruments (resource allocation) whereas the modern fast ICT tends to emphasize the operational instruments for coordination

181

Institutional context (external to the firm) The firm as an institutional context for operations (= structure)

Strategic coordination

Coordination by resource allocation

Operational coordination

So, it is understandable that when the capacity in society to process information increased, through higher levels of education and ICT, and the high growth of the trente glorieuse came to an end, economists pleaded for pushing back the complexity reducing institutions under the label of market liberalization, to create conditions for economic growth. With which came a higher level of complexity. From this follows that coordination is a multilevel function, which for the moment we can define as consisting of an institutional level (or context), the strategic level, the level of structure, the level of resource allocation,18 and the operational level (Fig. 8.3). These levels are parallel to the levels of strategy formulation, management control, and task control in the function of management control.19 The “zero”-definition of coordination proposed herebefore typical is at the level of operations (lean synchronization). The level of structure has its roots in task specialization, the associated concept of hierarchy and later in history Chandler’s structure follows strategy, in terms of business units and division. Structure is not an active element in the coordination of activities or the allocation of resources, it defines a system of accountable entities used in the coordination of activities. The idea and practice to define structure in the Weberian-hierarchical sense as a prerequisite to dynamic coordination is reflected in the structure school of coordination (as opposed to the process school of coordination). In the structure school of coordination, coordination is achieved ex ante through the design of structure, roles, and rules. This is also sometimes labeled as the design school of coordination; a school based on the assumption that an a priori systems can be designed with sufficient specificity and precision to allow individuals and specialized departments to complete the work in an efficient way.20 This structure school of coordination includes the traditional process of resource allocation, or 18

Casson (1997), Malone and Crowston (1990). Anthony and Govindarajan (2007), p. 7. 20 Okhuysen and Bechky (2009). 19

182

8 Complexity and Coordination

budgeting, as these budgets are based on departments or divisions defined by the structure, and budgeting is ex ante. The structure school has its operational origins in task specialization. In terms of organization theory, its roots are in Weber’s concept of bureaucracy and Taylor’s concept of tasks specialization to control the shop floor. The second school for coordination as defined in literature is process-based coordination. This type of coordination in general will build upon the ex ante structure-based coordination but focuses on a continuous readjustment of especially operational activities due to changes in demand, errors in production, capacity issues, breakdown of equipment, HR-issues, quality issues, etc. The process-based coordination has its origin with Henry Fayol.21 The structure school and the process school represent in terms of business administration different and complementary levels of coordination. These two schools as defined do not reflect the modern economy nor the present complex organization and their processes of creating value. Especially the structure school still is dominant in many managers’ mind; the first thing many managers think of when to execute a strategy is what structure needs to be created.22 Due to the declining costs of information and communication and thus declining costs of coordination, in combination with the changing nature of assets (more intangible), it is now possible to recombine resources from multiple business units, divisions and support departments in a cost-effective way and to coordinate operational activities on a planning-based way (as opposed to coordination through transfer prices) even across multiple legally independent firms. This implies that no longer the traditional structure in terms of managerial hierarchy necessarily defines the planning units and with that the units of coordination, nor in terms of resource allocation nor in terms of operational activities. In order to have higher returns on especially intangible assets, human capital, information capital, and organization capital, the execution of strategy today first is translated in strategic themes, especially in the combination of customer value propositions and the processes to develop and to deliver those customer value propositions. These processes are defined across all the departments, business units and divisions, shared service centers, including suppliers which are needed to make a contribution to that defined customer value proposition. These processes are defined in the system of internal governance as accountable planning and reportable dimensions, in addition to the existing structure. In the resource allocation process these CVP-based processes are the primary dimensions of resource allocation, the old structure now consigned the role of resource configuration, forming an infrastructure to enable a dynamic set of CVP-based processes and explorative and development projects. These processes and projects to an extent can be organized through self-coordination, both in terms of resource allocation (especially knowledge workers) and operational coordination. This implies that the model of coordination as depicted in Fig. 8.3 has transformed into a different model, in which the element of structure has developed

21 22

Wolf (2005), p. 85. Neilson et al. (2008).

8.2 How Is Coordination Achieved? Fig. 8.4 Levels of coordination in the modern firm. Flexibility and adaptability are achieved through ex ante techniques for coordination

183

Institutional context (external to the firm) The firm as an institutional context for development and operations (= platform) Strategic coordination Dynamic portfolio of CVPprocesses and strategic targets Allocation of resources to processes and projects Operational coordination

into an infrastructure or platform and a dynamic set of processes and projects (Fig. 8.4). This concept is to be found in the concept of the platform organization (see also § 12.3). In the case of products existing of many parts and subassemblies, e.g. cars, computer hardware, airplanes (these are products with a high engineering complexity23), the coordination of the production of those parts, often by third-party suppliers, the subassemblies, e.g. the dashboard of a car, the subassemblies and the final assembly is being coordinated by the bill of material (BoM) also known as the product-structure file.24 This document, usually a file in an ERP-system, lists all parts, subassemblies, semi-finished parts, raw material, their technical specifications, including manufacturing times and other planning information, of a complicated engineering product in a multilevel hierarchy (architecture). It is the basis for material requirement planning, setting the master production schedule, the planning of subassembly priorities and order, final assembly scheduling, product costing, etc., in a time-phased way so that the overall process, including that of third-party suppliers, is one smooth and well-timed process, as if carried out by one brain.25 This requires all kinds of additional information to the technical specifications, about how long it takes to produce a specific part, to complete a subassembly, to transport it to the location of final assembly, intermediate stocks, etc. The enormous amounts of information to be processed is done by specialized computer software, enterprise resource planning systems (ERP). The bill of material is an inevitable and effective tool for coordination in the case of complicated, well-defined and completely specified products. In later versions of the bill of material the product specification, the specification of subassemblies or subsystems have been structured through the concept of product architecture and 23

Collinson and Jay (2012), Mocker et al. (2014). Orlicky (1975), Slack et al. (2012), p. 345. 25 Orlicky (1975), p. 53. 24

184

8 Complexity and Coordination

modularity, which flows over into organization modularity.26 Modularity allows for mix-match flexibility in the case of variation of product specification asked by customers, as e.g. in the business model of Dell for personal computers, the variety in demand then can be met without additional costs. Modularity also allows for absorbing market uncertainty.27 Modularity also allows for outsourcing, not only of the manufacturing of a module, but also its innovation and improvements without the risk that this will disturb the overall system. The bill of materials as a tool for coordination does not reduce complexity in the coordination of the manufacturing and the assembly of complex products, it simply achieves the coordination with information commensurate to the complexity of the product. The complex information on parts and subassemblies is generated by CAD/CAM systems and therefor can be transferred effortless and accurately in the (digital) BoM. Underlying the coordination of the manufacturing and assembly of complex products like a Boeing 787, or a car are two concepts. The first concept is that all the physical artifacts, the product, its parts, subassemblies, the system, its functions, e.g. a Boeing 787, are completely digitalized, the product and its part are seen as a digital phenomenon, allowing to communicate the finest details of parts and subsystems irrespective of distance.28 The second concept is that the BoM in terms of coordination is accepted as a virtual organization which overlays the structure of assembler and all its suppliers and their internal organizations. Once decided and contracted, the BoM defines the coordination, the managers at various levels execute that coordination. The information in the BoM is structured through architecture and modularity. In order to have a mental comprehension (responsible simplification) of the overall complexity of, e.g., a car, the car, its development and its production are conceptualized as consisting of multiple hierarchies, consisting of market-need hierarchy, a product-function hierarchy (of systems and subsystems) and production-process hierarchy, to which are related the functional design, the structural design, and the process design.29 These architectures define the modularization of the product (e.g., the engine, the dashboard), and of production and processes. A module is defined by its performance (technical-functional specification in engineering terms) linking the product-function hierarchy and the product-structure hierarchy. By defining the performance of a module (built to performance) as opposed to defining all the engineering specifications how the module is constructed and manufactured (built to print) the amount of information to be exchanged between the manufacturer-designer of complex products and third-party suppliers is being reduced.30 However, it should be noted that the shift from “built to print” to “built to performance” implies a shift toward design and increases the design intensity of

26

Baldwin and Clark (2000). Clark and Baldwin (2002a, 2002b). 28 Nolan (2012). 29 Takeishi and Fujimoto (2003). 30 Kotha and Srikanth (2013). 27

8.2 How Is Coordination Achieved?

185

work, as opposed to engineering. Scott Lash interprets this as the shift from a “logic of manufacturing” to a “logic of information.”31 However, the case of the Boeing 787 teaches that despite modern CAD/CAM technology it is impossible to specify completely and flawless performance specifications, and in addition to this engineers in other countries may interpret provided information different from what is intended by the architects, as a result of which thick personal, F2F and high-definition video conferences were needed, as well as placing Boeing engineers at the sites of thirdparty suppliers to achieve a “clicked” together assembly (that is without additional engineering) of the final product. The applicability of the BoM as a tool for coordination is limited to engineered products and assumes sufficient predictability of market demand or a make-to-order business, given that modularity allows for some absorption of uncertainty. Perhaps more important is that the concept of the BoM induced a change in business from coordination through structure to coordination on the basis of the Logik der Sache, the nature of the products and subsequently required activities, resources and information, no longer the logic of structure and dominantly centralized planning. The BoM, especially in this age of information, in which the BoM is a digital document, turns physical products into virtual engineering objects (VEO). The use in the BoM of the concepts of architecture and modularization defines the processes to produce components, subassemblies, the final assembly, irrespective of the structure of the organizations involved, as defined in the traditional sense. That is, the product architecture in the BoM defines virtual engineering processes (VEP).32 Virtual in the sense that the BoM creates a virtual organization overlaying the organizations of both the orchestrating firm and its suppliers. This virtual organization defines the manufacturing and assembly activities. Due to ambiguities in engineering, errors, and flaws in human judgement, ingenuity and decisions are needed in this virtual organization, but such decisions are guided by the BoM, not by (commercial or administrative behavior) objectives of departments or suppliers. Such interests have been reconciled in contracts between the parties involved, these contracts constitute the legal framework within which the BoM can be applied and function. The emergence of IT-systems and digital communications, email, ERP-systems, etc., within the context of vertically integrated divisions created a shadow real-time network with the capability of enabling horizontal self-coordination, replacing the slower managerial-imposed coordination through the traditional structure.33 From a viewpoint of knowledge governance this change should have been consolidated by defining such horizontal (coordination) processes as accountable entities in the system of internal governance and in the system of resource allocation.34 A second issue is that this horizontal coordination assumes full transparency with respect to

31

Lash (2002), p. 26. Shafiq et al. (2015). 33 Nolan (2012). 34 Kaplan and Norton (2008). 32

186

8 Complexity and Coordination

product specification and subassemblies for all parties involved in the coordination process.35 This full transparency, although intended, was initially missing in the case of the virtual organization of the development and building of the Boeing 787 but was a core feature in the building information system by Autodesk, explaining the success of the latter.36 The tool of BoM and the related ERP-systems, architecture, and modularization are limited to engineering type products, be it that modularization and architecture also allow for design, innovation, and engineering to be outsourced, allowing manufacturers access to the best knowledge in the world for specific technologies and engineering. Now we have an understanding what coordination is and how it is being achieved, we can turn to the question on the relation between coordination and complexity.

8.3

Coordination in Complex Organizations

Is it possible to factor concepts of complex organizations into the described levels and methods of coordination or imply concepts of complex organization new concepts and new methods for coordination? To explore this question, we need to specify what a complex organization may be. From the foregoing discussion of the various types of complexity it follows that different types (levels) of organizational complexity need to be discerned: 1. Complexity of organization induced by product complexity and/or production process complexity. 2. Complexity of organization as defined by Ashby’s Law of Requisite Variety (this includes the complexity under 1 but added is complexity due to the variety in competition, the complexity of an industry, and other context dimensions like regulation). 3. Complexity of organization as in matrix organizations defined by Galbraith, the multinational company matrix, the matrix of projects over specialized departments, end-to-end processes over departments, etc. 4. Complexity of organization as defined by Herbert Simon, that the processes of programming between hierarchical levels (that is the process of translating the mission, values, and strategy into ultimately task control) have the nature of loose programming and loose control, allowing for “localized instances of adaptive behavior in response to new situations” of which the learning is shared by the whole organization, thus contributing to the capability of adaptation and thus to be in-control in a changing environment.

35 36

Nolan (2012). Nolan (2012), Admondson and Rashid (2010).

8.3 Coordination in Complex Organizations

187

5. Complexity of organizations as implied by the nature and role of uncodifiable, personal knowledge, which needs to be inputted in the production process, or more precisely the processes of development and innovation, through interaction between knowledge workers (as opposed to the economically conventional transactions), allowing for combinatorial innovation with a degree of unpredictability of outcomes. The type of complexity defined under 3 and 4 relates to the concept of intelligent complex adaptive systems (ICAS), as defined in § 3.3. These five types of complexity do not exclude each other but might be seen as constituting a scale of degree of complexity, from a passive variety to an active ICAS. Coordination in the first level of organizational complexity, due to product complexity and/or process complexity is simply solved by the concept of BoM and ERP-system as described here before. A second level of organizational complexity is that defined by Ashby’s Law of Requisite Variety. An organization satisfying this criterion implies that an organization has the capabilities to match or surpass the complexity of its (factor)market (s) and/or its industry as needed to survive, that is to be in-control. As we have seen before, market complexity may be about market segments, consumer preferences, product variety, distribution channels and such. In the period of modernism in organizations, starting in the first quarter of the twentieth century, this variety in markets initially was answered with the concept of the business unit organization and the multidivisional organization.37 This approach of coordination is labeled the structure school of coordination (as opposed to the process school). The selfcontained organized business unit and, respectively, the product-market combination-based division were designed to reduce the internal coordination by allocating each product-market combination its own dedicated resources, to avoid the coordination of specialized and functional activities across the whole organization. The costs implied by the multiunit concept doubling of functional departments and some operational activities initially were lower as would be the costs of coordinating such activities corporate wide. It sets however a limit on the return of investments especially in knowledge. This relationship of costs shifted due to the lowering costs of information and communication, and a shift toward more generic resources, as well an increased pressure for a higher return on investments in knowledge. This resulted in the eighties of the twentieth century in the emergence of shared service centers, which eventually would evolve into the concept of the platform organization. This development implied a de-integration of the functionally integrated division and business unit, whereas the vertical integration already was subsiding due to outsourcing. With that the complexity in the internal organization of firms increased, especially the linear hierarchy became abandoned. This was not really new as multinational organizations operated their MNC-matrix organization, that is the combination of

37

Dale (1960), Drucker (1946), Sloan (1962/1986), Chandler (1962).

188

8 Complexity and Coordination

global product divisions and national subsidiaries. In the seventies, Galbraith defined complex organization as having specialized (by technical discipline) activities with across these departments organized projects for final product definition, design, and assembly.38 This type of complexity basically is subjective complexity as the attributed complexity results from describing such organization from the perspective of the Weberian linear hierarchy and its mode of operation. For companies like Shell and Cargill this MNC-matrix is not complex at all, the non-linearity of their type of organization is solved by a specific type of resource allocation process, different from that described by Bower for the unit-organization, in which a choice is made what the primary dimension is for target setting and subsequently what are the contributions to be made to the primary dimensions by the secondary dimensions. Alike for the type of organizations defined by [projects] X [specialized departments] of Galbraith’s concept of complex organization. The specific resource allocation process needed for this type of complexity would only in 2008 be specified by Kaplan & Norton and is still missing in the standard textbooks for management control, despite being practiced for much longer by companies like Shell and Cargill.39 The coordination in this type of complex organizations is about the matching of market segments and, respectively, the products and/or services for defined market segments with resources. Hence, that from an administrative viewpoint, as in the cases of Shell and Cargill, the focus of coordination is on the allocation of resources. Beneath this level of coordination there will be operational coordination at the supply chain level. The fourth type of complexity, that defined by Herbert Simon, requires an additional type of coordination as the coordination in the first two types of complexity implicitly is through imposed or hierarchical coordination. Simon’s type of complexity introduces the concept of bottom-up causality. Following the cybernetic concept of control in which adaptation is a core function, Simon’s concept of complexity introduces decentral, local adaptive behavior allowing workers to find answers to new, unpredicted challenges from the demand side in the market and opportunities at the supply side, contributing to the firm to be in-control. Such local adaptive behavior or bottom-up initiatives should in the end serve the firm, and not be guided by parochial interests. These bottom-up initiatives can be understood in two different processes, which were not specified by Herbert Simon. The first type of bottom-up initiatives (emergence) is that workers within the context of a given product and/or production process and customer interaction come up with answers to new demands from the market or new opportunities. This may be about new product variations, new materials to be used, new functions for products to answer new needs from the market, etc., to this type of bottom-up initiatives there are two coordination issues. The first is that the bottom-up initiatives should contribute to the overall performance of the firm. The economist Arrow suggest by implication this to be coordinated through calculation. Arrow defines a decentralized

38 39

Galbraith (1973). Kaplan and Norton (2008).

8.3 Coordination in Complex Organizations

189

organization by that as many of its workers or teams can calculate by themselves which of their alternative initiatives and decisions will contribute most to the overall performance of the firm.40 This assumes that these workers have available a description of the business model of the firm, defining the causal relations allowing to make a calculation and have available or have access to all the data needed to make a calculation. The second dimension in the coordination of bottom-up initiatives is that the new knowledge created by applying existing knowledge to new demands of customers is made available to all the workers in the organization. For these various systems of knowledge management and knowledge sharing, formal and informal are applied by organizations, e.g. transactive memory systems. The second type of bottom-up initiatives is that in Bower’s bottom-up resource allocation process (RAP). This bottom-up RAP firstly has the function to execute the strategy as defined by senior management. In this execution it is the explicit function of the RAP to make a best use of all insights, information, and knowledge in the organization and to absorb lower-level market developments not seen by the higherlevel senior management. Departments are asked to provide bottom-up input with respect to new market opportunities and new market demands as seen at their level, by proposing initiatives how (investments in products and manufacturing processes) the top-down defined strategy best can be executed. At first sight these initiatives seem to be coordinated by the top-down set strategic goals. However, the bottom-up initiatives by nature and objective may be as much be defined by administrative behavior. Behavior of individuals in an organization results from the interaction between personal attributes and the specifics of the systemic context, the reward systems, assessment criteria, perceived career patterns, available information and such. To which come cognitive aspects like belief conservation, preference for routines, for rule-following behavior, denial of disruptive technologies, etc. A third factor is budget-gaming, that the discretion to propose investment plans more is aimed to get as much as possible of the available investment funds into the own department, to avoid the effort to make efficiency improvements, to misrepresent market opportunities to safeguard personal bonusses. As a consequence, in many cases, the well-intended bottom-up resource allocation process resulted in perverse behavior, a not well-executed strategy, a lack of exploitation of synergies between divisions, a lack of absorbing new developments in strategy and in operations, and a failure to transform or restructure the firm in time.41 As Bower observed to his disappointment, in many cases executives failed to translate the new strategy in a new systemic context for lower-level managers as required to facilitate attitude and behavior as needed for the new strategy.42 Bower also observed that his bottom-up RAP is inadequate to deal with the new generation, more complex business models as it is based on the concept of the unit-organization

40

Arrow (1974). Sull (2005a, 2005b), Jensen (1993, 2001, 2003), Donaldson (1994). 42 Bower and Gilbert (2005). 41

190

8 Complexity and Coordination

(the M-form) and with that on the black-box budget-driven method for strategy execution. Bower also observed that his method has no solution for dealing with the role and nature of intangible assets. Earlier Porter had concluded that the capital allocation system in US firms, including the method of Bower, biases toward investments in tangible assets resulting in underinvestments in intangible assets, whereas these emerged as driving the growth of the economy.43 What is to be concluded from this is that the loose programming assumed by Herbert Simon or the downward causality in systems theory is not simply defining a strategic intent but requires a subtler set of administrative instruments, the issue of creating and maintaining a fruitful systemic context as befitting both the strategy and the worker. Downward causality is that people, groups, to some extend, but not completely determined, let themselves influence by a mission statement, values, a strategy, which will depend of the degree of identification by the individuals with the firm, its products, markets, technology, etc.44 Downward causality works best when it is inspirational, providing an identity and purpose, stimulating people to make initiatives that are useful for the company.45 Such initiatives serve to adapt the organization to changes in its environment in order to survive with efficiency, identity and its values. So, those at lower levels in the organization or working decentralized need to know what the ultimate purpose of (local) adaptation is. Upward causality is that through variations, “mistakes,” experimenting a response to a new customer demand, process improvement, a new input from a supplier, new knowledge acquired e.g. through membership of a trade association, more efficient processes are invented, new products, new knowledge, that influences higher-level choices, strategies, and also contributing to the sound survival of the firm. The coordination in the complex organization as defined by Herbert Simon, as the experience with Bower’s bottom-up process teaches us turns out to be a combination of content (the strategy) and context (the systemic context to facilitate productive behavior).

8.4

Coordination at the Fifth Level of Complexity: Stigmergic Coordination

8.4.1

Is Coordination Possible in High Complex Organizations?

How can efficient coordination be achieved at the 5th level of complexity? This level of complexity is characterized amongst others by a higher causal density, but also by more fluidity in both the elements of coordination and tools to be used for coordination. Further, rules of design and solving problems increasingly have to be discovered, either through reconceptualization or from big data. Because at the level of fifth 43

Porter and Wayland (1992). Farnsworth et al. (2017), Capra and Luisi (2014), Mobbs et al. (2009). 45 Simon (1991). 44

8.4 Coordination at the Fifth Level of Complexity: Stigmergic Coordination

191

coordination the activities are more about design and development, the interaction between knowledge workers will be more complex, not only because of the process of designing and creating solutions, but also by the complexity of diverse social identities, which are needed for diversity in reframing problems. Also, the context of this interaction both by technology (social media) and the role of the domain of social production is relatively new in theories of coordination. These developments as sensed for some decennia, reduce the effectiveness of traditional managerial or bureaucratic control, raising the question whether there is an alternative available. The alternative is to be found in the organization of information and processes. The capital market persists in its emphasis on shareholder value, dividends and break-up value. In some industries this results in a linkage of coordination, risks, finance, and ownership, resulting in isolating the risks related to complex coordination in separate legal entities and ownership, that is seeking comfort in structure, which is counter productive.

8.4.2

A Kind of Fluidity46

At the fifth level of complexity the coordination is more about the development and design of the value proposition as it is about to routinize production of defined products and services. At this level intangible assets even play a more important role in value creation as is the case in manufacturing and routinized services. The implicit assumption underlying coordination in the industrial era, respectively the routinized manufacturing of defined goods and services, is the ex-ante designability of both products and processes. It was assumed, and to a large extent rightfully, that products, processes, organizational systems, processes and procedures, roles and accountabilities could be specified sufficiently complete and accurate, and independent of personal attributes, that on the basis of planning and management by instructions, individuals and teams could complete, and often accomplished, the work assigned and achieve stated objectives with efficient coordination.47 The shift from tangible assets to intangible assets, especially the role of non-codifiable, personal knowledge, in combination with a shift toward information goods, in the context of social media and big data, all imply an increasing higher causal density in the creation, marketing, delivery, and experience of goods and services, with increasing dynamics in these causal relations. Especially the informational nature of products, services, and markets and, respectively, the mediation of physical products imply a higher causal density and a higher pace of reciprocal relations between entrepreneurial decisions, business models, markets, customer preferences, and financial performance, making it more difficult to use the technique of induction to identify positive rules to be used in coordination of activities. Subsequently, it becomes more difficult to predict the shareholder value impact of

46 47

Schreyögg and Sydow (2010). Okhuysen and Bechky (2009).

192

8 Complexity and Coordination

various alternative potential courses of action.48 Consequently, the management is faced with a higher risk in financial performance and finds itself squeezed between the school of thinking emphasizing that financial performance results from the capabilities of the organization and the school that starts with the objective of shareholder value to define activities to achieve that value. This higher causal density also challenges the concept of decentralization as defined by Arrow, that as many teams or individuals for themselves can calculate which of their alternative actions will contribute most to the overall performance of the firm. Whereas this phenomenon of higher causal density plays out both in the organization as in its context, implying the need for decentralization in terms of sensing, sensemaking, initiatives to find solutions, as one of the requirements to be in control as defined by Herbert Simon. In his article The Nature of the Firm the economist Coase explained the existence of firm vis-à-vis the market as a coordinating mechanism based on the price mechanism, that firms have reason to exist as long the firm, its management, coordinates the specialized activities and functional activities within the firm and the activities of the firm on its markets, more efficient as the market mechanism itself is capable to do. Implicit in Coase’s theorem is that these activities are about tangible goods, being tradable based on alienable property rights. Another implicit condition around 1900, and thus an implicit assumption underlying conventional tools for coordination were the high costs of information and of communication. These conditions have fundamentally changed by the declining costs of information and of communication due to the introduction of the digital ICT. “The coordination technologies of the industrial era—the train and the telegraph, the automobile and the telephone, the mainframe computer—made internal transactions not only possible but advantageous.” But with the introduction of powerful personal computers and broad electronic networks—the coordination technologies of the twenty-first century—the economic equation changes. “Because information can be shared instantly and inexpensively among many people in many locations, the value of centralized decision making and expensive bureaucracies decreases”49 This first modernity was characterized by a logic of structures, whereas the second modernity (> ±1975) is characterized by a logic of flow.50 The logic of structure also implies or assumes downward causality, whereas in the fifth level of complexity there is to be acknowledged downward causality, upward causality, and horizontal causality. In terms of organization design the shift from structure to flow is the shift from Chandler’s “structure follows strategy” to “process follows proposition.”51 These

48

Manzi (2012), p. 148. Langlois (2001). 50 Lash (2002), p. 205. 51 Lash (2002, p. 207) describes this change from the logic of structure to the logic of flow as a change from the process highest differentiation (task specialization) to a process of indifferentiation. But indifferentiation is not the reverse of task specialization, the concept of flow expresses that the 49

8.4 Coordination at the Fifth Level of Complexity: Stigmergic Coordination

193

processes are additional accountabilities in the system of internal governance, and as such an expression of the phenomenon that: “the indifference of flows starts solidifying in their own new territories . . . These new territories are not new structures, institutions, organizations, and organic systems. They are instead such entities as platforms, brands, non-places, junks space, and cybernetic, open systems”.52 In the latter organization design starts with the design of the business model, in which the central element is the customer value proposition for which the processes then are designed to deliver that proposition. These processes are designed as end-toend processes, based on the architecture of the proposition to be delivered, decoupled from the existing configuration of resources, that is across the traditional departments. These processes may extend into suppliers and into the distribution. This is to achieve the required complementary organization of intangible assets to create value, including the interaction between knowledge workers and to achieve a higher level of resource allocation.53 Note that in this way of designing the organization the structures of the first modernity remain intact (departments as resource configurations), but the deployment (coordination) of those resources is defined by the end-to-end processes. This illustrates that a complexity dimension of modern society is that we experience not a transformation of the first modernity to a second modernity, but a transformation in which a second modernity is layered upon the institutional elements of the first modernity, this layering in itself is a source of (subjective) complexity.54 This shift from the logic of structure to the logic of flow does not capture completely the nature of the fifth level of complexity. The idea of “logic of flow” relevant as it is in the modern economy is still based on ex ante designability and discrete products, services, and processes. The higher and more dynamic causal density implies that in designing value propositions, including their marketing, that design rules are not ex ante available and those rules available most likely are obsolete. The traditional method of induction to find rules is losing its effectiveness, or its results are short lived in validity. One answer to this is trial-and-error, based on fast feedback information.55 This approach however, is confined to existing business models; it is unlikely that this approach will sufficiently result in the required reconceptualization as needed for long-term, sustainable value creation. This trial-and-effort is reflected in the second-generation machine learning. Traditionally the desired output was identified by combining rules and data. In the

coordination of specialized tasks is not through managerial hierarchy, but by the logic of the architecture of the value proposition to be achieved, compared to managerial hierarchy more horizontal by nature in terms of work flow. 52 Lash (2002), p. 207. 53 Kaplan and Norton (2004). 54 Beck and Lau (2005). 55 Manzi (2012).

194

8 Complexity and Coordination

modern complex world, with its high causal density it is the desired output + data which, in a trial-and-error procedure results in (new) rules, be it with some guidance (supervised or unsupervised learning), with defining a representational space for the type of machine learning56 in which optimized rules are identified and adjusted.57 This approach appears to be equivalent with abductive-open design thinking, in which neither the thing, the what is known, nor the how, the working principles, but the value to be achieved is known. Through framing and reframing by the designer ultimately the what and how are solved.58 Alike it may be that in the case of the 5th level of complexity the rules for coordination are to be found in a similar way. Another answer to the higher causal density is the second-generation or bottomup artificial intelligence in which the computer program, based on a data set, a value function, and an initial generic causal model, finds itself relevant rules. The issue at this fifth level of complexity is to define the value function, or value proposition. As the engineering type of design results in solutions to problems, it is the abduction open-system design that tries to achieve sought-after value in society, by consumers, by market, precisely in situations in which neither the what or thing nor the how or rules are known.59 This requires processes of reframing or reconceptualization. This is for two reasons. Data itself has no meaning, it is through concepts that data is turned into information, in the sense of understanding what to do and what to decide. Concepts, e.g., in the form of economic models or management models, implicitly reflect causal relations more or less in combination with an objective. Often the underlying assumptions of a—successful—management concept are not known or ignored as people have a strong tendency to stick to their existing concepts, frames, and worldview, even when talking about new developments. Abductive open-system design thinking is about liberating ourselves from the conceptual past and finding our way in the conceptual future, grasping new opportunities.60 This reconceptualization or reframing as constituent process of design thinking and with this the development of products, services and processes, is not only a job for the CEO, this reconceptualization tends to be distributed through all of the organization, including in the interaction with customers and suppliers. This reframing is an undefined process of intellectual thinking, creative moments, blurring rationality and intuition, developing empathy, radical collaboration, rapid prototyping, combinatorial innovation by combining solutions, etc. As a design in most cases will have multiple dimensions, psychological, technological, esthetics, social, anthropological, legal, informational, software, multiple knowledge workers of a variety of discipline need to work together to create the sought-after design with value for its users (consumers) and the firm. 56

Domingos (2015). Littman (2020). 58 Dorst (2011). 59 Dorst (2011). 60 Normann (2001), p. 200, Bolman and Deal (2014). 57

8.4 Coordination at the Fifth Level of Complexity: Stigmergic Coordination

195

From the perspective of the CEO or senior management the question, at least the traditional question will be how to achieve efficient cooperation and coordination between such a motley crew of knowledge workers. It cannot be based, as in the case of the bill of material, on the Logik der Sache, simply because this Logik first needs to be found or developed. Time is being spent and thus (shareholder) money, and preferably ex ante a return on this investment is to be calculated and targeted. Such design processes typical involve creative knowledge workers carrying personal knowledge that is impossible or very difficult to codify. This uncodifiability implies that this type of knowledge needs to be inputted in the development respectively production process through interaction.61

8.4.3

The Complexity of Interaction

The idea of interaction has a long history in economic thinking. Under the label of stigmergic coordination it is suggested that through free interaction between individuals, responding to each other’s indirect or direct output, complex products can be achieved without explicit central planning.62 Examples usually presented as proof are Wikipedia and open source software. With that it is suggested that free interaction between knowledge workers is a solution to the issue of coordination at the fifth level of complexity. The idea of free interaction originates from the Dutch-Anglo philosopher Bernard Mandeville (1670–1733) suggesting in his The Fable of the Bees (1714) that, depended on proper vices, individuals interacting freely would achieve public benefits. Adam Smith was inspired by this idea of free interaction resulting in the idea of a free market, free from the king’s interference or regulation, and that an invisible hand would ensure both an equilibrium in that market and a public benefit.63 This idea of free interaction is a constant theme in economic thinking and is also to be found in the earlier, mechanical complexity theory or even chaos theory. On closer inspection the idea and phenomenon of free interaction is not that simple at all; it is itself a complex phenomenon. Implicitly underlying the idea of free interaction is that the actors in the economy have unity of property, decision rights and that the economic consequences of their decision immediate and direct accrue to their personal income and wealth and thus have an unmitigated incentive to maximize their utility. As Etzioni argues, individuals not only tend to maximize, or more often to satisfice their utility, they also want to abide to their moral commitments.64 Although the latter differs by individual, culture, region, and nation. As is being acknowledged in the economic theory of new-institutionalism, this (free) interaction

61

Jensen (1998), Foss and Michailova. Heylighen (2016a, 2016b). 63 Friedman (2021), Medema (2009). 64 Etzioni (1988), pp. 4, 36, 63–64. 62

196

8 Complexity and Coordination

between individuals, entrepreneurs, knowledge workers, tradesman, etc. is being played out in an institutional context, which amongst others defines property rights, contracts, but also metric system and includes both formal and informal institutions defining a, be it changing, set of rules of the game. Be it that especially through the digital technology, the Internet, social media, the role of institutions becomes perceived in different ways compared to the generations who developed those institutions. The reason that in the modern economy free interaction is emphasized is partly the acknowledgement of personal knowledge, that it serves combinatorial innovation across departments and organization thus making a best use of knowledge and that this interaction provides chances for solving problems in an unanticipated way. Hence, that cross-departmental projects and processes are defined as mechanisms for knowledge governance, that is to have a highest possible return on investments in human capital.65 Consequentially, individual communicative capacities become more important in the economic process of information and cultural production, compared to the capacity to aggregate financial capital.66 However, it must be noted that the monetizing of intangible assets requires technological, digital infrastructures and hence the control by traditional capital shifts into these infrastructures. The question to be asked is what is interaction between knowledge workers different from the interaction between members in an organization with hierarchical coordination. A definition of free interaction between (knowledge-)workers is: Interaction: a mutually engaged co-regulated coupling between at least two autonomous agents, where (a) the co-regulation and the coupling mutually affect each other, and constitute a self-sustaining organization in the domain of relational dynamics, and (b) the autonomy of the agents involved is not destroyed, although its scope may be augmented or reduced.67

The relevance of this definition is that it emphasizes three elements in interaction: co-regulation, autonomy, and self-sustaining organization. The definition does not refer to a purpose or function of interaction. Neither does the definition refer to a hierarchal setting; it suggests self-organization. In economic terms interaction has purposes: to input personal, non-codifiable knowledge to develop new propositions, to achieve combinatorial innovation, to have diversity in reframing in design processes and for micro-coordination, this irrespective of the possibility of all kinds of unintended consequences of interaction. Interaction implies that individual communicative capacities become more important in the economic process of information and cultural production, compared to the capacity to aggregate financial capital.68 From a perspective of the firm there 65

Foss and Michailova (2009). Benkler (2006), p. 52. 67 Gallagher (2020), p. 98. 68 Benkler (2006), p. 52. 66

8.4 Coordination at the Fifth Level of Complexity: Stigmergic Coordination

197

are two consequences to this shift. A first is the appropriation issue: if managerial control no longer if (fully) based on the control over the ius utendi northe ius abutendi on tangible assets (e.g., a lathe),69 how is the firm to appropriate the value created in this interaction? Due to the digital technology it is increasingly possible to exploit knowledge without it being embodied in discrete physical products. A second issue is that due to the digital technology of personal computers and other digital devices the dependency of workers on capital intensive tools, e.g. the lathe, has been eliminated, the mutual dependency and with that the equilibrium between capital and labor in a number of professions no longer exists.70 This has opened the possibility that knowledge workers engage, both privately and related to their paid-for work, in nonproprietary exchange and development of knowledge and culture as well alike in the development of information goods and culture goods. The most well-known examples of these are open source software (Linux) and Wikipedia. The motives for knowledge workers to do so are various, these may be altruistic and/or pursuing individual interests. These motives may be social, economic, political or religious as well, about which Benkler writes: “From our friendships to our communities we live life and exchange ideas, insights, and expressions in many more diverse relations than those mediated by the market. In the physical economy, these relationships were largely relegated to spaces outside of our economic production system.”71 Benkler labels this domain of nonproprietary exchange and production the domain of social production. This domain of social production, as demonstrated by open source software, is for many firms, especially those operating in the information industry, a source for ideas, for inputs in the production function, and as we will see, a source of new concepts. But being nonproprietary this domain is not subject to coordination based on market prices as are the usual proprietary-based markets for resources. As knowledge also is a social phenomenon, in this exchange of knowledge and development of information and culture products, identities, group and individual play a role, reputations and values. This implies that not only knowledge and information resources are exchanged, but as much is at stake “Giving reasons to justify oneself and reacting to the reasons given by others are, first and foremost, a way to establish reputations and coordinate expectations.”72 That is, different from the situation in the second industrial era, the domain of social production stimulates the full person, including her or his political stance and views on society, to be at play in work. Especially in those firms producing information goods, this stance in the domain of social production flows over in the internal organization of the firm: “The promise of the networked information economy is to bring this rich diversity of social life smack

69

Furubotn and Richter (2000), p. 77. Hardt and Negri (2004). 71 Benkler (2006), pp. 52–53. 72 Mercier and Sperber (2017), p. 143. 70

198

8 Complexity and Coordination

into the middle of our economy and our productive lives.”73 This adds an additional complicatedness in processes of self-coordination between (knowledge-)workers.74 Whereas in the old situation the unitarist perspective of human resource management implied that employees submit themselves unrestricted to the objective of the firm this is maximizing shareholder value, the twenty-first century workers, grown up in a media society, in an image culture, with gaming, tend to be more motivated by status-based judgement of pride and respect for themselves, above shareholder interests. This generation tends to have a double motivation, one focusing on personal commitment, personal needs, norms and goals, personal advancement and thus career commitment (but not the traditional careers paths). The second motivation is that this generation values a (or multiple) social identity, the self as a member of a team or an organization, but this may be multiple organizations, including those in the domain of social production. This demonstrates itself in forms of organizational citizenship, loyalty, and a combination of rule-following behavior and extra-role behavior.75 In the modern economic endogenous growth theory, knowledge is not only created in R&D-facilities or institutions of academic research, as much it is created there where knowledge workers apply their existing knowledge to new problems, be they submitted by customers or suppliers. This relates to ICAS-complexity, these knowledge workers have ideas and concepts of their own, are intelligent in their ability of mastering multiple levels of abstraction, have theory on extra-organizational networks for identity and ideas, and have adaptive capability in ideas and personal choices. Knowledge workers with uncodifiable knowledge also have a more complex agenda. They want to participate in those projects in which their knowledge will make a highest contribution and in those projects in which their knowledge will develop best in view of economic value in the longer term. Also, related to wanting a large as possible market for their knowledge,76 they may participate in the domain of social production.77 This emancipation of the knowledge worker implies that the employer needs to acknowledge the legitimacy of the personal interests of the knowledge worker in balancing this with the need to identify with the interests of the firm.78 One of the practical consequences of this development is that in the case of a projects-based business model, in which knowledge workers make contributions in multiple

73

Benkler (2006), p. 53. In the WSJ issue dated May 3 2021, the journalist Katherine Bindley writes about the CEO Jason Fried of the software company Basecamp who intended to curb political conversations at work, but meeting resistance of employees, and making a number of them, after discussion of the CEO decision on LinkedIn and Twitter, to leave the company. Before that the use of president Trump of social media aroused discussion between workers at Google and Apple. 75 Haslam (2004), p. 78. 76 Rosen (2004). 77 Benkler (2006). 78 Simons (2005).. It must be noted however that Simons operational recommendations how to achieve this are wanting. He fails to see the role of the social identity theory, as well as the role of the interactive perspective model. 74

8.4 Coordination at the Fifth Level of Complexity: Stigmergic Coordination

199

projects, the coordination of the allocation of resources is not through resource allocation, which is typical for physical capital, but through resource mobilization, that is that knowledge workers themselves identify in which projects they can make a best contribution and in which their knowledge will grow best.79 This resource mobilization however, requires certain social conditions, that junior members receive the coaching as needed for their development, that political games are curbed like factions, and that the degree of loyalty and sociability is well balanced to maintain an open, organic organization.

8.4.4

Stigmergic Coordination

One proposed solution to the sketched fluidity here before to achieve coordinated cooperation where conventional tools for coordination fail is stigmergic coordination, which in its most simple description suggests a free interaction between knowledge worker to produce value for others, without imposed coordination. As we will see, this is a bit more complicated, but a free interaction is a core element in this type of coordination. The concept of stigmergic coordination originates from biology where it is used to describe how, e.g., insects (termites) of limited intelligence without apparent (for human observers not visible) explicit coordination or blue print, manage to collaboratively build a (complex) nest.80 Although multiple definitions of stigmergic coordination exists, the core is that the coordination of the activities of the insects is achieved through the stimulus provided by the work of one actor, direct or through changes in their environment, enticing others to continue the job.81 Understandably the phenomenon of stigmergy has appeal to those that seek selfcoordination, self-organization and count on the emergence of order out of chaos. Especially to those supporting the idea that emergent behavior resulting in value of society, can result from three simple type of rules only: rules that define the purpose, rules defining the boundaries of behavior, and rules about incentives. It is obvious that a concept from biology may inspire how to solve problems in business, but it cannot straightforward be applied. In a business environment factors play a role like property rights, requirements of efficiency, contractual relations, an institutional context, etc. In the case of human actors interacting laterally, at least a number of actors have the inclination and capability to understand the system beyond their own local situation up to the system as a whole.82 Such individuals are capable to define common purpose, common values and to define rules at the level of the community, beyond their personal interests. These individuals are able to

79

Doz (2005). Heylighen (2016a). 81 Heylighen (2016a, 2016b). 82 Bennet and Bennet (2004), pp. 25–36. 80

200

8 Complexity and Coordination

discern between their personal interests and the interests of the group or the system and act altruistically. In social systems the political altruistically personality understands how to achieve power but will use that power in an unobtrusive way in the interest of a community.83 Hence we see the development of the concept of cognitive stigmergy as opposed to the entomology stigmergy: Cognitive stigmergy overcomes criticized constraints of entomology stigmergy by recognizing that humans have interpretive and creative capabilities and that human actors respond to stimuli to more than simply coordinate.”84 The concept of cognitive stigmergic coordination understandably is being used to explain the success of free collaboration and free interaction in the cases of the Internet encyclopedia Wikipedia, open source software, and other free communitybased platforms on the Internet.85 What can be observed is that at the level of research communities of academics, through the publication of articles in refereed journals, a kind of stigmergic coordination existed avant la lettre. From these interactions it is difficult to define alienable property rights. This has been partly resolved through the concept of open innovation and partly that universities have incorporated start-ups and other venture to monetize public intellectual knowledge. This is a model somewhat between the purely private investment in combinatorial innovation resulting in private goods and the collective action model resulting in public goods.86 Therefore, a more precise understanding of stigmergic coordination is needed, especially in what conditions this can be effective in business. The working of cognitive stigmergy in terms of causality is more about ideals and rules as it is about deterministic causal relations. The contributors to Wikipedia, which can be anyone, need to observe a few rules. The first is that they comply with the ideal of an encyclopedia, as promoted as part of the French enlightenment with the editors of the Encyclopedie (1750–1776), Diderot and d’Alembert. That is the style of writing a lemma has to be that of an encyclopedia; no opinions, no speculations.87 Contributors are free either to edit an existing lemma, by making contributions or improvements, or initiate a new lemma, but the content needs to be fact based. Especially the editing of lemmas might be interpreted as an example of stigmergy, in which contributors respond to contributions of others. However, this bottom-up editing process is supervised by editors of Wikipedia and in some cases is also coordinated by those editors. Each lemma has a talk page, on which contributors and editors can discuss contributions and quality issues. Further, it has to be noticed that lemmas can be related or unrelated, these do not necessarily depend on each other. Contributions to Wikipedia are made under the copyright of Creative Commons and

83

Jensen and Meckling (1998). Majchrzak and Malhotra (2020), p. 230. 85 Doyle and Marsh (2013), Rezgui and Crownston (2017), Secretan (2013), Bolici et al. (2016). 86 von Hippel and Krogh (2003). 87 https://nl.wikipedia.org/wiki/Wikipedia:Vijf_zuilen. 84

8.4 Coordination at the Fifth Level of Complexity: Stigmergic Coordination

201

the GNU-license for free documentation (GFDL) and may not violate existing copyright. Contributors are volunteers, they receive no monetary rewards. In the case of open source software, the leading counterexample was and is the proprietary operating system (OS) by Microsoft. The idea for OS was and is to create a nonproprietary OS and other open and free software, in a number of cases as a response to the high license fees of proprietary but highly functional software like MS Words, MS Excel and such. The quality rule for contributors is simply that others can verify whether a contributed module of software functions properly, by itself and imbedded in larger systems and complies with the specific definition of open source software, including the copyrights.88 Important in this case is that the source code of all software modules, contrary to proprietary OS, is published. Open source software (Linux) is widely used by commercial parties, especially to run servers. This example suggests, as relevant for business, the providing of a general functionality, without a detailed blue print in combination with some rules, but with fast feedback on contributions is sufficient to create through interaction a product that is of economic importance and plays a key role in the software industry. Also, in this case, there are strict rules for software licenses, and different from Wikipedia in certain cases it is possible for contributors to make money with open sources software or its complementary products.89 Within the open source community the way the development of open source software is not defined in terms of stigmergy, but in terms of the cathedral (hierarchy, central planning) versus a bazaar-type of organization.90 Some suggest that the bazaar-type of organization for developing open source software also is an alternative governance model for the development of complex products, as opposed to the proprietary, hierarchical firm-type governance model as the traditional governance form.91 This is to be questioned, as the pressure of (active) investors and shareholders for especially dividend will constitute a force for value appropriation, at least partly to be achieved through propriety-based production. As monetizing of intangible assets requires physical—digital— infrastructures, the focus on traditional capital shifts to the control over these infrastructures. Examples of effective, useful stigmergic coordination are also to be found in disaster response.92 Stigmergic coordination may also result in adverse effects for society. In the case of the US war on drugs at the Mexican border, stigmergic processes between enforcement officers and criminal agents resulted in narcotic systems resilient against the enforcement architecture.93 Lewis suggests that stigmergic coordination played a role in the 9-11 terrorist network.94

88

https://opensource.org/osd. von Hippel and Krogh (2003). 90 Raymond (2000). 91 Demil and Lecocq (2003). 92 Lewis (2013). 93 Nieto-Gomez (2016). 94 Lewis (2013). 89

202

8 Complexity and Coordination

“Of course, this type of dispersed sensing and signaling is not a panacea and needs to be used with caution, in the appropriate context.”95 Stigmerigic coordination is not practical for all organizations and industries, as many strategies require the orchestration, cooperation, and effort of the organization as a whole. Some organizational settings may not be conducive to dispersed experimentation and emergent organization.96

8.4.5

The Upside and the Downside of Free Interaction

Free interaction between knowledge workers in itself does not automatically result in positive results. This interaction in a complex information space is subject to two opposing forces, a positive and a negative. The positive force is that living in, being a member of multiple fields contributes to being aware of situations, their specifics, it helps to see differences, and with that feeds the reframing, the abductive design thinking to find new solutions, solving the restrictions of obsolete paradigms and conventions. Especially the combination of different disciplines may result in innovations unthought of in mono-disciplines, what some call the Medici effect, referring how the renaissance scholars of different disciplines, but some of them, especially the polymaths, created new academic insights and laid the foundation of science.97 This supports the idea that knowledge workers free to interact with others by ideas and choice of persons will produce boundary shifting insights and solutions. In de period of the Medici, the period of Renaissance, starting in the fourteenth century in Italy, the exchange of ideas was both more fundamental, especially on mathematics, and practical at the same time, e.g. the perspective in drawing and in painting. In the present time the exchange of ideas in the social domain is at multiple levels, but related to what is relevant for business: “It can be remarked that these ‘knowledge markets’ typically transfer solutions, not cognitive assets— resources, matrices, capital.”98 In the case of open source software these solutions are answers to problems implied by the overall functionality of the software system being developed contrarian to the existing proprietary systems. For other domains of problems in society or exploring new solutions of value for society, typically in the mode of design thinking (re)framing is needed. The free interaction resulting in innovative solutions, solving the restrictions of the conceptual past, assumes a background or context of rich community and a core value of commitment to building understanding from diversity. If workers are not used to cooperate with people different from themselves, they may have difficulty to develop trust as is needed for productive interaction with others from different occupations and education, to say nothing of races and genders.99

95

Puranam et al. (2014). Felin et al. (2017). 97 Johansson (2017), Burke (2020), Wootton (2015). 98 Grandori (2001). 99 Heckscher (2015), p. 157. 96

8.4 Coordination at the Fifth Level of Complexity: Stigmergic Coordination

203

Interaction like action always plays out in a context. “Action is never possible in isolation; to be isolated is to be deprived of the capacity to act.”100 This context itself is a field of complexity. This context may define the three dimensions in which actors interact: content, form and stance.101 Interactive Theory (IT) emphasizes the importance of context and circumstance, and the role of communicative and narrative practices.102 One of those forms of communication are memes as defined by Dawkins as “a unit of cultural transmission, or a unit of imitation.” To which Shifman objects that this definition does not apply to the coordination of processes to construct an artifact beyond the capabilities of an individual. So, different from Dawkins’s behavioral memes, Shifman defines mentalist-driven memes which are ideas or information that resides in the brain.103 This concept of memes relates to the concept of modularity and the earlier observation that in interaction players tend to exchange solutions. To which Hayles comments: “Memes do not allow new insights to emerge as a result of establishing correlations between existing databases and new datasets; rather, they serve as catalysts for reframing and reconceptualizing questions that are given specific contexts by the writer appropriating the memes for his or her own purposes.”104 The autonomy assumed in the definition of interaction may be undermined by reification.105 This reification may be that the other is being treated as an object from a third-person perspective denying its identities and that certain memes are unquestioned and used in a routine way, that is in a noncommunicative nor narrative practice.106 “Reified and pre-packaged ways of interacting lack dynamic spontaneity, impose a mechanistic order, and can undermine the autonomous processes implicit in genuine forms of interaction; accordingly, they also distort intersubjective understanding.”107 To which Interactive Theory adds that understanding other people is primarily based on neither theoretical inference nor internal simulation, but rather on forms of embodied practices.108 In these times of digital technology allowing for micro-coordination or fine-grained coordination as required in multiple forms of cooperation, many players have a need to constantly update their mutual expectations to foster trust in the sense of being reliable.109 This however can result in hyper-coordination, the experience of enhanced, anxiety provoking relational dependence enabled through communication with a mobile device. Where the mobile phone, or social media, is being used over and above the instrumental,

100

Arendt (1958), p. 188. Shifman (2014), p. 420. 102 Gallagher (2020), p. 99. 103 Shifman (2014), p. 402. 104 Hayles (2012), loc 3475. 105 Gallagher (2020), p. 198. 106 Gallagher (2020), p. 99. 107 Gallagher (2020), p. 198. 108 Gallagher (2020), p. 97. 109 Pentland (2014). 101

204

8 Complexity and Coordination

task-oriented micro-coordination to achieve innovative solutions, expressing social and emotional communication, in-group discussions, in which is at play agreement about proper forms of self-presentation, including structures regarding the presentation of the self, may result in intense forms of social control.110 That is, technology can liberate individuals, as well reduce their autonomy. As a consequence, the dark side of free interaction may be that, within especially the traditional professions, although vocally promoting renewal, innovation and adaptation, this interaction hold their members back, to preserve their profession but also because of a lack of understanding their own paradigms, institutional basis and thus not seeing how changes in the basic conditions in society and the economy erodes their institutional basis. E.g. management accounting, despite the economic evidence, still does not acknowledge intangible assets as an element in the capital base of the firm. Risk management, especially the audit school in risk management is defined by the sociologist Becker as precisely a source of risk, because this field tries to solve risk problems in the era of the late or second modernity with tools based on certainties valid in the first modernity.111 Networking between knowledge workers is no guarantee for the emergence of breakthrough ideas. Johansson: “It is not that the network is holding you back on purpose. There is no conspiracy. But your network will promote, support, and highlight ideas that are valued within it. And it squashes or removes ideas that are not. This inherent characteristic creates a difficult paradox for anyone pursuing an intersectional idea: If we wish to succeed at the intersection of fields, we have to break away from the very networks that made us successful.”112 Counterbalancing the tendency of an echo-chamber effect in interaction is the social fact that the younger generation, not being part of the old organizational cultures, are faster in understanding and accepting the contribution to purpose as is a formal position in a hierarchy.113 This is partly due to the effect of the gaming generation. As a result of which there is a shift from motivation based on control over resources to motivation based on acknowledged contribution. From this it might be concluded that to avoid a negative spiral in free interaction, a focus is needed on the value for others to be achieved as in design thinking, of which examples are Wikipedia and open source software. But as much the employer has to acknowledge that there must be freedom in those interactions and that knowledge workers have a need to communicate above and beyond the tasks at hand about their personal values and stances.

110

Katz and Aakhus (2002), Hayles (2009), Ling and Yttri (1999). Beck (1999). 112 Johansson (2017), pp. 162–163. 113 Heckscher (2015), p. 157. 111

8.4 Coordination at the Fifth Level of Complexity: Stigmergic Coordination

8.4.6

205

Managing the Risks in Free Interaction

From a perspective of the firm the money invested in free interaction, mainly salaries, need to produce at some moment a return. As explained before, in a situation of higher causal density it is more difficult to predict shareholder value from activities. A response to this new situation is trial-and-error, based on fast feedback information.114 This approach however, comparable with machine learning, is confined to existing business models, it is unlikely that this approach will sufficiently result in the required reconceptualization as needed for long-term, sustainable value creation, dependent on what the nature is of the context within which individuals interact with each other. North wrote in 1990: “In a world of uncertainty, no one knows the correct answer to the problems we confront and no one therefore can, in effect, maximize profits. The society that permits maximum generation of trials will be the most likely to solve problems through time. . . . Adaptive efficiency, therefore, provides incentives to encourages the development of decentralized decision-making processes that will allow societies to maximize the efforts required to explore alternative ways of solving problems.”115 That is, at this level of complexity coordination cannot be based on the Logik der Sache. The existing engineering either fails to solve the problem or fails to produce the value proposition sought after. The idea is through design thinking to find a new logic. This requires an intellectual process of framing, reframing, reconceptualization, forms of intuition, borrowing from other fields (see the Medici effect). The question for the employer investing money in such a process is how to control such an investment. The employer-investor depends on the effort of the knowledge workers and on their duty of good faith, that solutions and opportunities found will be the property of the firm and that knowledge workers do not run away with it. In working on new problems also the knowledge of the worker grows and with that its value. To this applies Jensen’s principle of alienation: such workers should not be employed through a labor contract but through a supplier’s contract. The value of which is determined by the economic value of the solution ultimately delivered to the firm. Exclusivity of use is an element to be contracted depending on the preferences of the parties. The solution for this we see in e.g. start-ups and corporate venturing, in which the knowledge workers are also the entrepreneurs, either with financial capital of their own of capital acquired at the capital market. This is to be found in bio-tech, the development of new medicines, software/apps, etc. The market and financial risks are shifted to those who are the owner of personal, uncodifiable knowledge. In the case of the pharmaceuticals firms they control this risk by remaining the owner for the system and expertise for getting new medicines approved, as well the control over distribution. This phenomenon is also explained in open innovation, be it that open innovation is a two-way system. To master this requires the firm be competent 114 115

Manzi (2012). North (1990).

206

8 Complexity and Coordination

in scouting start-up initiatives, having the capability of valuing take-overs and financing this and absorbing new found solutions in their manufacturing and distribution system. The question could be raised whether an intermediate solution can be found, between open innovation and conventional employment based on a labor contract. Especially because not all knowledge workers have an aptitude to be entrepreneurial. An example of this could be the Dutch firm TMC in Eindhoven. TMC places knowledge workers circulating over a number of high-tech firms in the Eindhoven area, thus furthering knowledge spill-over and increasing the growth of knowledge with individual knowledge workers, organized in specialized business cells, under a contract labeled as “work-entrepreneurship” (werkondernemerschap), with longterm contracts and individual profit sharing. This model benefits both the cluster of high-tech firms as the individual knowledge workers.116 Another solution seems to be in Bower’s idea of contribution measurement infrastructure, in which the contribution of each individual knowledge worker over multiple projects can be measured through multidimensional information and reporting dimensions, as is demonstrated in the case of Duane Morris.117 The latter case is also strong examples of values creating a well-defined social context, with these values consistently and completely being codified in the supporting systems and decision-making of the firm. So, in a way, but at a higher level of understanding the complexity involved we are back by three basic tenets of complexity: values, information, and modularity. In a way it is also a return to the economic principle of co-location: those being the owner of assets (knowledge) also have the decision rights what to do with those assets and will bear all the economic consequences on those decisions in their income and wealth.

8.5

Complexity of Markets, Products, and Consumers

Complexity of a national economy as a driver for GNP-growth is by various authors operationalized as complexity of markets, defined as the variety of products produced and exported.118 From a perspective of the organization of the firm there are more dimensions to market complexity that need to be understood. This is relevant as Ashby’s Law of Requisite Variety implies that in order to survive the organization of a firm needs to be higher as that of its market. Therefore, we need to understand what market complexity may be, beyond the variety of products in a market. We need to distinguish between:

116

Blok (2013), pp. 137–140. Gardner and Lobb (2014), Groysberg and Abrahams (2008). 118 Hidalgo and Hausmann (2009), Cristelli et al. (2013). 117

8.5 Complexity of Markets, Products, and Consumers

207

• Complexity of a market or industry in terms of the variety of (specialized) producers • Complexity of a market in terms of its distribution channels, both in terms of alternative channels and number of sequential channels119 • Complexity of market with respect to the organization of transactions, whether the four flows constituting a transaction, the information flow, the goods or service flow, the title flow, and the value flow are organized unitary or separately (as e.g. in the mobile phone industry), in one step or in multiple steps • The complexity in terms of vertical integration versus network type organization of production as in e.g. apparel, including the organization of especially open innovation. Network type organization of especially complex products usually is orchestrated by a firm owning the architecture of a product or system, defining modules produced by other parties120 • The complexity of the pattern of value creation: simple, as in the vertical integrated firm with arm’s length complete transactions, versus complex as in network industries, in which value (integration value) is created by the end-user who combines system elements from various independent producers, as e.g. in HiFi-systems, home entertainment and in the case of the building industry by contractors121 • Complexity of competition: simple—over the dimension of integrated products, versus complex—horizontal over de the dimensions of components, modules, and architecture, as in e.g. the PC-industry122 • Complexity in terms of convergence of industries or fluidity of boundaries between industries as in, e.g., entertainment and ICT • At industry level there may be (detail) complexity in regulation as in, e.g., the financial industry • The complexity of markets in terms of variety of products and services • The complexity of market in terms of (especially preference) variety in customers or consumers • The complexity of the price mechanisms, e.g. whether products and services have published fixed prices or whether prices are time sensitive or IP-address sensitive, to counteract the transparency induced by the Internet by intransparency in pricing (and product versioning)123 Next to these dimensions of complexity of markets and industries, product complexity and complexity of consumers need to be distinguished. There may be a relation between product complexity and the complexity of an industry, as in the case of modular composition and production of complex products. Especially the

119

Majaro (1982). Nohria and Eccles (1992), Chesbrough (2006). Nohria and Eccles (1992), Chesbrough (2006). 121 Shy (2001). 122 Yoffie (1997). 123 Carr (2014), Pasquale (2015). Carr (2014), Pasquale (2015). 120

208

8 Complexity and Coordination

European car industry is an example of the relation between modularity and industry structure.124 Some authors define complex product systems (CoPS). “CoPS are defined as high cost, engineering-intensive products, systems, networks and constructs. The term ‘complex’ is used to denote the high number of customized components, the breadth of the knowledge and skills required, and the extent of new knowledge involved in development and production.”125 This is a definition from a perspective of engineering and of the perspective operations and supply chain management, and the use of the qualifier complex in CoPS at best refers to detail complexity. To understand the complexity of products from the perspective of general management (strategy, investments, organization, risk management) a distinction needs to be made between the product or a service as a proposition (the functional complexity of the customer value proposition, aka solution), and the way a product is composed and build respectively a service is being delivered, the technical complexity.126 The customer value proposition as a functional description of the product from the perspective of the customer has a minimal number of dimensions, functionality, technical specification, price, information, but it may include more dimensions and especially the functionality (what the product does) can range from simple to complex. ERP-systems have a complex functionality, as opposed to a light bulb. Separate from the customer value proposition is the complexity of how a product or system is constructed, including its development, construction, and operation. For an air traveler a Boeing 747 has a simple functionality, although more complex for its pilots and air carrier, but a Boeing 747 consists of over a million components. These two dimensions of the complexity of a product or service need to be distinguished because these have different consequences of how to deal with these complexities and, respectively, the consequences of these complexities. So far, the use of the phrase “complexity” to characterize products implicitly assumes detail complexity, not complexity in a dynamic sense, displaying emergence. One might argue that true product complexity is when the complexity of the proposition and within that especially the functionality of the product or service interacts with the requirements, the uses and the values of the user, the customer or consumer, resulting in new, not-foreseen, or unpredicted behaviors, insights, options, actions, needs, etc. This is what is to be observed in the case of the combination of the Internet, personal computers, mobile devices, software like Twitter, Facebook, etc. By using these systems and software individuals, groups, etc., discover new possibilities in terms of organization, as e.g. in the Spring Revolution, that new types of social power can be developed as counter-power, but also that it changes our self-image, the way we read, think, select, and buy

124

Sabel and Zeitlin (2004), Sturgeon (2002), Takeishi and Fujimoto (2003), Langlois (1999). Acha et al. (2004). 126 Marti (2007). 125

8.5 Complexity of Markets, Products, and Consumers

209

products and services, changes our preferences, in an mostly not foreseen, sometimes surprising way.127 Especially computers or IT-systems may have such a strong generative complexity in their use. This is not only to be attributed to the nature of such information systems, but as much to the fact that the roles and types of information in the functioning of organizations still is not understood. The unit-organization implicitly is based on, or aims to compensate for, the high costs of information, the high costs of communication and the slow speed of communication, as this was the situation in 1918, when DuPont implemented this organization form. ICT has reduced the costs of information very strongly, thus lifting constraints in designing organization forms.128 But because information is ill incorporated in theories of management and organization, other than management accounting information, the required conceptual understanding lags behind technological developments. The application of IT-systems, especially in the eighties of the twentieth century, became an undefined field of applying ICT to mechanize existing processes, to organize in new ways, to enforce changes that otherwise did not work. Initially this chaotic dimension of the application of information systems tended to be reduced by submitting IT-applications to the Business-IT-Alignment paradigm. That is the functionality of an IT-application was to be based on the existing processes, functions, and especially information processing in a given business.129 But soon it was acknowledged that the developments in information and communication technology, resulting in a decline of the costs of information and communication and in an increase in the speed of communication changed industries, changed markets, introduced information products, changed marketing and distribution, thus requiring new business models. In combination with the fact that especially large ERP-systems had a longer lifetime, as did the new business models plus that competition shifted to innovation of business models, the Business-IT-Alignment paradigm became a trap for business.130 This trap has been solved by defining distinct categories of IT-applications, a stable, complexity absorbing technical infrastructure of servers and communication, a layer of recording transactions, also stable and having the capability to serve multiple business models, as well to absorb, e.g., compliance induced complexity, a layer of tools for proposition and business developments, analytical tools, design tools, a dynamic layer and a more dynamic layer of transformational applications in interactions with customers and consumers, e.g. apps, information goods, websites, etc.131 The latter two layers allow for generative complexity, the first provides

127

Carr (2011), Fuchs (2008), Benkler (2006). Jorgenson (2001), Alchian and Demsetz (1972), Stinchcombe (1990). 129 Henderson and Venkatraman (1993). 130 Shpilberg et al. (2007). 131 Chapter 9 Information Capital Readiness, Kaplan and Norton (2004). 128

210

8 Complexity and Coordination

stability and absorbs institutional complexity. This system of four layers is also underlying the concept of exponential organizations; the first two layers constituting an infrastructure on which new initiatives in the two last layers can grow fast without high investments.132 The technical complexity of a product can be described in terms of its architecture and modularity, especially the levels of architecture, as in specific products or systems a module, e.g. an engine itself may have an architecture. Products architectures can be open (e.g., that of the PC) or closed (e.g., that of Apple).133 Another dimension of technical complexity of products is product versioning at cost, e.g., through the use of product platforms.134 Modularity of products allows for mixmatch flexibility, which is adapting the product to specific consumer preferences on the basis of a module catalog (e.g., in the case of cars and computers), through which the unpredictable variety in consumer preferences is translated in a matching variety of product versions in a profitable way. Versioning especially applies to information products, but due to the immateriality of information products the versioning can have a higher degree of variety compared to physical products.135 Consumers may also be described in terms of complexity. In the neoclassical economy the consumer is supposed to be a utility maximizing being expressed in the price she or he is willing to pay for a product or service. The consumer thus is represented as a one-dimensional behaving system element. Karl Marx complicated this one-dimensionality with his distinction between use value and exchange value. The field of marketing was soon to discover that consumer may see even more different values in products, e.g., to express a social status, actually or desired. We therefore might say that consumers could be characterized on a metric called value complexity, varying from simple, buying on (lowest) price only, to selecting and preferring products based on, e.g., hedonistic or altruistic values as in the case of fairtrade products. Figure 8.5 provides an overview of the possible complexity of values that may define consumer preferences and behavior.

132

Downes and Nunes (2014), Ismail et al. (2014). Christensen and Raynor (2003). 134 Alblas and Wortmann (2014). 135 Picot et al. (2008), p. 303, Shapiro and Varian (1999), p. 3. 133

Bibliography

211

Time% (moment)%

Context of% ac5vity%

Extrinsic) Ac#ve& Self/ oriented)

Intrinsic) Play%(Fun) %

Economic) Value)

Hedonic) Value)

Reac#ve&

Excellence% (Quality)%

Aesthe5cs% (Beauty)%

Ac#ve&

Status%(Success,% impression)%

Ethics%(Virtue,% Jus5ce)%

Reac#ve&

Esteem% (Reputa5on,% materialism)%

Other/ oriented)

Experience%

Efficiency%(O/I;% convenience) %

Place%

Social) Value)

Time% (dura5on)%

Altruis: c) Value) Spirituality% (Faith)%

Civil%values%

Fig. 8.5 Consumer preferences are far more complex as assumed in the utilitarian concept of neoclassical economy. Holbrook (2006)

Bibliography Acha, V., Davies, A., Hobday, M., & Salter, A. (2004). Exploring the capital goods economy: Complex product systems in the UK. Industrial and Corporate Change, 13(3), 505–529. Admondson, A. C., & Rashid, F. (2010). Integrated project delivery at autodesk, Inc. Case series Teaching note. Retrieved from Boston, MA. Alblas, A. A., & Wortmann, J. C. (2014). Function-technology platforms improve efficiency in high-tech equipment manufacturing. International Journal of Operations and Production Management, 34(4), 447–476. https://doi.org/10.1108/ijopm-06-2012-0221 Alchian, A. A., & Demsetz, H. (1972). Production, information costs and economic organization. American Economic Review, 62(5), 777–795. Anthony, R. N., & Govindarajan, V. (2007). Management control systems (12th ed.). McGraw-Hill/ Irwin. Appleby, R. C. (1994). Modern business administration (6th ed.). Pitman. Arendt, H. (1958). The human condition. University of Chicago Press. Arrow, K. J. (1974). The limits of organization. Norton. Baldwin, C. Y., & Clark, K. B. (2000). Design rules: The power of modularity. MIT Press. Barnard, C. I. (1948). The functions of the executive. Harvard University Press. Beck, U. (1999). World risk society. Polity Press. Beck, U., & Lau, C. (2005). Second modernity as a research agenda: Theoretical and empirical explorations in the ‘meta-change’ of modern society. The British Journal of Sociology, 56(4), 525–557. Benkler, Y. (2006). The wealth of networks: How social production transforms markets and freedom. Yale University Press. Bennet, A., & Bennet, D. (2004). The intelligent complex adaptive system. In Organizational survival in the new world (pp. 25–36). Butterworth-Heinemann.

212

8 Complexity and Coordination

Blok, P. (2013). Human Resource Management: Institutionele grondslagen, historische ontwikkelingen en perspectieven voor de economie van de 21ste eeuw. (Proefschrift), Universiteit van Amsterdam, Vossius Pers UvA. Bolici, F., Howison, J., & Crowston, K. (2016). Stigmergic coordination in FLOSS development teams: Integrating explicit and implicit mechanisms. Cognitive Systems Research, 38, 14–22. https://doi.org/10.1016/j.cogsys.2015.12.003 Bolman, L. G., & Deal, T. E. (2014). How great leaders think: The art of reframing. Jossey-Bass, A Wiley Brand. Burke, P. (2020). The polymath: A cultural history from leonardo da vinci to susan sontag. Yale University Press. Capra, F., & Luisi, P. L. (2014). The systems view of life: A unifying vision. Cambridge University Press. Carr, N. G. (2011). The shallows: What the Internet is doing to our brains (Norton pbk. ed.). W.W. Norton. Carr, N. G. (2014). The glass cage: Automation and us (1st ed.). W.W. Norton & Company. Casson, M. (1997). Information and organization: A new perspective on the theory of the firm. Clarendon Press. Chandler, A. D. (1962). Strategy and structure: Chapters in the history of American enterprise. MIT Press. Chesbrough, H., Vanhaverbeke, W., & West, J. (Eds.). (2006). Open innovation: Researching a new paradigm. Oxford University Press. Christensen, C. M., & Raynor, M. E. (2003). The innovator’s solution: Creating and sustaining successful growth. Harvard Business School Press. Clark, K., & Baldwin, C. Y. (2002a). The fundamental theorem of design economics. SSRN eLibrary.https://doi.org/10.2139/ssrn.312419 Clark, K., & Baldwin, C. Y. (2002b). The option value of modularity in design: An example from design rules, Volume 1: The power of modularity. SSRN eLibrary.https://doi.org/10.2139/ssrn. 312404 Coase, R. H. (1937). The nature of the firm. In O. E. Williamson & S. G. Winter (Eds.), The nature of the firm: Origins, evolution, and development. Oxford University Press (1991). Collinson, S., & Jay, M. (2012). From complexity to simplicity: Unleash your organization’s potential. Palgrave Macmillan. Cristelli, M., Gabrielli, A., Tacchella, A., Caldarelli, G., & Pietronero, L. (2013). Measuring the intangibles: A metrics for the economic complexity of countries and products. PLoS One, 8(8), e70726. https://doi.org/10.1371/journal.pone.0070726 Dale, E. (1960). The great organizers. McGraw-Hill. Demil, B., & Lecocq, X. (2003). Neither market nor hierarchy or network: The emerging bazaar governance. Retrieved from Lille: demillecocq.pdf Domingos, P. (2015). The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books, a Member of the Perseus Books Group. Donaldson, G. (1994). corporate restructuring: managing the change process from within. Harvard Business School Press. Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. https://doi.org/10.1016/j.destud.2011.07.006 Downes, L., & Nunes, P. (2014). Big bang disruption: Strategy in the age of devastating innovation. Penguin Group. Doyle, M. J., & Marsh, L. (2013). Stigmergy 3.0: From ants to economies. Cognitive Systems Research, 21, 1–6. https://doi.org/10.1016/j.cogsys.2012.06.001 Doz, Y. L. (2005). Resource allocation processes in multidimensional organizations: MNCs and alliances. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy. Oxford University Press. Drucker, P. F. (1946). Concept of the corporation (revised 1993 Transaction Publishers ed.). John Day.

Bibliography

213

Etzioni, A. (1988). The moral dimension: Towards a new economics. The Free Press. Farnsworth, K. D., Ellis, G. F. R., & Jaeger, L. (2017). Living through downward causation: From molecules to ecosystems. In S. I. Walker, P. C. W. Davies, & G. F. R. Ellis (Eds.), From matter to life: Information and causality (p. xxii, 494 pages). Cambridge University Press. Fayol, H. (1918/1999). Administration Industrielle et Générale. Dunod. Felin, T., Lakhani, K. R., & Tushman, M. L. (2017). Firms, crowds, and innovation. Strategic Organization, 15(2), 119–140. Foss, N. J., & Michailova, S. (2009). Knowledge governance: Processes and perspectives. Oxford University Press. Friedman, B. M. (2021). Religion and the rise of capitalism. Alfred A. Knopf. Fuchs, C. (2008). Internet and society: Social theory in the information age. In Routledge research in information technology and society, 8 (p. x, 398 p.). Retrieved from http://www.loc.gov/ catdir/toc/ecip0719/2007021600.html Furubotn, E. G., & Richter, R. (2000). Institutions and economic theory: The contribution of the new institutional economics. The University of Michigan Press. Galbraith, J. R. (1973). Designing complex organizations. Addison-Wesley. Gallagher, S. (2020). Action and interaction. Oxford University Press. Gardner, H. K., & Lobb, A. (2014). Collaborating for growth: Duane morris in a turbulent legal sector. Retrieved from Boston Gilbert, C. G. (2005). Restoring the bottom-up process. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy. Oxford University Press. Grandori, A. (2001). Neither hierarchy nor identity: Knowledge-governance mechanisms and the theory of the firm. Journal of Management and Governance, 5, 381–399. Groysberg, B., & Abrahams, R. (2008). Duane morris: Balancing growth and culture at a law firm. Retrieved from Boston, MA. Hardt, M., & Negri, A. (2004). Multitude: War and democracy in the age of empire. Penguin Books. Haslam, S. A. (2004). Psychology in organizations: The social identity approach (2nd ed.). Sage. Hayles, N. K. (2009). RFID: Human agency and meaning in information-intensive environments. Theory Culture & Society, 26(2–3), 47–72. https://doi.org/10.1177/0263276409103107 Hayles, N. K. (2012). How we think: Digital media and contemporary technogenesis. The University of Chicago Press. Heckscher, C. C. (2015). Trust in a complex world: Enriching community (1st ed.). Oxford University Press. Henderson, J. C., & Venkatraman, N. (1993). Strategic alignment: Leveraging information technology for transforming organizations. IBM Systems Journal, 32(1), 472–484. Heylighen, F. (2016a). Stigmergy as a universal coordination mechanism I: Definition and components. Cognitive Systems Research, 38, 4–13. Heylighen, F. (2016b). Stigmergy as a universal coordination mechanism II: Varieties and evolution. Cognitive Systems Research, 38, 50–59. Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proc Natl Acad Sci U S A, 106(26), 10570–10575. Holbrook, M. B. (2006). Consumption experience, customer value, and subjective personal introspection: An illustrative photographic essay. Journal of Business Research, 59(6), 714–725. Ismail, S., Malone, M. S., & van Geest, Y. (2014). Exponential organizations: Why new organization are ten times better, faster, and cheaper than yours. Diversion Books. Jarzabkowski, P. A., Lê, J. K., & Feldman, M. S. (2012). Toward a theory of coordinating: Creating coordinating mechanisms in practice. Organization Science, 23(4), 907–927. https://doi.org/10. 1287/orsc.1110.0693 Jensen, M. C. (1993). Modern industrial revolution, exit, and the failure of internal control systems. SSRN. Jensen, M. C. (1998). Foundations of organizational strategy. Harvard University Press.

214

8 Complexity and Coordination

Jensen, M. C. (2001). Corporate budgeting is broken, let’s fix it. Harvard Business Review. https:// doi.org/10.2139/ssrn.321520 Jensen, M. C. (2003). Paying people to lie: The truth about the budgeting process. European Financial Management, 9(3), 379–406. Jensen, M. C., & Meckling, W. H. (1998). The nature of man. In M. C. Jensen (Ed.), Foundations of organizational strategy. Harvard University Press. Johansson, F. (2017). The Medici effect: What elephants and epidemics can teach us about innovation: With a new preface and discussion guide (With a new preface and discussion guide. ed.). Harvard Business Review Press. Jones, G. R. (2013). Organizational theory, design, and change (7th ed.). Pearson. Jorgenson, D. W. (2001). Information technology and the U.S. economy. The American Economic Review, 91(1), 1–32. Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business School Press. Kaplan, R. S., & Norton, D. P. (2008). The execution premium: Linking strategy to operations for competitive advantage. Harvard Business Press. Katz, J. E., & Aakhus, M. A. (2002). Perpetual contact: Mobile communication, private talk, public performance. Cambridge University Press. Kotha, S., & Srikanth, K. (2013). Managing a global partnership model: Lessons from the boeing 787 ‘Dreamliner’ Program. Global Strategy Journal, 3(1), 41–66. https://doi.org/10.1111/j. 2042-5805.2012.01050.x Landes, D. S. (2000). Revolution in time: Clocks and the making of the modern world (Rev. and enl. ed.). Harvard University Press. Landes, D. S., Mokyr, J., & Baumol, W. J. (Eds.). (2010). The invention of enterprise: Entrepreneurship from ancient Mesopotamia to modern times. Princeton University Press. Langlois, R. N. (1999). Modularity in technology, organization, and society. Retrieved from http:// ssrn.com/abstract=204089 Langlois, R. N. (2001). The vanishing hand: The modular revolution in American business. Lash, S. (2002). Critique of information. Sage. Lewis, T. G. (2013). Cognitive stigmergy: A study of emergence in small-group social networks. Cognitive Systems Research, 21, 7–21. https://doi.org/10.1016/j.cogsys.2012.06.002 Ling, R., & Yttri, B. (1999). Nobody sits at home and waits for the telephone to ring: Micro and hypercoordination through the use of the mobile telephone. Littman, M. L. (2020). Introduction to machine learning. The Great Courses. Majaro, S. (1982). International marketing: A strategic approach to world markets (revised ed.). Allen & Unwin. Majchrzak, A., & Malhotra, A. (2020). Unleashing the crowd: Collaborative solutions to wicked business and societal problems. Palgrave Macmillan. Malone, T. W., & Crowston, K. (1990). What is coordination theory and how can it help design cooperative work systems. Malone, T. W., & Crowston, K. (1994). The interdisciplinary study of coordination. ACM Computing Surveys, 26(1), 87–119. Manzi, J. (2012). Uncontrolled: The surprising payoff of trial-and-error for business, politics, and society (p. xvii, 300 p.). Marti, M. (2007). Complexity management: Optimizing product architecture of industrial products. (PhD), Universität St. Gallen. Medema, S. G. (2009). The hesitant hand: Taming self-interest in the history of economic ideas (p. xiii, 230 p.). Mercier, H., & Sperber, D. (2017). The enigma of reason. Harvard University Press. Milgrom, P., & Roberts, J. (1992). Economics, organization and management. Prentice-Hall. Mobbs, D., Lau, H. C., Jones, O. D., & Frith, C. D. (2009). Law, responsibility, and the brain. In N. Murphy, G. F. R. Ellis, & T. O’Connor (Eds.), Downward causation and the neurobiology of free will (Vol. 5, p. e103). Springer.

Bibliography

215

Mocker, M., Weill, P., & Woerner, S. L. (2014). Revisiting complexity in the digital age. MIT Sloan Management Review. Neilson, G. L., Martin, K. L., & Powers, E. (2008). The secrets to successful strategy execution. Harvard Business Review (June), pp. 61–70. Nieto-Gomez, R. (2016). Stigmergy at the edge: Adversarial stigmergy in the war on drugs. Cognitive Systems Research, 38, 31–40. https://doi.org/10.1016/j.cogsys.2015.12.005 Nohria, N., Eccles, R. G., & (Eds.). (1992). Networks and organizations: Structure, form, and action. Harvard Business School Press. Nolan, R. L. (2012). Ubiquitous IT: The case of the Boeing 787 and implications for strategic IT research. The Journal of Strategic Information Systems, 21(2), 91–102. https://doi.org/10.1016/ j.jsis.2011.12.003 Normann, R. (2001). Reframing business: When the map changes the landscape. Wiley. North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge University Press. North, D. C. (1991). Institutions. The Journal of Economic Perspectives, 5(1), 97–112. Okhuysen, G. A., & Bechky, B. A. (2009). Coordination in organizations: An integrative perspective. The Academy of Management Annals, 3(1), 463–502. https://doi.org/10.1080/ 19416520903047533 Orlicky, J. (1975). Material requirements planning; the new way of life in production and inventory management. McGraw-Hill. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press. Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. The Penguin Press. Picot, A., Dietl, H., & Franck, E. (2005). Organisation: Eine ökonomische Perspektive (4th ed.). Schäffer-Poeschel Verlag. Picot, A., Reichwald, R., & Wigand, R. (2008). Information, organization and management. Springer. Porter, M. E., & Wayland, R. (1992). Capital disadvantage: America’s failing capital investment system. Harvard Business Review, 70(5), 65–82. Puranam, P., Alexy, O., & Reitzig, M. (2014). What’s “new” about new forms of organizing? Academy of Management Review, 39(2), 162–180. https://doi.org/10.5465/amr.2011.0436 Raymond, E. S. (2000). The cathedral and the bazaar. Retrieved from http://www.catb.org/~esr/ writings/cathedral-bazaar/cathedral-bazaar/ Rezgui, A., & Crownston, K. (2017). Stigmergic coordination in Wikipedia. Rosen, S. (2004). Markets and diversity. Harvard University Press. Sabel, C. F., & Zeitlin, J. (2004). Neither modularity nor relational contracting: Inter-firm collaboration in the new economy. A critique of langlois and lamoreaux, raff, and temin. Enterprise and Society, 5(3). Schreyögg, G., & Sydow, J. (2010). Organizing for fluidity? Dilemmas of new organizational forms. Organization Science, 21(6), 1251–1262. https://doi.org/10.1287/orsc.1100.0561 Secretan, J. (2013). Stigmergic dimensions of online creative interaction. Cognitive Systems Research, 21, 65–74. https://doi.org/10.1016/j.cogsys.2012.06.006 Shafiq, S. I., Sanin, C., Szczerbicki, E., & Toro, C. (2015). Virtual engineering object/Virtual engineering process: A specialized form of Cyber Physical System for Industrie 4.0. Procedia Computer Science, 60, 1146–1155. https://doi.org/10.1016/j.procs.2015.08.166 Shapiro, C., & Varian, H. R. (1999). Information rules: A strategic guide to the network economy. Harvard Business School Press. Shifman, L. (2014). Memes in digital culture. The MIT Press. Shpilberg, D., Berez, S., Puryear, R., & Shah, S. (2007). Avoiding the alignment trap in information technology. MIT Sloan Management Review, 49(1), 51–58. Shy, O. (2001). The economics of network industries. Cambridge University Press. Simon, H. A. (1991). Organizations and markets. Journal of Economic Perspectives, 5(2), 25–44.

216

8 Complexity and Coordination

Simons, R. (2005). Levers of organization design: How managers use accountability systems for greater performance and commitment. Harvard Business School Press. Slack, N., Brandon-Jones, A., Johnston, R., & Betts, A. (2012). Operations and process management: Principles and practice for strategic impact (3rd ed.). Prentice Hall/Financial Times. Sloan, A. P. (1962/1986). My years with general motors. Penguin Books. Staehle, W. H. (1991). Management: Eine verhaltenswissenschaftliche Perspektive. Franz Vahlen. Stinchcombe, A. L. (1990). Information and organizations. University of California Press. Sturgeon, T. J. (2002). Modular production networks: A new American model of industrial organization. Industrial and Corporate Change, 11(3), 451–496. Sull, D. N. (2005a). No exit: The failure of bottom-up strategic processes and the role of top-down disinvestment. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy (pp. 133–175). Oxford University Press. Sull, D. N. (2005b). When the bottom-up resource allocation process fails. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy (pp. 93–98). Oxford University Press. Takeishi, A., & Fujimoto, T. (2003). Modularization in the car industry: Interlinked multiple hierarchies of product, production, and supplier systems. In A. Prencipe, A. Davies, & M. Hobday (Eds.), The business of systems integration. Oxford University Press. von Hippel, E., Krogh, G., & v. (2003). Open source software and the “private-collective” innovation model: issues for organization science. Organization Science, 14(2), 209–223. Weihrich, H., & Koontz, H. (1993/1955). Management: A global perspective (10th ed.) McGrawHill. Wolf, J. (2005). Organisation, management, unternehmensführung: Theorien und kritik. Gabler. Wootton, D. (2015). The invention of science: A new history of the scientific revolution. Allen Lane an imprint of Penguin Books. Yoffie, D. B. (1997). Introduction: CHESS and competing in the age of digital convergence. In D. B. Yoffie (Ed.), Competing in the age of digital convergence. Harvard Business School Press.

Part III Complexity in Practice

9

Examples of Mastering Complexity

9.1

Complexity and Learning from Successful Cases

In business, despite entrepreneurship being abductive thinking, the standard question in case of a new proposed solution is, do examples exist as proof of concept? The answer is yes. To which must be added immediately that these cases are described in case studies, most of which are published by Harvard Business Press, the principles of these cases are not yet codified in text books. To which the question comes from science whether n = 1 can generate a general valid statement applicable in other situations. The rule of generalization of n = 1 was used in the spread of the multidivisional organization and the unit-organization after the Second World War. Was this because the multidimensional organization has general validity or was this because executive adapted themselves to the concept of the multidivisional organizations? The author in his practice of management consultancies came across at least two cases, both in the chemical industry in which the concept of the unitorganization was applied on the basis of product market combinations whereas by the source of basic materials for productions these units either were operationally linked through chemical processes including the production of energy (basic chemicals like sodium and chlorine) or had one source, a chemical cracker that based on temperature gradients in its column produced different types of plastics each with its own range of applications (automotive, toys, packaging), but changing the volume for one type of output of such a process (as input for one of the BUs) changed the output of the other outputs (and thus the inputs for the other BUs), inescapable due to the chemistry of the process. In the case of base chemicals the inappropriateness of appling the BU-model, was attempted to be corrected by using internal transaction costs (which didn’t really work, until some younger controllers modeled all the related BUs and process in one spreadsheet to optimize the performance of the group, unknowing to senior management) and in the case of the plastics business the controller of the group of BUs organized an overall operational planning process to optimize the performance of the group of BUs. Due to the differentiation of business models one needs to be careful to force a # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_9

219

220

9 Examples of Mastering Complexity

successful concept to other firms, to other situations. A complicating factor is that the effectiveness of solutions may be dependent on the (national) context or institutional context of a firm, whereas in traditional management books such contexts are ignored. Government policy, especially economic or industry policy may play a role, a geopolitical policy, e.g. the US policy of economic and military hegemony in the world and the Chinese political and economic policy do set a specific context for business policies, the national systems of industrial or labor relations are different across various nation states, e.g. France, Germany, the USA. In § 6.3, the regulatory context was explained as a moderating variable between complexity and economic growth. Government policy in terms of overall objectives to be achieved and the system of labor relations is to be distinguished from the regulator context, which is more specific. The question is whether the context of government policy and industrial relations, labor laws, etc. do stimulate the understanding of CEOs of new developing situations or more specific, foster their capability of reconceptualizing, looking for new simplicities beyond new complexities. Or is it that CEOs with the capability to reconceptualize develop such a capability irrespective of national context?. Phelps suggests that on average US CEOs are better in reconceptualizing as are European CEOs, but offers no explanation for this difference.1 So, there are insights to be learned from cases, but learning does not consist of blind copying operational practices from one firm to the other, as was often attempted in the practice of best practices and tends to be a consulting model. To which must be added that best practices in an innovative and differentiating world always are obsolete practices and trends are traps. To learn from successful cases today, different from the past, the underlying principles need to be understood, but usually are not in case descriptions. Another bias in case descriptions may be that editors tend to emphasize what they think will easily be accepted by readers or understood, which may imply that essential technical details, e.g. semantic data standardization in the transaction systems, are left out. Case descriptions tend to focus on what managers decided and what they communicated, less on their thinking, their theories-in-use. As any description of a real situation there tends to be an element of simplification in a case description in that the author(s) use a guiding theory to select what they think is essential in the case or what they think the lessons to be learned are. A case description best is to be read with a mindset asking questions like, what is the bias of the case author, what is being left out but might be important in a different context.

1

Phelps (2013).

9.2 The Case IBM

9.2

221

The Case IBM

An example of mastering complexity is IBM.2 In 1992, IBM was organized as a traditional MNC-matrix, country organizations, grouped in four regions, America’s, Europe, Asia, and ROW, also constituting its legal structure, and a number of global product divisions. IBM had local, regional, and global customers, some of these only purchasing a single product line, others purchasing hardware, software, and services. To solve the financial crises IBM was in in 1992, IBM’s new CEO, Louis Gerstner, defined a new strategy consisting of two elements: 1. Global key account management and 2. Integrated solutions as opposed to selling separately hardware, software, and services. The traditional design rule for organizations used to be Chandler’s “Structure follows strategy, but the market is the common denominator.” But in the case of IBM the market has a regional dimension, a global dimension, and industry dimension and a dimension defined by distribution channel. That is, as in so many cases, the market is multidimensional. Besides, customer may want integrated products, but also, for reasons to control their purchasing power, to purchase separate components through different channels and/or intermediaries, and organize the system integration themselves. Such a market complexity cannot be answered by a single structure of product-based divisions, or region-based divisions. Gerstner decided to leave IBM’s MNC-matrix as it was and which it still is, but made a small number of consequential constitutional decisions. The first was to create one global transaction system, based on unequivocal semantic data standardization, on which all IBM’s entities and divisions operate. A second decision was to record all internal and external transactions with multi attributes, to allow a multidimensional reporting of IBM’s performance. A third decision was to allow all members of the IBM organization access to the database created in this way. A fourth decision was to change in the accounting system the primary profit center from being the countries to the customer being the first profit center. A fifth decision was to report the performance of IBM simultaneously over multiple dimensions, customer, product, country/region, and industry. Thus, turning IBM from a traditional MNC-matrix into a multidimensional organization, commensurate with the for IBM’s success critical dimension in its markets. Obviously, Gerstner understood what he was doing, in terms of understanding this fundamental change away from the traditional MNC-matrix with its power relations, identities, roles, etc. In such an MNC-matrix the traditional power positions are the country managers, because they control essential information based on transactions with suppliers and customers being written on the title of the country organization as the constituting legal entities. By creating one global ledger and organizating this in global shared service centers for finance and ICT disembedded from the country organization, the on information based power of the national managers was taken away. By defining the customer as the primary profit center a customer centric organization was created, in which turnover, gross profit, share of wallet, retention, etc., are

2

Strikwerda (2008).

222

9 Examples of Mastering Complexity

reported per individual customer weekly, irrespective through which channel or direct from IBM the customer purchases products, services, or integrated solutions. Such a multidimensional organization is more complex as is an MNC-matrix, but is needed to be in-control in a complex market (Ashby’s Law of Requisite Variety). This is not to say that all of its workers understood IBM’s multidimensional organization. Quite some managers needed to be coached or to be replaced as well the staffing of the finance department needed to be upgraded in order to emphasize performance-oriented business control over conformance-oriented financial control. All employees of IBM were empowered to take initiative toward a (potential) customer, provided an integrated business case, considering the effects of an initiative on other departments, was presented. A number of IBM-employees needed time to get used to be open to colleagues in writing and discussing a business case, being open to them with respect to assumptions and uncertainties.

9.3

The Case Procter & Gamble

Another example of increasing the complexity of the organization and mastering it is Procter & Gamble.3 In 1997, it was decided within P&G to reorganize Procter & Gamble, from a traditional MNC-matrix into four types of organizational entities: Global Business Units (product development, brand management), Market Development Organizations (product to market, sales), Global Business Services (back office), and Corporate Functions. P&G did so to answer changes in the market and the shift toward the knowledge economy. Until then product development was concentrated in the USA and in Brussels. The development of markets worldwide implied an increase in variety (complexity) in consumer preferences, but also an increase in sources of new ideas for products. The P&L responsibility in the case of developed countries was with the GBUs, in the case of emergent markets with the MDOs. In addition to this Lafley decided that the MDOs are responsible for the first customer experience and the GBUs are responsible for the second customer experience. Compared to the traditional MNC-organization this is an increase in complexity in the organization. Basically, in the P&G organization the exploitation of resource is split from the exploitation of market opportunities, whereas in the traditional M-form the division manager is both responsible for managing market opportunities and resource exploitation. Especially in consumer products the variety in market across the world increases for certain categories of products, by taste, size of package, sampling, etc. (not for some high-end merit goods), a variety that needs to be answered in the organization. By creating its GBU P&G is better able to absorb ideas for new products or products improvement across the world, compared to the old situation of two development centers. The differentiation in P&L responsibility answers the required differentiation in focus implied by market requirements. This increase of complexity of the organization of P&G, in view of the financial and 3

Kanter and Bird (2009).

9.4 The GIOCA Expert Centre in Amsterdam

223

market performance of P&G suggests that P&G is successful in mastering the increased complexity, but not without problems. Specific attention needed to be paid to HR-policies, purpose, values, principles which needed to be codified in processes and systems, managers needed to be re-trained, etc. Reportedly a number of senior managers left P&G. Consumer goods retail is another example of increased complexity in markets. Consumer preferences, of at least a certain group of customers, may vary dependent on time, day, context of activities, etc. Markets cannot no longer be segmented by one dimension, as e.g. was the case Sloan did with General Motors on the basis of income brackets.4 The Dutch consumer goods retailer Albert Heijn has answered this increase in complexity of its markets in two ways. Its traditional organization was (and is) based on category management being the P&L entities, the shops being costs centers all having the same brand with the same shop formula. Albert Heijn made two changes to answer the increase in the complexity in its markets. The first was building an information system that stores all sales slips digitally, to which data about the weather at the time of sales and consumer identities based on a loyalty card are added. The sales slip contains date, time, and place as well. Category managers use this database for ordering, pricing, marketing, supply chain management, etc. The second change was to introduce a differentiation in shop formula, by residential area, X-large for shopping centers, convenience shops (AH ToGo) at railway stations and in hospitals, etc. The pricing of products is differentiated by shop formula, as is the product range. Retained is the purchasing power, and gained is the responsiveness to increased market differentiation. The older generation of category managers initially had problems with the new system. They tended to make decisions on a rule of thumb, e.g. no fresh bread supplies after four o’clock. But in the case of the AH ToGo customers expect fresh bread also at 19:00. The category managers needed to get used to an increase at their level of decision-making and to the fact that now formula managers defined customer service levels, not category managers.

9.4

The GIOCA Expert Centre in Amsterdam

Hospitals traditionally are organized asset-focused, that is by medical departments, whereas there is a demand for a disease-focused organization. In the case of the asset-focused approach the organization is simple for medical doctors and the executive board, but may be complex for certain types of patients, e.g. gastrointestinal oncology patients who need to make appointments with up to six medical specialists to achieve a proper diagnosis and treatment plan. The organization can be made simple for such patients by introducing care paths, as e.g. in the case of gastrointestinal oncology patients in the Amsterdam Medical

4

Sloan (1962/1986).

224

9 Examples of Mastering Complexity

Centre.5 These patients now need only to make one appointment and on the basis of that appointment and a defined, cross-departmental medical protocol the required medical doctors are scheduled. This improvement for the patient increases the complexity of the organization of the hospital, the care path has to be defined in the resource allocation process as an additional planning dimension to the medical departments for capacity planning and scheduling, priorities need to be set, costs need to be reallocated, etc. The medical doctors involved do not experience an increase in complexity, this way of working fits their medical training, their care for patients and a mutual understanding on the basis of a common basic training as medical doctors and a common body of knowledge, the human body. The administrative functions have experienced more problems as their one-dimensional view of the organization of the hospital needed to be readjusted and especially the resource allocation process. Discussing this case with the chairman of the executive board of another general hospital evoked the response by this chairman: “but the organization of the hospital should be simple!” To which the question was asked: “for who?” It was concluded that the organization needs to be simple from the perspective of the patient or patient–doctor interaction and that the executive board and supportive administrative organization need to be able to handle the consequential complexity. This latter example suggests that the issue is not so much complexity or simplicity, but that complexity and simplicity also is an issue of perspective, of position of purposes to be achieved. The combination of complexity and simplicity also has to do with the reconciliation of conflicting economic requirements. Patients want expert, specialized medical knowledge and at the same time a patient-centered treatment.

Bibliography Basta, Y. L. (2017). A blueprint for multidisciplinary fast track gastrointestinal oncology care. (Dr.), Universiteit van Amsterdam. Kanter, R. M., & Bird, M. (2009). Procter & Gamble in the 21st century (A): Becoming truly global. Retrieved from Boston, MA. Phelps, E. (2013). Mass flourishing: How grassroots innovation created jobs, challenge and change. Princeton University Press. Sloan, A. P. (1962/1986). My years with general motors. Penguin Books. Strikwerda, J. (2008). Van unitmanagement naar multidimensionale organisaties. Van GorcumStichting Management Studies.

5

Basta (2017).

How CEOs Cope with Complexity

10.1

10

CEOs and Complexity

Successful CEOs “wade into complexity.”1 These CEOs do so, not for the sake of complexity itself, but because they understand that the old, familiar simplicity no longer will bring the successes they are after and they understand that a new reliable simplification of a new complexity only results from an understanding of that new complexity. In order to deal with the (new) realities of markets, business, and organizations, we need simplified models to make decisions and to communicate effectively with others in the organization. Napoleon and Churchill are reputed for their quip “make it as simple as possible, but not simpler.” To which Albert Einstein added: “the simple always is the simplified.” Hence O’Toole’s observation that successful CEOs look for simplicity beyond complexity and in doing so they develop new and better models for their business and their organization. Better models in the sense of adapted to and appropriate for the new situation. Such CEOs acknowledge new complexities, they do not let themselves be blinded by the mind’s tendency of belief conservation. They want to understand new complexities in order to define what can be new, responsible simplifications for the time being. Successful CEOs do not simply simplify, they represent their business in a simple way for those who need simplicity, e.g. knowledge workers or customers, while maintaining the necessary complexity, as induced by technology, specialization, markets, compliance, and regulations. To have complexity absorbed in a platform to facilitate workers by protecting these from complexity is an example of combining simplicity with complexity. In some cases, complexity is a matter of perspective. A traditional unit-organization is simple for the CEO, in terms of managing a portfolio of business lines through bilateral relations with unit-managers. For a unit-manager the unitorganization is simple, the unit-manager does not depend on other units, only on the

1

Martin (2007).

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_10

225

226

10

How CEOs Cope with Complexity

resources under his hierarchical control. But for a customer dealing with multiple units, or a patient depending on multiple medical departments for a diagnosis, such a type of organization is complex, in terms of appointments, scheduling, and flow. That dimension that is most important for the successful survival of a firm or institution defines the dimension of simplicity, shifting the preservation of complexity to other dimensions in the organization. So, when Gerstner in the 1990s identified the importance of integrated products for customers, he made the decision that the customer would be the profit center, no longer the discrete products and subsequently changed the process and system for target setting, resource allocation, and reporting. This “wading into complexity” needs to be well understood. Because what CEOs do in confronting a new complexity is not going into all the details, but they try to understand the new situations on a more abstract level. They try to reconceptualize the new situation. Levy, the former CEO of the marketing firm Publicis, after a first failed attempt to digitize his business, on closer inspection realized that digital technology not simply is another technology for marketing, but that digital technology, through digital visual media, would change the idea of an advertisement, the idea of marketing itself and that it would bring new players in the industry, (which later turned out to be Google), with new rules. In the resource-based view of the firm, or more specific due to the nature of the resource knowledge, in the variant of the dynamic capabilities view, it is acknowledged that one of the characteristics of successful CEOs is their capability to reconceptualize.2 This capability to reconceptualize new, emerging situations, is acknowledged in economic studies on innovation as well.3 The idea of reconceptualization itself is not new. Alfred Sloan reconceptualized the car industry in the early twenties from the Ford-model, introducing the year-model and the income-bracket based car models and market segmentation. Reconceptualization is also underlying the concept of avoiding market myopia and is underlying the success of, e.g., Amazon, Google, Apple, and Facebook. Such reconceptualizations produce concepts through which new complexities as induced by technology become comprehensible again, if not simple. CEOs sticking to conventional concepts may experience the new market realities as complex, simply because these conventional concepts do not have the capability to grasp the new complexities. This explains why Christensen in his Seeing What’s Next has identified the importance of seeing changes at industry level, not simply at the level of markets and businesses.4 The importance of reconceptualization is in the strategic management books acknowledged as cognitive framing.5 Cognitive framing in strategic thinking is the acknowledgment of new dimensions, seeing new dimensions in consumer behavior, industry rules, cost dynamics, etc., thus avoiding interpreting new situations through the lens of existing business models and existing

2

Helfat and Peteraf (2015). Phelps (2013). 4 Christensen et al. (2004). 5 Gilbert (2005). 3

10.2

The Power and Risks of Abstract Thinking

227

dominant logics of which the often tacit assumptions do not fit the new situations. Cognitive framing requires a cognitive structure not only beyond traditional management models, but beyond the field of business administration, in sociology, macro-economics, the arts, political movements, even philosophy, etc. Cognitive framing requires an understanding of processes of emergence in society at large.

10.2

The Power and Risks of Abstract Thinking

Basically, we do not know how CEOs think, despite the research on this. Certainly, a number of psychological insights can be found in the literature on administrative behavior. But any modeling of thinking of CEOs runs the risks of being codified in AI or machine learning creating the risk of a mechanization of thinking, which would be the end of thinking. In terms of being able to cope with new situations effectively, the safest strategy is that of a continuous de novo thinking by responsible, wise, and intelligent individuals who do not let themselves trap in conventions. There are various ways CEOs, managers, members of an organization, as well as academics and practitioners deal with complexity, also because as we have seen there are various types of complexity. One author who provides a description on how to deal in a constructive and productive way with complexity is Brink Lindsey in his book Human Capitalism.6 Lindsey claims that economic growth breeds complexity, imposing more stringent demands on our mental capabilities, requiring greater investments in human capital, that is more education. Lindsey sees complexity as an intermediate variable in the relation between economic growth and human capital, whereas others may state the causal relation in the other direction, an increase in human capital will allow for an increase in complexity and that will contribute to economic growth. In the modern philosophy both causal directions are assumed under the title of reflexivity or double hermeneutics. To acknowledge this reflexivity is a first distinction in experience with complexity. There are people who feel that complexity happens to them, as the result of actions of anonymous others. Others, e.g. narcissistic entrepreneurs, will see complexity as the source of new opportunities, also because they impose their forceful worldview on others.7 Lindsey claims that the master strategy to deal with complexity is the capacity for abstract thought. This capacity for intellectual thought consists of three distinctive dimensions: • Intellectual abstraction: this allow us to make sense of the world around us through the use of broad concepts, symbols, and formal reasoning. • Social abstraction: this enables us to interact constructively with strangers by way of a highly elaborate game of role playing.

6 7

Lindsey (2012). Maccoby (2007), Martin (2007).

228

10

How CEOs Cope with Complexity

• Personal abstraction: this capability enables us to exercise meaningful autonomy through weighing how various choices will affect an imagined future self. Abstract thinking comes in varieties. One may think of the abstraction in mathematics, e.g. the field of topology, or theoretical physic. For business will be more important Simon’s “simplicity of the architecture of the mind.”8 We find this in the Greek art of mnemonic. The art of clustering large number of detailed information; names of persons, galaxy names, and linking those cluster to easy, more visual to remember symbols. This is what we do with the library index and with the functional architecture of a house, a computer, or a business. Thinking in terms of architecture implies solving a complex problem as a process, or a set of steps, but different from those of the well-structured problem. Simon thus defines his hypothesis of simplicity.9 This hypothesis states that we use the simplicity of process to deal with, but also to maintain the complexity of state. This simplicity of process is not the reductionist simplification of science, but the method of design. In this method the designer uses levels of abstraction in terms of function, functionality, modules and such to master complex systems like computers, airplanes, cars, etc. This type of abstract thinking enables the designer and therefore the problem solver to zoom out of the problem and to zoom into parts of it without losing oversight. This type of architecture is not unlike the hierarchy of an organization, most likely they have the same origin, but architecture in problem-solving is functional, not Weberian positional. Intellectual abstraction may be either conventional or it may be creative or interpretive. Conventional abstraction to manage complexity by reducing it is to be found in, e.g., the adoption of the M-form, traditional methods for decisionmaking, and developing a dominant logic.10 The philosopher Husserl made a distinction between “inauthentic thinking,” which comprehends mathematical calculus (operations research), thoughtless logical operations, and empty conceptual constructions, versus “authentic thinking,” which is seeing new realities, especially in terms of new connections between phenomena, which are consequential for interests and thus for acting.11 To this Heidegger added that authenticity also is about questioning assumptions held by others. This authentic abstract thinking follows a process of intuition, of a becoming aware of, in which intuition according to the philosopher Bergson has duration, it is not thinking at one glance, it is not Napoleon’s coup d’œil he required from his generals. Bergson’s intuition is a process of discovering freedom where it was not seen before. Lindsey observes, as did Heidegger and Sartre before him, that the individual through technology is freed from “a life rooted in a remembered past to one focused on an imagined future.” The question to be asked is, is intellectual abstraction used to make sense of the world expressed in conventional organization forms, management

8

Simon, p. 86. Simon (1996), p. 87. 10 Bower (1986), Chandler (1962), Simon (1997), Prahalad and Bettis (1986). 11 Bernet (2014). 9

10.2

The Power and Risks of Abstract Thinking

229

practices, concepts like culture or is intellectual abstraction used to reconceptualize organization forms, to reconceptualize how formal organization of firms function in a knowledge economy? When Max Weber coined his rule-of-law based bureaucracy, to overcome the limitations of the medieval person-based bureaucracy, he assumed, in line with old feudal relations in society, that everybody would know, understand, and accept his or her position in society, as a basis of the functioning of his bureaucracy.12 That is, implicitly Weber assumed complexity reducing institutions, as later would be explained more explicit by the German sociologist Luhmann, for the working of Weber’s concept of bureaucracy, itself as much a complexity reducing institution.13 Subsequently many sociological organization theories and models, including e.g. functionalism not only served to explain social situation but served to feed management theories for personnel management, and in this combination served to reduce complexity. Weber himself observed that the complexity of society only could be understood through abstraction, by using idealist types, thus ignoring the absolute variety in social life.14 Weber’s abstraction was a reductionist abstraction with is at odds with the present and future society. At this point Martin observes that those CEOs who deal in a constructive, productive way with complexity amongst others believe that models, as used in abstract thinking, do not represent reality. Such CEOs tend to see these models as time-bound simplifications reflecting the issues of the time in which these models were conceived, e.g. in the case of the M-form the economic conditions as this prevailed till about 1980.15 Conflicting models are not a problem for such CEOs, each of these models is a simplification with a specific intent, confined to a specific period in the economy and a specific interest. These CEOs understand that today multiple viewpoints are necessary, as well that models have an expiration date. Subsequently such CEOs believe that better models can be constructed and can be turned into concrete reality (as e.g. in the case of Procter & Gamble). With that these CEOs are comfortable with “wading into complexity,” and are confident they will emerge out of it with a better solution. The latter is precisely what economists expect of entrepreneurs, moving the boundaries of the production function. That is, such CEOs do not make sense of the world on basis of conventions, but move beyond these conventions. Neither Lindsey nor Martin is very specific how such CEOs think, by tools or education, to see a new simplicity beyond new complexity. One might ask the question whether specific training exists for such a stance. Has it to do with personality, with cognitive structure, or does luck play a role? The concept of market myopia may be a clue about the type of thinking. Does a manufacturer take pride in building a car for its customer, or does the manufacturer observe the consumer in a more abstract way, providing the customer comfortable, safe, and economic

12

Weber et al. (1992), Weber (1947). Luhmann (1968). 14 Weber and Shils (1949), p. 87. 15 Martin (2007), pp. 111–112. 13

230

10

How CEOs Cope with Complexity

transportation? A function of a product is more abstract as is the product itself. With respect to organizations a comparable difference in reasoning can be observed. Organization forms, e.g. the M-form can be seen as a standard to organize managerial control and to organize clear roles for the members of the organization, including focus, accountability, which is to reduce complexity. In the cases of IBM and P&G CEO’s apparently did perceive the organization more in terms of knowledge exploitation and knowledge creation, not in terms of reducing flows of information. Such thinking may be based on the works of a number of economists like Hayek, Arrow, Stiglitz, Becker, but also in around 1990 it became known what the implicit assumptions were underlying the multidivisional organization as used in 1918 by DuPont. Such insights can be found only in a limited way in more popular management books. But apparently it takes a visionary, empathetic but also knowledgeability with respect to business administration, CEO, to translate such insights into a specific concrete new organization form. A specific issue in this is that these CEO’s actively manage the staff departments, which with their specialisms, their own paradigms, concepts, external relation, e.g. accounting rules. Which therefore usually have little interest in the overall architecture of the firm, nor are such specialists very strong in understanding their own fundamentals and their time-bound limitations.16 Staff departments are not to be left to themselves. Books like The Age of Heretics suggest that those CEOs and managers who reinvented concepts of corporate management, did so by stepping outside the conventional social circles and outside the conventional thinking (of staff specialists). The author, Art Kleiner, defines an organizational heretic as “. . . someone who sees a truth that contradicts the conventional wisdom of the institution to which he or she belongs—and who remains loyal to both entities, to the institution and the new truth.”17 That is, such heretic thinkers are go-betweens between old truths and new truths. In his book Originals Adam Grant suggests that it are the non-conformists [CEOs, managers] that move the world.18 Michael Maccoby suggests in his book Narcisistic Leaders that that type of personality is better able to deal with complexity and uncertainty, which has foresight, are system thinkers, which organize uncertainty by projecting their vision on it, thus creating frames for others within which the world has a meaning and can be acted upon. There are some downsides to narcissistic leaders, but in a time of new opportunities, uncertainty, and change, their benefits outweigh their dark side.19 Most likely there are no recipes for required abstract thinking, if anything is to be said to further this art most likely it will not be beyond the concept of nature and nurture. It also might well be that the art of thinking as a capability to deal with uncertainty and complexity includes a level of thinking about thinking in

16

Martin (2007), p. 79. Kleiner (2008), p. xi. 18 Grant (2016). 19 Maccoby (2007). 17

10.2

The Power and Risks of Abstract Thinking

231

the context of society as is suggested by Katherine Hayles in her books Chaos and Order, How We Became Posthuman and How We Think.20 Weick makes a distinction between interpretation and sensemaking of (new) situations.21 An interpretation of a situation is creating an understanding of that situation in terms of familiar concepts, theories, and/or norms, as e.g. in law. Sensemaking is creating new concepts, stories, images, frames to give sense to a new situation or development. A classic example of this is Newton who in explaining a falling apple came up with the then new concepts of force, mass, and acceleration. This mental activity of sensemaking is driven by a deep need of men to make their world intelligible to themselves. Men, dependent on personality and context, also will tend to understand new situations in existing, familiar concepts, models, and explanations. We have a tendency to describe, understand, and interpret new phenomena, developments, and experiences in ways that make them consistent with prior beliefs, to conserve our worldview and to maintain a consistent, positive self-conception.22 Sensemaking refers to Perrow’s third-order control, consisting of (unconscious) assumptions, definitions that in an unobtrusive way, often through the language used, control how we define situations, problems, limit the search for alternative solutions, frame the search for information and its interpretation. Sensemaking is a process in which a person, either intuitively or more conscious, sees the limits of such premises, or that e.g. as a result of technological developments, these premises no longer hold, and that new options have come into existence or can be created. That is, sensemaking touches on the paradigms of business. Sensemaking requires a deep understanding, perhaps not always in a conscious or analytic way, but in an intuitive way, of underlying principles and mechanisms of the name of the game of industries, markets, business, competition, value creation, organization, administration, etc.23 This intuition or slow awareness in itself is not sufficient. Sensemaking must also include a communication to others; sensemaking requires a creative mind.24 Most people do not think in terms of principles, paradigms, or theories, but in terms of solutions, what actions to perform, what decisions to make. The executive that sees the limits of existing rules must communicate not the limitations, but the new options, and in a way that his audience will not feel estranged from their past experience, but provides them with a sense of coherence between themselves and the world. Lindsey’s tool of abstract thinking to master complexity needs a modification. If it is used in a Weberian way as done in economics, and the resulting models are used as a basis of policy decisions, it creates the risks of blindness to essential realities, resulting in the great crisis of 2008–2009. If abstract thinking is used to create new concepts, which are used to open up new possibilities, not ignoring the realities of

20

Hayles (1991, 1999, 2012). Weick (1995), p. 6. 22 March (1994), p. 183, Weick (1995), p. 23. 23 Simon (1987). 24 Gardner (2007), p. 77. 21

232

10

How CEOs Cope with Complexity

life, that type of abstraction of reconceptualization will be productive. With this in mind we can now modify Bazerman’s concept of System 1 and System 2 thinking. System 1 thinking, fast thinking, may consist of thoughtless applying a heuristic to a decision-problem. Whether this is wrong or right is defined by the outcome. It is not impossible that by chance a heuristic applied to a new problem produces an acceptable or even good outcome.25 It is not merely a matter of change. In quite some situation a fast decision that seems to be suboptimal is better than a decision that is too late or not making a decision. A situation of paralysis analysis needs to be avoided. But alike especially professionals tend to stick to existing, familiar heuristics and tend to overlook or ignore new data that should question the applicability of the familiar heuristics.26 The second type of fast thinking is intuition. According to Herbert Simon intuition in a positive sense is fast decision-making, based on pattern recognition, that in its turn is based on fast knowledge and extended experience by the decision maker.27 This type of intuition must be distinguished from the claim on intuition that results from making decisions under stress in a psychological state of fear and uncertainty that needs to be concealed from the environment. This type of claimed intuition is linked to a type of accountability avoidance. For in a system of corporate governance it can be perfect that decisions or strategic choices result from the first type of intuition, but that does not deny the duty to be accountable according to the system of corporate governance to test that solution or choices for plausibility. Lindsey’s social abstraction may also need some deeper understanding. His “elaborate role playing” in order to be able to interact constructively with strangers, actually is a complexity reducing mechanism, as typical for the Second Industrial Revolution, with defined roles in especially the trade but also in engineering. The social abstraction actually is to be seen with, e.g., the gamer generation, with their emphasis on contribution instead of the old emphasis on positions (and thus fixed roles). Another example of social abstraction is an emphasis on competencies and less on professional profiles or formal exams. Apart from the fact that competencies are used as a control technique as well (because traditional control techniques no longer are effective), competencies tend to characterize individuals irrespective, although perhaps not completely, from traditional roles, more aimed at soughtafter contributions, or to reduce the variety in behavior. The shift from position and role orientation to reduce social complexity toward contribution and competencies in order to deal with a higher social complexity assumes an understanding what one wants to achieve and what may be needed to achieve this. This part of dealing with an increasing complexity may also have distractive elements. A focus on competencies may also express an assumption that a mix of certain competencies will produce sought-after results, without either understanding or making an effort in the precise processes that will produce the sought-after results.

25

Gigerenzer and Goldstein (1996). Pfeffer and Sutton (2006). 27 Simon (1987). 26

10.2

The Power and Risks of Abstract Thinking

233

Often, we see that firms emphasize openness, trust, the competence of teamwork, the competence of sharing as preconditions in implementing cooperation and projects across business units. One might wonder whether this is not an expression of both the Fundamental Attribution Error (this is the tendency of our mind to attribute errors and other wrong doing to individual, whereas in most cases the cause is the context of those individuals) and the tendency to stick with the simplicity of the existing organization (belief conservation), instead of creating a context fostering cross-unit cooperation by applying, e.g., the new management control system as defined by Kaplan & Norton. Personal abstraction expresses the traditional question whether one defines him or herself by position or by purpose. The latter emphasizes the meaningful autonomy and enables the individual to define its way in the increasing complexity in society, whereas the person looking for security in the position finds itself under attack of the disruptions and transformations in business. Personal abstraction as a way to deal with an increasing complexity is related to the issue of authenticity. Authenticity is usually used in to ways, a romantic way and in an existentialist meaning. The romantic concept of authenticity often is used in marketing and also in counseling. The romantic concept of authenticity idealizes the natural, pure person, outside the influences of the modern image industry, marketing, to be true with oneself as a basis on which to act upon as well as a refuge from the complex world. The existentialist view of authenticity denies the uncorrupted state of man and defines authenticity as the willingness, the consciousness to face the new choices in life (and in organization, business, economics), that is not to seek comfort in traditions, to acknowledge the existential uncertainties implied by these new choices, the willingness to face these and to make choices, accepting their uncertainties.28 To deal in this Sartre-way with existential uncertainty requires and understanding of the fundamental (final) values of life and society, as well that it assumes that individuals have purpose in life. Existential authenticity can be found with artists, musicians, and narcissistic CEO’s. These are persons who define their world as a strategy to cope with it. Personal abstraction is not completely context free. In the case of strong institutions, which can be trusted by individuals, there is a lesser need for personal trust.29 In case of weakening institutions, which is related to an increase in complexity in society, many people compensate the erosion of trust in institutions with an increase in personal trust. We see this, e.g., in the emphasis of building a personal network, an emphasis on trust as a value in organizations, an emphasis on being a team player, etc. However, an emphasis on personal trust carries a risk that individual becomes conservative by creating a selective network of individuals who confirm the individual in his worldview, that is simplify his world view as opposed to an explorative network feeding new insights and understandings to the individual.30 Lindsey’s statement “how various choices will affect an imagined future self” will 28

Strikwerda (2013). Nooteboom (2006). 30 Pentland (2014). 29

234

10

How CEOs Cope with Complexity

produce different outcomes for narcissistic personalities compared to Fromm’s marketing personality.31 The latter wants to be accepted by others and is willing to make sacrifices for that, especially with respect to his or her personal opinions. The marketing personality denies awareness beyond the opinions of the street and knows how to survive in complex situations through reflexes, not by reflection. Such rulefollowing behavior within organizations, at the level of CEO’s and lower levels results in submarginal performance. Typically, consultants tend to be Fromm’s marketing personality, thus slipping away in management services, but the same goes for most lawyers, accountants, auditors, HRM-professionals.

10.3

CEO Turnover and Complexity

Can CEOs be identified who have set or set an example of dealing with increasing complexity in their markets and in their organizations? If so, what can be learned from such CEOs? Little research exists on how CEOs deal with complexity. One strand of research focuses on organizational complexity and CEO turnover, suggesting that in case of diversified firms turnover of CEOs is lower due to the more scarce available cognitive complexity needed for diversified firms.32 This research suggests that in case of a diversified firm CEOs need to master multiple business logics, which indeed is a requirement, but overlook the fact that in case of especially unrelated diversified firms CEOs simplify their task by regressing to a financial control type holding, in which an in-depth understanding of diverse businesses plays a lesser role.33 The need to exploit synergies as induced by the knowledge economy, through the requirement set by the capital market to demonstrate parenting value, forced a number of firms to change from an unrelated diversified portfolio of businesses to a portfolio of related business or even homogenous businesses, in order to avoid an unmanageable complexity. Another strand of research emphasizes a variety or complexity in behaviors or roles CEOs play, suggesting that CEOs with a high “behavioral complexity,” that is the ability to play multiple roles executing their office of CEO, demonstrate an average higher performance in terms of growth and innovation, but not necessarily a higher financial performance.34 This research is flawed because the roles identified are presented as conflicting roles, e.g. Vision Setter and Motivator, whereas such roles are simply part of the seven tasks (roles) of CEOs as defined in the Fayol-doctrine for modern business administration.35 This research fails to acknowledge the issue of a need to be able to execute the seven tasks in the Fayol-doctrine in different styles, contingent on the specific situations of the firm and dependent on economic context. An 31

Maccoby (2008). Berry et al. (2006). 33 Goold and Campbell (1987). 34 Hart and Quinn (1993). 35 Strikwerda (2014), Fayol (1918/1999), Carroll and Gillen (1987). 32

10.4

Simplicity Beyond the (New) Complexity

235

example of this is the Blake–Mouton grid for management styles and the Vroom– Yetton scheme for styles of decision-making (e.g., autocratic versus participative). These two examples of complexity serve to deal with objective-detailed complexity in Fig. 4.1. From an economic viewpoint this type of complexity management is interesting to achieve efficiency, but from an economic viewpoint the question is which CEOs by which methods achieve organized complexity, producing innovation, new knowledge, endogenous growth, etc. Calori et al. suggest that CEOs with a high socio-cognitive complexity are better able to deal with complex strategies, growing complexity in markets, industries, and organization. The authors suggest that this socio-cognitive complexity develops with CEOs because of their practical experience in working with multinational corporations and their MNC-matrix, working in a range of markets and industries.36 However, the studies of Christensen et al. suggest that it is not sufficient to assimilate or even accommodate new and broader experiences and in doing so developing multiple views on a business and organization. What is needed is the capability to reconceptualize changes in the market, as e.g. Levi, the CEO of Publicis managed to do in the case of the digitalization of the world market for marketing and advertising.37 This capability of CEOs to be able to (re)conceptualize industries, markets, competition, consumer behavior, to explain success in itself is not new. Due to a higher relatedness between modern, especially digital technology and new rules, new types of (disruptive) innovations and especially the self-image of consumers, their behavior and preferences (e.g., in the case of smart phones) make the capability to reconceptualize more demanding. Especially the issues of converging industries (communication, entertainment, computers) and, e.g., changes from vertical competing to horizontal competing and modularization imply new rules that need to be understood in their consequences for the firm. Strategies have become more complex as a consequence, implying the need for mastering new strategic concepts.

10.4

Simplicity Beyond the (New) Complexity

An author who touches upon complexity is James O’Toole in his The Executive Compass.38 O’Toole observes that successful executives, politicians, presidents, businessman, understand the art of finding a new simplicity beyond the (new) complexity: “To move beyond the confusion of complexity, executives must abandon their constant search for the immediately practical and, paradoxically, seek to understand the underlying ideas and values that have shaped the world they work in.”39 O’Toole takes a more political-philosophical approach, based on four values, liberty, equality, community, and efficiency, to pursue an understanding not only of

36

Calori et al. (1994). Christensen et al. (2004), Kanter and Bird (2009). 38 O’Toole (1993). 39 O’Toole (1993), p. 6. 37

236

10

How CEOs Cope with Complexity

the ideas underlying management concepts, but more of society at large. Responsible CEOs will have one or multiple debates in mind on what business practices produce a good society. But the phrase: “seek to understand the underlying ideas and values” opens a more practical approach. That is, to understand why, based on what assumptions, paradigms, constraints, etc., we deploy specific organization forms, accounting techniques, HR-policies, etc. From an economic entrepreneurial view CEOs are supposed to discover and exploit new opportunities, not to delve in the past. Understanding history, especially from which period management concept date, may be helpful to understand their assumptions and limitations, and what new development, technology, the changing nature of assets employed may open up new options for management and organization.

10.5

They Wade Into Complexity

In his book The Opposable Mind Roger Martin mentions a number of CEOs, Lafley (Procter & Gamble), Bob Young (Red Hat), Michael Lee-Chin as examples of integrative thinkers, that is persons with a high conceptual complexity operating in an environment with a high integrative complexity. Martin compares or contrasts integrative thinking versus conventional thinking.40 To complexity in terms of number of elements, number of relationships, number of states, Martin adds the issue of complexity of causality (Textbox 1). In the traditional MBA reductionist thinking, including a simple Newtonian view of causality was dominant. System thinking was acknowledged, but more as an alternative to the dominant simplified MBA-models. Consistent with the sociology of Ulrich Beck of the second modernism, a first characteristic of integrative thinkers is that these take into account a broader scope of salient factors of a problem or situation. Integrative thinkers tend to define the boundaries of a problem or situation wider as is common in typical MBA-concepts. A second characteristic of integrative thinkers is to take into account feedback relations, multicausality, nonlinear causality as is typical in, e.g., models used in dynamic complexity. That is, integrative thinkers also tend to take into account the law of unintended consequences. A third characteristic of integrative thinkers is that they do not break a problem into independent pieces and work on each piece separately. “They keep the entire problem firmly in mind while working on its individual parts.”41 This third characteristic results in superior coordination between functions in a firm. The fourth characteristic is that integrative thinkers do not think in terms of trade-offs, but try to synthesize, find a creative solution to conflicting demands. So far one might say that integrative thinkers are the solution to the oversimplification of the MBA-tradition, but is integrative thinking the answer to the growing complexity in our economy and the subsequently needed higher complexity in

40 41

Martin (2007). Martin (2007), p. 41.

10.5

They Wade Into Complexity

237

Fig. 10.1 Martin’s integrative thinker knowledge system which is quite different from Herbert Simon’s decision-making and from the apodictic style of US management books. Martin (2007), p. 191

organizations? Or is Martin making a mistake in attempting to define a simple model to convey a complex issue, when he defines a integrative knowledge system (Fig. 10.1)? The six features under stance in Fig. 10.1 are basically a variation on the same theme, not sticking to existing models or convictions, but a way of trial-and-error in order to find out what else might work better. It is a form of what is called in the economic theory of entrepreneurship self-discovery and in the field of logics, abducting thinking as opposed to inductive and deductive thinking. The three features of tools in Fig. 10.1 basically also, at a more practical level are forms of abductive reasoning, of finding possible new solutions and options. The first feature, generative reasoning is abductive reasoning at a more practical level, it seems, it is unclear what it adds to those features under stance. The second feature, causal modeling is applying system dynamics, that is feedback loops, nonlinear causality, into a model of a business problem. With that it addresses the issue of handling dynamic complexity. More interesting is “assertive inquiry,” this is exploring the assumptions underlying one’s own and others opposing models. But Martin describes this as a subjective, interpersonal process. With that it may be restricted by a social context as a result of which it may not lead to understanding at a more intellectual and institutional level assumptions underlying models, concepts, their today validity, etc. Martin’s concept does not explain the reasoning by which IBM, Procter & Gamble, Publicis, Nestlé, arrived at breakthrough reconceptualizations of their organizations. It is one thing to conceptualize abstract new models; it is another to think of the concrete, practical changes in the organization to implement such models. In the case of Gerstner played a role a new and deeper understanding

238

10

How CEOs Cope with Complexity

about the role of information in the organization, as in the case of Nestlé. In the case of Procter & Gamble played a role an understanding of a change in the market with respect to product development and its consequences for the organization of product development. Whereas Durk Jager defined this as a problem of structure, Lafley understood that complementary performance parameters needed to be defined to foster the needed new self-coordination (that is, in terms of traditional performance management, an increase in the complexity of performance parameters). A dimension usually not found in books on organization design. It is consistent however with Herbert Simon’s observation of the role of the factoring of decision-making in the design of organizations. Publicis also is an example of the need to understand the changes in the marketing and advertising industry due to digitalization, before understanding its consequences for the organization of Publicis in order to be able to deal with the new complexity. The question needs to be asked whether Martin’s concept of assertive inquiry is sufficient strong to understand the limitations of salient models.

10.6

Some Lessons from Successful CEOs

New models as an answer to new complexities need to be implemented in an existing institutional framework. This framework is not static; it is growing in complexity in itself. Basis institutions as the core family, the separation of capital and labor, etc., are weakened empirically, but especially to codification of the separation of capital and labor remains codified in the law, especially in the rights of shareholders.. Complexity reducing institutions are weakened by market liberalization, leading to re-regulation to curb the unforeseen and unaccepted consequences. From available documentation it is unclear how the firms mentioned, respectively, their CEOs managed the transformations in such institutional frameworks. That is, despite the study of Martin we do not have yet a clear or full understanding how successful CEO’s on the basis of which concepts managed their new complexities. For the time being we think a number of preliminary lessons can be drawn, as summarized in Fig. 10.2.

10.7

Complexity Leadership Versus Transactional Leadership

Complexity leadership questions transformational leadership because the latter assumes top-down planning and coordination, especially with respect to transformations as needed for survival of the firm. Complexity leadership is about creating conditions in the organization in which localized instances of adaptive behavior of members of the organization can emerge in response to (sudden, unexpected, deep) changes (threats, conditions, opportunities) as need to accomplish the mission of the firm or institutions, while preserving its identity, values, and

Bibliography

239

From

To

Mission as motivation

Mission as goal-information, for sensing and sensemaking

Values to guide behavior

Values as axiological information, to evaluate bottom-up initiatives

Organization as structure

Emphasis on business models as effect-information Multi-objective, multi-criteria objective setting Organization of information o self-coordination

Control through hierarchy, tight control

Control through 1. a guiding system; 2. tools; 3. platform (fast feedback information) Information based empowerment Loose programming/control Decentralized sensing and sense-making o pro-active behavior, emergence

Reporting through hierarchy

Access for all to multidimensional information, multidimensional performance management Institutional requirements, the annual report, being one of the dimensions

Simple organization forms

The guiding system as simplicity The platform to absorb complexity

Fig. 10.2 Some preliminary lessons from CEOs dealing successfully with complexity. This is not limitative and will change over time. Also, every CEO will tell her or his own perception of changes

integrity.42 With that complexity leadership is about creating organized complexity to enable innovation, creation of new knowledge, adaptation, and transformation, including disruptive innovations. Complexity leadership decentralizes leadership beyond the wish of proactive behavior and the wish of workers taking responsibility, by creating the material conditions in the organization, especially its systemic context to enable, legitimate, and reward proactive behavior and taking responsibility in the sense of taking initiatives. Complexity leadership builds on Herbert Simon’s concept of loose programming and Weick’s concept of (decentralized) sense making in organization.43 That is complexity leadership is akin to information-based empowerment. The challenge of complexity leadership is to establish and operate organized complexity, which is using loose control, loose programming opposing an institutional context that tends to emphasize tight control.44

Bibliography Bernet, R. (2014). The limits of conceptual thinking. Journal of Speculative Philosophy, 28(3), 219–241.

42

Uhl-Bien and Marion (2008), Marion (2008). Weick (1995). 44 Tight control is promoted in one of the main textbooks on management control and performance management Merchant and Van der Stede. 43

240

10

How CEOs Cope with Complexity

Berry, T. K., Bizjak, J. M., Lemmon, M. L., & Naveen, L. (2006). Organizational complexity and CEO labor markets: Evidence from diversified firms. Journal of Corporate Finance, 12(4), 797–817. https://doi.org/10.1016/j.jcorpfin.2005.04.001 Bower, J. L. (1986). Managing the resource allocation process. Harvard Business School Press. Calori, R., Johnson, G., & Sarnin, P. (1994). CEOs’ cognitive maps and the scope of the organization. Strategic Management Journal, 15(6), 437–457. Carroll, S. J., & Gillen, D. J. (1987). Are the classical management functions useful in describing managerial work? The Academy of Management Review, 12(1), 38–51. Chandler, A. D. (1962). Strategy and structure: Chapters in the history of American enterprise. MIT Press. Christensen, C. M., Anthony, S. D., & Roth, E. A. (2004). Seeing what’s next: Using the theories of innovation to predict industry change. Harvard Business School Press. Fayol, H. (1918/1999). Administration Industrielle et Générale. Dunod. Gardner, H. (2007). Five minds for the future. Harvard Business School Press. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669. Gilbert, C. G. (2005). Restoring the bottom-up process. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy. Oxford University Press. Goold, M., & Campbell, A. (1987). Strategies and styles: The role of the centre in managing diversified corporations. Basil Blackwell. Grant, A. M. (2016). Originals: How non-conformists move the world. Viking, an imprint of Penguin Random House LLC. Hart, S. L., & Quinn, R. E. (1993). Roles executive play: CEOs, behavioral complexity, and firm performance. Human Relations, 46(5), 543–574. Hayles, N. K. (Ed.). (1991). Chaos and order: Complex dynamics in literature and science. University of Chicago Press. Hayles, N. K. (1999). How we became posthuman: Virtual bodies in cybernetics, literature, and informatics. University of Chicago Press. Hayles, N. K. (2012). How we think: Digital media and contemporary technogenesis. The University of Chicago Press. Helfat, C. E., & Peteraf, M. A. (2015). Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strategic Management Journal, 36(6), 831–850. https://doi.org/10. 1002/smj.2247 Kleiner, A. (2008). The age of heretics: A history of the radical thinkers who reinvented corporate management (2nd ed.). Jossey-Bass. Lindsey, B. (2012). Human capitalism: How economic growth has made us smarter-and more unequal. Princeton University Press. Luhmann, N. (1968). Vertrauen: Ein Mechanismus der Reduktion sozialer Komplexität . (4 Auflage ed.). Lucius & Lucius. Maccoby, M. (2007). Narcissistic leaders: Who succeeds and who fails. Harvard Business School Press. Maccoby, M. (2008). Leaders for healthcare. Harvard Business Review. March, J. G. (1994). A primer on decision making: How decisions happen. The Free Press. Marion, R. (2008). Complexity theory for organizations and organizational leadership. In M. Uhl-Bien & R. Marion (Eds.), Complexity leadership - Part 1: Conceptual foundations (pp. 1–15). Information Age Publishing. Martin, R. L. (2007). The opposable mind: How successful leaders win through integrative thinking. Harvard Business School Press. Nooteboom, B. (2006). Social capital, institutions and trust. Retrieved from Tilburg. O’Toole, J. (1993). The executive’s compass: Business and the good society. Oxford University Press. Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. The Penguin Press.

Bibliography

241

Pfeffer, J., & Sutton, R. I. (2006). Hard facts, dangerous half-truths, and total nonsense: Profiting from evidence-based management. Harvard Business School Press. Phelps, E. (2013). Mass flourishing: How grassroots innovation created jobs, challenge and change. Princeton University Press. Prahalad, C. K., & Bettis, R. A. (1986). The dominant logic: A new linkage between diversity and performance. Strategic Management Journal, 7(6), 485–501. Simon, H. A. (1987). Making management decisions: The role of intuition and emotion. The Academy of Management Executive, 1(1), 55–64. Simon, H. A. (1996). The sciences of the artificial (3rd ed.). The MIT Press. Simon, H. A. (1997). Administrative behavior: A study of decision-making processes in administrative organizations (4th ed.). Free Press. Strikwerda, J. (2013). Authenticiteit in het werk: Romantiek of realiteit? Holland Management Review, 30(152), 66–72. Strikwerda, J. (2014). Bespiegelingen over governance, bestuur, management en organisatie in de 21e eeuw. Koninklijke Van Gorcum. Uhl-Bien, M., & Marion, R. (Eds.). (2008). Complexity leadership part I: Conceptual foundations. Information Age Publishing. Weber, M. (1947). Theory of social and economic organization (A. M. Henderson & T. Parsons, Trans.). Free Press. Weber, M., & Shils, E. (1949). Max Weber on the methodology of the social sciences (1st ed.). Free Press. Weber, M., Parsons, T., & Giddens, A. (1992). The Protestant ethic and the spirit of capitalism. Routledge. Weick, K. E. (1995). Sensemaking in organizations. Sage.

Tools Executives Use to Deal with Uncertainty and Complexity

11.1

11

What Connects a Variety of Tools?

A variety of tools exist for CEOs and others in business to deal with uncertainty and with the (growing) complexity in the economy and in business. Most of these tools have been with us for a long time, but now their mutual relationships and underlying logic and functions can be understood better. What these instruments have in common is that these increase the capability of information processing, both at the level of conceptual transformations and data processing, in the context of purpose, or ambition (which may include the responsibility to maintain a good society), the growth of new options due to technology and the abundance of data. The idea of purpose expresses what complexity is about, survival of the kind through adaptation, whether this adaptation is by natural selection or by design. For investors and entrepreneurs natural selection is a too passive attitude to achieve their objectives, and design needs to balance aimed for breakthroughs with uncertainty in the market. Abstract thinking, centering on principles, is a tool to deal with complexity, but is not sufficient in itself. Many people have no inclination for abstract thinking, but prefer the concrete. In an organization formal decisions need to be made with respect to mundane issues like reportable dimensions, attribution of decision rights, performance parameters, budgets, finance, marketing, product mix, etc. Viewed at the level of such details there is an almost overwhelming array of details. So, the question is what are tools to order such details in a way that the requisite variety in entrepreneurial initiatives and innovation does not get lost in an unorganized variety of administrative details. As explained before, complexity theory is not new, intuitively it has been applied in various forms and tools, so it is possible to assemble an inventory of various tools and order these in a way so to master required complexity and have productive organized complexity. The list is the following: 1. A clear mission 2. A hierarchy of values # The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_11

243

244

3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

11 Tools Executives Use to Deal with Uncertainty and Complexity

Reconceptualizing, reframing Holistic thinking, system thinking Scenario planning Preparedness and rolling forecasts Creating an open, organic, and learning organization Multidimensional organization of information, -reporting, and -analysis Information-based empowerment Loose programming, loose control HR-policy aimed for talent, diversity, originality, and the provenance of ideas Architecture and modular organization The resource allocation process Open innovation and open business models Real option method, phased funding, discovery driven planning The resource allocation process Using mathematical models for risk-analysis The platform concept Fast feedback information

Mastering complexity and dealing with uncertainty are intrinsically interwoven due to the nonlinear causal relations and openness of systems. Hence that in the list are also scenario planning and discovery-driven planning included. In the next paragraphs each of the tools are explained at a management level; details are to be found in original sources mentioned. The idea is that mastering complexity requires keeping oversight on the interrelatedness of these nineteen tools; in complexity using one tool only never can do the job.

11.2

Mission

A first tool to be observed in dealing with complexity, consistent with the cybernetic theory of information, is that those firms having a clear mission or purpose, guiding them through choices to be made and uncertainty, are more successful in the longer term.1 A mission is what elsewhere is labeled as a principle or an underlying idea to which sound CEOs refer to in undefined situations, it is one of the elements in abstract thinking. Formulating a mission is not that easy, as it should avoid the phenomena of market myopia. A mission is an answer to the question “what business are we in?” but technological innovations and consumer behavior may imply that the definition of business needs to be reconceptualized. A mission is not a nice to have, or a motivation tool, although a good mission is inspiring, nor is a mission a marketing instrument. Following cybernetic information theory, a mission is the pivotal element in business administration. A mission is goal information and with that part of the organization of information, especially in the concept of the

1

Drucker (1973), Mourkogiannis (2006), Cha and Edmondson (2006).

11.3

A Hierarchy of Values

245

information-based empowerment. The cases of Procter & Gamble and Duane Morris demonstrate that a mission is not only a statement taken to heart by management and employees, it also needs to be codified in all systems and processes of the organization. A mission is a reference in case of doubt, ambiguity, and newness. It refers to the question with which the German military-philosopher Von Clausewitz trained his officers to ask in new, undefined situations: “Warum handelt es sich hier eigentlich?”2

11.3

A Hierarchy of Values

A second tool use to deal with complexity and its associated uncertainty is using a hierarchy of values, as in the case of the pharmaceutical Johnson & Johnson.3 Just values are not sufficient, our brain needs a hierarchy of values about what has or should have priority.4 In the case of Johnson & Johnson the choice was made that good products have priority over profit. Other firms may make other decisions in such matters. There is some confusion about values. Some firms tend to define labels as teamwork, transparency, integrity as values, but these are not values but norms with respect to behavior, which in themselves will be useful but are to be questioned in view of the Interactive Perspective Model from organizational behavior, as required behavior in the first place needs to be facilitated by a properly designed and implemented systemic context. A value is a statement of an individual or by a group what that person or group wants to be true or not to be true, irrespective of the actual facts.5 Values are strongly judgmental and guide the conduct, in the sense of making decisions, of the value holder. Values are to be discerned from beliefs, which are about assumptions and/or interpretations of the world, or about worldviews. To be aware of one’s assumptions is an essential element in premise control and in discovery-driven planning, to manage the assumptions-to-knowledge ratio. Values and mission statements should not be confused with vision. That is, different types of visions are to be discerned. A first type of vision is the projection of a state of the world, of a business, of a position as to be achieved, as an ambition, by the visionary, a CEO, in an uncertain and/or complex world. This is what narcissistic leaders do: to create a comprehensible worldview that inspires others to act.6 In doing so uncertainty is suppressed, doubts eliminated from the perspective of the position holder. Cases like Steve Jobs at Apple demonstrate that this type of

2

Clausewitz (1833). Collins and Porras (1994, p. 59). 4 Cha and Edmondson (2006). 5 Rollinson and Broadfield (2002). 6 Maccoby (2000), Maccoby (2007). 3

246

11 Tools Executives Use to Deal with Uncertainty and Complexity

vision can be successful. However, dependent on the personality of the holder of the vision, having a strong vision may also result in the vision trap. Either the vision becomes too inspirational, being more poetry than product and/or it blinds the organization about the-changing-realities of the outside world.7 A second type of vision is an interpretation of what the state of the world, a market, or industry will be independent from the ambition of a person, respectively from an altruistic viewpoint, to which is second the foreseen position, positive or negative, of the vision holder in that worldview. A third type of vision is vision as eidetic information, the interpretation of (possible) changes in markets, in industries, in society, especially in terms of consequences for the firm in terms of strategic change needed to survive. The first type of vision will be akin to the concept of value. Whereas the third type of vision needs a mission and values to be produced, that is sensemaking. The first type of vision may blind the vision holder for changes in the environment, change blindness, and may block to produce necessary eidetic information.

11.4

Reconceptualizing, Reframing

At multiple places in this book, reconceptualization was mentioned as a tool to dealing with complexity. Reframing and sensemaking more or less are synonyms. What actually is reframing and is it possible to specify this into a number of operational steps to achieve this? A number of examples, IBM, Procter & Gamble, Publicis, were mentioned, as well the reconceptualization of shared service centers in platforms. Reconceptualization does have an aspect of creativity, but it is not simply creativity. It is even by effort and nature more remote from a popular expression like thinking-outside-the-box, although it definitely is about escaping the limitations of obsolete paradigms. To an extent the nature of reconceptualization is expressed in a quote by Richard Norman: “If one’s current operational position can be clearly defined, one’s intellectual position must be much wider and must imply the continuous questioning of the operational position against the background of greater forces impinging on the larger context.”8 This requires first to question the institutional context, especially in the combination of understanding when institutions lose their fundaments and fail to be fundaments of society, or more specific for business, function as a fundament for an industry. An example of this is Lévy of Publicis, who sensed that and how the digital technology would transform the advertising industry, as it does. Reconceptualizing might be understood as the art from escaping the conceptual past and pulling the conceptual future to the present and at the same time moving up and down the axis of systemic logic, using upward causality and downward causality to operationalize the conceptual future (Fig. 11.1).

7 8

Langeler (1992). Normann (2001, p. 80).

11.4

Reconceptualizing, Reframing

247

The higher systemic logic

The conceptual past

The conceptual future

The lower systemic logic

Fig. 11.1 Two dimensions in reconceptualizing. Managers often show enthusiasm for new concepts, but to ride in their theory-in-use of the conceptual past is far more difficult as embracing new ideas. The conflict between the higher system logic and the lower systemic logic is to be solved by a superior organization of information Normann (2001, p. 200)

With that a first dimension or stream in reconceptualization is the authentic abstract thinking described in Sect. 11.1. To be aware and acknowledge the obsoleteness of existing paradigms and institutions in itself does not shape new paradigms nor new institutions. As we have seen in Sect. 6.3, a simple abandoning of rules and regulations may create problems and the phenomenon of stigmergic coordination (Sect. 8.5) teaches us that some rules are necessary to achieve organized complexity. The conceptual future requires some visualization, new symbols, a new language to shape and to communicate this new conceptual future to a wider audience not involved in the intellectual process, but being more operational by interest. Sources for this visualization of the conceptual future are to some extent futuristic book, more specific in this is the role of science fiction and cyberpunk literature. E.g. Metaverse originated from Stephenson’s Snow Crash.9 But alike CEOs in their process of reconceptualization, which often is a process of slow thinking, slow intuition, can be inspired, subconscious sometimes, by work of arts, books, painting, musing, movies, etc. In addition to the analytical/intellectual dimension and the imaginary dimension, reconceptualization requires an administrative dimension. The latter is the skill to translate the identified new options, the visionary, into administrative measure that build on the conventional tools and techniques in business, but at the same time fundamentally changes the causal patterns in the organization, enabling new business models, as in the case of IBM, Publicis, and Nestlé. It is not only the visionary reconceptualizing CEO in this way transforms his own firm and its organization, due to the institutional nature of institutions to be changed, the industry, his vision, his or her transformation also need to resonate, need to be either accepted or adapted to (depending on market 9

Stephenson (1992).

248

11 Tools Executives Use to Deal with Uncertainty and Complexity

power) by other actors in the business or industry eco-system. Lévy set himself to the task not to transform Publicis, but to be Publicis the leading firm in the digital transformation of the advertising and marketing industry. Such a reconceptualization is not independent of the personality of the CEO. Not only is required an intellectual capability, an open inquiring mind, the ability to absorb ideas, concepts, conflicts of a wide variety, it not only requires a mastering of administrative techniques and especially a deeper understanding of these, what is required is a combination of courage and responsibility as defined at the sixth level of moral development defined by Kohlberg. That is to see what fundamental changes are needed in the interest of the organization, the community, even if this requires to break with existing routines, conventions, including some friendships.

11.5

Holistic or System Thinking

A fourth tool used to deal with complexity and uncertainty is using holistic thinking or systems thinking, with respect to an industry, market, the business model, the organization of the firm.10 Holistic thinking is the opposite of reductionist or functional thinking. A simple example of this is Porter’s value chain to depict a business as opposed to the (functional) organization chart. Porter introduced the value chain to correct the simplification of portfolio management induced by corporate finance. Portfolio management is typical black-box type thinking, thus simplification, which (existing) businesses or product-market combination produce the most optimal return-risk profile. Porter understood that this approach of (corporate strategy) is detrimental to innovation, to create new products, new, competitive value propositions, new innovative, more efficient processes, as these are needed for economic growth. Porter’s representation at the same time is a kind of simplification, but from an economic viewpoint a necessary new simplification. The emergence of the knowledge economy implies a needed higher degree of complexity as assumed in Porter’s value chain, asking for a.o. a free flow of knowledge that is free interaction of workers across the silos. This was expressed in the concept of the Strategy Map coined by Kaplan & Norton, in which the strategic themes may need to be organized across divisions, business units, staff departments, shared service centers and into suppliers.11 A more complex step in systems thinking is Osterwalder’s business model canvas.12 Osterwalder’s concept is more complete in suggesting which decisions to make to create value, including elements like a revenue model and the type of profit model. A practical, working example of systems thinking with respect to a business model is Ryanair (Fig. 11.2). Contrary to the elegant linear organization chart thinking in terms of a system or in terms of a business model as a set of causal relations may appear to be more

10

Churchman (1967), Martin (2007), Senge (1990). Kaplan and Norton (2004). 12 Osterwalder (2004). 11

11.5

Holistic or System Thinking

249

Fig. 11.2 Ryanair’s business model as an example of a mental model of the business in terms of causal relations, as opposed to viewing the business as a well-structured managerial hierarchy Casadesus-Masanell and Ricart (2011)

complex, but it provides a better understanding of causal relations, uncertainty in causal relations, unintended consequences, etc. In terms of cybernetic theory these causal relations are effect-information. To make this type of information explicit, as opposed as being implicit in systems and organization, facilitates more members of the organization to develop proactive behavior, to be aware of the externalities of their decision on other parts of the organization, to develop self-coordination and self-organization, and to deploy their ingenuity to anticipate situations and to solve new problems. Making explicit the business model as a set of causal relations also facilitates defining the organization in terms of process management, emphasizing (end-to-end) processes over structure, to achieve objectives under dynamic complexity. Thinking in processes allows for turning (new) causal relations into required governance mechanisms to anticipate or respond to new opportunities or other changes in the market, without being hampered by the structure or configuration of resources. A process, like a project and a strategic theme, is also a governance mechanism beyond the traditional structure to create productive combinatorial complexity and combinatorial innovation, which is combining tacit knowledge of (creative) knowledge workers into new products, services, and/or supply chains.

250

11 Tools Executives Use to Deal with Uncertainty and Complexity Privatized European competition

Regulated industry RPI-X

Maximum outsourcing to service firms and engineering firms

Vertically integrated

Unbundled

Existing situation, some local cooperation

Billing through public infrastructure Other activities in national cooperatives

Public ownership

Fig. 11.3 Example of a simple scenario formulation on the possible development of drinking water companies in the Netherlands. The two axes of fundamental uncertainty are public ownership versus privatized and vertically integrated and unbundled. This scenario helped the Dutch drinking water companies to define a course in which the threat of being overtaken by foreign multinationals was avoided in the interest of the Dutch public (By the author for fourth Awwarf – Kiwa CEO Conference Europe (2005), March 22, Amsterdam)

11.6

Scenario Planning

A fifth tool to deal with complexity and uncertainty is using scenario planning.13 Scenario planning consists of a variety of tools, varying from mere conceptual, via influence diagrams to computer programs. Core to its methods is that core-, non-reducible uncertainties for a firm are identified with respect to technology, markets, economy, regulation, societal developments, etc. Using nonlinear influence diagrams combinations of uncertain events are used to generate Gestalts, a set of possible but logically related, especially external events or trends that describe a state that is consequential for the firm in terms of its mission, values, strengths, and weaknesses. Such a Gestalt is the core of a story that tells the link between present and the scenario. A scenario at least is a story, depicting a possible and plausible future state and may be quantified, usually based on computer models. An example is presented in Fig. 11.3. The example is about the Dutch drinking water companies, asking themselves what to prepare for in view of the political debates on privatization of these public utilities, whose shares are held by counties and provinces, the fact that, e.g., French drinking water companies developed themselves in multinationals, and in view of market pressures for outsourcing and de-verticalization. Of these the two main uncertainties identified were ownership,

13

van der Heijden (1996), Ringland (1998).

11.7

Preparedness and Rolling Forecasts

251

public or private, and organization integrated or not (unbundled), because that effects completion and options for defending existing positions. This created to the existing situation three scenarios as in Fig. 11.3, each of which could be evaluated in terms of effects, benefits, threats, strengths, desirability in view of the public interest of safe, reliable, and affordable drinking water. Scenario planning is about exploring the combinations of the most fundamental independent uncertainties with respect to a firm, in a broad defined societal context, political, economic, technological, cultural, etc. Through scenario planning possible configuration of events are charted and their possible consequences, opportunities, and threats, for the firm. Scenario planning takes on the complexity of society as it develops or may develop and includes building models including feedback relation, multiple causalities between varieties of diverse types of events. Scenario planning with that addresses especially the dynamic objective complexity in the environment of the firm. These can be described in terms of stories, Gestalts, of logical possible combinations, which can be quantified in their effects, especially with respect to the consequences on the firm. In a number of contemporary specialized books various techniques to be used can be found.14 Often in developing scenario’s used is made of scenario’s developed for countries by national planning bureaus, e.g. the Central Planning bureau (CPB) in the Netherlands and those published by the OECD.15 The purpose of scenario planning is not to accurately predict the future, but its purpose is to open up the often unconsciously mental models of executives.16 That is, scenario thinking is a tool to grow the cognitive complexity of executives and others.

11.7

Preparedness and Rolling Forecasts

A more operational and structured tool to deal with uncertainty and to foster preparedness in the organization is working with rolling forecasts as opposed to fixed year budgets. An organization no longer can be based only on the existing business model or existing markets based on Chandler’s structure follows strategy . . . but the market is the common denominator. An organization also needs to be prepared for new business models, new market patterns, etc. Today the issue how structure can enable new strategies. Therefore, the issue is how to foster preparedness in the organization, a proactive attitude, initiatives, but balanced with the need for sufficient focus on the short term and operations. To be prepared is a moral duty, it simply is implied by the duty of care. But also, luck tends to favor the prepared mind (Louis Pasteur). The question of course is what to be prepared for. Increasingly design decisions need to be made, with respect to systems, organization, technological choices and such. The art of making design decisions is to avoid choices that block future

14

Schoemaker and Tetlock (2012), Ringland (1998), van der Heijden (1996). E.g. OECD: On Future Global Shocks (2011). 16 de Geus (1997). 15

252

11 Tools Executives Use to Deal with Uncertainty and Complexity

options. An example of this is the software program EPICS for electronic medical patient records. Before the introduction of this software program a care path for oncology patients organized across multiple departments to offer patient-centered fast track diagnostics, this care path was supported by an information system organized along the dimension of the care path. EPICS is based on the vertical structure of vertical medical departments and does not allow for organizing information over the dimension of the care path, with the result that in the oncology care path data now needs to be combined manually. That is to say, an information system must not be aligned with the existing business model, it must allow, be prepared, to service future, new business models and to enable the emergence of new business models by trial-and-enter. A trade-off needs to be made between investing now in more functionality and capabilities of an ERP-system versus the costs later in time of adding new functionalities. But in this trade-off (the business case of an IT-system) it must be considered that information itself, availability, redundancy, access, is also a facilitator of innovation and adaptation, apart from being a tool for preserving, coordinating, and absorbing complexity to facilitate simplicity for knowledge workers. For which reason it is better to use a different concept of making investment decision in IT-system. This different system defines four categories of IT-investments, technical infrastructure (servers, partly today in the cloud), a common transaction recording system for all activities of the firm, irrespective of legal organization, country or business operations (one-data base concept), a layer of investments in tools for product development, analyzing big data, etc., and a fourth layer of transformation applications with customers, to create added value for customers, solutions, experiences, etc.17 The latter comprises the well-known apps. The last, fourth layer is dynamic (agility); the first two are stable over time. To foster preparedness, we need to be aware what factors work against being prepared, alertness, and proactive behavior. At a more philosophical level postmodernism, the marginalization of the grand stories, fosters presentism, living in the hic et nunc, with no awareness of past and future. This may appear to conflict with the talk in the town on new technological developments, new gadgets, how society changes under the influence of technological, economic, and demographic developments, but for many there is in these developments not always the promise of a better personal life and future. Consequentially a category in society tends to shield itself from the communication on dynamics, and focus themselves on short-term survival. Within organization there is often a tension between the promise of freedom for knowledge workers, self-organization, self-determination, but in quite some cases this promise is overruled by traditional performance management, short term, serving shareholder value. Therefore, the purpose of the balanced scorecard at the operational level is to balance between orientation and targets on the short term and those on the long term. To achieve this, the balanced scorecard in itself is not sufficient, this balance between the short term and the longer term needs to be the

17

Kaplan and Norton (2004, p. 251).

11.8

The Organic Organization

253

basis of the management control system or resource allocation system.18 A first decision to achieve this to abandon the year-budget system, in which targets are set once a year, on which most focus, causing to forget the changes in the environment, externalities, and unintended consequences. Dependent on the dynamics of an industry firms replace the year-budget with a system of dynamic budgets, with especially rolling forecasts.19 With respect to planning and budgeting the new norm is to have a two-level rolling forecast. The first level is to forecast each month market developments with a time horizon of 12 or 18 months, the time horizon will depend on e.g. lead times in investments, operational planning, and life cycles of products. The second level is a rolling forecast each month with respect to the performance of the firm, with a time horizon appropriate for the specifics of the firm and its context. A rolling forecast is not setting budgets. The purpose of these rolling forecasts is not to have a contest in accurate prediction; that would be most futile. The objective of rolling forecasts is to develop and maintain an understanding of the market and of the performance of the firm in relation to market developments. This understanding is to prepare the mind on what may happen, whereas the classical method of budgeting for many tends to close the mind. Preparedness of the organization is not so much an issue of structure, although the creation of shared service centers and using these to develop a platform organization will be a help in achieving a combination of agility (in market activities, innovation of products and services, conquering new markets) and stability (provided by the platform). Preparedness of the organization has more to do with the training, the development of people, having a surplus of knowledge and insights beyond an existing business, having a broader worldview, and in addition to that to have systems (accounting, management control, IT) that are designed and built that these easily can be adapted to new needs or prospects. Rolling forecasts are needed, but need to be used properly, to understand changes in the business and to see changes before these manifest themselves in performance. However, one needs to be aware that rolling forecasts in generating preparedness are restricted to the business models that define those forecasts and scenario thinking is a higher level of awareness of uncertainties and possible changes in the industry, business, and markets.

11.8

The Organic Organization

A seventh tool in dealing with complexity, changes, and uncertainty is fostering and operating an organic organization as opposed to a mechanical organization (Fig. 11.4).

18 19

Kaplan and Norton (2008). Kaplan and Norton (2008, p. 187).

Organic organizat ion

M echanic organizat ion

failure to perceive and act on critical environmental changes reactive, selective responses ‘don't care' attitude to community values and issues

constant scanning of environment and appropriate adaptation

initiative in external relations

well-defined concept of social responsibility

Fig. 11.4 The characteristics of an organic organization and a mechanic organization. Adapted from: Buchanan & Huczynski. The superiority of the organic organization has been proven over time. Today its reason and workings can be better explained as well as it can be better organized in terms of administrative instruments. Because working in an organic organization requires some more mental energy, there is always the tendency to slip away into the mechanical organization Huczynski and Buchanan (2007, p. 562)

unclear signals: 'what did the boss mean by that?' apparently arbitrary rewards

source of knowledge: the organization

Source of knowledge: external

just and equitable rewards

the FUJIAR syndrome: 'fuck-you-Jack-I'm-all-right'

mutual trust, support, respect

accurate, timely performance feedback

secrecy, gossip, failure to listen, database fragmented by structure and applications

politicking and defensive cliques

power recognizing mutual influence what the boss says goes'

contempt for individuals and groups (the POPOS: pissed on, passed over)

commitment to personal growth (growth of skills, knowledge and professional development)

flexible, participative decision making, seen as learning processes information openness, absence of information asymmetry, one data-base concept

bureaucratic rigidity, or constant change without rationale narrow, repetitive jobs with little lear ning opportunity

consistent, clear procedures which evolve purposefully

meaningful, varied work with learning opportunity

focus on immediate pressing problems

ill-defined or unknown goals, ghost mission and values no link between goals and structure, structure fragments processes

flexible forward, discovery-driven planning

processes have priority over structure, are based on CVP

clearly defined goals, clear mission and values

254 11 Tools Executives Use to Deal with Uncertainty and Complexity

11.8

The Organic Organization

255

The benefits of the organic organization over the shortcomings of the mechanic organization are well known for about 50 years.20 Organic organizations demonstrate a better capability to adapt themselves to changes in the environment, including transformations, to achieve successful continuity. Before jumping to this concept enthusiastically the question is to be asked: if we know this, why then so often and so many firms and institutions regress to running a mechanical organization? An organic organization is more complex as is a mechanic organization simply because its members are freer and behave freer to interact across structures and formal lines of authority. Organic organizations have a better chance to be in-control, according to the resource dependency view of in-control, compared to mechanic organizations. This is because organic organizations have complexity as defined by Herbert Simon, that is loose control, allowing for local (decentralized) adaptive behavior to new situations and the capacity of the organization as a whole to learn from that and to adapt.21 But to maintain such a complexity requires energy and technology.22 This is where Lindsey’s social abstraction and personal abstraction comes into play (Sect. 10.1). To understand and define new roles and a higher variety of roles requires effort (as opposed to thoughtless routines) in the form of knowledge and in terms of careful observations in social relations. To exercise autonomy meaningful requires effort in having a personal purpose, and communicating it to others and to resist role attribution by others of roles conflicting with the personal autonomy. It requires consciousness, new knowledge, the understanding of evolving new relations to avoid to regress in routines, fixed roles, that is regressing into a mechanical organization. This tension is comparable with the continuous trade-off between exploration and exploitation, between being focused on the longer term versus being focused on the short term. To maintain an organic organization requires a continuous flow of energy into the organization in the form of new knowledge, exploration, new ambitions, and visions. Leadership therefore might also be interpreted that it is about putting new energy in the organization and facilitating that workers bring in new energy as well. Technology, especially digital technology, is needed to preserve complexity, e.g. multidimensional information in view of multidimensional markets, or to absorb complexity, e.g. induced by regulation, and to coordinate complexity, e.g. in the case of dual organizations with market units and resource units.23 This will be elaborated in Sect. 12.3 on the platform organization. An organic organization has a number of intuitively attractive characteristics and it is tempting to lump these together under the heading of “culture.” Indeed, the culture of an organic organization is distinct from that of a mechanical organization. But the concept of culture, also because many do not search for the provenance of the idea of culture, has multiple, often subjective meanings and most often is being used

20

Burns and Stalker (1963). Simon (1962), Simon (1973). 22 Tainter (1988). 23 Zuboff and Maxmin (2002, p. 292). 21

256

11 Tools Executives Use to Deal with Uncertainty and Complexity

as a nonanalytic sign in communications and with that is reductionistic. Culture-as-asign ignores the requirements in the material organization or systemic context needed to facilitate the behaviors described by the concept of the organic organization. One characteristic of an organic organization is that its members, e.g. through memberships of professional associations, acquire knowledge about new developments and changes in the environment better compared with those working in a mechanical organization. Another difference between an organic organization and mechanical organization is that in the first there is a lower chance that a dominant logic develops that frustrates interpretations of new developments with an open, exploring, innovative mind, also because it may allow experiments with dominant logics or variation in logics.24

11.9

Multidimensional Information

An eighth tool to deal with complexity is creating an information system allowing for simultaneous planning, tracking, and reporting the performance of the firms over multiple dimensions. Such multidimensionality allows for synergy exploitation cross divisions, proper organizing strategic projects, project portfolio management, using shared service centers and coordinating this higher variety of types of accountable units. Traditional information within a firm was organized on the basis of accounting rules according to the linear hierarchy and the budget structure related to that hierarchy. This was the simple structure of the ROI-tree to judge performance of self-contained organized business units. In a world of multidimensional markets, the need to exploit purchasing synergies, customer synergies, knowledge synergies, etc., alternative views on the organization are needed in addition to the traditional unit structure. Even more, as projects and processes are governance mechanisms for fast recombination of (tacit) knowledge across the existing structure of divisions or units, information needs to be available on the dimension of those processes and projects to assess their performance and to support the interaction between the knowledge workers in those processes and projects. There is something deeper underlying the need for multidimensional information, and that is that an increase of complexity requires an increase of information to reduce entropy. In a complex organization as a system there are more possible states of the system, more choices, and subsequently higher entropy. Entropy often is confused with chaos, or a degeneration of the system and is therefore to be avoided. But a higher entropy is not chaos but a higher number of possible states of a system. We will experience this as chaos if we lack the information to describe all these states. As the physicist Boltzmann (1844-1906) observed, later elaborated by the Nobel prize laureate Prigogine, entropy can be reduced if there is an increase of

24

Prahalad and Bettis (1996), Bettis and Prahalad (1995).

11.10

Information-Based Empowerment

257

information. Or stated otherwise, we can preserve needed complexity without chaos, if we increase the information about the system.25 This is what is to be seen in the IBM-case. IBM decided to record transactions, internal and external, with multiple attributes to be able to understand the performance over multiple dimensions in view of a growing variety of customer behavior and in view of IBM’s strategy to offer integrated solutions instead of discrete products or services only. Multidimensionality of information also allows for fit-to-market in case of a multidimensional market, without a complex structure. Adding to this the organization of information disembedded from the internal structure, making it accessible for all, as in the case of IBM, enables self-coordination, decentralized initiatives, facilitates interaction between knowledge workers, etc. The combination of a clear mission, a hierarchy of values, a well-defined objective function, and accessible and shared information are elements for organized complexity, fostering innovation and the creation of new knowledge. Shared information direct accessible for all, also creates fast feedback information, allowing for trial-and-error to achieve objectives in an innovating way.26 Multidimensional information is created through practical measures like multiattribute transaction recording, semantic data standardization, query-software, financial shared service centers, IT-shared service centers. But this technology requires managers and a workforce with a mindset and training capable to deal with such a complexity of information in terms of deciding what is relevant and how to interpret data. Providing members of the organization with more pragmatic of management information only creates confusion; data itself has no meaning. Members need a mission, a value hierarchy, strategic guidelines, an understanding of the business model of the firm (effect-information) to interpret data into information, which are decisions, choices, and actions. The trend to make the business model explicit and to communicate it to its workers is also a tactic to deal with complexity, that workers are facilitated to take useful initiatives themselves, not being dependent on their bosses.

11.10 Information-Based Empowerment A ninth tool is information-based empowerment. Information-based empowerment is that workers have access to and understand to all the types of information as defined in cybernetics, goal information, axiological information, material information, causal information, pragmatic information, in order to take sensible initiatives, to calculate which of their alternative initiatives or decisions will contribute most to the performance of the firm, and to be able to have useful proactive behavior. This implies that sensing and sense making are distributed in the organization, not restricted to, e.g., staff departments for market research. Through these types of

25 26

Hidalgo (2015). Kanter (2009), Manzi (2012).

258

11 Tools Executives Use to Deal with Uncertainty and Complexity

information also the role of mission and values becomes clear, and why in, e.g., the case of Procter & Gamble these have been codified in all systems and processes of the firm, not only are communicated to workers. Effect-information, this is about cause-and-effect relations, never can be perfect.27 In addition to this is a fact that causal relations, that is effect-information due to the reflexivity of this information or double hermeneutics, are constantly changing, apart from the fact that entrepreneurial acts are about discovering new causal relations (abductive thinking). To deal with imperfection, the question needs to be asked and answered by mission and values, what is it all about? In interpreting external data on changes this interpretation is not sufficient at the level of process optimization, or product flexibility, but as, e.g., in the case of disruptive technologies in healthcare, need also to be interpreted in terms of the mission and value of an institution or firm. Digital imaging turned out not to be simply a replacement of the photo-chemical film as carrier of images, digital imaging changed the concept, the idea of photography, by motives, use, application, users, players, etc. This is why in the production of eidetic information reconceptualization may be so important. The interpretion pragmatic or management information into actions was in the past programmed in the system of performance management. Herbert Simon’s concept of loose programming, of loose control, as needed for learning, adaptation, to be in-control, implies that the interpretation of management information is to be by workers, e.g. a category manager, on the basis of his or her understanding of the business, understanding of the causal relations in the business (effect-information), and changes in the market (material information). To provide workers with all possible management information (data) without an IF THEN ELSE --statement to turn data into information, that is choices, decisions, is of little use. At the same time no perfect set of conditional statements can be written, apart from the fact that the underlying causal relations are continuously changing. Empowerment requires recourse by individual knowledge workers direct to the strategy and its eidetic information, the hierarchy of values and ultimately the mission of the firm. In addition to this, fast feedback information is needed for information-based empowerment, to correct mistakes and to learn from decisions. Information-based empowerment explains why in the modern concept of business models the emphasis is on processes, no longer on structure. These processes reflect causal relations and are the governance mechanisms through which knowledge, including tacit knowledge is (re)combined resulting in the value propositions for customers. From that perspective the old structure of divisions is reduced to the resource configuration of specialized knowledge and equipment, and becomes an infrastructure for processes delivering the value propositions. These processes have agility and can be achieved through a degree of self-organization, provided the players involved have information available on the knowledge, skills, and attitude of colleagues and their commitment in other processes. Therefore, we can conclude that information-based empowerment is a form, possibly other

27

March (2006).

11.11

Loose Control and Loose Programming

259

forms exist as well, of organized complexity. The control based on Weberian hierarchies is replaced by information, but performance still has to be judged, and knowledge workers interpreting themselves the mission, values, and the strategy in initiatives and decisions have replaced the traditional command.28

11.11 Loose Control and Loose Programming A tenth tool, related to the concept of organic organization, is the application of loose control and loose programming in target setting, monitoring, and control.29 Loose control and loose programing need to be understood as opposites of tight (financial) control. The methods of loose control and loose programming are applied to ensure an adaptive capability of the organization. In the context of the knowledge economy loose programming, loose control, loosely coupled organizations are also used to create learning organizations. That is the acknowledgment that applying knowledge in operations, to (new) demands of customers, not only exploits that knowledge, but also creates new knowledge.30 This new knowledge, created locally, that is decentralized, in the operations, needs to be absorbed by the organization as a whole, which assumes free communication, sharing of knowledge, in the case of tacit knowledge through interaction between members of the organization. This new knowledge may also affect existing strategies, business models, processes, etc. Loose programming may apply to three aspects of operations31: HOW WHAT þ ðthingÞ ðworking principleÞ

VALUE leads to

ðaspiredÞ

In engineering the value to be achieved is known or set, and the how, the working principles are known, only the what, the means to deliver the value, needs to be invented or designed. In design thinking, that is abductive thinking, working principles are not taken as a given, but as a field of discovery to achieve innovative breakthrough solutions. The loose programming is loosening up the working principles. The value to be achieved may be subject to loose programming, in that the value for the consumer is not taken face value, but that by thinking through at the level of deeper needs, especially in the context of changes in life styles or demographic changes results in a redefinition of the value to be delivered. In the car industry the value to be achieved for a long term dominantly was that users want to own a car. All kind of changes in (urban) society result in that a considerable part of the younger generation do not want to own a car, but want to have access to a car the moment and place they need it. 28

Alberts et al. (1999). Simon (1962). 30 Stiglitz and Greenwald (2014). 31 Dorst (2011). 29

260

11 Tools Executives Use to Deal with Uncertainty and Complexity

Loose programming and loose control are related to trial-and-error to achieve objectives.32 This trial-and-error is to be controlled however, according to that entrepreneurship is taking calculated risks. The two control instruments for trialand-error, a guiding system and platform, also apply to loose programming and loose control. An issue is that in the profession of accountants and controllers a tendency exists to emphasize tight control.33 One author of the field of management accounting, Robert Simon, warns against tight control as being detrimental to the adaptive capability of the firm.34 But different authors have different aspects of the organization in mind when writing on tight control. Simon’s loose programming refers to the discretion workers should have how to achieve a set target. The target to be achieved should be specified sufficient specific and complete, although the nature of specification will be different between, e.g., building mechanical equipment or a policy objective. Merchant’s tight control in accounting primarily refers to the requirement that transactions be recorded timely, complete, accurate, and retrievable. The related process control and financial control may be based on tight control, but that should not be translated, e.g., through procedures for risk management, into tight control for workers how to achieve their tasks. Another possible confusion might be with the concepts of strategic control, strategic planning, and financial control as coined by Goold & Campbell in the 80s.35 This relates to the style of control between headquarters and divisions or business units in a multi-business firm. In this case, financial control focuses on financial performance only, leaving the management of a division or business unit all freedom how to achieve those financial performance. In the case of strategic control not only financial results are to be performed, but as well certain strategic objectives like the development of new capabilities, new markets, etc. The model of financial control, which is related to the portfolio strategy of corporate finance of an investment strategy in unrelated activities to optimize a risk-return profile, results in a simplicity of governance from the perspective of the executive board, but foregoes synergies and easily results in a positive break-up value of the firm. This is because the capital market expects that the value of a multi-business firm is higher as is the sum of the market value of the individual businesses of the multi-business firm. The capital market expects the system of the multi-business firm to be more as the sum of its individual parts. To achieve this, synergies are to be defined and exploited in a planned and controlled way, making the organization of the multi-business firm more complex as in the case of the model of financial control.

32

Manzi (2012). Merchant and Van der Stede (2012). 34 Simons (2005). 35 Goold and Campbell (1987). 33

11.12

Management Development and HR-Policy

261

11.12 Management Development and HR-Policy An eleventh tool to deal with complexity is management development and HR-policy. From the interrelatedness of aspects of an organization it follows that a HR-policy needs to be integrated in the business model of the firm to achieve organized complexity. The traditional HR-function was part of the system of bureaucratic control, through systems of job descriptions, selection of workers, systems for performance assessment and rewards, etc. HR has shifted into issues like culture, values, competency management, talent management, diversity, etc. The issue for HR is that to create organized complexity in the organization, it needs to acknowledge that a number of administrative tools needed for organized complexity are not in the field of HRM. Selection, coaching, management development, are, but not the factoring of decision-making, that is defining complete goals as to enable self-organization and self-coordination, nor the access to information and fast feedback information. These instruments reside under business control. Especially in transition phases to organized complexity sets serious challenges to HRM with respect to selection of managers and workers, coaching into new roles and identities and other measures to avoid stress, anxiety, burnout as reported in relation to knowledge work.36 Some authors suggest that dealing with complexity requires a certain complexity in the cognitive map of executives.37 As a consequence to deal with complexity and uncertainty implies that specific selection criteria are set to recruit executives, managers, and employees. In view of the role of cognition and type of thinking, a shift may be needed in HR-policy from a service-oriented competence management to more emphasis on the cognitive base, an aptitude for learning and exploration, and a style of thinking that is more holistic design type as opposed to reductionist engineering type of thinking. As an organization in general needs to combine agility with stability most likely it needs an appropriate mix of people, comparable with the Belbin-roles in a team, some more focused on dealing with complexity, uncertainty, and dynamics, and others focused on stability. At first many will be tempted to see the entrepreneurial roles as roles to deal with complexity and uncertainty and the staff for functions in roles for stability. But it is precisely the professionals in the functions which need to design and operate an infrastructure enabling agility and complexity, and this staff needs to step outside the conventions of their functions. This is especially the case for the finance function, where multidimensionality beyond the one-dimensionality of the traditional financial performance management and reporting is needed. HR, as in the case of Google, needs to rethink its policies and processes, as in the case of the interview questions asked at Google, to identify nonconventional thinkers.38 Martin criticizes the traditional MBA education as being too much focused on analysis and

36

Wiholm (2006), Nieuwenhuijsen et al. (2010), Kämpf (2015). Calori et al. (1994). 38 Bock (2015), Poundstone (2012). 37

262

11 Tools Executives Use to Deal with Uncertainty and Complexity

proof, on simplification and specialization, and to less on design.39 Moldoveanu & Simon observe: “The integrator solves through action what the narrow specialist can often not solve even in theory.” This is not to say that the MBA-training should not provide students with a thorough understanding of underlying academic insights and scientific methods. From an economic viewpoint MBA-students are supposed to use this knowledge to create new opportunities and business. Like music students will be versed in music theory, but the purpose of that is to express oneself through music, not to master the theory. With that the question is whether the existing MBA-educational model will be sufficient to answer the growing need in the economy for integrative thinkers. To this also applies what Maccoby is writing about (productive) narcissistic leaders, that these are better in dealing with complex situation because they simplify their worldview by focusing on what they want to change.40

11.13 Architecture and Modularity A twelfth tool to deal with complexity is conceptualizing a business in terms of architecture and modules.41 The architecture of a product (value proposition) is the specification of the functional design (hierarchy of predicted market needs) of a product, e.g. a car, in terms of value for its user. The architecture specifies the overall functional design into sub functions (modules, e.g. the engine of a care, its dashboard), its interfaces and the overall coordination of the modules. In complicated products a sub function itself can be the architecture of a next level of detailing sub functions. Essential is that the interface between the modules is minimal. This minimalism we see in many products, if e.g. the power pack of a PC is defunct, it simply can be replaced without an effect on the overall functioning of the product as a system. A consequence of this architecture-modularity is that an individual, team, or a manufacturing unit can concentrate on developing and producing a module, without the need to coordinate all possible relationships with other modules in the system, the frame is set by the defined functionality and the interfaces defined by the architecture. For the module maker the complexity of the overall product is reduced. The architect, on the other hand, can restrict himself to the relative simplicity of the decomposition of sub functions and interface, there is no need to be involved in the details within modules. Modularization of products, services, processes, organization, etc., makes complexity manageable, enables parallel work (including innovation of modules) and modularization is tolerant of uncertainty. In the architecture of a product, e.g. a car, the engineering complexity is made manageable by defining the functions of the modules and the interfaces between the modules. This technique also is used in

39

Moldoveanu and Martin (2008). Maccoby (2007). 41 Clark and Baldwin (2001), Baldwin and Clark (2004). 40

11.13

Architecture and Modularity

263

computer hardware and in software, and modular innovation is the basis of a number of disruptive innovations. Modularization of products and production changes industries in terms of outsourcing, relations between suppliers and manufacturers, and in changes in entry and exit barriers.42 Modularity not only applies to the design of products and services, it also applies to the design of organizations.43 Modularization initially was about managing detail complexity at an engineering level. As it created a market of modules not specific for defined architectures, a type of generative or organized complexity came into being. This enabled combinatorial or modular innovation, e.g. producing Uber and Airbnb.44 Modularization relates to Herbert Simon’s concept of problem complexity, problems may be decomposable, nearly decomposable, and non-decomposable.45 Due to improved process descriptions, based on techniques from Total Quality Management, in combination with the rise of open standards, many problems in business have become better decomposable, certainly at a level of engineering problems and processes. But decomposability should not be confused with reduction and simplification of problems. Decomposing a business problem, product, service, or organization is aimed at maintaining, fostering, using the requisite complexity. Modularity is also a technique to deal with uncertainties. If a product is not successful in the market or market demand change, usually only a few modules composing the product need to be replaced, safeguarding the investments in the other modules. Modularity, as demonstrated in the business model of Dell, allows for mix-match flexibility, that is products can be customized to specific customer needs without additional costs. Modularity also allows for combinatorial innovation as demonstrated in the case of exponential organizations.46 Architecture and modularization are prerequisite for outsourcing in innovative businesses, because the innovation of a module also can be outsourced.47 Architecture and modularity also change the nature of industries and are tools to understand the complexity of industries, as architecture and modules change power relations and thus the distribution of the profit pool in an industry. Modularization also introduces the distinction between integrated or interdependent architectures of products (e.g., Apple) and open architectures (e.g., the PC). Christensen links this to the phenomenon of disruptive innovation; modularity allows for disruptive business models.48

42

Langlois (2001). Langlois (1999). 44 Downes and Nunes (2014). 45 Simon (1962). 46 Ismail et al. (2014). 47 Sako (2003). 48 Christensen and Raynor (2003). 43

264

11 Tools Executives Use to Deal with Uncertainty and Complexity

11.14 The Resource Allocation Process A thirteenth tool to master complexity is the resource allocation process, as mentioned a number of times before, see also Sect. 12.4.6. What has changed, partly driven by efficiency motives, partly driven by Total Quality Management, partly by declining costs of information, partly by the insight of the role of intangible assets in value creation, is a shift, starting around 1990, from defining the organization of the firm in primarily terms of structure, to defining the organization of the firm primarily in (end-to-end) processes across that structure. A for managers practical insight from economic theory is that cross-divisional processes, teams, projects are also governance mechanisms for knowledge management and -exploitation to achieve fast and efficient recombination of all types of knowledge from multiple specialised departments, without the need for reorganization of the structure of the organization of the firm.49 As a consequence such processes, projects, etc., need to be added as an additional planning dimension (accountable entities), to the accounting system and in the resource allocation process. The system of internal governance, the accounting system respectively the management control system therefore will have a higher complexity as compared to the traditional M-form. This is not a really new idea, it can be found in, e.g., Lorange & Vancil published in 1977.50 The insight of knowledge governance how to organize for combinatorial innovation beyond traditional structures but in terms of management accounting is not to be found in the standard text books for management control,51 nor in the journals or books on project management or project portfolio management. Especially the textbooks on management control tend to stick to a one-dimensional accounting system, despite the need for multidimensional management information systems. The additional dimension as induced by the insight of Foss & Michailova is to be found in the publications of Kaplan & Norton, in their books Strategy Maps (2004) and The Execution Premium (2008). Kaplan publishes cases on firms deploying their method. At the same time a 2006 HBR article by Kaplan & Norton How to Implement a New Strategy Without Disrupting your Organization demonstrates an ambiguity in their presentation. In the article Kaplan & Norton explain that the structure of business units does not need to be changed in order to implement cross-business unit strategic themes. That statement is in itself is correct and will be a relief to all those who fear change of structure. But the introduction of strategic themes as accountable entities in addition to the existing structure of BUs (as the conventional accountable entities) does increase the complexity of the organization, including an increase in complexity in roles, an increase of the complexity of the resource allocation process, etc. The application of strategic themes as governance mechanism in the system of internal governance definitely implies a change of the organization. The confusion for many is that not the old structure changes as such, but that an additional dimension or

49

Foss and Michailova (2009). Lorange and Vancil (1977). 51 Merchant and Van der Stede (2012), Anthony and Govindarajan (1995). 50

11.15

Open Innovation and Open Business Models

265

structure is added (without this being a traditional matrix organization). Certainly, in the case of a material number of cross-divisional projects especially the social dimension of the organization due to the increasing complexity will be disrupted, which is more or less acknowledged by Kaplan & Norton in the 2008 book. The task of the BU-manager becomes more complex, in addition to the bottom line responsibility of the BU-manager he or she now also is responsible for making the right people of his BU available for one or more strategic themes, as budgeted by an additional budget category, STRATEX. One might also see the article by Kaplan & Norton to help managers to reduce their focus on structure and their tendency to changes structure in case of a faltering strategy implementation,52 and that the aim of the article is to add a variable to the mental models of managers other than structure (and other than culture, the other trap). The example of Kaplan & Norton also suggests that dealing with complexity not only is a matter of abstract thinking but it may also be a matter of applying clever, simple tools, more based on practicality than on a deep theory. Although of course practical solutions must be consistent with deeper economic principles, which they are in the case of Kaplan & Norton’s model, which is based on the economy of intangible assets. An adapted graphic representation of Kaplan & Norton management control system is to be found in Fig. 12.7.

11.15 Open Innovation and Open Business Models A fourteenth tool for managing uncertainty, but one that also induces complexity, is open innovation. In a dynamic complex market research and development projects and innovation projects need to be structured and organized different from the one-off fixed planning according to the old concept of project management. The solution to this is open innovation, a concept that based on practicality and opportunity within Philips Electronics during the 60s and the 70s. In a number of R&D and innovation projects Philips developed patentable technology and solutions that were not useful for Philips, but were exploited by Philips on the open market. Otherwise Philips has an older tradition, dating back to the period of the development of the radio, that Philips would buy technical solutions from radio amateurs to be exploited in its products. That in a later period developed into a more professional scanning of the market on new developments, often by small firms, of which the useful one would be acquired and be applied in Philips products and processes. In this way Philips Electronics reduced the financial risks of R&D and innovation projects and its Patent Office for a long period was an important cash generator of Philips Electronics. This phenomenon later was documented, explained, and advertised by Chesbrough under the title of open innovation.53 Open innovation now is a standard in virtually all industries as it is an effective concept to make a best use of available

52 53

Neilson et al. (2008). Chesbrough (2003).

266

11 Tools Executives Use to Deal with Uncertainty and Complexity

knowledge and ingenuity in the world market whilst reducing financial risks of innovation at the same time. Open innovation is also an illustration of how the uncertainty and thus high financial risks of product development in e.g. biopharma, are distributed in the market, reducing the financial risks for the large firm who continue their control over the market by owning the infrastructure to scale up products and controlling the distribution. The concept of open innovation is to be discerned from, e.g., the knowledge ecology, as in open innovation there is quite some technicality in terms of evaluating prospects by start-up, patenting research findings or new products, finance, deal-making, financing, etc. The concept of open innovation is being applied as open business models as well, which is more about outsourcing, and is basically the same as the concept of architecture and modularity to design and to manage outsourcing.

11.16 The Real Option Method, Phased Funding, and Discovery-Driven Planning A fifteenth tool for managing especially the uncertainty part of complexity is applying the technique of real options. This technique originates from the field of investment theory where it addresses the issue of uncertainty of the future value of resources needed in an investment project. An investment project by definition has a risk, but this risk can be reduced if the project can be broken up in a number of steps, each having strategic options.54 The completion of an investment project takes time, at the beginning there will be a number of uncertainties and assumptions made, but as the completion of the project progresses over time more knowledge and information will be available, thus reducing uncertainty, provided the project has options. In the traditional linear project organization there will be milestones. In a real options structured project at each mile stone the question will not be whether to start the next phase, but the first question will be asked what new options have come available, what has been learned from executing the project so far, and whether there are better options for the next phases as initially foreseen in the project planning. Valuating a project as one investment on the basis of net present will result in undervaluation of the project because alternative options are not considered. Structuring and managing a project on the basis of real options will create a higher chance that the project will have a better fit with external developments as these unfold during the completion of the project and subsequently there will be a higher chance on success. Real options theory applied to investment projects is structuring the projects in sub-decisions or sub-decision each of which is deferred as long as possible, within the overall time frame of the project, to make those choices on the basis of new information not available at the start of the project. Consequently, structuring a project according to real options will be a tool to deal with higher complexity in markets, because a higher complexity implies more options and more requirements for fit. 54

Grinblatt and Titman (2002), p. 426.

11.17

Using Mathematical Models for Risk Management and Managing Complexity

267

In doing so also moves of competitors during the time duration of the completion of the projects can be better answered in the case of real options compared to a linear defined project. This links the real options method to gaming to answer uncertainty and variety in competitor behavior.55 In a less mathematical way the real options method, especially the focus on seeing new opportunities has been described as discovery-driven planning.56 A standard technique for planning complex engineering projects is network planning by using specialized software for this, in which subprojects are related through input and output relations, which includes resource utilization, throughput times and allows for determining the critical path in the execution of the project. Such a type of planning assumes full knowledge in what needs to be done, what is needed, resources required, risks, throughput times, etc. For engineering projects like the Swiss Gotthard Base Tunnel, completed in 2016, within time and budget (CHF 9.74 billion). New product and process development in an uncertain, dynamic complex environment for two reasons need to be based on an open instead of a closed project planning. The first reason is that after a first phase there most likely will be a new situation in terms of (technical) options, and it may be that certain lessons have been learned during the first phase. There might be new options. A second reason is that during the time it took to complete the first phase more information has become available on market developments, either that certain uncertainties are reduced, or that the market offers new opportunities. This situation first was acknowledged in the real option theory, a method to keep options as long as is possible open and to make a best use of new options in the course of developing a project. The non-use of possible new option implies that valuing a new project on the basis of the net present value method systematically undervalues (investment) projects. The real options method provides a more accurate financial evaluation but is more complicated compared to the NPV-method. McGrath has popularized the real option method to structure, plan, and manage large complex projects with the concept of discoverydriven planning in which a tool is to manage the assumptions-to-knowledge ratio, that is to learn from each phase in a project plus that during the passing of time for that phase new information will have become available. In a way this is a more sophisticated version of the science of muddling through, but less incrementalistic.57

11.17 Using Mathematical Models for Risk Management and Managing Complexity From the field of mathematics concepts and techniques have developed to understand and deal with risk, be it that is started with gambling. Throwing a dice and not knowing which number will be on top is an example of complexity. On the other hand, because it is a closed system we do know that it is a number varying between

55

Smit and Ankum (1993). Discovery-driven planning was introduced by McGrath (1999). 57 Lindblom (1959), Migone and Howlett (2016). 56

268

11 Tools Executives Use to Deal with Uncertainty and Complexity

1 and 6 and that the chance of a six simply is one-sixth, the quotient of the wanted result and the total number of possible results. In this case the complexity is in the mechanics of the turning dice, the movement of the hand, and forces used in the game, a complexity we are unable to calculate, it is beyond Newton’s F = m x a. A core concept in probability is consequentially that probability is a measure for our ignorance, we do not know and it is impossible or extremely difficult to know the precise mechanics, but we do know the boundaries of the system by possible outcomes. So, since the Renaissance probability calculus has developed to include the mathematics for (life) insurance and from that into decision-theory. More advanced mathematics is being used to develop mathematical models for economic systems, including feedback relations, nonlinear causality, to simulate dynamic complex systems.58 The probability calculus was further elaborated by mathematicians introducing concepts like the normal distribution and the standard deviation. The use of probability calculus for decision-making emphasized the concept of expected utility (introduced by Daniel Bernoulli (1700–1782), which after WWII would be developed in the mathematical portfolio investment theory. Mathematical modeling is intensively used in financial markets for decision support, executing preprogrammed decisions, and calculating at value-at-risk in investment projects. However, the use of sophisticated mathematical models for risk management themselves can be a source of risk; called risk-risk. As with any mathematical modeling the risk is too much abstraction, leaving out or undervaluing factors that under normal situation do not play a role, but at specific confluences or new conditions may create problems. The banking crisis of 2007–2010 was triggered by mathematical models used in New York and in London because in these models was not taken into account the dislocation of short-term credit markets, the drying-up of liquidity, and the strong deterioration of the credit risk of instruments such as the Credit Default Swap and the AAA-assets backed securities.59 Mathematical modeling understandably is extended from risk management to modeling complex systems in science, e.g. biology, rheology which has typical non-Newton mechanics, and also in economics, understandably researchers work on modeling complex behavior of actors in the economic process, but even advanced mathematics requires simplification of the observed reality to be modeled. An example of such a simplification is the use of the mathematical kinetic theory for active particles (KTAP) to model, e.g., traffic flow, social dynamics, and psychological interactions.60 In the so-called agent-based modeling, which is directly based on the traditional complexity theory a number of more specific concepts are being used for modeling, e.g. game theory, multi-actor multiagent systems, Monte Carlo, population dynamics, social networks, including the issue of topology of networks,

58

Bernstein (1998). Terragnolo (2014). 60 Ajmone Marsan (2009). 59

11.18

The Concept of the Platform Organization

269

etc.61 Mathematical modeling of complexity is of great use of situations which can be structured as mentioned before, but at a higher level as in well-structured decision problems. But also, in the new methods for mathematical modeling, those supported with big data, reduction of complexity is needed.62 The corollary of this is that especially at strategic level there is no alternative for abstract thinking. Most situations cannot be modeled as a mathematical model, but the boundary is fuzzy, not only because of the fuzzy set-theory. As Einstein once quipped, the simple is always the simplified. There is a risk in using mathematical models for risk management, especially in the context of big data and algorithms running of ever more powerful computers, that consultants and managers will quantify aspects of business situations which cannot be quantified.63 Mathematical modeling is a strong tool for understanding the complexity of situations and perhaps their use is more in what the intellectual growth is from modeling than in achieving a reliable mathematical model. The mathematician-philosopher Alfred North Whitehead (1861–1947) taught us to look for simplicity, but to distrust it. There is also a simple logical argument why mathematical modeling has its limits in dealing with complexity. Mathematical modeling through combinatorial mathematics may help as, e.g., in the case of population dynamics to identify states or combinations which the human mind either by its limitations or biases, would not have identified. But the core of complexity is that it is unknowable, both because of bounded knowledgeability and because of emergence, both in states and topology of situations. What cannot be known cannot be modeled either.

11.18 The Concept of the Platform Organization The concept of bus modularity in computer architecture, a generic component on which a variety of modules can be plugged to create a variety of products, inspired to reconceptualize shared service centers as constituting a platform, a concept at the level of the organization, which is defined as a set of generic resources and processes that has the capability to service a variety of businesses which in a cost-effective way can be plugged on and off.64 Such a platform provides services for sales transactions, HR-services, accounting, management control, IT-services, logistics, real estate, facilities management, etc. Because corporate policies today increasingly are implemented by codifying these in such services, including their IT-systems, these platforms also absorb the complexity of compliance. This is what Zuboff & Maxmin refer to when they wrote: “In contrast, the new digital medium moves decisively

61

Ajmone Marsan (2009). Ajmone Marsan (2009). 63 Bernstein (1996). 64 Marti (2007, p. 71), Imai (2000), Strikwerda (2014). 62

270

11 Tools Executives Use to Deal with Uncertainty and Complexity

away from the logic of simplification and its counterpart in hierarchical oversight. Here for the first time is a technology that preserves and coordinates complexity.”65 More in general the concept of platform is a concept to combine stability, to preserve capital investment, and combinatorial innovation or agility. Platforms exists at the level of technology, for a family of products as, e.g., in the car industry, computer hardware industry, and in the software industry. Platforms as explained exist at the level of organization. Platforms exist at the level of industries, e.g. the architecture of the PC, iTunes by Apple, Google, etc.

11.19 Fast Feedback Information to Deal with Complexity? 11.19.1 Feedback as a Defining Element in Complexity? Feedback loops are a defining element in cybernetic systems.66 Due to the nonlinear and reciprocal causal relations in complex systems, feedback loops by some are also taken as defining elements of complex systems.67 Or, more precisely, feedback loops by many are seen as a tool to deal with complexity. As we will see in this section, this role of feedback is subject to limitations. Feedback, especially in social system itself is a complex phenomenon. Feedback is a subset of the category of effect-information (Sect. 6.3.8), as its purpose and effect are to control the behavior of a system. At the same time feedback information has a derived relation with material (context) information. A system has inputs, a throughput with process parameters, and an output. The feedback dimension of the system consists of (1) a sensor that measures the actual output (performance) of the system; (2) A collator that compares the actual performance to a preset standard (objective, norm) and in case of a deviance of the performance from the preset standard generates a signal which by a (3) effector is translated into information (feedback information) which causes adjustments in either the input and/or the process parameters with the aim that the performance is according to the preset standard. The thermostat that maintains the temperature of you room to the value you have inputted in the thermostat is the simplest and most ubiquitous example. Most of our equipment as we use it privately and in our manufacturing processes is unthinkable without programmed closed control loops. Its success in engineering applications has by management authors like Juran and Anthony, been translated into control loops in organizations. As we will see, in that domain feedback also plays a role in machine learning and in fine-tuning parameters in algorithms and with that in some of the artificial intelligence. Whether this results in optimal performance is to be seen. The use of feedback information and feedback loops in process control is given a boost by the declining costs of sensors, digital recording of transactions

65

Zuboff and Maxmin (2002, p. 292). Wiener (1961). 67 Bishop and Silberstein, p. 147. 66

11.19

Fast Feedback Information to Deal with Complexity?

Control inputs when

Control processes when

• It is difficult and/or costly to monitor processes or outputs (e.g. government policies) • Cost of input is high relative to value of outputs • If there is a long delay between time of input and time of output • If errors in input show themselves too late, are costly to repair

• Processes can be observed and/or measured • Cost of measuring/monitoring process is low, cost of monitoring output are high, output is difficult to measure • Standardization is critical for safety and/or quality • Causal relations are understood proprietary processes or process improvements can result in strategic advantage • No process innovation is needed or possible

Control outputs when • Outputs can be observed and/or measured • Cost of measuring/monitoring is low • Errors in output can be repaired fast and easy (short cycle production) • Causal relations are not understood by principal • Freedom to innovate processes is desired

271 Control outcomes when • The mission of the organization stipulates so (typical in case of institutions) • Innovations are needed in output (products, services), processes (incl. organization) and inputs to uphold the mission

Fig. 11.5 Whether to control inputs, outputs, or outputs under high costs of information and observation (adapted from (Simons, 2000, p. 67). In the era of high costs of information and of sensors, choices have to be made as suggested in this Figure. Today we have low costs of information and cheap ubiquitous sensors and thus a tendency exists to measure everything, using AI and machine learning. This in itself will not result in better performance

and process parameters, and the declining costs of information in general. The resulting emphasis on feedback however, has a shadow side. In the past, before sensors were ubiquitous and the costs of information were high, choices needed to be made in the case of the control of a process, whether to control inputs, to control processes or to control outputs, respectively, outcomes (Fig. 11.5).68 The declining costs of sensors and digital technology tend to everything being measured, real time, in the process eliminating the traditional statistical quality control. The question is whether measuring everything and subsequently to have continuous, real-time feedback on inputs and on all process parameters, will result in an improved state of being in-control respectively in a state of optimum efficiency. Or, will creating or allowing every possible feedback loop results in optimum handling of complexity? Or might it be that doing so creates an additional dimension of complexity to a system? The management author J.M. Juran wrote in the twentieth century: “So [management] control can be a cruel hoax, a built-in procedure to avoiding progress. We can become so preoccupied in meeting targets that we fail to challenge the target itself.”69 The success of feedback loops in control engineering not automatically implies its success in social systems, even not in intelligent complex adaptive systems (ICAS).

68 69

Simons (2000, p. 67). Juran (1995, p. 3).

272

11 Tools Executives Use to Deal with Uncertainty and Complexity

11.19.2 The Cognitive Role of Feedback Feedback information and feedback loops have a peculiar cognitive function. When James Watt constructed his flyball speed governor for steam engines in 1769 to control the speed of steam engines, he constructed an information processing system, avant la lettre, be it mechanical. The function of Watt’s speed governor was not only to eliminate human supervision and interference, in cognitive sense Watt’s governor system compensated for the limits of engineering knowledge. Despite being a mechanical engineering system, it is impossible to know and understand all the factors and causalities at work in a steam engine, due to wear and tear, variability in the quality of the coal, outside temperature, etc., to construct a mechanical system that is completely deterministic predefined to achieve and maintain a preset standard without feedback. Feedback information is an elegant device to compensate for limits of knowledge. This does not only apply to mechanical engineering or chemical processes, it also applies or perhaps better, is being applied to social systems. Especially in the case of bottom-up artificial intelligence systems, and connected to that, machine learning, feedback information is used in order that the AI-software will conclude causal relations, where these cannot not be identified through conventional induction, in order to improve decisions with respect to e.g. marketing to influence people. However, as is the issue with conventional management models, “There is a broad assumption underlying many machine learning models that the model itself will not change the reality it is modeling.”70 Which by the way, also was the weakness of many traditional management models. Not taking this effect of models on reality into account, has mislead many managers in the past. Understandably that in dealing with especially dynamic (social) complexity related to trial-and-error to deal with complexity fast feedback information is seen as a prerequisite to deal with such dynamic complexity.71 The question needs to be asked whether feedback information only, especially in the form of closed feedback loops can be sufficient for a complex system to survive successfully. Beyond the domain of control-engineering feedback loops may consist of a wider variety of types of feedback information, how feedback information is being generated and digested, as well that feedback information transgresses from effect-information for corrective actions into a resource in the production system.

11.19.3 Types of Feedback Loops To organize as a constituent element of organized complexity useful feedback information, it is necessary to discern different types of feedback and for each of these an appropriate organization. Types of feedback can be characterized by a number of dimensions:

70 71

Christian (2020, p. 48). Manzi (2012).

11.19

Fast Feedback Information to Deal with Complexity?

273

1. Feedback that regulates the input of a process, based on deviances from set standards of the output (negative feedback) or reinforces the production (in case of positive feedback); = performance feedback. Capra: “self-balancing (negative) feedback loops maintain the system in a stable but continually fluctuating state, whereas self-amplifying (positive) feedback loops may lead to new emergent structures.”72 To which March adds: “Positive local feedback produces strong path dependence and can lead to suboptimal equilibrium.”73 The idea of closed feedback loop suggests that the feedback operates on one specific parameter or input of the system. Especially in social systems, in which information is shared or accessible by many, a specific feedback information may have effects, of different kinds, in multiple parts of the system. Positive feedback may be the spark of life, but is no guarantee for survival. 2. Feedback that regulates the process parameters (throughput) of a process based on deviances in the output. This in general will result in higher efficiency. In complex processes machine learning is applied to identify and adapt to changing causal patterns. 3. Intrinsic feedback that corrects process parameters based on deviances of these parameters from set standards, this is a feedback information feedback loop within the system. In case the standards are based on (external) regulation, e.g. safety requirements, this is called conformance feedback. 4. Extrinsic feedback is originated from outside the system, e.g. deviances from standards set to output: (a) Extrinsic feedback that is organized by the system (the firm) itself (numbers 1 and 2), e.g. comparing quality and/or quantity of output, measuring customer satisfaction (b) Extrinsic feedback to the system (the firm) that is originated unsolicited by the environment, e.g. NGO’s on the environmental impact of production, on the social effects of some medicines, the impact of HR-policies on inclusiveness and diversity, etc. Due to the Internet, the democratization of information, the social media the sourcing policies of firms, their production processes and their products and services are scrutinized from multiple perspectives, interests, and value systems. That is, this unsolicited feedback is not defined and thus not constrained by the programmed control of the system (the firm). This unsolicited feedback originates from the domain of social production. With that this unsolicited feedback is not just feedback but it becomes a resource for the firm, an input in the development of strategy, market positioning and the development of products and services. (c) Extrinsic feedback may be at the level of products, the market (market feedback),74 it may also be at the level of the industry, the capital market or even the economy. A business operating in a market, dependent on size

72

Capra and Luisi (2014, p. 159). March (1991). 74 Beniger (1986, p. 378). 73

274

11 Tools Executives Use to Deal with Uncertainty and Complexity

and success, very often changes the conditions at industry level, it may attract new competitors, it may change consumer expectations and preferences, that is, especially a successful business may undermine the validity of the assumptions its strategy and operations are based on, by its own doing. This implies that monitoring at industry level is necessary to control the validity of premises. This is a task of management, Fayol’s prévoyer. Increasingly it is other parties, analysts at investment banks, journalists, academics who generate observations, analysis, and recommendations as feedback and feedforward on the strategies and policies of executives. (d) Feedback on secondary parameters. A CEO may choose to weigh feedback on, e.g., shareholder value and shareholders, despite this being the residual claim, because on the existing capital market takeovers or threats for this may influence the strategy. Also, a CEO may anticipate the effect of certain outcomes on her or his bonus over the wealth creation of the firm.75 5. Feedback by degree of semantics (a) Discursive feedback. This is feedback information in terms of unequivocal data (discursive information) in which the metrics are defined by fields like mechanical engineering, operations management, physics, chemistry, accounting systems and such. This is the type of feedback information assumed in the PDCA-cycle of Deming. The corrective actions to be based on discursive feedback information are usually defined by the causal patterns of the fields defining the metrics. This type of feedback information is used in fine-tuning algorithms by feedback and in machine learning in case causal relations are unknown. Often this type of feedback information is intrinsic feedback. Discursive information feedback loops also are labeled as first-order control loops. (b) Cognitive feedback. This is feedback in various forms, mostly found in education, but relevant in other social systems alike, through prompts, cues, questions, etc., that help learners to reflect on the quality of their problemsolving processes and solutions, so that they develop more effective cognitive schema’s. Whereas discursive feedback information fine tunes the parameters of the process, cognitive feedback information will result in improving the design of the process. Cognitive feedback is based on established fields of engineering, industrial engineering, physics, accounting and such. Cognitive feedback also may apply to the behavior of individuals and teams. (c) Intellectual feedback. This is feedback usually on manuscripts, either by a reviewer or by an interlocutor asked for by the author, in which the feedback consists of academic or scientific insights, sources, new developments, questions aimed at improving the intellectual quality of a paper or book and with that intellectual understanding. At first sight this seems less 75

Jensen et al. (2004).

11.19

Fast Feedback Information to Deal with Complexity?

275

relevant for business, except in R&D activities. As explained before, good CEOs always look for better models to deal with the developing complexity of their industry and their business. As the changing basic conditions in the economy and technology, and also changes in society, undermine the assumptions and the paradigms underlying conventional MBA-models and tools, intellectual feedback on the effects of such models and tools is needed to define new models and tools. This feedback is provided by some academics but such feedback only can be effective if CEOs have an open, inquiring mind for such feedback. In practical sense we see this in the phenomenon of reconceptualizing. With respect to reconceptualizing feedback itself is not sufficient, as described by Martin, active CEOs always “wade into complexity, and always are searching for better models, that is these CEOs are aware of the limited validity, temporal and spatial, of management models. This intellectual level of feedback, which is also related to the requirement of abstract thinking to deal with new complexities, is a requirement to be in-control, especially in the longterm. Here we see a tension in the social dimension of control. Often individuals and groups tend to stick to the familiar, routines, methods, concepts in a subjective feeling of maintaining control. Whereas it is precisely openness for new ideas, methods, concepts which certainly for the longer term is decisive for being in-control. “Absorption of knew knowledge will be slowed down or blocked when it threatens deeply and unconsciously held values or cherished attitudes.”76 It is precisely these deeply and unconsciously held attitudes that constitute with many in society a false sense of to be in-control, as can be observed in the phenomenon of re-regulation. The effectiveness of intellectual feedback depends on open, inquiring minds. (d) Emotional feedback. Individuals interacting with each other not only exchange facts, solutions, insights, opinions and such but also, consciously and unconsciously exchange affects, by choice of language, tone of voice, facial expressions, and other body language. In this there is a difference between interaction by digital means (email, social media, chat groups, video conferences) and in-person interaction. In-person, F2F-interaction has real-time rapid and complete feedback in terms of visual and body language cues. There is no anonymity in in-person interaction as there may be over the Internet. In-person interactions imply also a commitment in time to each other.77 Somewhat in between are the video interactions (FaceTime, Zoom, Teams and such), but these suffer from detailed and completeness of visual feedback and the time commitment is not so absolute compared to in-person interaction. In-person interaction includes concurrent feedback: information that arrives during our behavior, not waiting for the

76 77

Boisot (1995, p. 228). Storper and Venables (2004).

276

11 Tools Executives Use to Deal with Uncertainty and Complexity

completeness of a process or its results.78 These forms of emotional feedback may be aroused by the behavior of others as well as by facts and solutions presented in interactions, as these may affect social position of an individual. Emotional feedback may be both corrective, to correct behavior of others as it may constitute amplification in case of positive emotional feedback in terms of appreciation and acceptance, be it on behavior or on performance. Emotional feedback (and feedforward) is related to perception feedback, which is about the perspectives, feelings, and opinions individuals develop about their contexts and which they may use to be more effective with making contributions and/or achieving (personal) goals. Emotional feedback may be perceptive in that the individual observes, looks for and interprets explicit and implicit clues from its social contexts. Emotional feedback may be socializing by function, getting (new) individuals conform to the existing social system. Emotional feedback also may be manipulative to influence in an unobtrusive way the behavior and/or the thinking and with that decision-making of individuals, as in the case of cultural control. The latter often is through images or words used as images, as images have a more profound effect on emotions. Emotional feedback may also be related to issues of identity and of gender at the workplace and with that the system of emotional feedback is part of the open exchange of the organization with its societal context. Emotional feedback may affect cognitive dimensions in interactions as well as coordination processes. Emotional feedback is not just corrective by nature, as much it is for individuals a resource to develop themselves in terms of behaviors that serves them well.79 In the context of social media feedbackseeking behaviors may impede intellectual exploration.80 Feedback to individuals can be constructive or destructive. Individuals who receive criticism from peers tend to look for new relationships, for different social contexts. Emotional feedback also may have a shadow side with respect to innovation and problem-solving. “The abstraction needed for problem solving can be blocked by a need or attitude of immediacy and discouraging reflection, denying its value and of the time it requires.”81 (e) Social feedback. Feedback other than technical (control engineering) or financial performance, that is feedback as previously described under cognitive feedback and emotional feedback also can be labeled as social feedback. A specific dimension in social feedback is social media feedback. Social media create through patterns of communication and through its technology an information space that is a social force in itself. Different from traditional broadcasting and publishing, social media is a many-to-many

78

Huczynski and Buchanan (2007, pp. 116–117). Ashford and Cummings (1983). 80 Greenberg (2010, p. 417). 81 Boisot (1995, p. 228). 79

11.19

Fast Feedback Information to Deal with Complexity?

277

medium.82 It has multiple formats and basically social media consists of user-generated content. This may include direct feedback on products, services, and social behavior of firms, as a kind of autonomous feedback. Due to the openness of social media in which either by initiatives or random autonomous bubbles of communication and thus opinion shaping develops, firms actively need to research the domain of social media on moods and critiques on their products, services, and corporate behavior. With that the distinction between feedback and market research gets blurred. What also gets blurred are the knowledge boundaries between the firm and its environment. To survive a firm always needed to be open for new knowledge, as demonstrated by Burns & Stalker as early as in the 60s of the twentieth century.83 Now employees of firms use social media in addition to the firm specific knowledge management systems to find and discuss solutions and/or to participate either set to this by their boss or on their own initiative, in the opinion forming processes with respect to the firm. The social media have become part of the systemic context co-defining their behavior. With that weakening the systemic context as an administrative instrument in the governance of the firm. This may result in either public criticism of employees on employers’ policies, as e.g. in the case of algorithms used by Google in their HR-system, or on the political dimensions of services as in the case of Facebook. A further complication is that the social media is not a uniform system but it is characterized by compaction points or bubbles in which opinions are formed and maintained and specific groups of social media users flock and stay without deeper reflection or exploration. In addition to this the social media feed the echo chamber effect, creating a false sense of understanding expressions resulting in an exchange of simulacra instead of communication.84 This echo chamber effect of overconfidence in thinking that one understands what one sees on the social media and is repeated in F2F-contacts for reasons of social adaptation and belongingness is a cause of fads and financial bubbles.85 In a world of social media resulting in splinternet and echo chambers fads and panics tend to be dominant, in combination or not with fake news and other deliberate manipulations, in which social media is weaponized including “digital wildfires, using social media as a type of feedback, and as a type of resource, becomes a complicated phenomenon for firms.”86 This complicatedness plays at the level of the individual as well, to which many respond, or perhaps better flee by locking themselves up in one of those many bubbles of splinternet.

82

Khan (2017, p. 3). Burns and Stalker (1963). 84 Pentland (2014). 85 Pentland (2014, p. 37). 86 Pentland (2014, p. 41). 83

278

11 Tools Executives Use to Deal with Uncertainty and Complexity

An implication of social media is as well is that in stigmergic coordination not only there is a response to output in material sense, there may be response to behavior, apart from the fact that material response may evoke emotional responses with respect to one’s own standing, position, or reputation.87 Social feedback in the modern era served the individual to participate in the world by socialization. In the era of postmodernism, it requires a strong self-image, a personal purpose, if not also a strong Bildung, to use it in the same way.88 Whereby digital technology tends to externalize individual’s their memory and even identity. The paradox is that the richness of the modern information space, due to bounded knowledgeability at the individual level, turns it into an information maze. Feedback information is a dimension of complexity in itself. To an extent feedback information compensates for our lack of understanding of complex causal patterns, for our inability to design mechanical and engineering systems in a full deterministic way. So, in social systems, but in social systems (ICAS) feedback information and feedback loops exist beyond the scope of administrative instruments, in the domains of social controls and self-controls. These social feedback loops have the power to impair the effectiveness of administrative instruments and administrative feedback loops and -information. Operating through the social domain and at the level of self-control, feedback information, through spontaneous feedback, is not just control information, especially market feedback and social feedback is a resource, an input in the production process as well. This implies that the organization as an open system needs to have the capability to acknowledge and absorb autonomous feedback information, from whatever source, even if at first sight not relevant for the existing business model. Autonomous feedback may be of importance for long-term value creation and continuity. For that reason, closed feedback loops, including those used for machine learning, always need to be embedded in higher levels of open control systems, allowing for cognitive and intellectual feedback and with that for premise control. These types of feedback can be thought of as spanning a space, horizontally defined on a scale of local (closed loop) feedback via feedback on end-to-end processes, to feedback at the firm-level, to feedback in the economy. Vertically the boundaries of the scope vary from local, mechanical control engineering via process outputs, financial performance, to social learning loops, to intellectual and testing premises and worldviews. A third dimension of feedback is the time horizon, comparable as in the balanced scorecard.89 Such a space of feedback will be needed in order that feedback plays a constructive role in dealing with complexity.

87

Schein (1988). Davis (2017). 89 March (2006). 88

11.19

Fast Feedback Information to Deal with Complexity?

279

11.19.4 The Context of Feedback: Control The control engineering level of feedback aims for achieving and maintaining a standard (e.g., the rotation speed or as in management accounting achieving and/or maintaining a preset performance parameter, e.g. market share (a dynamic performance parameter in its operational consequences). The warning of the quoted Juran on the risk of being too preoccupied with meeting targets applies to feedback as alike. To understand the function and with that possible limitations of feedback, feedback information and feedback loops need to be seen and understood in the context of the overall function of control of an organization. To remind the reader from Sect. 4.3 the function of control is to acquire data on changes in the environment and processing these into information to adjust flows and transformation of matter, energy, and information in such a way that the living system survives in changing conditions. This is what control engineering-level feedback does, e.g. in the case of Watt’s flying balls speed regulator or the room thermostat, be it that the changes in the outside temperature are not measured in a direct way, but are “concluded” from the changes in speed and in temperature. In the present-day more sophisticated temperature control units for e.g. heating your house, an outdoor sensor is included which does measure directly changes in the outdoor temperature to generate some feedforward information, resulting in a more accurate temperature control. The control-engineering level feedback loops can be defined in the three-layer model of control defined by Beniger (Sect. 4.3) as being at the level of existence or being, that is maintaining a state of the system by a process parameter or performance, to counter entropy. This also applies to, e.g., the target of achieving or maintaining a market share. But a closed feedback loop is defined by the physicality of the system or process, it only observes what it is defined to observe. “The major shortcoming of such examples (thermostat) is that they draw attention to control behavior itself—especially feedback—and therefore away from the more fundamental aspect of control, namely programming.”90 Remind that “all control is programmed.”91 If this programming of control is wanting or impaired, especially with respect to the scope of the programmed causal density and/or influential environmental factors, feedback information will suffer the same impairment. Even in a context of machine learning with bottom-up artificial intelligence, the effectiveness of feedback information to discover new causal relations, or better correlations, will be limited. Therefore, in order to be in-control, Beniger’s second level of the programming of control, the continuous environmental awareness and scanning to observe whether objectives are still relevant and whether the scope or nature of the mechanical programming at the first level needs to be reprogrammed, is a prerequisite.

90 91

Beniger, p. 66. Beniger, p. 40.

280

11 Tools Executives Use to Deal with Uncertainty and Complexity

Feedback information, due to this first level mechanical programming, is discursive information defined by either the mechanical engineering, electronics, chemistry, or management accounting rules. E.g. in the case of a declining market share, which may have multiple causes, it might be that this decline only can be encountered by introducing a new product. By what process or system the most effective remedy is identified from these multiple causes? A process in which human judgment and thus psychological biases, specifically in this example the risk of the cashflow trap, does play a role? In the control engineering level of feedback loops, the translation of data on changes in output into information and into interventions, is programmed in the physicality of the system. In social systems this programming is at the level of mission, values hierarchy, but this type of programming requires human judgement. This issue is addressed in Robert Simons’ concept of management control, specifically in his concept of four levers of control.92 Simons four types of control are Belief Systems, Boundary Systems, Diagnostic Control Systems, and Interactive Control Systems. With that Simons takes management control and thus feedback, beyond measured performance. The dimension Belief Systems is about the mission, the hierarchy of values, which as we have seen, are necessary to deal with complexity, with new choices to be made. The hierarchy of values is also about the human relationships within the organization. The mission and hierarchy of values need to be codified in systems, processes, and procedures, on which feedback is needed for correctness as well, because nothing is deterministic a priori perfect, whether actual relations between the members of the organization are what these intend to be. Boundary Systems are about compliance, limits set to, e.g., markets, to risks to be accepted. Feedback is needed whether such boundaries are appropriately codified in objective functions and especially whether members of the organization in their behavior observe set limits and rules. Diagnostic Control Systems are about feedback on performance and corrective actions in case of negative deviances of preset standards of performance. Interactive Control Systems are about discussions between members of the organization about whether the premises on which a strategy is based and those underlying the operational plans, still are valid and whether adjustments of strategy and/or operational plans are needed. In the interactive control also new developments, changes in the market are being discussed in terms of initiative to adapt to such changes. Ineffectiveness of corrective action is discussed in this system to judge whether needed adaptation is beyond the capabilities of the existing system and thus a more fundamental adaptation or transformation is needed. Scenario planning also is a form of interactive control. Scenario thinking is about seeing those developments in the economy, in society, that are not seen by the programming of the existing control system.93 The term interactive refers to interaction between human beings, and that in this interaction it is about judgment, interpretation, development of vision, acknowledging

92 93

Simons (1995). de Geus.

Bibliography

281

uncertainties, and with that about decision-making in its proper sense, beyond the capabilities of artificial intelligence and machine learning. Interactive control therefore cannot be based on feedback only, as much it must be based on feedforward, monitoring the environment, exploration and taking in new concepts and insights. Interactive control is as much about imagination as it is about feedback, it is about reprogramming the system where reprogramming is due to achieve continuity. It is to be noted that Simons four levers of control assume the presence and effectiveness of an Internal Control System, this is about ensuring the integrity of data (in the sense of discursive information) as used in all other control systems and in operations, and that assets are safeguarded from theft, abuse, or accidental loss.94 Its practical form is that firms today have one (global) transaction database, organized disembedded from the structure and processes of the organization with a rigorous semantic data standardization as in the case of IBM. Simon’s concept of control, consistent with the earlier warnings of Juran, and those by Merchant, is that control must be future oriented, which implies that feedback loops as a sub function of the overall control function in the organization are programmed with a limited scope of “observation” of the environment, and therefore it in itself falls short of the criterion to be future oriented. Feedback loops must be subordinate to forecasting, feedforward information and to scrutinizing assumptions underlying strategies and operations. The foregoing makes clear that feedback does play a role in complex systems, but feedback processes are not the core of organized complexity. The core of organized complexity are the processes of control as defined in cybernetics, in which the dominant requirement is openness and exploration with an infinite time horizon, whereas feedback has a subordinate role with a limited time horizon. Feedback may be a defining characteristic of cybernetic systems, feedback is not a defining characteristic of complex systems, although complex system will have multiple feedback loops of different types. An important dimension in organized complexity is, addressed earlier in different terminology (e.g., availability bias) in the field of business administration, that biases to emphasize feedback over exploration need to be acknowledged and counteracted. This is the basic message in James March’s article Rationality, Foolishness, and Adaptive Intelligence, feedback is not the process to deal with complexity.95

Bibliography Ajmone Marsan, G. (2009). New paradigms towards the modelling of complex systems in behavioral economics. Mathematical and Computer Modelling, 50(3–4), 584–597. https://doi.org/10. 1016/j.mcm.2009.03.004 Alberts, D. S., Garstka, J., & Stein, F. P. (1999). Network centric warfare: Developing and leveraging information superiority (2nd ed.). National Defense University Press.

94 95

Simons (1995, p. 181). March (2006).

282

11 Tools Executives Use to Deal with Uncertainty and Complexity

Anthony, R. N., & Govindarajan, V. (1995). Management control systems (8th ed.). Irwin. Ashford, S. J., & Cummings, L. L. (1983). Feedback as an individual resource; personal strategies of creating information. Organizational Behavior and Human Performance, 32, 370–398. Baldwin, C. Y., & Clark, K. B. (2004). Modularity in the design of complex engineering systems. Retrieved from Connecticut. Beniger, J. R. (1986). The control revolution: Technological and economic origins of the information society. Harvard University Press. Bernstein, P. L. (1996). The new religion of risk management. Harvard Business Review (March– April), 3–6. Bernstein, P. L. (1998). Against the gods: The remarkable story of risk. Wiley. Bettis, R. A., & Prahalad, C. K. (1995). The dominant logic: Retrospective and extension. Strategic Management Journal, 16(1), 5–14. Bock, L. (2015). Work rules! : Insights from inside Google that will transform how you live and lead. Twelve. Boisot, M. H. (1995). Information space: A framework for learning in organizations, institutions and culture. Routledge. Burns, T., & Stalker, G. M. (1963). The management of innovation (2nd ed.). Calori, R., Johnson, G., & Sarnin, P. (1994). CEOs’ cognitive maps and the scope of the organization. Strategic Management Journal, 15(6), 437–457. Capra, F., & Luisi, P. L. (2014). The systems view of life: A unifying vision. Cambridge University Press. Casadesus-Masanell, R., & Ricart, J. E. (2011). How to design a winning business model. Harvard Business Review, 89(1/2), 100–107. Cha, S. E., & Edmondson, A. C. (2006). When values backfire: Leadership, attribution, and disenchantment in a values-driven organization. The Leadership Quarterly, 17(1), 57–78. Chesbrough, H. W. (2003). The era of open innovation. MIT Sloan Management Review, 44(3), 35–41. Christensen, C. M., & Raynor, M. E. (2003). The innovator’s solution: Creating and sustaining successful growth. Harvard Business School Press. Christian, B. (2020). The alignment problem: Machine learning and human values (1st ed.). W.W. Norton & Company. Churchman, C. W. (1967). Wicked problems. Management Science, 14(4), B-141–B-142. Clark, K., & Baldwin, C. Y. (2001). Modularity after the crash. SSRN eLibrary.https://doi.org/10. 2139/ssrn.270292 Clausewitz, C.V. (1833). Zum Kriege (At War). Kindle. Collins, J. C., & Porras, J. I. (1994). Built to last: Successful habits of visionary companies. HarperBusiness. Davis, E. (2017). Post-truth: Why we have reached peak bullshit and what we can do about it. Little, Brown. de Geus, A. (1997). The living company. Harvard Business School Press. Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. https://doi.org/10.1016/j.destud.2011.07.006 Downes, L., & Nunes, P. (2014). Big bang disruption: Strategy in the age of devastating innovation. Penguin Group. Drucker, P. F. (1973). Management: Tasks, responsibilities, practices. Harper & Row. Foss, N. J., & Michailova, S. (2009). Knowledge governance: Processes and perspectives. Oxford University Press. Goold, M., & Campbell, A. (1987). Strategies and styles: The role of the centre in managing diversified corporations. Basil Blackwell. Greenberg, J. (2010). Managing behavior in organizations (5th ed.). Prentice Hall. Grinblatt, M., & Titman, S. (2002). Financial markets and corporate strategy (2nd ed.). McGrawHill.

Bibliography

283

Hidalgo, C. A. (2015). Why information grows: The evolution of order, from atoms to economies. Basic Books. Huczynski, A. A., & Buchanan, D. A. (2007). Organizational behaviour (6th ed.). Financial TimesPrentice Hall. Imai, K.-I. (2000). Platforms and real options in industrial organization. The Japanese Economic Review, 51(3), 308–333. Ismail, S., Malone, M. S., & van Geest, Y. (2014). Exponential organizations: Why new organization are ten times better, faster, and cheaper than yours. Diversion Books. Jensen, M. C., Murphy, K. J., & Wruck, E. G. (2004). Remuneration: Where we’ve been, how we got to here, what are the problems, and how to fix them. Retrieved from http://ssrn.com/ abstract=561305 Juran, J. M. (1995). Managerial breakthrough (revised ed.). McGraw-Hill. Kämpf, T. (2015). “Ausgebrannte Arbeitswelt” - Wie erleben Beschäftigte neuwe Formen von Belastung in modernen Feldern der Wissensarbeit? Berlin J Soziol, 25, 133–159. https://doi.org/ 10.1007/s11609-015-0278-7 Kanter, R. M. (2009). Supercorp: How vanguard companies create innovation, profits, growth, and social good (1st ed.). Crown Business. Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business School Press. Kaplan, R. S., & Norton, D. P. (2008). The execution premium: Linking strategy to operations for competitive advantage. Harvard Business Press. Khan, G. F. (2017). Social media for government: A practical guide to understanding, implementing, and managing social media tools in the public sphere. In (p. 1 online resource (XXIV, 159 pages 158 illustrations, 111 illustrations in color). doi:https://doi.org/10.1007/978981-10-2942-4 Langeler, G. H. (1992). The vision trap. Harvard Business Review, 70(2), 8. Langlois, R. N. (1999). Modularity in technology, organization, and society. Retrieved from http:// ssrn.com/abstract=204089 Langlois, R. N. (2001). The vanishing hand: The modular revolution in American business. Lindblom, C. E. (1959). The science of “muddling through”. Public Administration Review, 19(2), 79–88. Lorange, P., & Vancil, R. F. (1977). Strategic planning systems. Prentice-Hall. Maccoby, M. (2000). Narcissistic leaders: The incredible pros, the inevitable cons. Harvard Business Review (January–February). Maccoby, M. (2007). Narcissistic leaders: Who succeeds and who fails. Harvard Business School Press. Manzi, J. (2012). Uncontrolled: The surprising payoff of trial-and-error for business, politics, and society (p. xvii, 300 p.). March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 1, 71–87. March, J. G. (2006). Rationality, foolishness, and adaptive intelligence. Strategic Management Journal, 27, 201–214. Marti, M. (2007). Complexity management: Optimizing product architecture of industrial products. (PhD), Universität St. Gallen. Martin, R. L. (2007). The opposable mind: How successful leaders win through integrative thinking. Harvard Business School Press. McGrath, R. G. (1999). Discovery driven planning. Merchant, K. A., & Van der Stede, W. A. (2012). Management control systems: Performance measurement, evaluation and incentives (3rd ed.). Financial Times/Prentice Hall. Migone, A., & Howlett, M. (2016). Charles E. Lindblom, “The science of muddling through”. In M. Lodge, E. C. Page, & S. J. Balla (Eds.), The Oxford handbook of classics in public policy and administration. Oxford University Press.

284

11 Tools Executives Use to Deal with Uncertainty and Complexity

Moldoveanu, M. C., & Martin, R. L. (2008). The future of the MBA: Designing the thinker of the future. Oxford University Press. Mourkogiannis, N. (2006). Purpose: The starting point of great companies. Palgrave. Neilson, G. L., Martin, K. L., & Powers, E. (2008). The secrets to successful strategy execution. Harvard Business Review (June), pp. 61–70. Nieuwenhuijsen, K., Bruinvels, D., & Frings-Dresen, M. (2010). Psychosocial work environment and stress-related disorders, a systematic review. Occup Med (Lond), 60(4), 277–286. https:// doi.org/10.1093/occmed/kqq081 Normann, R. (2001). Reframing business: When the map changes the landscape. Wiley. Osterwalder, A. (2004). The business model ontology: A proposition in a design science approach. (Docteur en Informatique de Gestion). Université de Lausanne. Pentland, A. (2014). Social physics: How good ideas spread-the lessons from a new science. The Penguin Press. Poundstone, W. (2012). Are you smart enough to work at Google?: Trick questions, zen-like riddles, insanely difficult puzzles, and other devious interviewing techniques you need to know to get a job anywhere in the new economy (1st ed.). Little, Brown and Company. Prahalad, C. K., & Bettis, R. A. (1996). Dominant logic. In M. Goold & K. S. Luchs (Eds.), Managing the Multibusiness Company (pp. 398–420). Routledge. Ringland, G. (1998). Scenario planning: Managing for the future. Wiley. Rollinson, D., & Broadfield, A. (2002). Organisational behaviour and analysis: An integrated approach (2nd ed.). Prentice Hall Financial Times. Sako, M. (2003). Modularity and outsourcing. In A. Prencipe, A. Davies, & M. Hobday (Eds.), The business of systems integration. Oxford University Press. Schein, E. H. (1988). Process consultation volume I: Its role in organization development. Addison-Wesley. Schoemaker, P. J. H., & Tetlock, P. E. (2012). Taboo scenarios: How to think about the unthinkable. California Management Review, 542(2), 5–24. Senge, P. M. (1990). Fifth discipline: The art & practice of the learning organization. Double Day. Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467–482. Simon, H. A. (1973). The organization of complex systems. In H. Pattee (Ed.), Hierarchy theory: The challenge of complex systems (pp. 3–27). Georg Braziller. Simons, R. (1995). Levers of control: How managers use innovative control systems to drive strategic renewal. Harvard Business School Press. Simons, R. (2000). Performance measurement & control systems for implementing strategy. Prentice Hall. Simons, R. (2005). Levers of organization design: How managers use accountability systems for greater performance and commitment. Harvard Business School Press. Smit, H. T. J., & Ankum, L. A. (1993). A real options and game-theoretic approach to corporate investment strategy under competition. Financial Management, 22(3), 241–250. Stephenson, N. (1992). Snow crash. Bantam Books. Stiglitz, J. E., & Greenwald, B. C. (2014). Creating a learning society: A new approach to growth, development, and social progress. Columbia University Press. Storper, M., & Venables, A. J. (2004). Buzz: Face-to-face contact and the urban economy. Journal of Economic Geography, 4(4), 351–370. https://doi.org/10.1093/jnlecg/lbh027 Strikwerda, J. (2014). Shared service centers: From cost savings to new ways of value creation and business administration. In T. Bondarouk (Ed.), Shared services as a new organizational form (Vol. 13, p. 15). Emerald. Tainter, J. A. (1988). The collapse of complex societies. Cambridge University Press. Terragnolo, J. (2014). The limits of mathematical models in financial risk management (04/2014). Retrieved from www.ipan.nl

Bibliography

285

van der Heijden, K. (1996). Scenarios: The art of strategic conversation. Wiley. Wiener, N. (1961). Cybernetics or control and communication in the animal and the machine (2nd ed.). The MIT Press. Wiholm, C. (2006). Advanced knowledge work and stress-related symptoms. Zuboff, S., & Maxmin, J. (2002). The support economy: Why corporations are failing individuals and the next episode of capitalism. Viking.

Organization Design and Complexity

12.1

12

Introduction: Is Complexity a Design Principle?

What role or roles do(es) complexity play(s) in designing organization forms? As has been explained in Sect. 5.4, there are different types of complexity to be discerned and there will be different degrees of complexity at different levels and aspects of organization and its contexts. Also, it was explained that a positive relation exists, although most likely concave, between economic growth and complexity. Can complexity be designed, is complexity a design principle, is complexity a design parameter, or is complexity a criterion or a constraint to be observed? Is complexity an issue to be addressed or solved (simplified) by design, or is complexity a solution to achieve sought-after performance?1 The author Peter H. Jones formulates a number of “shared systemic design principles,” in which system thinking and complexity are combined with some elements of design thinking.2 On closer reading of these design principles it turns out that these principles are mainly characteristics of complex systems, whereas design principles, when applied, should result in these characteristics. Complexity itself is not a goal, neither is complexity an administrative instrument to be applied. Complexity is a characteristic of a situation, an economy, a market, a product, a process, an organization. Related to complexity being a characteristic of situations is to be considered the capability of an organization, its managers, its workers, to deal with complexity. This capability to deal with complexity is subject of organization design, organization development, and management development. Complexity theory is a school of thought in the philosophy of science which questions linear reductionistic models, respectively, Newtonian causality, in our case in the economy, and in management and organization theory. In organization design as a process complexity in the first place is a space of thinking and communication.

1 2

Wilson (1975, p. 292). Jones (2014).

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_12

287

288

12 Organization Design and Complexity

Which raises the question what the relation is or should be between design thinking and complexity. Whether this relation can be translated into design principles is to be seen. The purpose of designing organization forms is to achieve an efficient organization for a firm, alliances, networks, etc., efficient as defined in welfare economics. That is, an organization form A is efficient if no alternative organization form A’ exists “that is universally preferred in terms of the goals and preferences of the people involved.”3 This criterion of efficiency is itself a complex phenomenon as different stakeholders involved in an organization project a variety of goals and preference onto the organization. No algorithm-type procedure exists to achieve this welfare type of efficiency. Designing organizations nevertheless aims for this efficiency, but under conditions of bounded knowledgeability and imperfect procedures to consider all the objectives and preferences of all of the stakeholders. A design may allow for some stakeholders to vote with their feet, others to accept suboptimal goals and sub-satisfaction of their preferences. In typical managerial terms, two questions stand out: how to seize the upside of complexity4 and how to curb the downside of complexity? The upside of complexity is to use phenomena like self-coordination, self-organization, emergence, to foster innovation, adaptation, and knowledge creation as contributors to the efficiency of organization, respectively, to maintain efficiency in a dynamic complex environment with recurrent relations constantly changing the causal texture. And thus, to achieve growth and command a competitive position in complex markets. The downside of complexity is that insufficient information, knowledge and understanding of (new) complex situations will result in (extraordinary) risks and costs in business.5 The question is to be raised what should be leading in organization design, should this be the complexity of products and services needed to be competitive, respectively, the complexity of the market in which a firm wants to be competitive, or should this be the (attainable) capability of the firm’s organization to deal with complexity? Following Kees Dorst there is a distinction between engineering-type design thinking, based on deduction and induction, to achieve results, and abductive design thinking aimed at creating value for the stakeholders of a design. In abductive design thinking both the “what” (the organization) and the “how” (working principles) are unknown, e.g. due to emergent new complexities.6 With that design thinking according to Dorst is the parallel of the creation of “the thing” (business, organization) and its “how” (its working principles) in order to create an innovative value for stakeholders. This can be related to complexity thinking, but needs some

3

Milgrom and Roberts (1992, p. 22). “Seizing the upside of complexity” is taken from https://hbr.org/2011/10/learning-to-live-withcomplexi.html. However, this report is based on a survey by IBM and has traditional reductionist management recipes as “solutions” incongruent with deeper insights in complexity theory. 5 Beck (1999). 6 Dorst (2011). 4

Introduction: Is Complexity a Design Principle?

Fig. 12.1 The economic welfare concept of value created by the firm Motta (2004). This shows that shareholder value is an element of a larger system, it is not at the apex of the business. But because shareholder value is simple to measure the psychological availability bias dominates in the capital markets, ignoring the complexity of value creation

289

Consumer’s maximum B willingness-to-pay

Value created

12.1

Consumer surplus

P

Price Firm’s profit

C Firm’s cost

elaboration in view of Tim Brown’s three criteria for a successful design: desirability, viability, and feasibility.7 Design thinking focusses on the ultimate value to be created for the stakeholders involved in an organization design, or impacted by it. What this ultimate value is, often depends on the perspective of those who raise their voice in defining this value. At the level of corporate governance basically four perspectives and subsequent ultimate or final values can be defined: • The liquidation value or shareholder value, typically the perspective of shareholders • The committed value, or the going-concern value of the corporation in the long term, today often expressed as creating sustainable long-term value creation • The stakeholder value, this is the value of the firm for all those affected directly and indirectly by the policy and operations of the firm, and which includes maintaining social capital and social cohesion, that is sustainability with respect to society’s institutions • The value of the firm for society, economic growth, growth of welfare, “The real issue is what corporate behavior will get the most out of society’s limited resources?”8 To which we today would add “in a sustainable way.” In this concept the value created by the firm is the difference between the maximumwillingness-to-pay and the costs of resources (Fig. 12.1). This value is divided by the market price P in the consumer surplus and the firm’s profit. Maximizing blindly the firm’s profit, e.g., through creating monopolies or market power, will push prices up and reduce the consumer surplus and thus welfare in society. 7 8

Brown and Katz (2009). Jensen (2000).

290

12 Organization Design and Complexity

These four types of value are not mutually exclusive, except for the liquidation value, as one of the stakeholders of a firm is society, and committed value may as well contribute to maintaining social capital and social cohesion. In a free society firms are free to choose their final values, but that choice will be made within the context of a political society and thus a political theory.9 That same political society also is a context to be considered in designing an organization. In designing an organization, multiple regulatory requirements have to be factored in the design, but the entrepreneurial agenda of Schumpeter’s Neukombinationen should not be restricted by that. Overriding organization design are economic motives like maximizing utility (but under the constraint of sustainability), a most efficient allocation of resources (which will include the most important resource human capital) and growth of productivity, not only labor productivity as basis of growth of personal incomes, but factor productivity, that is apart from labor productivity, materials productivity, energy productivity, and capital productivity. The value created by a firm is not the only financial dimension to organization design. An organization design at least will have five finance dimensions: 1. Will the design produce a sustainable (positive) business case? 2. Organization design as much is about identifying critical (new) (input, throughput, output) performance parameters 3. The design should include a financial infrastructure (support) to grow the designed business/organization 4. The financial infrastructure by design needs to be capable to facilitate “organized complexity” 5. The design of the management control system, especially the resource allocation process, needs to be capable to support the implementation and growth of the designed business/organization. Organization design at the operational level may be subject to different values, logics, and objectives compared to the level of corporate governance. At this level the value to be created by design is an efficient organization. Efficiency here is not to be understood as cost efficiency, but efficiency as defined in welfare economics. An organization has different relevant outcomes for different parties involved, shareholders, managers, workers, suppliers, customers, communities, society. These outcomes will be different by nature and in general will be incomparable in a mathematical way. And even when, Arrow’s impossibility theorem implies that the ranking by each of the stakeholders of preferred options how to organize cannot be turned into a collective ranking satisfying the ranking of each of the stakeholders.10 In terms of welfare economics an organization form (design) A is said to be efficient if no alternative organization form (design) A’ exists that has more value for at least one of the stakeholders without harming the value of the organization for one or

9

Danley (1994, p. 8). Maskin et al. (2014).

10

12.1

Introduction: Is Complexity a Design Principle?

291

more of the other stakeholders. A private equity investment firm often will invest in a company because they see opportunities for cost savings and thus higher profits, and therefore will see a more efficient organization form, but usually that organization form is detrimental to workers and suppliers, and therefore the organization form preferred by that private equity firm does not satisfy the welfare economic criteria of being an efficient organization. Because of Arrow’s impossibility theorem an efficient organization form is Pareto optimal.11 This should not be taken for a kind of satisficing behavior because a lower limit has to be observed, that is that the organization form needs to result in a sustainable, profitable continuity, preferably over a longer period. This is to say, organization design, to achieve efficient organization forms, needs to be played out in a political context and this context to an extent defines organization forms, as much as that innovative products and innovative organization forms over time changes the political-socio-economic context within which firms operate. That is to say, this context is ever changing, not fully predictable, there is an element of emergence and thus complexity in that context. This evolving context continuously offers new options (market liberalization) as well as new constraints (re-regulation), so basically in every design project this context needs to be looked at with fresh eyes. At firm level traditional design principles for organization used to be specialization of tasks, unity of command, unity of tasks, accountability and control over resources, a linear hierarchy and thus a linear chain of command. Given Schumpeter’s economic principle of Neukombinationen operationally efficiency was to be increased through tasks specialization, centralized knowledge, and through economies of scale and scope. In today’s organization with decentralized organization of knowledge and decentralized new knowledge creation, with a material role of intangible assets different from the material assets in the past, the design principle “process follows proposition” (Fig. 12.2) has replaced Chandler’s dictum “structure follows strategy, . . . but the market is the common denominator.”12 This change reflects that in these processes to develop and deliver the customer value proposition, a space exists in which apart from engineered processes for delivery, dependent on the nature of the proposition, there also may be combinatorial innovation based on new or even unplanned combination of tacit knowledge carried by knowledge workers. That is to say such processes may be spaces of organized complexity. Such processes need to be facilitated, both from a perspective of corporate governance as from the perspective of efficiency. Therefore, a process specially to develop a customer value proposition may be a tool to achieve organized complexity, but complexity itself is not the design principle, but in the design the concept of organized complexity will be used to foster a specific type of innovation processes.

11 12

Milgrom and Roberts (1992, p. 23). Chandler (1962, pp. 382–383).

292

12 Organization Design and Complexity

Delivery and development processes

Customer value proposition

Fig. 12.2 The new design principle, process follows proposition. Note that the processes are an overlay over the old structure which continues in the function of resource configuration and becomes an infrastructure for the processes. This reflects the shift from the logic of structure to the logic of flow, but it also implies that organizations become more complex

The example of “process follows proposition” illustrates that most likely it will not be possible to develop a design for an organization without some a priori building block types of concepts. This need for (conceptual) building blocks is implied by the fact that, certainly larger organizations, consists of uncountable details and at the same time—reliable—simplified model of the firm, respectively, its organization, in order to administer it effectively. No model is capable to comprehend the myriad of details going on in an organization. So, details of, e.g., accounting, processes need to be contained in building blocks or modules with defined interfaces to other modules and controlled by a limited set of parameters. In the 70s, Peter F. Drucker defined building blocks for the organization, like the revenue producing activities, result-contributing activities, and support activities.13 These concepts never gained popularity. The accounting concepts profit center, cost center, investment center, etc., became more popular. But as these were based on information asymmetry between levels of management and with that on the costs of information, the declining costs of information eliminated the distinction between profit centers and costs centers, it now is possible to measure contribution of cost centers to the overall performance of the firm.14 This illustrates that building blocks may be necessary to organize our thoughts and communication in thinking about organization, but over time, e.g. due to technological developments, these building blocks themselves may be subject to “re-design.” Typically for design thinking is a holistic view, but this holistic view is not about a two-dimensional network of causal relations or processes as in, e.g., Osterwalders business canvas, it is about a three-dimensional space, with different types of causal processes in terms of hierarchies and contexts. This relates to the concepts in modern

13 14

Drucker (1974, pp. 529–550). Kaplan (2007).

12.1

Introduction: Is Complexity a Design Principle?

293

systems thinking of upward causality and downward causality, whereas traditional system thinking tends to be restricted to what might be called horizontal causality.15 The concepts of upward causality and downward causality parallel two different approaches for organization design, top-down and bottom-up. Top-down design is from the perspective of corporate governance, the perspective of corporate finance, investments, and corporate strategy. The bottom-up design is that of industrial engineering and operations management, the operational processes to develop and deliver customer value propositions. These two approaches have different logics, each valid in themselves, but need to be reconciled somehow. Also, these two approaches may have strong interferences as in the example of the modularity in design, engineering, and manufacturing which is related directly to the real option theory in corporate finance. Crossing these two directions in organization design is the issue how to organize not only for proper incentives and fair rewards of workers, but especially for work that contributes to workers living a fulfilled live in terms of personal and professional development and being able to be a moral person by having responsibility toward others and the firm, and having discretion over one’s work. Sustainability is not only with respect to natural resources and the institutions of society, but as much with respect to the workers in an organization. Parallel to this dimension is that of regulation of various kinds imposed on the firm. We might this define as a third logic of organization design. The historical forerunner in organization theory and practice of this logic is the socio-technique.16 In this school of organization design it is assumed that, contrary to Frederick W. Taylor’s dictum “the one best way” [to achieve highest labor productivity], there are for a given technology, products, alternative ways to achieve efficiency, especially including ways of working, processes, that answer the need of workers for some discretion in their work, to take initiatives, self-control, that is to respect the worth of workers as human beings with efficacy, a sense of responsibility and a need for personal development and fulfillment through work. However, in view of modern knowledge work, the informationbased organization, the role of digital technology, this old field of socio-technology needs to be reinvented and to an extent is reinvented, e.g. in the concept of information-based empowerment.17 But it needs attention in view of the issue of stagnating productivity and the reported levels of stress and burn-out in work.18 This space of a multidimensional holistic view on organization design may be perceived to complicate organization design, if not making it complex in the eyes of many. What we need to cope with this is not the traditional simplification, but an equivalent of Herbert Simon’s hypothesis of simplicity: “we use the simplicity of process to deal with the complexity of state.”19

15

Capra and Luisi (2014). Emery and Trist (1960). 17 Simons (1995). 18 Pfeffer (2018). 19 Simon (1996). 16

294

12 Organization Design and Complexity

The main “tool” to deal with complexity is abstract thinking. The difference between abstract thinking and reductionist management models is that the latter are operational, in terms of what to do or only suggesting some dimensions of a situation without insight of relations as in, e.g., the 7-S-framework, or even the Balanced Scorecard (although the latter has an underlying economic theory), whereas abstract thinking is conceptually, but typical for business, is temporal and situational, providing a valid and useful insight for that situation and period. This abstract thinking is the reframing in design thinking, but not just by seeing from different perspectives and stakeholder’s interests, but seeing new options and new meanings, due to, e.g., technological developments and developing consumer preferences. So, complexity is not a design principle, the issue is what design principles, based on engineering, technology, economy, psychology, sociology, applied to organization design will result in organization forms that are efficient, including the capability of operating in complex environments and seizing the upside of complexity in its internal operations while curbing the downsides. The core of a firm, and thus its organization, is to develop manufacturing and bring to market products and services that are demanded by society and which contribute to the productive capacity of society.20 This implies that the design of an organization starts with the design of the customer value proposition (aka “solution”). Whether this customer value proposition stands on its own or whether this is part of a larger strategy, whether it is conventional or innovative is to be decided specific per firm. Relevant for organization design is the complexity of the customer value proposition. The design principle is: ‘process follows proposition’, that is, processes to develop, to manufacture, and to deliver the designed proposition are to be based on the proposition, preferably organized as end-to-end processes, across the structure of departments. In the case of a high product complexity, especially in the context of a dynamic complex market, the customer value proposition will be needed to be specified in terms of an architecture with modularity. This architecture and modularity in its turn then is leading for the design of the processes to develop, to produce and to assemble these modules, including planning and coordination processes. This design of the development and delivery processes for a given customer solution, given its Logik der Sache, depends in its effectiveness on a context redesigned to support end-to-end processes. A first design variable is whether the processes are part of a single product firm or, which is more common, are part of a multi-product or multi-business firm. In the case of a single product firms all support functions will be part of the process, apart from some outsourcing, in the case of a multi-product firm, support functions will be shared in a shared service centers or more modern, in a platform. A second design variable is the degree of vertical integration, for which the existing design rules from the field of industrial organization apply, rules that are changing in a dynamic of declining costs of information and communication technology. A third dimension of designing the

20

Landes et al. (2010, p. x).

12.1

Introduction: Is Complexity a Design Principle?

295

proposition-process configuration is defined by the dynamics of the portfolio of customer value positions and subsequent processes. In the case of a high dynamic business or industry, requiring that the portfolio consists of experiments, exploration, exploitation, new products, mature products, products to be phased out, as a consequence of wich these processes will have different degrees of uncertainty and are in different phase of their life cycle, different sets of input, monitoring and output parameters most likely need to be applied, depended on the nature of the proposition-product phase in the life cycle and dependent on its complexity. This sets specific requirements to the finance function; this function now needs to be capable to define and to manage different sets of parameters to avoid that simplicity in financial control destroys the dynamics of the firms needed in a dynamic complex market. A next level of context for the proposition-process configuration, especially in the case of multi-products and multi-business is that of the governance system. The governance system needs to satisfy two main functions. The first is that it needs to add value to each of the separate proposition-process configuration. The second function is that it must ensure that the firm will have available those resources, especially knowledge and financial capital, as needed for its continuity over a longer period, include a license to operate by society. The first required function implies that the corporate level needs to provide insights, knowledge, information, but also services (and a context) to the individual businesses so that these perform and develop better as when standalone organized and incorporated. This context for individual businesses includes exploiting synergies of different kinds, especially economies of scope. Adding value also is about corporate HQ being capable of providing individual businesses better with investment capital compared to the capital market, including protecting a new business in its embryotic phase from short sighted investors. The second role or function, seeing to long-term sustainable value creation, implies the creation and maintenance of an intellectual context nurturing scenario thinking, a peripheral vision and avoiding the dominant logic induced by success becoming a lens inhibiting seeing new and relevant developments as these may be relevant for future success. This sensitivity for weak signals is not only about strategy in the traditional sense. It is also about concepts, methods, routines, paradigms in all of the functions, HR, management control, accounting, IT-management, etc. Traditionally these functions not only were deployed to add value to the business, but also for control purposes. In the shift from a Weberian, bureaucratic type of hierarchy to a hierarchy of knowledge, insights and understanding functional departments shift toward support, and therefore are organized for the most part in supportive platforms, while control by nature shifts toward a cybernetic type of control, emphasizing survival instead of checking whether operations perform according to plan and instructions. This relates to complexity as defined by Herbert Simon, who has explained that one of the requirements to be in-control is the need for loose programming and loose control, to allow for local adaptive behavior to respond to changes in the environment, adaptation to which is necessary for continuity. This is another example that complexity is not a design principle in itself,

296

12 Organization Design and Complexity

but that by applying certain organizational principles, in the case of loose programming the concept of Auftragstaktik over Befehlstaktik, resulting in an organization capable of dealing with dynamic complexity. The main question to be answered in organization design is the relation between complexity and human relation policy in the firm. Much is being written on complexity in leadership, but within the context of organization design the question is to design an organization in which workers, of various levels of education, training, and skills, are capable to deal with complexity in a productive way at there own workbench. A distinction needs to be made between task complexity and the complexity of the context within which that tasks have to be performed. This context is simple in the case of a traditional functional organization of a selfcontained organized business unit or division. This context is complex in, e.g., a hospital with care paths organization across multiple medical departments. Also, the practice of end-to-end processes across departments as needed for a most efficient delivery of the customer value proposition implies a more complex organization. Knowledge workers participating in multiple projects, as implied by the need for interactions to turn tacit knowledge into combinatorial innovation, dependent on the specific situations, may be faced with a higher complexity in terms of roles, identities, objectives, way of working, etc. To be able to deal with such type of complexity requires sufficient professional knowledge and awareness, indirectly implied by the insight of Herbert Simon that much complexity is perceived complexity and is experienced as complexity resulting from insufficient broad and deep knowledge. That is, insufficient to what is needed to understand the—new—complex situation. At the same time, in case of new complex situations at the moment we are faced with such situations it is impossible to know what is sufficient information. With respect to the allocation of resources, especially the hours of knowledge workers, it is the task of the finance department to absorb this complexity in the finance function, this may not be left to the project managers and workers.

12.2

How to Factor Complexity into Organization Design?

To factor complexity into organization design requires to discern, at least, eight levels of thinking and decision-making. The first level is the role of complexity in concepts and theories used in design thinking. These concepts and theories have different degrees of stability and development and in varying ways will be subject to design thinking itself. A second level is that of complexity in the sense of detail variety, of the institutional, legal, and regulatory context within which the firms operate and on which its working to an extent depends. A third level is the knowledge and innovation within the various functions, HR, management control, accounting, IT-governance, etc. These functions themselves need to be redesigned to deal with complexity.

12.3

Kanter’s Concept of the Modern Organization

297

A fourth level is that of the design of the customer value proposition (product complexity) and the processes needed to develop and deliver that proposition (supply chain complexity). Through modularization, open innovation, and outsourcing this results in the complexity of business eco-systems.21 A fifth level is that of the planning and coordination processes, that is the system of management control (strategy execution, organization of information). A sixth level is that of organization design as a process of management development. A seventh level of organization design as a process of managing change. An eighth level is to factor complexity in the task and process of prévoyer, in scenario planning, forecasting, industry analysis, market analysis, and the Pestle— analysis. To factor complexity into these tasks and processes is in the first place to acknowledge the possibility of nonlinearity, unpredictability, but also aspects like multidimensionality and variety, and speed of development. Related to design this can result in choices for industries and/or market in terms of degree of complexity versus choice for more simple market, dependent on the achievable capabilities of the firm. The different types of complexity require different tactics to deal with the specifics of that complexity in design.

12.3

Kanter’s Concept of the Modern Organization

Kanter describes, without reference to the concept of complexity leadership, how a number of large organizations make themselves agile through emphasizing in the organization three elements (Fig. 12.3)22: 1. Guiding System 2. Tools 3. Platform These three elements emphasize the facilitation of knowledge workers, creating favorable conditions for interaction (organized complexity), resource mobilization, etc., in order to create new knowledge and innovations. Reality, of course, is more complex as in Fig. 12.3, there will be a legal organization, there will be some structure (regions, resource configuration, application, or market segments), but the emphasis is on purpose, processes, and people.23 In these three domains of the organization defined by Kanter there is a combination of simplicity and complexity. The simplicity is in the purpose, respectively, mission and hierarchy of values, and depended on the type of firm, also in its 21

Baldwin (2012). Kanter (2009). 23 Purpose, process, and people is taken from Ghoshal and Bartlett (1997). 22

298

12 Organization Design and Complexity

Guidance System: Mission, Values, Business Ethics, Codes of Conduct, Strategy

Tools (projects: enabling workers to take sensible initiatives, to experiment, to develop adaptive behavior

Purpose

Platform: providing access to data, providing fast feedback, codified corporate policies, etc.

Fig. 12.3 A graphic presentation of Kanter’s three defining domains of the modern organization. This overlays the traditional structure of divisions and or business units

strategy. For instance, Netflix acknowledges that growth of its organization induces more complexity in the organization that needs to be mastered by an adequate percentage of talent in its workforce, not by bureaucratic procedures. At the same time Netflix emphasizes simplicity in terms of a limited number of products. The platform absorbs complexity, e.g., through a well-defined information space, the capability of multidimensional reporting commensurate to the complexity of the market, codifying compliance requirements in systems and processes, producing external reports like the annual report, etc. The domain of tools, that is the domain of the knowledge workers, is the organized complexity, including processes of selforganization, combinatorial innovation, emergence, etc. Underlying the concept in Fig. 8.1 will be traditional structures like regions, legal organizations, offices, resource configurations of specialized knowledge workers, and hardware facilities. But, different from the traditional organization form those structures are not dominant in achieving the performance of the firm, neither these are the first instrument of business administration any more. With that the concept in Fig. 8.1 is how to create organized complexity as needed for innovation, exploration, in which individual workers can pursue their personal objectives while at the same time observing the required performance of the firm and the identity of the firm and thus being an acknowledged member of a group.

12.4

Complexity and the Design of Functions

12.4.1 General In designing the functions within the organization of the firm there are four “directions” of design:

12.4

Complexity and the Design of Functions

299

1. Alignment with the strategy, the processes to deliver the customer value proposition 2. Preparedness, to design capabilities and capacities for planned and foreseeable strategies, objectives, and developments 3. Discretion in how to achieve tasks 4. Adaptability, the capability, authority, and discretion to respond to contextual changes relevant for the function in terms of new options, challenges, and threats, to learn, develop new knowledge and achieve dynamic sustainability, not only for the function itself, but for the ultimate performance of the firm.

12.4.2 The Design of the Customer Value Proposition and Operational Processes The question might be asked whether in the design of the organization a specific function is core to the design or should be started with. In the end there will always be iterations between the various functions due to constraints, but the art of design thinking, of course, is to circumvent such constraints or lessen their role by reframing. The legitimacy of the firm exits meeting society’s demand for goods and services, in an efficient (no waste, no harm) and a sustainable way.24 Therefore, we start with the customer value processes and the processes needed to deliver these propositions. In terms of functions this is about operations management, which to a large extent is based for its execution on the field of industrial engineering, and specific for the customer value proposition also uses marketing, especially identifying consumer needs. Various (design) methods exist for designing a customer value proposition.25 Designing a customer value proposition is the core of entrepreneurship and with that it is an innovative field, thriving on technological innovations as well social innovation, but not less so thriving on imagination, especially in the USA being fed by the science fiction and cyberpunk literature. Customer value propositions in general will be simple for users, except perhaps for financial services, but the systems or products to deliver the customer value proposition may have extreme technical complexity in terms of number of parts, modules, and architecture, as e.g. in the case of smart phones. With that, through outsourcing in global supply chains, the processes to develop, manufacture, and deliver such products also can be extreme complex. This complexity is mastered by concepts like architecture, modularization, and the technique of bill of material, and by information and Internet-based communication, which may include highdefinition video conferencing, for coordination, transfer of knowledge and ultimately coordination.

24 25

(Cadbury, 1995). Osterwalder et al. (2014), Kim and Mauborgne (2005), Brown and Katz (2009).

300

12 Organization Design and Complexity

Uncertainty in view of dynamic markets to an extent can be mastered by modularity in design, innovation, development and in production, which may include concepts like open innovation and open business models.26 The customer value proposition—process configuration needs an infrastructure or support functions, legal, HR, management control, treasury, IT-governance, logistics, etc., for its functioning. These are dealt with in following sections, for an overview the business canvas model of Osterwalder will suffice.

12.4.3 The Governance System Designing the governance system is part of the constitutional task of the executive board. To operate the customer value proposition-process configuration, that business needs to be incorporated in a legal persona, a corporation, a limited company, a GmbH, etc., dependent on the jurisdiction. This is needed to separate private capital from the corporate capital and to serve as a nexus of contract in the case of multiple shareholders. The design of a corporate governance system is to be played out within the system of corporate governance as defined by the corporate law of the host jurisdiction. There is some leeway in making choices within the context of legally defined systems of corporate governance, especially since corporate governance codes suggesting specific norms where the law defines open norms, also leave it to companies to “comply or explain.” The question to be asked is whether in the design of a corporate governance system the role of complexity needs to be factored in.

12.4.3.1 The Design of the System of Corporate Governance and Complexity The system of corporate governance as codified in, depending on, specific national jurisdiction, civil codes, company laws, etc., is still based on the concept of the corporation as this was created by law at the end of the nineteenth century, based on the then existing basic conditions in the economy and the then existing need for capital cumulation to finance the high investments in the then dominant technologies. In that process of codifications some simplifications were introduced, which would work well in the following 75 years or so, ending around 1980. One of the simplifications was that the corporate law would only acknowledge tangible assets (including tradable intellectual property rights), a second was the separation of labor and capital, and that labor would not be part of the corporation but a purchased commodity. On this in turn would be based a system of management accounting and the annual report. That is to say, by foundations the system of corporate governance is not prepared for the complexities implied by non-codifiable, personal knowledge, that value is 26

Clark and Baldwin (2002), Chesbrough et al. (2006).

12.4

Complexity and the Design of Functions

301

created in an ecology of actors, including infrastructure and legal systems, is not prepared for emergence due to endogenous knowledge creation, and emergence due to higher educated workforce. Alike, management accounting is not prepared for that. Of course, entrepreneurs sensed correctly what was needed, accounting rules or not, and in many cases bot the law and accounting rules either due to their imperfection or by bypassing them allowed the emergence of what would be called the organic organization. Much of the bypassing is possible through the low costs of information, that is having both compliance and organized complexity. But this is only for a few firms, most firms are wrestling with a tension between the requirement of the modern economy and compliance requirements reflecting the twentieth century. Codes for corporate governance tend to ignore the requirements and the new options in the modern economy and reflect therefore the underlying principles of the modernism, in which complexity was a marginal theme in the management books.

12.4.3.2 The Complexity of Corporate Governance Systems A first question to be asked is whether the system of corporate governance as defined by law, e.g. the Company Act in the UK, Delaware corporate law in the USA, Book 2 of the Dutch Civil Code, itself has characteristics of complexity. Some publications characterize the system of corporate governance as complex, in the sense of complicated, due to a system of stakeholders, actual powers of stakeholders, complicated legal and financial structures and subsequently a complicated system of powers, decision rights, etc. The corporate governance defined by law in most jurisdictions is defined in open norms (duty of care, duty of loyalty, duty of good faith), which are elaborated into specific norms or best practices by national codes for corporate governance. The latter are revised regularly, which suggests a non-state-initiated capability of adapting the specific norms for corporate governance to changes in the economy and to changes in political norms in society, e.g. with respect to long-term sustainable value creation, or rebalancing the interests of shareholders versus stakeholders. Basically, the systems of corporate governance are not complex, be it that the corporate governance codes in specifying specific norms in a faster response as legislation is able to do, suggest a capability of complexity in the sense of adaptability. The question however, is whether the systems of corporate governance allow for adequate and timely complexity in business. 12.4.3.3 Epistemological Complexity in Corporate Governance The national legislation and codes for corporate governance are written and rewritten based on concepts of the firm as this came into existence at the end of the nineteenth century and which is still the basis of corporate law. That is, the concept of the firm underlying systems of corporate governance is the firm based on tangible assets, workers with a low level of training, in which the system of value creation coincides with the technical boundaries of the firm, a separation of capital and labor in which labor is not part of the firm as a nexus of contracts but a purchased commodity and in which labor was denied the economic power to turn their personal skills and tacit

302

12 Organization Design and Complexity

knowledge into economic bargaining power. This system has served our economies very well in terms of growth of welfare for the major part of the twentieth century. So much so that many find it difficult to discuss it in terms of relevance for the economic basic conditions in the twenty-first century, although many expresses something is wrong, especially with respect to the factor labor. Within the system of corporate factor, a dominant lens through which to view the firm is the lens of corporate finance, respectively, the lens of return on investments for the shareholders. This lens has proven to be very necessary in the 70s and the 80s to correct the perverse behavior of especially American CEOs due to their then stakeholder orientation and letting themselves being stuck in the cashflow trap. However, the accompanying increase of the power of the finance function following the growing role of the capital market, with the emergence of the CFO as a powerful executive in the board,27 in many cases resulted in neglecting the inner workings of the firm in terms of operations management and in terms of industrial engineering. The in itself positive role of the capital market in restructuring the US economy in the eighties, however reinforced the concept of the firm as a black box input-output model, for which it was assumed that pressure for financial performance would result in higher return on investments. Initially this worked simply because it corrected sloppy management.28 But financial performance management deemphasized the role of operations management in growing the productivity by re-engineering the operations, resulting in a slump in the growth of productivity. Re-engineering to reduce costs was emphasized by the emergence of the computer, especially the ERP-software around 1990, until the increase in differentiation in business models (an increase of a type of complexity in the economy) could not be dealt with by the standard ERP-software which therefore became restricted to accounting functions.29 Cost reduction may sometimes increase shareholder value, it is not creating value in an intrinsic way. The digital technology, with the exponentially declining costs of data, was seen by a number of entrepreneurs to offer opportunities to reconceptualize markets, consumer behavior, and also organization forms. The capital market appreciated these new models for their promise of a customer lock-in and therefore a high certainty of future cashflows, the bases of the valuation of companies since 1990, when intangible assets were acknowledged. This promise of higher and more certain future returns however conflicted with how to judge the quality of the executives of a company. In the case the firm is a portfolio of self-contained divisions (investment projects), the quality of the executives could simply be judged by the break-up value of the firm, whereas this is far more difficult in the case of the integrated firm. The integrated firm has the promise of a higher return on especially intangible assets by exploiting in a planned way multiple types of synergies, but its organization, at least looked at through the lens of the traditional H-form, is far more complex. Thus, a tension grew between the capital market and the executives of

27

Zorn (2004). Jensen (1993), Jensen (1993), Jensen (2000). 29 Davenport (1998, pp. 121–131). 28

12.4

Complexity and the Design of Functions

303

firms with respect to which concept to report the performance of the firm: the simple H-form, transparant for the capital market with its portfolio of independent investments and an additive profit model, or the integrated firm with its multiplier profit model, and thus a higher promise of future profits, but less transparent for the capital market. Meanwhile in various corporate governance codes the emphasis is still on the reliability of the annual report and the management accounting system needed for that, thus ignoring the far more important and much more complex management information and the more complex management control systems needed for that.

12.4.3.4 Complexity and Risk Management In these corporate governance codes there is an emphasis on risk management. Audit committees are asked to assess the system of risk management, in the company’s organizations risk managers became appointed, in some cases chief risk officers (CRO) are appointed in the board. There is a relation between complexity and risk management, the common denominator being knowledge and understanding. Risk is based on the mathematical concept of probability. Probability is a measure of our ignorance. The idea of risk as used in risk management is a response of our society to changes in the economy and the society at large. That is, an economy and society that is more complex in terms of causal relations, dynamics in relations, specialized activities, business model innovation and is more complex due to the reduction of complexity reducing institutions by liberalization, requires more knowledge and information to cope with this higher complexity. Where society allowed the economy and society to become more complex to achieve higher economic growth, it insufficiently invested in a commensurate increase in knowledge and understanding as needed to cope with the new complexity, thus opening the sluices for unintended consequences. Even more, the risks resulting from this lack of knowledge and understanding, especially the lack of holistic, non-reductionist systems understanding, are tried to be controlled with measures and techniques based on insights and norms dating back to the period of modernism and with that are themselves a source of risks.30 As a consequence “risk represents the perceptual and cognitive schema in accordance with which a society mobilizes itself when confronted with the openness, uncertainties and obstructions of a self-created future and is no longer defined by religion, tradition or the superior power of nature but has even lost is faith in the redemptive power of utopias. . . . The world is not as it is, rather its existence and its future depend on decisions, decisions which play off positive and negative aspects against one another, which connect progress and decline and which, like all things human, are bearers of error, ignorance, hubris, the promise of control and ultimately, even the seed of possible self-destruction.”31 This quote by Ulrich Beck relates to the

30 31

Beck (1999), Beck (2009). Beck (2009, p. 4).

304

12 Organization Design and Complexity

statement of Herbert Simon that most complexity is perceived complexity as the result of insufficient broad knowledge and lack of systems understanding. Corporate governance codes however do not heed the insights of Herbert Simon nor Ulrich Beck, but are indoctrinated by the modernist audit approach of risk, in which the complex nature of the economy is denied. The growing complexity in the economy, in business models, in business ecologies, cannot be mastered by risk management. The concept of risk is based on the mathematical concept of probability of an event, that is the quotient of the number of desirable outcomes over the number of possible outcomes, and with that assumes a closed well-structured situation. Whereas in complexity through emergence the nature is that of uncertainty, in which by definition no calculus can be applied. In the system of corporate governance there should be a proper monitoring on whether the executive board uses effectively the tools mentioned in Chap. 12 to deal with uncertainty. These tools primarily apply to the design of the customer value proposition, the design of processes, the organization of information, investment in human capital to increase knowledge, understanding, and insights. Also, with these tools uncertainties, e.g. through scenario thinking, can be factored into the strategy and in the system of strategy execution. Within that context, at the level of processes, the level of process engineering data-based mathematical risk management is to be applied, using sensors, data, and algorithms. This implies that many of the so-called best practices mentioned in codes for corporate governance or publications on corporate governance are either irrelevant or sometimes even contra productive when it is about creating and maintaining efficient organizations which are so by having an appropriate level of complexity in relation to the complexity of their environment.

12.4.3.5 Complexity in Supervision An issue in the process of supervision is the information asymmetry between the members of the supervisory board and the members of the executive board. This asymmetry is emphasized in the agency theory. The agency theory however applies only to the US jurisdiction because in that jurisdiction, especially the Delaware corporate law, the shareholders are co-owners of the corporation and are therefore seen as principals of the CEO as their agent. This is not so in continental Europe, in the various jurisdictions the shareholders are only the owners of their shares in a company, providing them certain rights, but the corporation has to be run by the executives in the interest of the corporation; jurisprudence implies that it is explicitly forbidden to run the corporation in the interests of the shareholders only. The executive board has to serve the interests of the corporation as stated in a ruling by the Dutch Supreme Court, even when this conflicts with the interests of the shareholder. As a consequence, neither the general shareholders meeting nor the supervisory board can set a strategy nor objectives to the executive board, as this board is autonomous. This autonomy does not deny that the supervisory board needs to ratify the strategy and is tasked to oversee the strategy execution. For this not only information in the sense of data is needed, much more is needed, especially because both the ratification of the proposed strategy and the monitoring of the execution of

12.4

Complexity and the Design of Functions

305

the strategy need to be an ex ante judgement, knowledge is needed with respect to— new—concepts for strategy, organization, execution, operations management, developments at industry-level, technology, etc. That is to which standards, to which norms, a supervisory board can or should judge a proposed strategy and the proposed organization of the strategy execution? In selection members for executive boards often the emphasis is placed on experience, preferably as a CEO or CFO, that is having intimate knowledge of business operations or the administration of an institution based on personal experience. This at best is contemporary and certainly will be based on past experiences and situations. But the aimed for objectives and performances of the firm will be in the future, and the future has no audit trail. Ideally a supervisory board judges a strategy, a system for strategy execution against causal patterns as these will be valid or develop in the period of strategy execution. Some of these may be aimed for, as part of the plan for strategy execution, some of these are subject of planning and design, but as much there will be through processes of serendipity, errors, recombination an emergence of new, unforeseen causalities. This is acknowledged in, e.g., McGrath’s concept of discovery-driven planning. But it is also the idea of organized complexity, to allow, within certain parameters of strategy and available capital, the emergence of new knowledge, not only for new products, but also for new causal patterns. Even at a lower level, more practically and in terms of proven concepts, we notice an information problem. Many firms, for reasons of the working of the modern knowledge economy, need to work through pan-divisional projects and processes, as we have seen before. Members of supervisory boards, confronted with such plans for strategy execution, both sense the need for such type of organizations and express a feeling that it won’t work, but cannot precisely explain why not, and thus seek this explanation in terms of people, culture and the proposed organization being too complicated. They are right in this opinion, when viewed through the lens of the twentieth century unit-organization with Bower’s bottom-up resource allocation process as system for strategy execution. They are wrong, viewed through the lens of the multidimensional organization, with a resource allocation process according to Kaplan & Norton. That is a supervisory board needs a conceptual complexity anticipating what the company will need in order to be successful, so a proposed plan, especially the plan for strategy execution can be judged on what is proposed, but on what should have been proposed implied by what the modern economy requires and what is possible in view of the modern technology. That is, between the supervisory board and the executive board there is a variant of Ashby’s Law of Requisite Variety at play; a proper future-oriented supervision requires a higher variety, a higher conceptual complexity on the part of the members of the supervisory board compared to that of the executive board. Often the intuition of the entrepreneurial executive board with respect to market opportunities is good, but partly due to conservatism and a too strong focus on risk avoidance in the finance function, there is a lack of knowledge how to organize a working organization with the required new complexity.

306

12 Organization Design and Complexity

12.4.3.6 Complexity and the To Be Ratified Strategy A first task of executive board is the ratification of the strategy (decision initiatives) as generated by the executive board.32 The executive board is tasked to judge the initiated strategy against the mission and the values of the corporation, to judge the strategy on plausibility in the context of industries, markets, technology, organization, that is on assumptions with respect to facts and causal patters in order to have assurance that the proposed resource utilization will result in acceptable financial and non-financial performance without undue negative side effects. The issue for supervisory boards is how to judge whether an executive board demonstrates an acknowledgement of the (changing) complexity in its industries and markets and whether this is adequately factored into the proposed strategy. A sign of denying complexity would be if a strategy is a mere (linear) extrapolation of the existing markets, using a classical portfolio theory, etc. Scenario thinking, especially when used to transform worldviews, would be a sign of acknowledging the unthinkable resulting from emergence, that is the unpredictable. That in itself is not yet insufficient to acknowledge complexity. Closer to this comes, this might be part of scenario thinking, when it is acknowledged that new players will come to the industry, like the CEO Levi of Publicis foresaw the coming of Google in the advertisement market and that the digital technology would change the idea of consumer, the idea of advertisement, the idea of marketing. This relates to the more recent insight that an essential characteristic of a good CEO is the capability to reconceptualize (new) situations, away from the conventional concepts, how successful these may have been or even more, still are. This goes back to an older insight among CEOs how to deal with emerging new complexities, simplicity beyond complexity, one first needs to understand new, emerging complexities, by wading into these before defining a new simplicity. To judge whether a CEO, respectively, a supervisory board deals in a sound way with new complexities in their industries and business, therefore not so much is to be judged by the initiated strategy, there might be a sound simplification of a new complexity, but by the mode of thinking and thus explanation of the CEO of the strategy. The criterium is whether a strategy, which in itself may be simple, answers the criterium of reconceptualization or whether it is stuck in the old business logic. Perhaps even more important is how an initiated strategy deals with uncertainty which is inherent to dynamic complexity. As elaborate before, partly this is through modularity in design of propositions, technology, processes, etc. 12.4.3.7 Complexity and Strategy Execution Critical in the success of a strategy is the resource allocation process by which the strategy is translated in task control, that is the definition and funding of projects, strategic themes, allocation of resources, operational objectives, the organization of required information, etc. In the resource allocation process two issues at least need to be addressed, related to complexity. The first is that the nature of intangible assets 32

Fama and Jensen (1998) (org. 1983).

12.4

Complexity and the Design of Functions

307

implies the need of a more complex combination of resources to have maximum return on those assets compared to the linear unit-organization. The second issue is that due to the higher dynamic complexity in industries and markets a higher degree of uncertainty needs to be addressed. The first issue, that of a more dynamic and more complex recombination of knowledge resources carried by knowledge workers is to be achieved by management control system having at least two planning dimensions compared to the one planning dimension of the unit-organization using Bower’s one-dimensional bottom-up resource allocation process. The multidimensional management control system by Kaplan & Norton is to be used, but is perceived by those familiar only with the unit-organization, as being (too) complex. But this is a complexity due to a lack of knowledge and understanding, of trying to see new situations through the lens of obsolete products.

12.4.3.8 The Complexity of the Organization As an aspect of judging the strategy, it is the task of the supervisory board to assess whether the capability of the organization to deal with complexity is commensurate with the complexity of the target market and the complexity of the knowledge and technology to be exploited. The requirement to be in-control is implied by Ashby’s Law of Requisite Variety. To develop such a capability requires an (intellectual) effort, in the first place by the executives themselves. That in itself may for some executives be a reason to refrain from choosing for a more complex business model and organization to execute it, another restriction may be the level of education of the workforce. If executives are reluctant to increase the complexity of their organization, or lack the capability to do so, and fail to master the new levels of complexity, they consequently need to restrict the firm to simpler markets and technologies, implying less growth and less market power.33 But complexity not only has an upside, it also has its downsides. There may be an objective complexity as implied by the market and as implied by a maximum return on (intangible) assets, but if the knowledge, understanding, and the capabilities of executives, managers, and (a sufficient part of) the workers is insufficient to this implied complexity, and related to that the systems of the organization do not process information of a sufficient complexity, this mismatch will be a source of risks. Hence, the market power strategy by large firms, with abundant resources, who define the structure of the industry and define markets as to reduce uncertainty. But in doing so create a new type of entry barrier: a difficult to imitate complexity of their operations. 12.4.3.9 Complexity, Information, and Supervision Although in general investments in information technology contribute to the performance of firms, cases exist in which a wrong management of investments in information systems took firms out-of-control.34 Also, cases exist in which high

33 34

Ansoff (1984, p. 28). Brynjolfsson and Hitt (2003), Davenport (1998).

308

12 Organization Design and Complexity

Fig. 12.4 The four types of (main) applications of IT, relating to agility versus stability and the organization of data Shpilberg et al. (2007), Kaplan and Norton (2004, p. 251), Ismail et al. (2014). This replaces the business-IT-alignment paradigm of the early 90s, which turned out to be a trap

investments in IT contributed to successful transformations of the firm.35 As with any investment, there are risks with investments in IT, to be managed by the executive board and to be monitored by the supervisory board. A first reflex to proposals for investments in IT-systems is to judge the risks of the project at the level of project management, whether the project will be kept within the budget, risks in developing software, bugs in the software, etc. This certainly is important but does not constitute the core of the problem. The first issue for the supervisory board is to assess the business case of the proposed project, whether the proposed investments will result in an acceptable return at acceptable risks. Because a number of IT-systems will have the nature of an infrastructure, as opposed to a discrete operational system compared by traditional equipment, it may be difficult to make a cost-benefit calculation for reasons of delineating the system and choosing the time horizon, effects may be in the longer term. Nevertheless, there must be a sound business reason, operational, strategic why to invest in an IT-system as an input for setting the budget. A second issue in judging a proposal for the investment in an IT-project, related to the first issue, is that of defining the functionality of the system, what does it need to perform in terms of the business of the firm. This functionality must be defined by a business manager, not by an IT-manager or as worse, an information manager. This implies that it is wrong if a proposed project includes the development of the functionality of the system. The definition of the functionality is not straightforward, as this needs to be future oriented, and thus is subject to uncertainty. Until about the end of the 90s, a design rule for the functionality of an IT-system was the Business-IT-Alignment Paradigm. This became the Business-ITalignment trap, because business models are being innovated at a faster pace as the technical life cycle of IT-systems; an IT-system needs to be able to execute new business models, not just the existing one. Apart from the fact that a development since the coinage of the Business-IT-Alignment paradigm is that information is not just support in the business system, information is an input in the production function, it is an output and it is in terms of the modern economy, a capital good. This implies that the functionality of an IT-system needs to be defined based on the

35

E.g. IBM, Nestlé, Publicis.

12.4

Complexity and the Design of Functions

309

nature of the strategy of the firm, and what is implied by that strategy what information is needed, that is, no longer from the perspective of individual users in the organization. This has implications for the IT-governance in terms of decision rights to define the functionality of systems and deciding investment budgets for IT-systems. Facing the future has also implications for the architecture of an IT-systems, or perhaps better of the organization of data in the organization of the firm. Instead of the Business-IT-paradigm the four-layer IT-application model of the MIT (Fig. 12.4) needs to be applied or a development of it, by which basic data is organized disembedded from the structure of the organization, allowing for multiple uses thus enhancing its value, and allowing for adaptation efficiency, transformation and, in specific cases, even for exponential growth.36 Within that context issue for monitoring are semantic data standardization, modularity in software and in applications, interoperability, depended on the complexity of the product, the use of the concept of the bill of material, etc. Overriding all this is the question whether the CEO, respectively, the executive board demonstrates “information leadership” as, e.g., in the case of IBM where Louis Gerstner and his senior management team acknowledged that IT was key to serving customers, and that in order to offer integrated solutions, IBM needed one worldwide transaction database instead of transaction databases per country. But even important, does the executive board understand that complex products (airplanes, smartphones) in order to be produced these in complex supply chains efficiently and reliable, these first need to be understood as information objects and secondary as hardware objects?37 In addition to this is an important question whether the executive board defines information technology, digital technology as a resource to create new markets, to create new customers, new products, to generate new and growing revenues, or only as a technology to reduce costs? If the first is lacking very likely the increasing complexity of business is managed by negative simplification, resulting in marginal performance.

12.4.3.10 Complexity and To Be in-Control In corporate governance systems there tends to be an emphasis on the reliability of the annual report and on risk management. Related to that the supervisory board is expected to judge whether the company is in-control. The COSO-framework to which especially auditors refer to is a typical example of trying to apply concepts and tools from the twentieth century to situations of the twenty-first century. The relation between complexity and to be in-control was defined by Herbert Simon that in order to be in-control a type of complexity is needed in the organization, that through loose programming and loose control, there is in the organization sufficient discretion and knowledge that at a local, decentralized level workers can respond to new demands from the market, to new opportunities, which are shared throughout the organization (a learning organization) in order that the organization adapts itself to changes in its 36 37

Kaplan and Norton (2004), Ismail et al. (2014). Nolan (2012).

310

12 Organization Design and Complexity

(PESTLE-) context in order to survive. That is, the supervisory board needs to judge whether risk management is achieved through a positive approach, more knowledge, better information, alike processes, or whether risk management is achieved through tight control with the risk that this type of risk management destroys the capability of adaptation, a prerequisite for survival in changing contexts.38 To be in-control in terms of the RBV of the firm requires that a firm is capable of acquiring, controlling, and exploiting those resources, of whatever nature, as required for a productive continuity of the firm in a state of defining its own course. This is to be in-control at a strategic level. Due to the changes in technology and in markets this level of being in-control may require a sufficient degree of integrative complexity with CEOs and of conceptual complexity in order to have sufficient absorptive capacity for new concepts and models in order to see what new relevant resources might be and its implications for management and organizations. Underlying this is the issue of integrative complexity related to turning material information into eidetic information. A distinction needs to be made between interpreting data on the basis of an existing business model and sensemaking, reconceptualization of data, of situations by defining new concepts, management models, and business models. At the level of resource allocation a capability needs to exist to deal with a variety of different projects, see Sect. 12.4. At the operations level, to be in-control requires a level of complexity, including speed of decision-making and implementation that is at least one degree more complex as is the market and one step faster as is the market (Ashby’s Law of Requisite Variety), apart from the required type of complexity as defined by Herbert Simon.

12.4.3.11 Organized Complexity and Culture To be successful in dynamic complex context, as explained in Chap. 12, requires an organic organization as opposed to a mechanic organization. The supervisory board, e.g., in the Dutch corporate governance code is expected to judge whether the culture contributes to long-term (sustainable) value creation. Culture itself has been attributed many meanings, creating a fog impairing a clear view on which administrative instruments to apply and how to achieve an organization capable of dealing with complexity. An alike problem has creeped into the idea of leadership. In a situation of dynamic complexity, the traditional transformational leadership won’t do any more, Herbert Simon’s concept of complex organization implies that sensemaking, reconceptualizing, is a decentralized distributed task. But this task needs to be facilitated by management development, appropriate styles of decisionmaking, a safe psychological climate, an understanding of the business, of the organization, access to information, tools for making analysis and for making calculations, feedback information, that workers are facilitated and stimulated to acquire new knowledge and insights, not only data but especially new concepts from outside and have discretion to apply this, commensurate decision rights, and an

38

Simons (2005).

12.4

Complexity and the Design of Functions

311

appropriate performance measurement system and an appropriate reward system. This in short is complexity leadership.39

12.4.3.12 Complexity in the Audit Committee In the audit committee of the supervisory board the object of supervision is the reliability of the annual report, risks management, and the management accounting information systems supporting this. In many publications the audit committee is defined through the lens of the external auditor and the European Commission has stated that at least one of the members of the audit committee has sufficient financial expertise, defined as expertise in management accounting and the annual report. This expertise is insufficient for judging especially ex ante whether the management of a company will be in control in a dynamic complex environment. The required expertise and object of supervision of the audit committee needs to be redefined commensurate with the role of complexity in the economy and its impact on what it means and what it requires to be in-control. 12.4.3.13 Concluding on the Role of Complexity in the Design of the Governance System The system of corporate governance itself is in all jurisdictions still based on the concept of the enterprise as this came into being at the end of the nineteenth century, of which the main characteristic that it is based on tangible assets and on arm’s length transactions. Implicitly it is based on a modernistic, mechanistic, bureaucratic type of control, whereas it today should be based on a cybernetic type of control.

12.4.4 Complexity and the (Strategic) Guidance System The idea of guidance system (Fig. 12.3) originates from Rosabeth Moss Kanter.40 The concept is core to the idea of organized complexity, by delineating the scope of activities of a corporation, its finance framework, defining a number of strategic themes, but given these creating teams and facilitating these to achieve strategic objectives through bottom-up initiatives, cross-business interaction, and self-organization. In a more operational sense the guidance system has been elaborated in the concept of the office of strategy management by Kaplan & Norton.41 A main task of the guidance system is to translate the overall strategy in a limited set of strategic themes, to set the business case of each of these and to decide their funding, this in combination with targets and criteria for the ongoing operations. Kaplan & Norton concept of the office of strategy management is still somewhat command & control type, whereas dealing with complexity and agility requires a different approach from that of the traditional staff department. Corporate

39

Uhl-Bien and Marion (2008), Uhl-Bien et al. (2007). Kanter (2001). 41 Kaplan and Norton (2005). 40

312

12 Organization Design and Complexity

headquarters not only needs to provide strategic guidance, but as well needs to protect the integrity of the firm in terms of safeguarding its values and assets and to ensure compliance and with that the integrity of the organization. From a managerial perspective strategic guidance not only is setting a strategic direction, it is as much about creating conditions in the organization and facilitating knowledge workers in achieving set strategic goals, including the required capabilities development (dynamic capabilities) aimed for long-term value creation. That is, the defined strategic direction, including assumptions on developments at industry level, needs to be translated into the support system, the platform, in terms of preparedness with respect to human capital and information capital. Corporate policies (including mission and values) and compliance need as much as possible to be codified in systems, processes, and procedures, in order that these will be factored in the calculations and decisions by the teams working on the strategic themes. A critical issue is defining the nature of the guidance system as a department. Will this be an extension of the department for corporate strategy or will it be an extension of the department of corporate control/accounting? Strategy execution is not part of strategic management but of the function of management control. In view of complexity within this function the type of control needs to be considered carefully to allow for complexity and agility. There will be two main issues. The first is the choice of parameters to monitor the teams working on the strategic themes. This choice is elaborated below; the finance function as part of the support function needs to be capable to deal with a variety of changing parameters over the life cycle of a project. The choice of these parameters will be higher-level decision based on strategic considerations. A second issue is the style in which the interactive control will be conducted, in terms of a safe psychological climate. Especially that those involved feel free and safe to table unforeseen issues as well as new options, that there will be continuous learning processes, that projects will develop on the basis of discovery-driven planning and progress and phased funding. This is what Herbert Simon defined as loose control, while maintaining the integrity and the financial hygiene of the firm. This implies that risk management need not to be separated from this process, nor to be organized in the support function, but must be part of the interactive control process, all those involved should feel safe to point out uncertainties, to propose scenario’s, to avoid fixation on plans blurring the view on ultimate goals. This of course is a balancing act. Some will counteract that this basically always has been the case in companies. The difference with the old routines is whether now uncertainties, unforeseen events, and frictions are viewed as deviations of a set course, a decided plan and budget, or whether these are seen as the nature of a dynamic terrain through which one has to discover a road to set objectives. This sets requirements to the persons conducting the monitoring process, in terms of cognitive base, exploring attitudes, holistic thinking, mastering the art of positive inquiry, keeping an eye on ultimate objectives, tolerance, and a sense of humor.

12.4

Complexity and the Design of Functions

313

12.4.5 Complexity and the Design of the Organization of Information Zuboff and Maxmin observed that in contrast to tools like simplification, standardization, and hierarchal oversight, with the digital technology: “Here for the first time is a technology that preserves and coordinates complexity.”42 This observation goes back to the physicists Ludwig E. Boltzmann (1844–1906) who understood that an increase in information reduces entropy (disorder) while preserving (necessary) complexity. This can be seen, e.g., in the case of complex products, airplanes, automobiles, smartphones, in which the production, the assembly, the supply chain, product modifications, etc., are effectively achieved through a combination of the bill of material, a document in which the product is specified by components, architecture, and information on time-phased production and assembly, and ERP-computer systems handling all this information. In terms of organization design information is both pivotal and complex in itself. The reason for the latter is that information is being used in all functions in the organization, in different ways. This raises the question what role information plays in organization theory, respectively, in the development of organization forms, if even without the benefit of theory. An overview of the role of information technology (IT), which is not the same as information, can be found in an article by Zammuto et al.43 Zammuto et al. translate IT in a number of new “affordances,” new options for organizing. The new options identified mainly are with respect to (self-)coordination and decision support. These new options to organization for a part are explainable because implicitly underlying the concept of the unit-organization as introduced by DuPont in 1918, that is self-contained organized business units or division, based on a product-market combination, were the then high costs of information, high costs of communication, a slow speed of communication and restricted capacity of communication channels.44 Basic conditions which now hundred years later have been reversed. This has multiple consequences like the feasibility of shared service centers and end-to-end processes across divisions, diminishing the role of the traditional Weberian hierarchy in coordination, shifting imposed coordination into decentralized self-coordination resulting in more efficient processes and more efficient coordination.45 Prior to the deployment of IT was the digital mechanization of transaction recording and other accounting information, including the generation of financial reports. Initially computerized accounting information systems were based on existing structures in the organization and the related accountability. Only later this would change. In the 50s, resulting from WWII experiences with the applied mathematics of operations research, computers also were deployed for decision support, be it by nature limited to structured decisions

42

Zuboff and Maxmin (2002, p. 292). Zammuto et al. (2007). 44 Stinchcombe (1990). 45 Zammuto et al. (2007). 43

314

12 Organization Design and Complexity

support systems (SDSS).46 This led to the deployment of IT as management information systems (MIS). The first wave of this had limited impact and contributions because it was overlooked that management information also comprises goal information, eidetic information, effect-information (causal relationships), and attempts to base MIS on AIS therefore faltered.47 The awareness of a deeper, more fundamental role of information in organizations and in markets, was needed to maximize the utility of information and digital technology. The economist Kenneth Arrow laid bare that information is not a cost in the organization (as assumed by Peter F. Drucker), but is part of the capital base of the firm. In the modern economy and statistics information capital is acknowledged as an intangible asset, being material to the value of and value creation by the firm. Be it that information capital does not create value on its own, but in complementarity with human capital and organization capital. A second understanding was that in order to make a best use of the modern computer power, it was needed to see, understand, and define, in addition to the application described here before, products, services, processes, objects, events as information objects. One example of this is the use of the concept of the (mathematical) materials resource planning (MRP) and its concept of the bill of material, to describe complex products like airplanes and automobiles, completely by architectures, modules, components, lead times for production, internal logistics, assembly, testing, etc., to be executed on ERP-systems to achieve an efficient production of complex products and systems. A third shift, already acknowledged by Michael Porter in the 80s was the role of information systems in creating and defending the competitive position of the firm.48 Although the military concept of information superiority as need to be the more powerful is not explicit in the management literature.49 This would be elaborated by Shapiro and Varian who made clear that information also is input and output in the production function of the firm.50 Algorithms, in themselves a type of information, is the modern machinery to process data and turn data into value. With that it is understood that since about 1990 the intangible assets human capital, information capital and organization capital, organized complementary are more important for value creation as are tangible assets, dependent on the specific nature of the firm. So understandable Herbert Simon wrote in the 90s that in the twenty-first century, the factoring of decision-making (= processing of data and information) and the organization of information will be more important as will be the traditional Weberian structure. To which Neilson et al. would add that in strategy execution the first important factor is the organization of information.51 With the organization of

46

Markus et al. (2002). Johnson and Kaplan (1987, p. 261). 48 Porter and Millar (1985). 49 Hayes-Roth (2006), Alberts et al. (1999). 50 Shapiro and Varian (1999). 51 Neilson et al. (2008). 47

12.4

Complexity and the Design of Functions

315

information is meant access to external and internal information, structured and unstructured, information available and accessible over multiple dimensions of performance, planning and performance, fast feedback information to facilitate experiments, etc. That is, in order to have the highest value of information, it needs to be organized disembedded from the traditional structure of the organization. This implies that the declining costs of information, its central roles in the value creating processes, representing elements in the business model, both facilitate and require new organization forms beyond the traditional restrictions of conventional organization forms.

12.4.6 Complexity and the Design of the Support Functions In the traditional organization form there are a number of staff departments, corporate legal, corporate strategy, finance, human resources, public relations, compliance, risk management, etc. These staff departments often have combined roles of planning, development, coordination, control, and support, in underlying weight different by their nature. Since the end of the 80s under the banner of shared service centers, there was a movement to distinguish between staff activities being strict corporate support for the executive board and those supporting the business, the latter in combination with the decentralized staff departments. These shared service centers eventually transformed, conceptually and operational into platforms, providing support to the businesses. The digital technology, the use of corporate wide databases, etc., imply new options how to design this support function, as well that new requirements are implied by the concept of organized complexity. The quoted Zuboff and Maxmin also observed the “infrastructure convergence.” These authors, writing in 2002, are too descriptive and too apologetic to be of use for design thinking let it be operations, but there is a valid point in these ideas of preserving complexity and convergence. What needs to be organized, following Zuboff & Maxmin, is a proper support for all these projects and processes operated on the basis of self-coordination, interaction between knowledge workers to achieve combinatorial innovation, etc. This support needs to be provided by the finance function, the HR, function and IT-governance, and other services like facilities. An overview of what needs to redesigned is depicted in Fig. 12.5. There will not be a complete convergence of staff departments because of differences in conceptual knowledge and expertise. However, the staff departments will share the database of the organization, simply because information will be organized disembedded from the traditional structure of the internal organization. In some cases, this has resulted in the management of labor contracts, being transferred to the purchasing departments. Evaluation of workers by HR departments is being based on information recorded by the accounting department, and the latter also has the lead in the incentive structure, although this may be dependent on specific labor laws in different jurisdictions.

316

12 Organization Design and Complexity

Redesign the ITgovernance; eliminating information symmetry, increase of multidimensional information

Redesign of the HR-new roles and identities of resource managers, developing conceptual complexity

Redesign of the accounting system: adding reportable / planning dimensions; Decision rights

Delivery and development processes

Redesign of the resource allocation process; linking process planning to resource departments

Customer value proposition

Redesign of the performance management infrastructure: integrated objective function , individual contribution measurement; Incentive system

Fig. 12.5 Overview of the to be redesigned support functions to facilitate the level of tools in the concept of Kanter. That is, in these functions proper conditions need to be created ex ante to facilitate for self-coordination and self-management in teams

In terms of design, a large part of the staff functions is to be organized as one integrated support platform (Figs. 12.3 and 12.5). The planning task of the staff departments for the corporate part remains corporate, the planning of the capabilities and the capacities of the platform becomes twofold. For the long term there will be a process of alignment and preparedness between corporate strategy and the platform, for the short term this process of alignment and preparedness will be between the projects and processes at the toollevel and the platform. The coordination tasks in various staff departments shift partly into the guiding system, for e.g., the planning of strategic themes and project portfolio management, partly the coordination function goes into the self-coordination at the level of tools, in the concept of Kanter, partly it goes into the alignment processes between the projects and the end-to-end processes and the services to be delivered by the platform. Support is to be split between support of the executive board and support of the operations. The first will remain in a small number of staff departments, operational support will be organized in the platform and possibly be outsourced, where appropriate in economic and in strategic terms. More complicated is the distribution of the function of control. Control is programmed in all of the constitutional elements of the organization. The ex ante control or feedforward control is to be integrated in the processes for preparedness, alignment, and resource allocation, at the levels mentioned before. The feedback control is multiple level, to start with at the level of the individual in the form of selfcontrol and social control. There needs to be self-control at the level of individual projects for which the platform needs to provide fast feedback information.

12.4

Complexity and the Design of Functions

317

The corporate office needs to monitor the projects and processes for (financial) performance. This monitoring is ex ante, ongoing and it is ex post. There are two design dimensions with respect to the finance functions. A first one is implied by the concept of having a portfolio of projects and processes. Different from a one-business firm, or a multi-business firm operated as a financial holding, the finance function in the case of a synergetic, dynamic firm will need to have a capability to deal with different sets of parameters for ex ante input planning, process control, and ex post performance judgment, dependent on the nature of the projects, processes, and the phase in their life cycle. This is to avoid that a too rigid, or too small variety in the choice of parameters will not match specific characteristics of projects to the effect that this will kill especially explorative and innovation projects.52 In terms of complexity, a possible need in variety of critical, financial, and non-financial, input, process, and performance parameters as implied by the nature of projects and processes, needs to be matched by a one-degree higher variety in parameters the finance function is capable to deal with. But this is not about complexity in terms of emergence, this is complexity in terms of required variety. A second type of complexity the finance function needs to be capable to deal with is a more complicated resource allocation process, as opposed to the (linear) bottomup resource allocation process by Bower. As resources need to be allocated in a dynamic way to have combinatorial innovation based on personal knowledge a more complex resource allocation process like the one formulated by Kaplan & Norton, although preceded by companies like Shell and Cargill, is to be applied (Sect. 11.14). This is also needed to absorb at least a part of the complexity induced by the fact that a project manager or process manager has no hierarchical control over all of the resources needed to accomplish her or his assignment and objectives. That is to say, the redesigned resource allocation process needs to provide the project- or process manager with sufficient influence on and sufficient support by the resource departments, that she or he has sufficient assurance about the timely availability of the right resources.53 Managing projects in situations of dynamic complexity implies that the traditional linear phase-wise planning of projects will no longer bring the best results. There will be an idea, an objective to be achieved, but the path toward it must be open all the way to new insights, new opportunities, new situations. With respect to the dimension of resources this is addressed by concepts like open innovation and discovery-driven planning. Open innovation contributes to a higher success with lower financial risks for the firm, but it requires quite some technicalities at the level of corporate finance, evaluation of firms and intellectual property rights, legal issues and such, thus setting higher requirements to the finance function, including evaluation of proposals using the more accurate, but more complicated technique of real options.

52 53

Christensen et al. (2008). Simons (2005).

318

12 Organization Design and Complexity

The concept of discovery-driven growth implies a new concept for project control. Funding no longer will be a one-off, but it will be phased funding. Before entering a new phase first there will be a reassessment of the assumptions underlying the planned phased in terms of changes in the context, technological developments, learning from the completed phases, identifying new options to find solutions, to incorporate new conditions and options in the next phase for higher chance on success. The case of IBM shows that another uncertainty to be faced by the management control system is that of changing patterns of power in the market. The sub-unit power principle implies that the factor in the market being most critical for the success of the firm, should have the most power in the internal organization. But what if the power structure in the market is not stable, also because playing out market power has become more prominent in corporate strategy, and this power is changing due to technology. Gerstner solved this problem, as did Nestlé, by creating one database for the corporation, especially one transaction system in which internal and external transaction are recorded with multiattributes allowing to read out performance on the dimension most critical for the issue and the moment, allowing it to be another in the next period, without any change in the organization. Even more, reporting and analyzing issue has become one and the same act.

12.4.6.1 The Capability of the Finance Function to Deal with Complexity Especially in large firms there will be a variety of (investment) projects, differing by exploitation versus exploration, degree of innovativeness, phase in the life cycle, time horizon, nature of the business model, etc. Therefore, there may be differences in critical performance parameters, by outcome, output, throughput, input, and context, between those projects. These parameters first need to be decided at the level of the individual project, often changing between phases of the project, on the basis of the principle of management information, that is, which parameter at any time is most critical for the success of the specific project. At the same time the integrity of the firm as whole needs to be preserved implying that financial monitoring is needed on inputs, progress, outputs, to achieve the set business case. Then the challenge is to find a degree of financial control that allows for differences between the projects, their nature and objectives, that is mainly to differentiate between controlling inputs, throughputs and outputs and time horizon, including allowing for discovery-driven growth and phased funding, that combines nurturing conditions for the projects and maintaining the strategic and financial integrity of the firm. To have one set of monitoring parameters for all projects, risks may be killing, even for critical projects.54 Not only a variety may be needed in the choice of monitoring parameters, but also a variety will be needed in the values (targets) to be met or observed by the project teams (Fig. 12.6). In addition to this, the finance function needs to be able to bridge the differences between management information and accountability information, and regulatory 54

Christensen et al. (2008).

12.4

Complexity and the Design of Functions

319

Fig. 12.6 An example from the medical industry on the relation between complexity and type of parameters to be used in management control (Christensen et al. 2009, p. 380). Even more this illustrates a possible relation between uncertainty and type of parameter to focus on in management control

requirements. This implies the need for an adequate level of conceptual complexity with the leadership in the finance function, a mindset of managing risks not through simplification, but by broadening and deepening knowledge and understanding, in combination with organizing more information. Especially is to be observed that the system(s) for management information are not based on systems primarily designed for external reporting, compliance, and auditing requirements.55 The idea of organized complexity implies that a team of interacting knowledge workers has the capability, the tools, and the information, to calculate themselves which of their alternative ideas and initiatives will contribute most to the overall performance of the firm.56 This implies for the finance function, especially the sub-function of business control, to assist those teams in making such calculations by providing them with information, organizing access to information, and assisting in developing tools for making calculations, especially defining (mathematical) models, based on non-financial causalities to make business ideas calculable. In the case a project runs (business) experiments, or a new business model is being tested (to deal with a new unknown situation), the finance function needs to provide those teams with fast feedback information.57 Complexity in business also implies consequences for the resource allocation process. The concept of knowledge governance, that is to facilitate interactions

55

Johnson and Kaplan (1987, p. 261). Arrow (1974). 57 Manzi (2012). 56

320

12 Organization Design and Complexity

Fig. 12.7 The resource allocation process designed for more complex organizations, working with cross departmental projects, end-to-end projects, strategic themes and such. Adapted from (Kaplan & Norton, 2008). This type of resource allocation process replaces the bottom-up resource allocation process by Bower of 1970. This scheme is applicable to many types of complex organizations

between knowledge workers from different departments and from outside, implies the need for a more complex resource allocation process, that defined by Kaplan & Norton (Fig. 12.7), as opposed to the simpler (albeit in practice complicated) bottomup resource allocation process defined by Bower. But as organized complexity also implies a need for self-organization, this implies that the management information system needs to allow for resource mobilization, that is that knowledge workers for themselves can decide in which projects their knowledge will contribute most and will develop most, respectively, that project managers themselves will be able to select the most appropriate knowledge workers for her or his projects.58 However, at the same time this management information system, in combination with proper HR-management, needs to protect knowledge workers against the debilitating effects of the possible political nature of horizontal resource mobilization on individual knowledge workers, and needs to avoid overexploitation of individual knowledge workers, especially when operational tasks need to be combined with working in projects. Also, such an information system needs to facilitate and to monitor the trade off, often at the level of the individual, time spent on the short term (exploitation) and time spent on the long term (exploration). 58

Doz (2005).

12.4

Complexity and the Design of Functions

321

This new way of organizing also implies consequences for the system to generate information needed for the assessment of the performance or contribution by knowledge workers. Because knowledge workers contribute in multiple projects, often in shifting roles, a contribution measurement infrastructure is needed to record contributions of individual knowledge workers and managers to cross-business activities and as she or he shifts projects and roles.59 The declining costs of information make it possible that the basic unit of performance measurement in the organization is the individual worker. To what extent and in what situations this is to be applied also may depend on the system of labor relations in a country. The individual as unit of performance measurement also is an issue in relation to what type of employment contracts is being closed. Apart from motive for capacityflexibility by the employer to contract self-employed (knowledge)workers, many knowledge workers themselves decline working under standard employment contracts or supply contracts. A company must be prepared to organize, also in their systems for a variety of types of contracts, varying from traditional employment contracts to supply contracts and even contracts that include property rights and/or equity.

12.4.6.2 Designing for Complexity at the Levels of Tools Now we have dealt with design at the level of the guidance system and the level of support, the question is to be answered what at the level of tools (Fig. 12.3)? This is the level of the dynamic portfolio of projects, processes, strategic themes, varying from exploitation to exploration and innovation. This level of tools is where the organized complexity should be productive in terms of innovation, adaptation, dealing with uncertainty, where human capital is being made productive, while at the same time (knowledge)workers at this level should be facilitated in dealing with this complexity through simplicity of process, because especially the support function absorbs a number of complexities. What is needed at this level are people who are knowledgeable, bring in new knowledge, have a high conceptual complexity, are eager to learn and to explore, but are also capable to deal with multiple roles, multiple projects, some role ambiguity, simply because they are focused on results (and personal reputation). The level of tools, given proper project definition, access to information, and administrative support, is an issue of talent. Note that not is meant talent management, as real talent does not let itself manage, they want to be challenged and want to have the opportunity of self-discovery for new opportunities. Which brings us to the HR-function. 12.4.6.3 HR and Complexity The paradox of the knowledge economy, in which human capital is the most important capital, is that the instruments for facilitating the knowledge worker to a large part are in the domain of the finance function, not in the domain of HR. This is not to say there is no role for HR, at the contrary, but especially conditions in the 59

Bower (2003).

322

12 Organization Design and Complexity

material organization, the organization of information is a responsibility of finance. The measurement of the contributions of individual knowledge workers cannot be isolated from the overall system for performance management, neither the management of their employment and supply contracts, and the allocation of their costs to the individual projects. With that the role of HR will be concentrated on attracting, selecting, contracting, and retaining workers that bring in the knowledge that is valuable for the company and that is capable to deal with complexity, in order that the company not regresses into rules and procedures in order to deal with the increasing complexity.60 In the USA companies like Amazon, Netflix, Apple have available a large (mobile) labor market and in combination with available funds for a high compensation can be and are highly selective to get the talent they need. In Europe this is different, despite a fairly mobile labor market for knowledge workers across Europe, but language differences and a stronger role of labor regulations imply European firm have a less large pool to select from. Therefore, in Europe there is a higher need for companies to develop workers with respect to understanding and seeing new developments, and to broaden their knowledge and understanding as need to have less subjective complexity and to deal with a higher variety of roles, types of contributions and responsibility. This requires a well-designed program of management development in combination with facilitating self-education. In-company management development courses are needed as an extension of the guidance system, that there is an understanding of a common body of ideas, visions, familiarity with new ideas, concepts, developments, knowledge, etc., to facilitate teamwork, especially explorative and innovative and adaptation. Self-education is needed to grow the variety of ideas and concepts, especially because through selfeducation of workers and managers more new concepts will be acquired outside the company and brought into it. Not every worker will be at ease with a growing degree of complexity in the organization. A transparent contribution measurement system favors those with an extrovert personality, they want to demonstrate to others their superior quality and the extroverts will perform better. Not so the introverts, they tend to perform less in a situation of transparency. This implies an HR-policy that results on placing the right people in for them most fitting positions. Atleast a dual HR-policy will be needed, it would be too simple that only high performers, highly talented and extrovert individuals can or should be hired. Ashby’s Law of Requisite Variety implies that also a variety of personalities is needed in the organization, as is diversity needed to have multiple viewpoints and lenses through which new developments are seen and interpreted. Another issue requiring attention by HR is the style of management (not to be confused with the style of leadership). It will be necessary to select managers who are aware that power should not be abused and to coach these in order to have a safe psychological climate in the organization. Simply because this helps in getting new

60

Netflix (2013), McCord (2014).

12.4

Complexity and the Design of Functions

323

ideas on the table, but also if not more important, that failures, and there will be failures, are dealt with in a constructive way. Related to psychological climate and somewhat bridging it to organizational culture is how the organization scores on sociability (the degree to which the members of the organization have friendship relations) and solidarity (the degree of a shared understanding of tasks and objectives). If these are too weak, new members will not properly be integrated in the organization and self-coordination will be poor. If these two are too strong, there may be cliques hampering decision-making and poor performance will not be addressed. Sociability and solidarity are a balancing act. It is tempting to state that it is the task of HR to create a culture capable to seize the upside of complexity. Often it is the founder who is decisive for a specific culture, e.g. in the case of Nike. In other cases the fast way to change or create a specific culture, especially by a new CEO, is applying the embedding mechanisms for culture change as defined by the psychologist Edgar Schein: What leaders consistently and persistently pay attention to, measure and control, a second one is how leaders respond to critical incidents and crisis (are mission and values maintained?), a third one is the consistency in criteria by which leaders allocate scarce resources. These are three of the in total 12 embedding mechanisms for shaping a culture according to Schein.61 These embedding mechanisms primarily are in the scope of the finance function, not in HR. To which immediately must be added that Schein’s mechanisms are based on behaviorism and therefore are not robust for errors, change of leadership, and cultural change based on this can be reversed as quickly and seamlessly as these have been brought on.62 A more robust way to shape culture is through the approach of cognitive psychology, by developing knowledge of individuals, to broaden it, to deepening it, as explained by the psychologist Argyris and consistent with the insights of Herbert Simon. This cognitive psychology approach is from a viewpoint of operational management more indirect as it requires a continuous investment in management development programs and other training of managers and key workers and it assumes that the mission and the values of the firm are clearly formulated and are codified, in a consistent and a complete way, in all systems, procedures, and processes of the organization, including in objective functions, in order that workers both can identify with the mission and values and understand how to live up to these. An unsolved problem in the modern-day complex organization, defined by Herbert Simon as the local or bottom-up adaptive behavior to new unforeseen demands and options in the environment of the organization, is the assumption that a critical body of workers identify themselves with the mission, the identity, the products, the technology, the customers, etc., of the form as a condition for generating useful bottom-up initiatives.63 The question could be asked whether in this era of job-hopping, temp work, self-employed workers contracted as suppliers,

61

Schein (1999). Gardner (2004). 63 Simon (1991). 62

324

12 Organization Design and Complexity

which may work for multiple companies at the same time, and in which this identification does not develop, may hamper the generation of useful bottom-up initiatives. At one hand, it seems that this lack of identification is tried to be substituted by culture programs. But especially values cannot be imposed on workers as this results in resistance to control by resistance through persistence and resistance through distance, as well as accountability avoidance.64 Alike, identification only can develop by the individual herself or himself, it cannot be imposed on the worker. In generating bottom-up initiatives other factors are at play as well, cognitive psychology and self-interest being the most important. Cognitive psychology relates to the capability of individuals and groups to generate transformational thinking and reframing of problems and opportunities, and thus feeding the innovation of the firm, especially in contexts, markets, competition, technology, that are turbulent and transformational.65 Individuals do have personal interests, also as employees and certainly when contracted as self-employed worker. Consciously or unconsciously workers will balance in developing initiatives between their personal or local interests and the interest of the firm at large. Or, as Robert Simons phrases it, there will be a tension between self- or local interests (a department or business unit) and commitment to others, that is commitment to the corporation at large.66 This is not a trivial issue in terms of top-down command-and-control as that violates the principle on an complex organization of the need for loose control and loose programming, as expressed in the phrase “strategic guidance,” which is not “strategic planning.” The situation is not as bleak as it may seem because, different from behaviorism, but compatible with cognitive psychology, the social identity theory describes that many individuals are motivated by two factors. The first is personal identity, in which persons seek to through career commitment and personal advancement to satisfy personal needs, norms, and goals. That is, they serve themselves. The second factor is that through social identity, to be accepted by a preferred reference group, through group serving by loyalty, rule following, and extra-role behavior they will strive to achieve social needs, norms, and goals.67 Partly because this satisfies a basic need of the individual itself, also because an unconsciousness awareness that human beings depend for survival on as well an immediate and broader surrounding social system. Most likely it is individuals whose motivation is based on that others acknowledge their contribution to the performance of the group, respectively, the firm. Whereas the traditional unit-managers were selected for being motivated by control over resources, most of the new generation knowledge workers are motivated by acknowledge contribution. That is, here is a selection issue in recruiting for HR. There may also be a caveat. Wanting to belong to a reference group, to be accepted by a group is a typical characteristic of individuals displaying what the psychiatrist Fromm labeled

64

Rollinson and Broadfield (2002, pp. 552–558). Sull (2005). 66 Simons (2005). 67 Haslam (2004). 65

12.4

Complexity and the Design of Functions

325

the marketing personality. These persons tend to do virtual everything to be accepted by others, especially by adapting themselves to new situations. The do well in interactions with other people are not committed to an organization, but to their career. They solve problems, but are prone to the phenomenon of the echo-chamber effect, that is they use phrases whose provenance they often do not know, but use these phrases because they sense these are what other people want to hear.68 This conflicts with what is needed to master complexity, the capability of abstract thinking, “to wade into complexity” to understand new situations, to experiment with new models, to the requirement to be able to reconceptualize situations, etc.69 The echo-chamber effect is to be seen in the use of the phrase “culture.” Since its original meaning by Margaret Mead, used also by Hofstede, “the collective programming of the mind,” to the three-layer model of Schein, and the reduction of this model to values, culture has become a sign, a simulacra, which does not refer to an existing reality. The sign-use of the word ‘culture’ both is a cloak to hide how things really work, as it is a residual factor to explain all that which cannot be explained by known or existing management models. Culture has become a non-analytical sign that stops exploring to understand a situation and which is frustrating the executive rule “simplicity beyond complexity,” that is, one needs first to understand a new complex situation before defining a new simplicity.70 In the social realm there is a new complexity, that an individual is not part of one culture, as implicitly assumed by Mead, or that in one’s working life, with lifetime employment, the corporate culture of the employer is dominant, in today’s world many members of society participate in multiple cultures. There are variations, some regress to living in a self-defined or self-chosen bubble for which they will find confirmation on the Internet, others are creating and defining their culture by sampling memes from different cultures and communicating these through gifs. This is how these individuals deal with the social side of complexity, that is a proliferation of roles and identities, by their capability of social abstraction and personal abstraction.71 Social abstraction enables the individual to interact constructively with strangers by away of a highly elaborate game of role playing and personal abstraction empowers the individual to exercise meaningful autonomy through weighing how various choices will affect an imagined future self.72 Both are part of the master strategy to deal with complexity: abstract thought.73 Therefore, to develop a workforce that is capable to deal with complexity, there may be a tendency, apart for selecting talent, to engage in programs for coaching and training workers to deal with complexity. This would be wrong without first having organized in the material organization, in the system, as explained before, required

68

Maccoby (2007), Pentland (2013). Martin (2007), Helfat and Peteraf (2015). 70 O’Toole (1993). 71 Lindsey (2012). 72 Lindsey (2012). 73 Lindsey (2012). 69

326

12 Organization Design and Complexity

conditions that complexity is absorbed in the finance function by different types of information. The then remaining complexity is the complexity about roles and identities, for which indeed training (broadening the cognitive base) and coaching will be helpful.

12.4.6.4 Complexity and Empowering Workers The nature of complex organization, as defined by Herbert Simon, and the concept of organized complexity, assumes that workers in the organization are empowered. Empowerment is not just attributing decision rights and to allocate resources over which workers have discretion. In a way empowerment is distributing the entrepreneurship throughout the organization, in terms of seeing opportunities, sensemaking, taking initiatives, making risk-bearing decisions, controlling profitable positions in markets, defending these and to be accountable for their actions and decisions. The distributed entrepreneurism within a (large) corporation needs to be performed such that the identity and the integrity of the corporation is preserved and that Coase’s criterion of coordination will not be violated. That is to say, this distributed entrepreneurism needs to be guided and will be bounded. Distributed entrepreneurialism implies that those entrepreneurial workers have the possibility to organize resources to materialize their entrepreneurial initiatives. This implies self-organization, but as there will be multiple claims on the same resources and/or scarcity of resources, some process is needed to decide which allocation of resources will be most valuable for the corporation. In empowering workers, it needs to be acknowledged that individual knowledge workers will seek an as large as possible market for their individual, personal, and non-codifiable knowledge.74 Knowledge workers will also understand that the value of their individual knowledge will depend on interaction with other knowledge workers, as interaction is the typical process by which to input tacit knowledge in a process of innovation and/or production. Knowledge workers will seek opportunities, in terms of projects and de novo demands from customers in which applying their knowledge will grow that value and enhance its market value. So, what is needed might be labeled knowledge- and information-based empowerment.

12.5

Conclusion

Complexity is an undeniable phenomenon in real life, in the economy, in business, and thus in organizations. Knowing to deal in a positive way with complexity, through positive simplification as opposed to negative simplification and knowing how to balance the required complexity with the capability of the organization in dynamic way will make the difference between the more efficient organizations and the less efficient organizations. In the past, the decennia after WWII there was a

74

Rosen (2004).

Bibliography

327

convergence between the more efficient organizations and the less efficient organizations, although always a gap remained. Now the OECD research suggest we have a divergence between the more efficient firms and the less ones, with detrimental effects on society in terms of income distribution, political polarization, and social unrest. This implies that those responsible for designing organizations need to work on growing the capability of organizations to master the new levels of complexity in society. Especially an issue will be how to distribute the complexity so, in view of the fact that the capability to deal with complexity differs between individuals and groups in society, that as many as possible can participate in the economic process in a way that is worthy in terms of income and being able to live a fulfilled live.

Bibliography Alberts, D. S., Garstka, J., & Stein, F. P. (1999). Network centric warfare: Developing and leveraging information superiority (2nd ed.). National Defense University Press. Ansoff, H. I. (1984). Implanting strategic management. Prentice Hall. Arrow, K. J. (1974). The limits of organization. Norton. Baldwin, C. Y. (2012). Organization design for business eco systems. Journal of Organization Design, 1(1), 20–23. Beck, U. (1999). World risk society. Polity Press. Beck, U. (2009). World at risk. Polity Press. Bower, J. L. (2003). Building the Velcro organization: Creating value through integration and maintaining organization-wide efficiency. Ivey Business Journal, 68(2), 1–10. Brown, T., & Katz, B. (2009). Change by design: How design thinking transforms organizations and inspires innovation (1st ed.). Harper Business. Brynjolfsson, E., & Hitt, L. M. (2003). Computing productivity: Firm level evidence (4210-01). Cadbury, A. (1995). The company chairman (2nd ed.). Director Books. Capra, F., & Luisi, P. L. (2014). The systems view of life: A unifying vision. Cambridge University Press. Chandler, A. D. (1962). Strategy and structure: Chapters in the history of American enterprise. MIT Press. Chesbrough, H., Vanhaverbeke, W., & West, J. (Eds.). (2006). Open innovation: Researching a new paradigm. Oxford University Press. Christensen, C. M., Kaufman, S. P., & Shih, W. C. (2008). Innovation killers-how financial tools destroy your capacity to do new things. Harvard Business Review (January). Christensen, C. M., Grossman, J. H., & Hwang, J. (2009). The innovator’s prescription: A disruptive solution for health care. McGraw-Hill. Clark, K., & Baldwin, C. Y. (2002). The fundamental theorem of design economics. SSRN eLibrary.https://doi.org/10.2139/ssrn.312419 Danley, J. R. (1994). The role of the modern corporation in a free society. University of Notre Dame Press. Davenport, T. H. (1998). Putting the enterprise into the enterprise system. Harvard Business Review, 76(4), 121–131. Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. https://doi.org/10.1016/j.destud.2011.07.006 Doz, Y. L. (2005). Resource allocation processes in multidimensional organizations: MNCs and alliances. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy. Oxford University Press. Drucker, P. F. (1974). Management: Tasks, responsibilities, practices (1st ed.). Harper & Row.

328

12 Organization Design and Complexity

Emery, F. E., & Trist, E. L. (1960). Socio-technical systems. In C. W. Churchman & M. Verhulst (Eds.), Management science, models and techniques (Vol. 2, pp. 83–97). Pergamon. Fama, E. F., & Jensen, M. C. (1998 (org. 1983)). Separation of ownership and control. In M. C. Jensen (Ed.), Foundations of organizational strategy. Harvard University Press. Gardner, H. (2004). Changing minds: The art and science of changing our own and other people’s minds. Harvard Business School Press. Ghoshal, S., & Bartlett, C. A. (1997). The individualized corporation: A fundamentally new approach to management. HarperBusiness. Haslam, S. A. (2004). Psychology in organizations: The social identity approach (2nd ed.). Sage. Hayes-Roth, R. (2006). Hyper-beings: How intelligent organizations attain supremacy through information superiority, p. 204. Helfat, C. E., & Peteraf, M. A. (2015). Managerial cognitive capabilities and the microfoundations of dynamic capabilities. Strategic Management Journal, 36(6), 831–850. https://doi.org/10. 1002/smj.2247 Ismail, S., Malone, M. S., & van Geest, Y. (2014). Exponential organizations: Why new organization are ten times better, faster, and cheaper than yours. Diversion Books. Jensen, M. C. (1993). Modern industrial revolution, exit, and the failure of internal control systems. SSRN. Jensen, M. C. (2000). Value maximalization and the corporate objective function. In M. Beer & N. Nohria (Eds.), Breaking the code of change (pp. 37–57). Harvard Business School Press. Johnson, H. T., & Kaplan, R. S. (1987). Relevance lost: The rise and fall of management accounting. Harvard Business School Press. Jones, P. H. (2014). Systemic design principles for complex social systems. In Social systems and design (pp. 91–128). Kanter, R. M. (2001). Evolve!: Succeeding in the digital culture of tomorrow. Harvard Business School Press. Kanter, R. M. (2009). Supercorp: How vanguard companies create innovation, profits, growth, and social good (1st ed.). Crown Business. Kaplan, R. S. (2007). The demise of cost and profit centers. Retrieved from Boston, MA.: Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business School Press. Kaplan, R. S., & Norton, D. P. (2005). The office of strategy management. Harvard Business Review (October), 1–11. Kaplan, R. S., & Norton, D. P. (2008). The execution premium: Linking strategy to operations for competitive advantage. Harvard Business Press. Kim, W. C., & Mauborgne, R. (2005). Blue Ocean strategy: How to create uncontested market space and make the competition irrelevant. Harvard Business School Press. Landes, D. S., Mokyr, J., & Baumol, W. J. (Eds.). (2010). The invention of enterprise: Entrepreneurship from ancient Mesopotamia to modern times. Princeton University Press. Lindsey, B. (2012). Human capitalism: How economic growth has made us smarter-and more unequal. Princeton University Press. Maccoby, M. (2007). The leaders we need: And what makes us follow. Harvard Business School Press. Manzi, J. (2012). Uncontrolled: The surprising payoff of trial-and-error for business, politics, and society (p. xvii, 300 p.). Markus, M. L., Majchrzak, A., & Gasser, L. (2002). A design theory for systems that support emergent knowledge procsses. MIS Quarterly, 26(3), 179–212. Martin, R. L. (2007). The opposable mind: How successful leaders win through integrative thinking. Harvard Business School Press. Maskin, E., Sen, A., & Arrow, K. J. (2014). The arrow impossibility theorem. Columbia University Press. McCord, P. (2014). How netflix reinvented HR. Harvard Business Review (January–February). Milgrom, P., & Roberts, J. (1992). Economics, organization and management. Prentice-Hall.

Bibliography

329

Motta, M. (2004). Competition policy: Theory and practice. Cambridge University Press. Neilson, G. L., Martin, K. L., & Powers, E. (2008). The secrets to successful strategy execution. Harvard Business Review (June), pp. 61–70. Netflix. (2013). Netflix Culture: Freedom and Responsibility. Nolan, R. L. (2012). Ubiquitous IT: The case of the Boeing 787 and implications for strategic IT research. The Journal of Strategic Information Systems, 21(2), 91–102. https://doi.org/10.1016/ j.jsis.2011.12.003 O’Toole, J. (1993). The executive’s compass: Business and the good society. Oxford University Press. Osterwalder, A., Pigneur, Y., Bernarda, G., & Smith, A. (2014). Value proposition design: How to create products and services customers want. Wiley. Pentland, A. (2013). Beyond the Echo Chamber. Harvard Business Review, 91(11), 80. Pfeffer, J. (2018). Dying for a paycheck: How modern management harms employee health and company performance--and what we can do about it (1st ed.). HarperBusiness, an imprint of HarperCollins Publishers. Porter, M. E., & Millar, V. E. (1985). How information gives you competitive advantage. Harvard Business Review, 63(4), 149–174. Rollinson, D., & Broadfield, A. (2002). Organisational behaviour and analysis: An integrated approach (2nd ed.). Prentice Hall Financial Times. Rosen, S. (2004). Markets and diversity. Harvard University Press. Schein, E. H. (1999). The corporate culture survival guide: Sense and nonsense about culture change. Jossey-Bass Publishers. Shapiro, C., & Varian, H. R. (1999). Information rules: A strategic guide to the network economy. Harvard Business School Press. Shpilberg, D., Berez, S., Puryear, R., & Shah, S. (2007). Avoiding the alignment trap in information technology. MIT Sloan Management Review, 49(1), 51–58. Simon, H. A. (1991). Organizations and markets. Journal of Economic Perspectives, 5(2), 25–44. Simon, H. A. (1996). The sciences of the artificial (3rd ed.). The MIT Press. Simons, R. (1995). Control in an age of empowerment. Harvard Business Review, 73(2), 80–88. Simons, R. (2005). Levers of organization design: How managers use accountability systems for greater performance and commitment. Harvard Business School Press. Stinchcombe, A. L. (1990). Information and organizations. University of California Press. Sull, D. N. (2005). When the bottom-up resource allocation process fails. In J. L. Bower & C. G. Gilbert (Eds.), From resource allocation to strategy (pp. 93–98). Oxford University Press. Uhl-Bien, M., & Marion, R. (Eds.). (2008). Complexity leadership part I: Conceptual foundations. Information Age Publishing. Uhl-Bien, M., Marion, R., & McKelvey, B. (2007). Complexity leadership theory: Shifting leadership from the industrial age to the knowledge era. The Leadership Quarterly, 18(4), 298–318. Wilson, H. (1975). Complexity as a theoretical problem: Wider perspectives in political theory. In T. R. La Porte (Ed.), Organized social complexity: Challenge to politics and policy (p. xiv, 373 p.). Princeton University Press. Zammuto, R. F., Griffith, T. L., Majchrzak, A., Dougherty, D. J., & Faraj, S. (2007). Information technology and the changing fabric of organization. Organization Science, 18(5), 749–762. https://doi.org/10.1287/orsc.1070.0307 Zorn, D. M. (2004). Here a chief, there a chief: The rise of the CFO in the American firm. American Sociological Review, 69(3), 345–364. Zuboff, S., & Maxmin, J. (2002). The support economy: Why corporations are failing individuals and the next episode of capitalism. Viking.

Complexity and Management of Change

13.1

13

The Butterfly-Effect

At first sight it is to be expected that complex organizations are more difficult to change compared to simple organizations. Related to that is the idea that changing large organizations requires large-scale interventions. On the other hand, chaos theory, especially its butterfly-effect, suggests that small interventions may have large consequences. To which immediately must be added that this butterfly-effect is not an empirical phenomenon, but was used by the meteorologist Edward Lorentz as a visualization of a phenomenon in mathematical models that the predictions resulting from such models may be oversensitive dependent on small variations in a single input-parameter. That is what Lorentz actually discovered in 1961 when he entered in a model for a parameter 0.506, where before he had entered 0.506127 to discover that this difference of 0.000127 created a huge difference in the behavior of the model, the magnitude of which did not correspond with the difference of 0.000127. In mathematics this is called an oversensitivity of the model for a specific parameter that results from a divergent algorithm, these are algorithms in which large errors result from small errors in the input or the loss of digits due to the finiteness of the mantissa of the parameters in the algorithm (modern computers now have larger mantissa and error due to loss of digits in a calculation is less an issue today as it was in Lorentz’s time). Nevertheless, the idea of the butterfly-effect raises the question whether it is possible that small interventions are capable of—controlled—largescale system changes. It turns out that this indeed is the case. To understand this, we first need an understanding of the mainstream schools for management of change.

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_13

331

332

13.2

13

Complexity and Management of Change

Planned Change

The first and probably most influential school is that of planned change, as formulated, based on the theories of Talcott Parsons, by Bennis, Benne & Chin.1 This school is through the theories of Parsons based on the concept of punctuated equilibrium of the market and it is based on the idea that an organization in order to survive needs to be adapted to changes in its environment, but that this process of adaptation as a period of disturbance of an equilibrium in the organization, needs as fast as possible to be turned in a new phase of equilibrium as needed for efficiency. As a consequence, the school of planned change focuses on overcoming resistance to change in the organization. This school today often goes under the name of management of change (MoC), but has lost its original perspective of adaptation.

13.3

Organization Development

A second school is that of organization development (OD).2 Organizational development focuses on behavioral science interventions in terms of teamwork, interpersonal competences, self-coordination, decentralized problem-solving and decision-making. With that OD is less on planning as it is on emergence. At the same time, it is an intervention based on the assumption that investments in people, their interactions, proactive behavior, at individual, group- en team level, the performance of the organization will improve. With that OD is less based on planning and assumes emergence. OD as a school of change is not only based on behavioral science, but as much on the economic theory of Hayek (1945) that decentralized decision-making will improve the information processing capacity of organization and hence will contribute to the growth of the organization. OD anticipated the role of investment in human capital as a driver for economic growth. OD does not seem to be an intervention in the organization as a systemic context for behaviour. As OD aims to increase the variety in behavior by individuals and groups, OD might make a contribution to organised complexity provided its interventions are coordinated with those necessary in the organisation as a systemic context, facilitating more complex behaviour. Also, OD today has to take into account the effects of the digital technology on interactions between individuals as explained in § 8.5.

13.4

The Emergent School for Change

A third school is the Emergent school, advocated by, e.g., Weick.3 The Emergent school discards both the idea of planned change and the idea of episodic change, and sees organizations in a continuous ongoing process of change, evolving and

1

Bennis et al. (1962). French and Bell (1999) [1978], Cummings and Worley (2001). 3 Weick and Quinn (1999). 2

13.4

The Emergent School for Change

333

cumulative. This can be related to Herbert Simon’s concept of complex organization with loose control and loose programming, in which the members of the organization at all levels respond to new challenges, both external and internal by experimenting new solutions and learning from doing so and sharing this learning in the organization, creating a capability of adaption as needed for survival. Continuous change as a stable state of the organization therefore does not seem to require an intervention in terms of management of change. However, Herbert Simon’s concept of complex organization with its loose control and loose programming was described, based on empirical research, more specifically by Burns & Stalker in the concept of the organic organization versus the mechanic organization (Fig. 11.4). Although it is well known that organic organizations are better in adaptation and therefore have a higher survival rate, in many generations a complex of forces is at work tending to push organizations from being organic organization into mechanic organization. As a result, specific administrative instruments or interventions need to be applied to achieve and maintain a state of being an organic organization. These interventions partly are aimed at individuals, but dominantly are aimed to create a specific context for the member of the organization (§ 11.8). Hierarchy and structure are being deemphasized in the organic organization in favor of goals, access to information, free flow of ideas, acknowledgment of expertise, etc. The Emergent school assumes that specific conditions are set in order to have continuous and emergent change and adaptation. Also, the Emergent school assumes that the continuous changes processes are the outcome of cultural and political processes in organizations. From the failures in the resource allocation process due to selfish and parochial behavior, we know that political processes in the organization may result in the demise of the firm. The Emergent school also assumes that incremental changes over time will result in transformations of the organizations. Whether this is hope, fact or simply reflects a lack of understanding of economic processes is the question to be asked about the Emergent school. A fact is that in the economic process there is an ongoing stream of failing businesses, mergers, liquidations, transformations, and start-ups, also called change as an ecological process of births, growth, and dying firms. Incrementalism is not consistent with Beniger’s third level of control: transformation. The purpose of change is that the firm survives, in a successful way, in a changing environment. From the perspective of the resource dependency view of the firm, survival requires that the firm will have access to (new) resources, of whatever kind, as necessary for this survival. This implies as a first step that the firm as a system acquires information about changes in its environment and has the capacity to interpret this information in such a way that required changes are timely implemented with respect to new products, services, processes, etc., to ensure survival. This may require changes in the internal system of power relations. The criterion of subunit power-base implies that the dimension in the industry or market of the business, (e.g. manufacturing, marketing, finance) which is most critical for the success of the firm, should have the most power and must have priority in the resource allocation process. If cost efficient manufacturing is most important for a low-cost strategy, then manufacturing will need to be the most important department, if design is the most important factor to be

334

13

Complexity and Management of Change

successful, consequently the department for design will be the most powerful department. As power has the tendency to maintain its power, it must be questioned whether existing power relations in an organization will produce successful adaptations. The Emergent school may relate to the phenomenon of emergence as a property of complex systems, and to spontaneous organization, but more is needed. On the other hand, the example of IBM, in which Gerstner changed the organisation of information by a few simple decisions, the old power base was effectively eroded, opening up a process of three successful transformations, also a form of emergence.

13.5

The General Management View on Change

A fourth school in management of change might be labeled as the general management view on change.4 This school of management of change is based on the Interactionist Perspective Model from the field of organizational behavior. This model states that behavior of an individual results from the joint influences of personal attributes (genetic disposed, personality, and learned) and the nature of the context in which this behavior occurs.5 This raises the issue of what is the object for change interventions, the individual or the system. Beer et al. contrast the Programmatic Change Assumptions with the Task Alignment Assumptions.6 In the Programmatic Change Assumptions it is assumed that problems in behavior as a function of individual knowledge, attitudes, and beliefs, consequently the primary target of change should be the content of attitudes and ideas. With that it is assumed that behavior can be isolated and changed individually and the primary object of change is the individual. In the Task Alignment Assumptions, it is assumed that individual knowledge, attitudes, and beliefs are shaped by recurring patterns of behavioral interactions. Consequently, the primary target of change should be behavior; attitudes and ideas, knowledge should be secondary. It is acknowledged that behavior results from a circular pattern between personal attributes and context, but the effects of the organizational system are greater than those of the individual on the system.7 Consequently interventions for change should be at the level of roles, responsibilities, and relationships. This observation from psychology should be related back to the definition of system. Do the elements of the system, people, define the behavior of the system, or vice versa? The issue is that multiple types of elements in the system need to be acknowledged; active elements, people with their ideas, agenda’s motivation, views, etc., and passive elements, rules, material elements like information, the accounting system etc. The General Management View of change acknowledges that for most people the system or context within which they work determines more their behavior, initiatives, problem-solving, as do

4

Bower (2000). Greenberg (2010, p. 70). 6 Beer et al. (1990, pp. 60–62). 7 Greenberg (2010, pp. 61, table 63–62). 5

13.6

Systemic Change

335

their personal attributes.8 Therefore, to facilitate new behavior in an organization its general management first needs to change the system of the organization (the material organization) as the context for its members. This is precisely the issue of Bower in his resource allocation process, to implement a new strategy by bottom-up initiatives, first the new strategy needs to be translated in a new systemic context, mission, values, information, career paths, remuneration systems, performance parameters, etc., before asking for bottom-up initiatives, because the systemic context is of more influence on the content of bottom-up initiatives as is the content of the new strategy. In the context of a changed systemic context it usually will be necessary to explain the why and objectives of change, assist workers in internalizing this, providing training, coaching, with respect to changes in roles, identities, required knowledge, skills, etc. The issue of course is that general management must see such necessary changes in the systemic context, therefore the general management view on changes assumes that general management itself satisfies the Programmatic Change Assumptions, that is that their knowledge, attitudes, and beliefs are not shaped by the organization, but is of a more individual nature. That is, it is assumed that general management will be part of the organization-as-a-system, but at the same time have consciousness about the system and are able to see themselves in a different relation to the system, in relation to changes in the economy.

13.6

Systemic Change

The general management view on change leads to a fifth school of change, systemic change.9 This school states that in order to achieve large-scale change, or even transformation, not a large-scale intervention is needed, but the change of a limited number of system parameters. Kanter mentions 10 elements for systemic change, but cases like IBM and Procter & Gamble throw a brighter light on the nature of systemic change. In the case of IBM Gerstner created transformations by mandate through the rigorous semantic data standardization creating one global general ledger (therefore the general ledger was organized per country), with multiattribute recording of transactions and by mandate provided all members of the organization access to all and the same information. With that an important pillar of the power of national managers was removed. To facilitate the integrated solutions for customers as set out in the strategy, Gerstner changed in the accounting system and in the reporting system the profit center from being based on products/countries to being the customer. This forced functional departments to cooperate. The new global general ledger also allows for reporting the performance of IBM simultaneously over four dimensions, information that is available to all IBM-ers and allows them to quickly identify the source of issues. Gerstner also introduced the rule that everyone

8 9

Pfeffer and Sutton (2006). Kanter (2011).

336

13

Complexity and Management of Change

was permitted to take an initiative toward a customer, be she or he a product manager, a distribution manager, or an account manager, provided she or he produced an integral business case, based on first optimizing the profit of IBM on that customer. In the case of Procter & Gamble, to have distributed product development making best use of knowledge from all over the world, its CEO Lafley split the traditional division concepts, with its integral responsibility for managing market opportunities and for resource exploitation, into separate responsibilities for market development and for product development/supply. This of course creates an issue of coordination through cooperation, which Lafley facilitated by making the marketing side responsible for the first customer experience and the product development for the second customer experience. In making such changes in the system, accompanied with communication on mission and values, in both firms new behavior was facilitated, by first changing the system and secondly explanations to and involvement of individuals and groups. This is not to say that systemic change is without difficulties and challenges, in both cases some managers could not cope with it, could not adapt themselves to the new system, it demonstrates the suggestion implied by chaos theory that large systems can be changed, even transformed, through a limited number of changes in the system. Hence the title of Gerstner’s book Who says elephants can’t dance?.10 It is the art of identifying the few leverage points in the system.

Bibliography Beer, M., Eisenstat, R. A., & Spector, B. (1990). The critical path to corporate renewal. Harvard Business School Press. Bennis, W. G., Benne, K. D., & Chin, R. (Eds.). (1962). The planning of change: Readings in the applied behavioral sciences. Holt, Rinehart and Winston. Bower, J. L. (2000). A general management view of change. In M. Beer & N. Nohria (Eds.), Breaking the code of change. Harvard Business School Press. Cummings, T. G., & Worley, C. G. (2001). Organization development and change (7th ed.). SouthWestern College Publishing. French, W. L., & Bell, C. H. (1999[1978]). Organizational development: Behavioral science interventions for organization improvement (6). Prentice Hall. Gerstner, L. V. (2002). Who says elephants can’t dance?: Inside IBM’s historic turnaround. HarperBusiness. Greenberg, J. (2010). Managing behavior in organizations (5th ed.). Prentice Hall. Hayek, F. (1945). The use of knowledge in society. American Economic Review, XXXV(4), 519–530. Kanter, R. M. (2011). The change wheel: Elements of systemic change and how to get change rolling. Pfeffer, J., & Sutton, R. I. (2006). Evidence-based management. Harvard Business Review (January), 63–74. Weick, K. E., & Quinn, R. E. (1999). Organizational change and development. Annual Review of Psychology, 50, 361–386.

10

Gerstner (2002).

A Final Word

14

Complexity is not simple, but to live the life we want, we cannot without some degree of complexity. The most elegant way to deal with complexity, to reduce its complicatedness without forfeiting the benefits of complexity, is to have a proper education, knowledge, a fitting style of thinking, which is a sufficient complex cognitive structure. This can be accomplished at an individual level, but is more difficult in a social setting with vested interests, sound bite type media, and pressure for immediate results, in which one needs some benign aggression to create time for thinking and in which communicating new insights is hampered by a sign type use of language. Understandably the narcissistic persons will be the best to deal with the increasing complexity. They do so by having a vision to change the world and to create meaning. Productive narcissists are independent thinkers, are willing to take risks, in a way simplify their world by not listening to other people, although they can be oversensitive to criticism. Narcissistic leaders only can be successful as far their ideas fit in the Zeitgeist, like Bill Gates and Steve Jobs. But the world has more types of personality who also are entitled to some ontological security in life through understanding instead of a continuous struggle of reflexes. We therefore need to educate each other, in the workplace and in institutions of educations. A challenge will be to produce management books that do not have the reductionist simplification and neither the escapism of romantic complexity.

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2_14

337

Index

A Abstraction intellectual, 34, 228, 276 personal, 233, 255, 276, 325 social, 37, 59, 229, 232, 255, 275, 325 Abstract thinking, vii, 4, 20, 34, 107, 134, 135, 154, 162, 168, 227–234, 243, 244, 247, 265, 269, 275, 294, 325 Accountability, 79, 88, 117–118, 128, 144, 162, 191, 193, 230, 291, 313, 318 avoidance, 232, 324 paradigm, 88 Accountable entities, 46, 181, 185, 264 Accounting information, 59, 118, 128, 130, 132, 209, 313 Accounting information systems, 58, 59, 311, 313 Accounting rules, 27, 91, 101, 130, 158, 230, 256, 280, 301 Account management, 126, 148, 221 Ackoff, R.L., 3, 52, 68 Adaptation, vii, 11, 12, 16, 20, 24, 27, 56, 57, 59, 63, 67, 68, 70, 71, 76, 85, 106, 110, 114, 123, 178, 186, 188, 190, 204, 239, 243, 252, 258, 277, 280, 288, 295, 309, 310, 321, 322, 332–334 Adaptive capability, 9, 198, 259, 260 Adaptivity, 51, 56, 74, 77 Administrative behavior, 24, 185, 189, 227 Administrative instruments, 21, 64, 178, 180, 190, 254, 277, 278, 287, 333 Agenda theory, 155 Agility, vii, 9, 10, 20, 55, 77, 79, 84, 252, 253, 258, 261, 270, 308, 311, 312 AH ToGo, 43, 223 Airbnb, 70, 86, 263 Albert Heijn, 43, 131, 223 Alienation rights, 15, 19, 40, 41, 53 Allen, J., 35

Amazon, 97, 226, 322 Amsterdam Medical Center, 36 Apple, 97, 198, 210, 226, 245, 263, 270, 322 Architecture closed, 210 opens, 10, 87, 207, 210, 263, 265 Argyris, C., 119, 323 Aristotle, 66 Arrow, K.J., 55, 78, 128, 155, 188, 189, 192, 230, 290, 291, 314 Artificial intelligence (AI), 102, 194, 270, 272, 279, 281 Ashby, W.R., 6, 24, 35, 60, 70, 131, 186, 187, 206, 222, 305, 307, 310 Assets cognitive, 39 intangible, 6, 9, 10, 19, 22, 58, 118, 124, 158, 167, 182, 190, 191, 193, 196, 201, 204, 264, 291, 302, 306, 314 tangible, 6, 10, 19, 25, 148, 158, 190, 191, 197, 300, 311, 314 Assurance, 118, 129, 142, 306, 317 Audit committees, 303, 311 Auditors, 16, 36, 169, 234, 309, 311 Auftragstaktik, 296 Authenticity existentialist, 233 romantic, 233 Authority paradigm, 88 Autodesk, 186 Autonomy, 51, 196, 203, 204, 228, 233, 255, 304, 325 Autopoiesis, 66 Availability bias, 77, 112, 281, 289 B Bachelard, G., 60 Balanced scorecard, 76, 118, 158, 252, 278, 294

# The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Strikwerda, Organized Complexity in Business, Future of Business and Finance, https://doi.org/10.1007/978-3-031-25237-2

339

340 BASF, 125 Baudrillard, J., 42, 46, 103 B2B-market, 176 B2C-market, 176 Beck, U., 12, 25–27, 45, 62, 89, 101, 167, 169, 193, 204, 236, 288, 303 Beer, S., 40, 45 Befehlstaktik, 296 Behavior proactive, 55, 108, 110, 163, 239, 332 satisfying, 33, 116 Behavioral complexity, 234 Behaviorism, 323, 324 Beliefs, 11, 39, 59, 64, 109, 114, 116, 139, 189, 225, 231, 245, 280, 334, 335 conservation, 11, 39, 114, 116, 189, 225 systems, 280 Benjamin, W., 104 Big Data, vii, 33, 41, 103, 120, 128, 144, 190, 191, 252 Bill of material (BOM), 58, 122, 133, 134, 183, 184, 195, 299, 309, 313, 314 Biology, 5, 20, 55, 59, 66, 67, 74, 100, 111, 118, 133, 199, 268 BMW, 44 Boeing, 51, 133, 184–186, 208 Boltzmann, L.E., 256 Boolean value, 120 Bottom-up resource allocation process, 64, 154, 166, 189, 305, 307, 317, 320 Boundary systems, 280 Bower, J.L., 64, 91, 133, 154, 155, 166, 188– 190, 206, 228, 305, 307, 334, 335 Boyd, J., 141 Brown, T., 289, 299 Budget-gaming, 148, 189 Building information system, 186 Built to performance, 184 Built to print, 184 Bunge, M., 32–34 Bureaucracy, 20, 38, 51, 182, 192, 229 legalistic rational, 38 Burnout, 76, 261 Business administration, 4, 12–14, 20, 33, 53, 60, 79, 90, 161, 227, 230, 234, 244, 281, 298 administration, doctrine, 161, 234 control, 222, 261, 319 model canvas, 248 models, 17, 19–24, 35, 56, 57, 62, 67, 72, 73, 75, 78, 88, 102, 109, 113, 114, 117, 122, 124, 126–131, 133, 147–149, 156, 163, 165, 168, 169, 184, 189, 191, 193,

Index 198, 205, 209, 219, 226, 244, 247–249, 251–253, 257–259, 261, 263, 266, 278, 300, 302–304, 307, 308, 310, 315, 318, 319 units, 10, 38, 68, 75, 146, 147, 181, 182, 187, 222, 233, 248, 256, 260, 264, 296, 298, 313, 324 Business IT-alignment paradigm, 19, 58, 63, 130, 133, 209, 308 Business-IT-alignment trap, 19, 130, 308 Butterfly-effect, 331 Buurtzorg, 21 C Capital human, 15–17, 19, 26, 118, 158, 196, 198, 227, 290, 304, 312, 314, 321, 332 allocation system, 190 market, 16, 19, 37, 84–86, 128, 157, 158, 164, 169, 170, 191, 205, 234, 260, 273, 274, 289, 302, 303 Care path, 36, 89, 223, 224, 252, 296 Cargill, 188, 317 Cartesian worldview, 52 Cash flow trap, 157 Causal density, 111, 190–194, 205, 279 Causality downward, 5, 55, 102, 120–123, 135, 190, 246, 293 multi-, 62, 236 social, 55, 120, 122 structural, 120 Causal relations, 4, 20, 23, 34, 45, 62, 70, 75– 78, 102, 103, 119–123, 132, 141, 143, 149, 159, 160, 167, 189, 191, 194, 200, 227, 244, 248, 249, 258, 270, 272, 274, 279, 292, 303 non-linear, 119, 121, 244 Cellular phones, 70 Central Planning Bureau (CPB), 251 CEO, vii, 39, 65, 91, 116, 117, 141, 142, 146, 154, 160, 161, 168, 194, 195, 221, 225– 239, 245, 247, 248, 274, 304–306, 309, 323, 336 Ceteris paribus, 32 CFO, 37, 128, 302, 305 Change emergent school, 332, 333 general manager view, 208, 334–335 planned, 332 programmatic, 164, 165, 334 systemic, 45, 57, 164, 335–336

Index task-alignment, 165 Chaos, 59, 66, 199, 231, 256, 257 Chaos theory, 66, 195, 331, 336 Christensen, C.M., 39, 112, 210, 226, 235, 263 Christian redemption, 27 Churchill, 35, 225 Classifications, 120 Classifier, 120 Codification, 33, 75, 98–104, 108, 238, 300 Coding, 99, 102, 129 Cognitive framing, 72, 77, 226, 227 Cognitive manager model, 115 Cognitive psychology, 323, 324 Collator, 270 Co-location principle, 58, 145, 176 Command and control, 55, 103, 324 Communication theory, 170 Communicative capacities, 196 Competence management, 19, 46, 261 Complex individual, 46, 115 Complexity absorption, 275 algorithmic, 63, 129 behavioral, 21, 91, 92 cognitive, 11, 34, 42, 64, 76, 111, 229, 234, 261, 303, 312, 326 combinatorial, vii, 3, 85, 127, 249, 269, 296, 298 conceptual, 15, 60, 65, 70, 100, 111, 115, 121, 122, 124, 125, 134, 163, 170, 209, 236, 243, 250, 305, 310, 319, 321 constructions, 53, 63, 83, 124, 208 of consumers, 10, 15, 43, 105, 120, 168, 207, 235, 306 definition, 12 details, 34, 61–63, 68, 70, 105, 120, 134, 177, 184, 207, 208, 226, 243, 244, 262, 263, 296 discovery, 59 disorganized, 65–68 downsides, 230, 288, 294, 307 dynamics, 4, 5, 9, 22, 35, 61–62, 67, 68, 85, 111, 129, 163, 208, 209, 226, 236, 249, 251, 252, 261, 265, 268, 269, 272, 288, 294, 296, 303, 307, 310–312, 317, 321, 326 epistemic, 64 evolutionary, 66 generative, 66, 67, 209, 263 integrative, 40, 79, 111, 115, 310 irreducible, 63 knowledge, vii, 3–5, 7, 9, 12, 15, 19, 22, 25, 28, 35, 37, 38, 40, 43, 45, 46, 60, 63, 66, 67, 74, 75, 78, 79, 85–88, 92, 100, 101, 124, 126, 127, 129, 133, 134, 163, 168,

341 187, 191, 193, 205, 206, 222, 224–226, 229, 230, 232, 234, 237, 239, 248, 249, 252, 255, 261, 262, 265, 266, 275, 288, 291, 295, 296, 298–300, 303–305, 307, 309, 310, 312, 317, 319–322 Kolmogorov, 32, 33, 105 limited, 15, 25, 28, 38, 39, 100, 115, 180, 230, 275, 281, 298, 311 of markets, 4, 11, 12, 24, 26, 44, 59, 73, 84, 87, 88, 112, 176, 181, 188, 206, 207, 222, 225, 226, 229, 235, 250 modern, 3, 7, 25–28, 38, 60, 74, 101, 105, 106, 132, 134, 185, 193, 227, 233–235, 301, 305 objective, 11, 13, 15, 16, 18, 23, 46, 54, 63– 65, 68, 70, 105, 165–167, 170, 177, 191, 194, 220, 243, 249, 251, 296, 298, 306, 307, 312, 317 ontological, 32, 34, 63–65 optimum, 77, 271 organized, 4–6, 10, 12, 13, 19, 21, 37, 54, 60, 63, 65–68, 70, 74, 78, 89, 98, 124, 127, 139, 163, 168, 177, 187, 188, 221, 223, 235, 239, 243, 248, 252, 261, 263, 265, 272, 281, 291, 294–296, 298, 301, 312, 315, 319–321, 325 post-modern, 18, 20, 25–28, 45 of products, 61, 63, 67, 86, 88, 125, 126, 133, 176, 206–208, 210, 222, 223, 238, 262, 269, 288, 298 science, 4, 5, 9, 14, 32, 59–61, 63, 66, 74, 76, 85, 100, 101, 105, 124, 179, 228, 268, 287 semiotic, 32 social, viii, 11, 17–19, 25, 27, 35, 38, 42, 44, 66, 85, 99–101, 105, 124, 126, 127, 166, 180, 191, 208, 210, 265, 268, 327 subjective, viii, 4, 6, 11, 13, 18, 34, 60, 63– 65, 70, 91, 188, 193, 275, 322 theory, vii, 3–5, 7, 9, 11, 13–21, 27, 32, 40, 41, 52–54, 59, 60, 63–67, 70, 74, 84, 89, 92, 99–101, 105, 134, 168, 176, 195, 209, 229, 243, 262, 264–266, 268, 287, 294, 296, 306 upside, 288, 294, 307 Complex number, 32 Complex product systems (CoPS), 208 Complicatedness, 13, 23, 35, 60, 198, 277, 337 Conceptual future, 194, 246, 247 Conceptual past, 194, 202, 246, 247 Consumer behavior, 11, 16, 120, 121, 226, 235, 244, 302 Consumer preferences, 16, 43, 62, 63, 106, 110, 113, 119, 165, 187, 210, 211, 222, 223, 294

342 Consumer preferences (cont.) complexity of-, 43, 187, 210, 222 Contract materials, 15, 146 Control, 3, 12, 38, 53, 90, 105, 143, 175, 221, 226, 244, 290, 333 bureaucratic, 57, 58, 73, 295 cybernetics, 17, 20, 27, 54–56, 58, 72, 77, 105, 106, 110, 113, 122, 132, 188, 281, 295 financial, 19, 44, 105, 191, 221, 259, 278, 311, 312, 318 levels of, 13, 40, 72, 122, 132, 181, 198, 278 loose, 17, 27, 44, 312, 324 meta-, 122, 134 premises, 231, 245, 274, 278, 280 rights, 9, 40, 58, 77, 90, 144, 145, 148, 265, 317 strategic, 55, 105, 114, 181, 260, 310, 312, 318, 324 style of, 260 tight, 24, 27, 44, 169, 259, 260, 310 Coordination defined, 177 design school, 181 efficiency, 37, 87, 178, 180, 181 ex ante, 181–183, 191, 193, 195, 305, 311, 316 explicit, 163, 177, 195, 199, 276 feedback, 23, 179, 236, 275, 278 feed-forward, 179 fine-grained, 203 hierarchical, 133, 176, 188 horizontal, 37, 163, 185 hyper-, 203 implicit, 179, 191, 192, 203, 276 imposed, 22, 163, 176, 185, 188, 199, 313 instruments, 177–179, 181 managerial, 175–178, 182, 191 micro-, 196, 203 process-based, 182 process school, 181, 182, 187 self-, 74, 123, 169, 178, 182, 185, 198, 199, 238, 249, 257, 261, 288, 313, 315, 332 stigmergic, 178, 195, 199–201, 247, 278 structure school, 181, 182 Copyrights, 200, 201 Co-regulation, 196 Corporal, 55 Corporate account management, 10, 37, 130, 146 Corporate finance, 19, 22, 27, 98, 101, 145, 157, 158, 248, 260, 293, 302, 317 Corporate governance, 3, 4, 27, 41, 65, 114, 116, 139, 144, 145, 150, 160, 162, 169,

Index 232, 289–291, 293, 300, 301, 303, 304, 309–311 complexity in-, 300, 301, 309 Corporate law, 25, 27, 40, 41, 58, 90, 91, 108, 144, 160, 161, 179, 300, 301, 304 COSO, 17 Cost centers, 146, 292 Costs of resources, 16, 289 Creative Commons, 200 Critical success factors (CFS), 34, 62, 128 CRM-software, 37 Culture corporate, 325 definitions, 54, 55, 99, 109 embedding mechanism, 323 goods, 197 Customer value proposition (CVP), 23, 37, 84, 125, 163, 177, 182, 193, 208, 291, 293, 294, 296, 297, 299, 300, 304 CVP, see Customer value proposition (CVP) Cybernetics, vii, 4, 5, 14, 20, 45, 54–60, 74, 99, 103, 105–135, 162, 193, 244, 249, 257, 270, 311 Cyberpunk, 247, 299 D Data, vii, 5, 11, 21, 31–33, 45, 71, 72, 74–77, 79, 84, 97–100, 102, 103, 106, 110, 113, 114, 119–121, 123, 124, 128–135, 143, 149, 159, 166, 167, 189, 193, 194, 220, 221, 223, 232, 243, 252, 257, 258, 269, 274, 279–281, 302, 304, 308–310, 314, 318, 335 semantic standardization, 220, 221, 257, 281, 309 Database transactions, 75, 130–133, 221 Datasets, 203 Data standardization semantics, 220, 281, 335 Dawkin, R., 203 de Bono, E., 34, 36 Decentralization, 17, 55, 64, 109, 122, 165, 166, 169, 192 Decision control, 145, 146, 148 defined, 20, 22, 26, 54, 58, 64, 78, 98, 103, 105, 116, 120, 124, 127–129, 139, 140, 145, 146, 148, 151, 155, 160, 166–168, 185, 221, 223, 232, 257, 301, 303, 308, 312, 326 externalities, 249, 253 framing, 148 theory, 98, 99, 105, 106, 140, 268

Index well-structured, 99, 152, 269 Decision-making decentralized, 205, 309 rational, 26, 116, 142, 149, 158 rule following, 116, 149, 156, 165 styles of, 151, 152, 154, 235 Decision rights attribution, 146 complexity, 19, 166, 167, 176, 206, 326 partition, 146 Dell, 184, 263 Democratic systems, 118 Design criteria, 23, 261, 287–289, 291 functions, 19, 37, 78, 128, 194, 196, 209, 261, 262, 292, 294, 295, 299, 308, 313, 315, 317, 321 value propositions, 191, 193, 194, 205 thinking, 22, 72, 90, 194, 202, 204, 205, 259, 287–289, 292, 294, 296, 299, 315 Dewey, J., 33 Diagnostic control systems, 280 Diamond framework, 121 Digitas, 168 Dijksterhuis, E.J., 52 Disaster response, 201 Disclosure, 41 Discovery driven growth, 44, 126, 318 Discovery-driven planning, 20, 84, 123, 244, 305, 312, 317 Disinformation, 101–106, 119 Diversity, 6, 27, 28, 42, 58, 67, 72, 101, 104, 115, 158, 191, 196, 197, 202, 244, 261, 273, 322 DNA, 57, 58, 123, 133 Doctrine of business administration, 161 Dominant logics, 39–41, 72, 77, 112, 122, 155, 160, 226, 228, 256, 295 Dorst, K., 144, 194, 259, 288 Drinking water companies, 250 Drucker, P.F., 16, 21, 23, 135, 165, 187, 244, 292, 314 Dualities, 25, 101, 102, 104 Duane Morris, 41, 206, 245 Duty of good faith, 205, 301 Dynamic capability view, 70 E Echo chamber effect, 11, 13, 103, 204, 277, 325 E-commerce, 78, 148, 149, 166 Economic growth, vii, 3, 4, 13, 25–27, 38, 42, 47, 83–92, 125, 126, 156, 168, 175, 181, 220, 227, 248, 287, 289, 303, 332

343 Effect-information, 20, 73, 119–124, 249, 257, 258, 272, 314 Effector, 270 Efficiency adaptive, 205 Einstein, A., 32, 144, 225, 269 Emergence, viii, 14, 16, 51, 61, 70, 77, 83–85, 99, 112, 118, 121, 135, 167, 185, 187, 188, 199, 204, 208, 227, 248, 252, 269, 288, 291, 298, 301, 302, 304–306, 317, 332, 334 Empowerment information-based, 257, 258 Encyclopedia, 200 End-to-end processes, 76, 123, 132, 148, 177, 186, 193, 249, 264, 278, 294, 296, 313 Enlightenment, 58, 200 Entropy, 56, 97, 134, 256, 279, 313 EPICS, 252 ERP-systems, 133, 185, 186, 314 Etzioni, A., 26, 195 Eucity, 111 Evaluation functions, 120 Executive tasks of, 175 Extra-role behavior, 198, 324 F Fake news, 97, 277 Fallibility theorem, 23 Fayol, H., 161, 175, 177, 182, 234, 274 Feedback autonomous, 277, 278 cognitive, 251, 272, 274, 276, 278 conformance, 273 discursive, 134, 274, 280 emotional, 275, 276, 278 extrinsic, 273 intellectual, 54, 237, 274, 278 intrinsic, 273, 274 market, 56, 77, 78, 113, 165, 257, 273, 277– 280 social, 44, 54, 78, 113, 237, 270–276, 278, 280, 316 unsolicited, 273 Feedback loops types of, 272 Feminism, 27, 42 F2F-interaction, 275 F-form, 146, 148 Financial capital, 196, 205 Financial control, 27, 130, 222, 234, 260, 295, 318

344 Firm multidimensional, 10, 24, 132 nature of, 6, 9, 11, 14, 19, 76, 112, 119, 125, 129, 131, 148, 155, 160, 192, 226, 247, 295, 317, 318 resource-based view, 20, 226 First Industrial Revolution, 23 Five-forces-framework, 121 Florida, R., 42, 163 Flow production, 176, 177 Fluidity, 190, 191, 199, 207 Folksonomy, 127 Foremen system, 23 Fourth Industrial Revolution, 13, 15, 18–21, 41 Framing cognitive, 72, 226, 227, 272, 274 Friedman, M., 17, 157, 195 Fromm, E., 103, 114, 234, 324 Fukuyama, F., 58, 59 Functional design, 262 Fundamental Attribution Error, 109, 233 Furubotn, E.G., 40, 179, 197 G Galbraith, J., 14, 24, 68, 91, 186, 188 Gaming, 78, 198, 204, 267 Gastrointestinal oncology patients, 36, 223 Gender, 27, 42, 202, 276 General Electric, 125 General ledger, 130, 134, 335 General meeting of shareholders, 146, 160 General Motors, 21, 165, 223 General Problem Solver, 143 General Systems Theory, 53 Gerstner, L.V., 92, 221, 226, 237, 309, 335, 336 Gestalts, 52, 250, 251 GNP, 91, 206 GNU-license, 201 Gödel’s Incompleteness Theorem, 101 Google, 44, 67, 73, 97, 119, 168, 226, 261, 270, 277, 306 Gordon, R.J., 27, 89 Governance overhang, 157 GPS, 70 Grant, A., 125, 230 Groupthink, 72, 78, 112 Guiding system, 74, 260, 297, 316 H Habermas, J., 33, 53, 107, 108, 117 Hayek, F.A., 59, 66, 85, 122, 165, 176, 178, 230, 332 Hayles, K., 11, 203, 204, 231

Index Healthcare, 89, 258 Heidegger, M., 228 Heuristics, 4, 34, 39, 63, 101, 102, 109, 119, 122, 124, 134, 140–142, 149, 151, 155, 170, 232 Hierarchy, 3, 10, 12, 14, 15, 17, 21, 23, 36, 38, 55, 66, 73, 75, 87, 88, 91, 101, 106, 107, 109, 110, 123, 125, 128, 135, 162, 163, 175, 176, 181–184, 187, 188, 201, 204, 228, 243, 245, 249, 256, 257, 259, 280, 291, 292, 295, 297, 313, 333 Higher systemic logic, 247 Holistic thinking, 14, 20, 143, 244, 248, 312 HR departments, 315 Human capital, 15–17, 19, 76, 118, 133, 158, 182, 196, 227, 290, 304, 312, 321, 332 Husserl, E.G.A., 228 Hyper-coordination, 203 Hypothesis of simplicity, 152, 228, 293 I IBM, 21, 45, 63, 79, 92, 126, 131, 148, 167, 221–222, 230, 237, 246, 247, 257, 281, 309, 318, 335, 336 Identification, 74, 122, 176, 190, 324 Identity of the firm, 76, 179, 298 If-then-else-statement, 75 Ill structured problem, 143, 144, 158 Incompatibility, 45 In-control, vii, 17, 56, 59, 70, 73, 79, 103, 112, 114, 131, 132, 142, 169, 186–188, 192, 222, 255, 258, 271, 275, 279, 295, 307, 309–311 Individualism methodological, 59 Induction, 119, 121, 128, 191, 193, 272, 288 Industry standards, 87, 178, 179 Information accountability, 117–118 age, 97 allelopathic, 118 asymmetry, 45, 146–148, 165, 167, 292, 304 axiological, 66, 105–107, 110, 116, 134, 257 biological, 54, 102, 122 causal, 39, 77, 118–128, 134, 257, 274, 278, 314 conceptual, 102, 103, 106, 112, 119–127, 315 costs of, 3, 19, 21, 22, 25, 38, 41, 78, 79, 131, 148, 152, 155, 169, 180, 182, 187, 192, 209, 264, 270, 271, 292, 294, 302, 313, 321

Index culture, 103, 104 cybernetic, vii, 4, 5, 14, 60, 77, 105, 106, 134, 135, 162, 244 discursive, 100–106, 122 economy, 4, 97, 197 eidetic, 77, 111, 113, 114, 116, 118, 119, 258, 310, 314 external, 36, 58, 98, 110, 116, 128, 130– 132, 155, 167, 221, 257, 258, 266, 273, 298, 311, 315, 319 goal, 54, 58, 66, 74, 105–108, 113, 116, 127, 128, 134, 189, 198, 244, 257, 261, 276, 312, 314, 333 goods, 191, 197, 209 interface, 119, 131 material, 52, 54, 77, 97, 109–114, 116, 117, 119, 121, 124, 126, 128, 129, 133, 152, 176, 257, 258, 265, 270, 278, 310, 314 multidimensional, 16, 63, 78, 79, 112, 134, 206, 221, 255–257, 264, 298, 305 organization of, 4, 19, 36, 45, 63, 98, 105, 129, 131, 139, 146, 148, 166, 175, 180, 186, 187, 197, 207, 238, 244, 257, 264, 309, 314 overload, vii, 104, 105, 129 pragmatic, 120, 127–129, 257, 258 processing capacity, 22, 177, 332 reproductive, 133–134 semantic, 97, 98, 101, 131, 221, 257 Shannon-type, 21 society, vii, 38, 97–100, 167 spaces, 4, 5, 16, 21, 55, 84, 103, 105, 106, 134, 135, 202, 276, 278, 298 transaction, 78, 119, 129–134, 176, 192, 207, 209, 221, 252, 257, 270, 309, 313, 335 unifying theory, 100 Infrastructure contribution measurement, 321 Initiatives bottom-up, 36, 71, 74, 163, 188, 189, 323, 335 Innovation combinatorial, 10, 67, 72–74, 133, 134, 187, 194, 196, 200, 263, 264, 270, 291, 317 disruptive, 263 opens, 72, 76, 84, 110, 200, 205, 206, 244, 276, 300, 317 Institutions business, 4, 27, 44, 58, 91, 225, 233, 246, 247, 305 complexity reducing, 4, 12, 13, 25–27, 91, 180, 238, 303 formative, 25, 179 Integrated patient units, 89

345 Integrated reporting, 16, 117 Integrative complexity, 111, 115, 116, 236, 310 Integrative thinkers, 236, 262 Integrative thinking, 14, 236 Integrity of the firm, 23, 312, 318 Intel, 55 Intelligence organizational, 72 phase, 143 Intelligent complex adaptive systems (ICAS), 51, 55, 70–79, 187, 271, 278 Interacting pre-packaged, 203 Interaction complexity of, 195–199 definition, 203 free, 10, 44, 75, 76, 195, 196, 199–202, 204, 248, 259 Interactive control systems, 280 Interactive perspective model, 6, 72, 116, 122, 164, 198, 245 Interactive theory, 203 Interactivity, 52 Internal governance, 76, 126, 127, 144, 146, 148, 163, 166, 167, 182, 185, 193, 264 Internet, 10, 24, 35, 58, 70, 72–74, 77, 84, 88, 104, 106, 114, 127, 164, 196, 200, 207, 208, 273, 275, 325 Interpretation, 4, 38, 55, 72, 74, 75, 77, 101, 104, 111, 114, 115, 117, 129, 231, 245, 246, 256, 258, 280 Intuition, 4, 7, 20, 90, 108, 140, 141, 149, 194, 205, 228, 231, 232, 247, 305 Inventory records, 129 Investment center, 292 Investors fundamentals, 118 Invisible hand, 66, 84, 195 Invoices, 129 IT-applications categories of, 209 IT-governance, 5, 13, 296, 300, 309, 315 Ius abutendi, 197 Ius utendi, 176, 197 J Jager, D., 238 James, W., 33 Japan, 126, 157, 158 Jensen, M.C., 15, 40, 41, 75, 111, 145, 150, 155, 157, 168, 189, 195, 200, 205, 274, 289, 302, 306 Jobs, S., 245, 337 Johnson & Johnson, 245

346 Just-in-Time (JIT), 177 K Kanban, 178 Kanter, R.S., 45, 74, 91, 110, 222, 235, 257, 297, 311, 335 Kant, I., 103 Kaplan, R.S., 76, 91, 128, 132, 133, 156, 158, 185, 188, 193, 209, 233, 248, 252, 253, 264, 265, 292, 305, 307, 309, 311 Kleiner, A., 230 Knowledge centricity, 71, 75 codified, 14, 20, 25, 85, 125, 206, 223, 312, 323 management, 75, 168, 189, 264 management systems, 134, 277 personal, viii, 10, 15, 20, 22, 38, 40, 72, 75, 78, 85, 134, 163, 168, 187, 195–197, 204, 205, 293, 298, 300, 301, 305, 317, 326, 334 tacit personal, 25 uncodifiable, 15, 25, 40, 75, 187, 198, 205 Kohlberg, L., 248 L Labor laws, 25, 27, 40, 41, 58, 90, 179, 220, 315 Lafley, 222, 236, 238, 336 Language, 5, 7, 11, 13, 14, 21, 40–47, 57, 59, 64, 70, 79, 92, 101, 102, 106, 169, 178, 179, 231, 247, 275, 322, 337 Law of Requisite Variety, 6, 24, 35, 36, 45, 60, 70, 77, 131, 186, 187, 206, 222, 305, 307, 310, 322 Law of unintended consequences, 26, 27, 236 Leaders narcissistic, 230, 245, 337 Leadership complexity, 70, 122, 238, 239, 297, 311 transactional, 238, 239 Lean Six Sigma, 123, 126 Lean synchronization, 177, 178, 181 Legal persona, 160, 300 Levi, 168, 235, 306 Lex parsimoniae, 31 Liberalization, 26, 59, 85, 88, 91, 92, 167, 168, 181, 238, 291, 303 Liberal market theory, 66 Lindsey, B., 160, 227–229, 231–233, 255, 325 Linux, 197, 201

Index Local interests, 324 Logical positivism, 26, 52, 98 Logic of information, 185 Logic of manufacturing, 185 Logik der Sache, 185, 195, 205, 294 Lorentz, E., 331 Lower systemic logic, 247 Luhmann, N., 25, 38, 170, 229 Lyotard, J.-F., 42 M Machine learning, 33, 57, 102, 120, 121, 193, 194, 205, 227, 270–274, 278, 279, 281 Management by instruction (MBI), 23, 175 Management by objectives (MBO), 23 Management by values (MBV), 23 Management development, 36, 261, 287, 297, 310, 322, 323 Management information, 4, 59, 74, 118, 127– 130, 132, 147, 257, 258, 303, 314, 318– 320 Management information systems (MIS), 78, 132, 264, 314 Management of change, 57, 68, 85, 164, 331– 336 Management rights, 145 Mandeville, B., 195 March, J.G., 35, 64, 114, 116, 123, 149, 156, 165, 231, 258, 273, 278, 281 Market equilibrium, 15, 16, 25, 68 Market myopia, 140, 229, 244 Martin, R.L., 3, 53, 90, 159, 170, 225, 227, 229, 230, 236–238, 248, 261, 262, 275, 325 Marxism, 27 Material resource planning (MRP), 314 Mathematical communication theory, 56, 74 Mathematical modelling, 61, 70, 268, 269 Mathematical theory of information, 4 Maximum-willingness-to-pay, 289 Mead, M., 46, 55, 67, 325 Media society, 198 Medici-effect, 202 Memes, 203, 325 Meta-control, 122, 134 Metaverse, 247 M-form, 64, 65, 146, 148, 155, 156, 166, 190, 222, 228–230, 264 Micro-coordination, 196, 203, 204 Microprocessor, 55 Microsoft, 55, 201 Miller, D., 11, 34–36 Mini car, 43

Index Mintzberg, H., 156 Misinformation, 97 Mission, vii, 4, 14, 21–23, 55, 58, 60, 73, 74, 77, 105–110, 113, 116–118, 122, 123, 128, 129, 134, 135, 140, 162, 163, 186, 190, 238, 243–246, 250, 257–259, 280, 297, 306, 312, 323, 335, 336 Mix-match flexibility, 184, 210, 263 MNC structure, 45 Modern Management Tradition, 90 Modernism second, 18, 103, 187 Modularity, 125, 133, 183–185, 203, 206, 208, 210, 263, 266, 269, 293, 294, 300, 306, 309 Modules, 63, 70, 86, 88, 125–127, 133, 134, 184, 201, 207, 210, 228, 262, 263, 269, 292, 294, 299, 314 Moral commitments, 195 Moral development, 115, 248 More, T., 54 Motivation, 21, 45, 85, 108–110, 117, 198, 204, 244, 324, 334 based on contribution, 324 Multicausality, 52, 62 Multiculturalism, 27 Multidimensionality, 11, 71, 77, 78, 132, 256, 257, 261, 297 Myopia of learning, 34, 134 N Napoleon, 35, 225, 228 Netflix, 21, 46, 72, 73, 88, 119, 298, 322 Network, 22, 47, 78, 83, 84, 115, 185, 192, 198, 201, 204, 207, 208, 233, 267, 268, 288, 292 Networking, 204 Newtonian physics, 60 New York Stock Exchange Commission on Corporate Governance, 117 NGOs, 119, 160, 273 Norman, R., 194, 246, 247 Norton, D.P., 76, 91, 128, 156, 158, 185, 188, 193, 209, 233, 248, 252, 253, 264, 265, 305, 307, 309 Noxity, 111 NPV-method, 267 Nutella, 44 O Objective functions, 16, 73, 105, 120, 143, 145, 146, 159–161, 257, 280, 323

347 OECD, 132, 251, 327 One database, 318 OODA-loop, 141 Open source software, 197, 200–202, 204 Operating system (OS), 55, 201 Oracle, 133 Organization ambidextrous, 71 bazaar-type, 201 designed, 36, 65, 66, 73, 128, 133, 181, 187, 193, 253, 287, 294 efficient, 36, 68, 107, 151, 161, 163, 176– 178, 180, 181, 190, 248, 264, 290, 291, 294, 296, 304, 313, 326, 333 information-based, 6, 17, 21, 88, 135, 169, 221, 244, 293 matrix, 14, 91, 112, 265 mechanic, 12, 44, 71, 254, 255, 310, 333 multi-divisional, 91 organic, 6, 12, 24, 44, 70–79, 90, 193, 253– 256, 259, 310, 333 self-sustaining, 196 virtual, 184–186, 325 Organizational citizenship, 198 Organizational complexity, levels of, 23–24, 186, 187 Organization design, 12, 13, 19, 37, 44, 76, 91, 192, 193, 238, 287–291, 293, 294, 296, 297, 313 Organization development (OD), 287, 332 Osterwalder, A., 122, 248, 292, 299, 300 O’Toole, J., 3, 20, 34, 170, 225, 235, 325 Outsourcing, 10, 86, 88, 126, 176, 184, 187, 250, 263, 266, 294, 297, 299 Overcode, 41 P Pain-avoidance syndrome, 112, 123 Parsimony, 31 Patent law, 179 PDCA-cycle, 274 Peirce, C.S., 33 Performance management, 16, 19, 44, 56, 57, 90, 128, 144, 238, 239, 252, 258, 261, 302, 322 Performance parameters, 73, 128, 129, 238, 243, 279, 290, 317, 318, 335 Permeability, 74, 76 Personal interests, 117, 141, 154, 198–200, 324 Personality marketing, 103, 114, 233 PESTLE, 297, 310 Phelps, E., 92, 168, 220, 226

348 Philips electronics, 61, 90, 112, 125, 265 Platform, 5, 65, 78, 131, 183, 187, 193, 200, 210, 225, 244, 246, 253, 255, 269, 270, 294, 295, 297, 298, 312, 315, 316 Plato, 66 P&L responsibility, 222 Polymath, 202 Porter, M., 22, 53, 74, 79, 120, 121, 158, 190, 248, 314 Postmodernism, 12, 27, 28, 42, 101, 103, 104, 278 Pragmatism, 33 Preparedness, 244, 251–253, 299, 312, 316 Presentism, 46, 252 Prigogine, 66, 256 Principal-agent theory, 147 Problems professionally induced, 164–165 Problem solving styles of, 154 Process control, 260, 270, 317 Procter & Gamble, 21, 126, 222–223, 229, 236–238, 245, 246, 258, 335, 336 Productivity labor, 22, 83, 157, 290, 293 Product-structure file, 183 Profit center, 37, 45, 90, 132, 146–149, 221, 226, 292, 335 Profit maximization, 16, 109 Profit model, 148, 248, 303 Programming DNA-based, 59 levels of, 22, 58, 59 loose, 3, 12, 20, 24, 56, 70, 71, 75, 77, 105, 123, 135, 169, 178, 186, 190, 239, 259, 260, 295, 309, 333 Project funding, 306, 312 Projects, 10, 11, 18, 21, 47, 68, 76, 78, 79, 87, 91, 123, 126, 127, 129, 130, 133, 146– 148, 162, 163, 182, 183, 186, 188, 196, 198, 199, 206, 233, 249, 256, 264–268, 288, 291, 296, 302, 305, 306, 308, 310, 312, 315–322, 326 Property law, 25, 40, 90, 117 Property rights alienable, 192, 200 Proteins, 57 Provenance of ideas, 18, 103, 244 Psychological climate, 73, 310, 312, 322, 323 Publicis, 126, 168, 226, 235, 237, 238, 246– 248, 306, 308 Purchasing orders, 129 Purpose, vii, 14, 22, 34, 55, 71, 74, 75, 102, 103, 105–108, 115, 119, 129–131, 135, 148, 190, 196, 199, 203, 204, 223, 224,

Index 233, 243, 244, 251–253, 255, 262, 270, 278, 295, 297, 333 Putting out system, 23 Q Query-software, 257 R Rajan, R.G., 41 Rational-inductive ideal, 149 Real options, 140, 143, 244, 266, 267, 293, 317 Reconceptualization, 24, 34, 40, 57, 72, 92, 103, 113, 122, 142, 149, 154, 168, 190, 193, 194, 205, 232, 246–248, 258, 306, 310 Reconceptualizing, 4, 56, 57, 59, 102, 114, 140, 149, 162, 203, 244, 246–248, 275, 310 Reductionism, 13, 20, 40, 53, 60, 70, 154 Reflexivity, 24, 62, 106, 114, 117, 159, 165– 166, 227, 258 Reframing, 34, 57, 113, 144, 191, 194, 196, 202, 203, 205, 244, 246, 294, 299, 324 Regulations, 12, 14, 57, 61, 63, 68, 85, 89, 91, 92, 105, 118, 119, 143, 186, 195, 207, 225, 247, 250, 255, 273, 293, 322 Regulators, 44, 57, 89, 105, 118, 157, 169, 220, 279 Regulatory context, 41, 220, 296 Regulatory state, 6, 89 Reification, 203 Renaissance, 54, 202, 268 Requisite variety, vii, 3, 78, 243 Re-regulation, 89, 91, 92, 118, 238, 275, 291 Reserved powers, 144, 146 Resistance through distance, 324 Resistance through persistence, 324 Resistance to control, 324 Resource allocation, 37, 67, 73, 76, 90, 123, 126, 154, 181, 182, 185, 193, 199, 226, 253, 310, 316 Resource allocation process, 11, 14, 16, 65, 76, 89, 91, 132, 133, 155, 162, 163, 182, 188, 224, 244, 264, 290, 305, 306, 317, 319, 320, 333, 335 Resource-based view, 20, 226 Resource configuration, 21, 182, 193, 258, 292, 297, 298 Resource dependency view, 56, 110, 132, 255 Resource mobilization, 67, 76, 126, 133, 199, 297, 320 Revenue center, 146 Revenue model, 248 Reward system, 73, 144, 145, 189, 311

Index Richter, R., 40, 179, 197 Rights intellectual property, 19 Rights of alienation, 58, 176 Risk, 6, 12, 13, 26, 27, 33, 34, 39, 46, 64, 78, 86–89, 104, 109, 112, 134, 146, 152, 155, 157, 169, 184, 191, 192, 204–206, 227–234, 260, 265–269, 279, 280, 288, 303–305, 307, 308, 310, 311, 317–319, 337 management, 12, 26, 27, 41, 44, 169, 204, 208, 260, 268, 269, 303, 304, 309, 310, 312, 315 society, 6, 26, 44 RNA, 123 ROI-tree, 22, 256 Rolling forecasts, 244, 251, 253 Rorty, R., 33 Rosen, S., 75, 198, 326 Rule of law, 54, 58, 59, 117, 229 Ryanair, 124, 248, 249 S Sales slip, 97, 223 SAP, 133 Sartre, J.-P., 228 Scenario planning, 164, 244, 250, 251, 280, 297 Schumpeter, J., 84, 290, 291 Science fiction, 54, 72, 247, 299 Second Industrial Revolution, 7, 13, 15, 19, 21, 23, 25, 26, 38, 40, 41, 74, 89, 90, 232 Security ontological, 12, 337 Self-discovery, 67, 84, 106, 162, 237, 321 Self-education, 322 Self-management, 76, 108, 110, 316 Self-organization, 17, 51, 58, 59, 66, 67, 76, 108, 110, 123, 133, 169, 196, 199, 249, 252, 258, 261, 288, 298, 311, 320, 326 Self-organizing teams, 23 Self-reproduction, 66, 67 Sensemaking, 192, 231, 246, 310, 326 Sensing, 109, 110, 113, 116, 163, 192, 202, 257 Sensors, 110, 270, 271, 279 Shannon, C.E., 4, 5, 21, 56, 60, 63, 74, 76, 98–100, 102, 103, 106, 129 Shared service centers, 64, 65, 131, 146, 182, 187, 221, 246, 248, 253, 256, 257, 269, 294, 313, 315 Shareholders, 37, 41, 85, 111, 114, 118, 126, 141, 145, 157, 160, 161, 169, 191, 192, 195, 198, 201, 205, 238, 252, 274, 289, 290, 300–302, 304

349 Shell, 188, 317 Shipping documents, 129 Signals, 98, 99, 103, 110, 122, 155, 170, 270, 295 Signs, 18, 42, 44, 46, 59, 97, 103, 105, 256, 306, 325, 337 Simon, H.A., 3, 4, 12, 17, 24, 44, 54, 56, 61, 70, 71, 85, 90, 105, 129, 139, 143, 144, 149, 154, 164, 176, 186, 188, 190, 192, 232, 237–239, 255, 258, 263, 293, 295, 296, 304, 309, 310, 312, 314, 323, 326, 333 Simplicity algorithmic, 32, 33 epistemological, 33 experimental, 33 notational, 33 pragmatic, vii, 33, 34, 134 psychological, 33, 64, 147 technical, 32, 33, 252 Simulacrum, 42 Sloterdijk, P., 18, 53 Smith, A., 66, 83–86, 195 Sociability, 199, 323 Socialization, 18, 22, 57, 73, 278 Social production, 191, 197, 198, 273 Socio-cognitive complexity, 235 Solidarity, 323 Solutions, 10, 17, 46, 53, 68, 102, 109, 112, 123, 143, 148, 149, 152, 154, 165, 167, 170, 190–192, 194, 195, 199, 202–206, 208, 219–222, 229, 231, 232, 236, 237, 252, 257, 259, 265, 274–277, 287, 288, 294, 309, 318, 333, 335 Sony Europe, 154 Source code, 201 Specialization, 10, 15, 22, 25, 35, 38, 60, 63, 67, 83–87, 89, 134, 146, 175, 181, 182, 225, 262, 291 Splinternet, 277 Staff departments, 64, 124, 230, 248, 257, 311, 315, 316 Standards, 17, 24, 61, 62, 70, 84, 86, 87, 126, 139, 148, 175, 188, 219, 230, 263–265, 267, 268, 270, 272, 273, 279, 280, 302, 305, 321 Stephenson, N., 247 Stiglitz, J.E., 27, 168, 230, 259 Stigmergy cognitive, 200 entomology, 200 Strategic planning, 260, 324 Strategic themes, 21, 76, 78, 133, 147, 148, 156, 182, 248, 249, 264, 265, 306, 311, 312, 316, 320, 321

350 Strategy business, 22, 67, 148, 248, 258, 260, 273, 295, 306, 308, 311, 336 corporate, 22, 260 execution, 13, 67, 73, 118, 126, 147, 148, 166, 190, 297, 304, 305, 312, 314 maps, 76, 118, 248, 264 Stress, 64, 76, 232, 261, 293 Structure organizational, 22 Structured decision support, 99 Structured decisions support systems (SDSS), 313 Subunit power-base criterion, 333 Sub-unit power principle, 37, 318 Supervision complexity in, 311 Sustainability, 16, 27, 28, 109, 117, 289, 290, 293, 299 Synergies, 19, 37, 64, 79, 148, 169, 189, 234, 256, 260, 295, 302 Systemic change, 45, 57, 122, 164, 335, 336 Systemic context, 19, 73, 155, 165, 189, 190, 239, 245, 256, 277, 335 System interventions, 280, 332, 334 System thinking, 3, 9, 11, 14, 20, 26, 45–47, 52, 53, 55, 77, 236, 244, 287, 293 mathematical school, 46 System 1 thinking, 141, 232 System 2 thinking, 141, 232 T Talent management, 40, 73, 261, 321 Task to be accountable, 162 constitutional, 300 to coordinate, 162 to foresee, 162 to monitor, 162 to organize to provide leadership, 162 Taxes, 117, 132 Technology digital, 168, 197, 203, 226, 235, 246, 269, 271, 278, 306, 309, 313 Tesco, 131 Thinking abductive, 22, 194, 202, 259, 288 authentic, 18, 228, 247 deductive, 237 inauthentic, 228 inductive, 237

Index transformational, 324 Third Industrial Revolution, 26, 27 TMC, 206 Tolerance, 12, 42, 73, 75, 156, 312 Tools administrative, 13, 19, 22, 23, 72, 243, 247, 261, 310, 321 Total factor productivity, 25, 91 Toyota, 126 TQM, 123, 124, 126, 157 Transaction costs, 88, 176, 219 Transactive memory systems, 134, 189 Transfer prices, 41, 132, 182 Transformation, 27, 45, 56, 57, 66, 67, 72, 77, 92, 113, 127, 168, 193, 233, 238, 239, 243, 247, 248, 252, 255, 279, 280, 308, 309, 333, 335 Trial-and-error, 44, 45, 77, 84, 123, 126, 135, 159, 160, 169, 193, 194, 205, 237, 257, 260, 272 Trust blinds, 111 explorative, 111, 233 institutional, 17 personal, 233 U Uber, 70, 86, 263 Uncertainty fundamentals, 230, 233, 250, 251 Unitarist perspective, 198 Unit-organization, 47, 89, 124, 165, 187, 188, 209, 219, 225, 305, 307, 313 US Defense, 55 UTC, 178 Utilitarianism, 27 Utility, 15, 16, 55, 105, 142, 195, 210, 250, 268, 290, 314 Utopian thinking, 12, 54 V Valuation, 106, 109, 118, 149, 154, 165, 302 Values committed, 289, 290 definition, 257, 259 final, 109, 289, 290 function, 74, 120, 163, 177, 194, 197, 201, 262, 276, 295, 299, 300, 302, 314, 323 hierarchy of, 22, 74, 77, 108, 129, 140, 162, 245, 258, 280, 295 instrumental, 74, 109

Index liquidation, 289, 290 stakeholder, 28, 117, 157, 160, 162, 169, 288–290, 301, 302 Vector, 32, 120 Verne, J., 54 Viable system model, 45 Virtual engineering object (VEO), 185 Virtual engineering processes (VEP), 185 Visible hand, 66, 84, 195 Vision as eidetic information, 246 Vision trap, 246 Von Clausewitz, C., 245 Von Mises, L., 66 W Walmart, 97, 119, 131 Watt, J., 56, 57, 105, 272, 279

351 Weber, M., 38, 51, 113, 229 Weick, K.E., 70, 114, 231, 239, 332 Well-structured decisions, 149 Well-structured problems (WSPs), 139, 143, 144, 147, 149, 152, 153, 156, 158, 161 Wertebalance, 110 Whitehead, A.N., 31, 32, 36, 39, 269 Wiener, N., 54, 98, 270 Wikipedia, 195, 197, 200, 201, 204 Wintel, 55 Work-entrepreneurship, 206 Workers, creative knowledge, 75, 163, 195, 249 Z Zingales, L., 41 Zook, C., 35