Drivers of Organizational Change: A System Dynamics Analysis Integrating Environmental Determinism and Managerial Choice

Nicole Zimmermann Dynamics of Drivers of Organizational Change GABLER RESEARCH Nicole Zimmermann Dynamics of Drive...

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Nicole Zimmermann Dynamics of Drivers of Organizational Change

GABLER RESEARCH

Nicole Zimmermann

Dynamics of Drivers of Organizational Change With a foreword by Prof. Dr. Dr. h. c. Peter Milling

RESEARCH

Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.d-nb.de.

Dissertation University of Mannheim, 2010

1st Edition 2011 All rights reserved © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011 Editorial Office: Stefanie Brich | Nicole Schweitzer Gabler Verlag is a brand of Springer Fachmedien. Springer Fachmedien is part of Springer Science+Business Media. www.gabler.de No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright holder. Registered and/or industrial names, trade names, trade descriptions etc. cited in this publication are part of the law for trade-mark protection and may not be used free in any form or by any means even if this is not specifically marked. Cover design: KünkelLopka Medienentwicklung, Heidelberg Printed on acid-free paper Printed in the Netherlands ISBN 978-3-8349-3051-4

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Foreword The design of organizational change has become a permanent managerial task in order for organizations to adapt to their environment or to exert influence on it. Accordingly, change in organizations can be reactive and proactive; either as a response to new demands which exert influence on the organization from outside, or as a deliberate measure to gain competitive advantage. Drivers of change and its management are the focus of Nicole Zimmermann’s dissertation. It explains change in organizational structures and processes by the interaction of internal and external stimuli. Here, it addresses the question of why some organizations are able to recognize new challenges early on and face them adequately, while others persist in their habituated structures and behavior patterns and are finally driven out of the market. Two major research questions are at the core of the investigation: (i) what are the drivers of organizational change, and (ii) is a history of successful change processes helpful or obstructive for further change? As such, problems are investigated as to why companies in a more specific sense and organizations in a more general sense have difficulties to change their strategies which have proven successful in the past whenever circumstances change. It is an important methodological characteristic of the stated problem and indeed a trivial fact that organizational change is a highly dynamic phenomenon. However, much research that addresses this topic employs methods that are adequate for static subjects of study. Often, this results in considerable differences in organizational theories and in their statements concerning change. The example of the New York Stock Exchange (NYSE) represents the basis of an extensive investigation of processes of inertia and change in an organization that has been successful for a very long period of time and then ran into difficulties that threatened its continued existence. The case study is used in order to collect data of a concrete example, to formulate hypotheses and to test them. Methodologically, the system dynamics approach is used because it is equally appropriate for theory formulation and the analysis of a case study. The author develops a formal model which can later be used for computer simulation. It is based on a multitude of also qualitative data that she derived predominantly from weblog entries and a number of interviews with employees of the New York Stock Exchange. She succeeds at developing hypotheses, at postulating causal relationships, at testing them by analyses, and at developing them further. The research question focuses on the transition from manual to electronic securities trading, which had been delayed for a long period of time, but which has since been implemented very quickly. Different periods, resistance, and impulses for change are analyzed, formed into hypotheses, and transferred to a model format. The comparison of simulated behavior over time with empirical data shows high consistency. The combination of exogenous pressure for the abandonment of manual

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Foreword

trading with endogenous actions by the management team creates a process that quickly transforms the habituated behaviors at the NYSE. The statements and insights which have been derived from the case study are generalized in order to derive the basic elements of a theory of organizational change. For this purpose, the original model that analyses a specific situation is transferred into a generic one that represents an entire class of applications. Elaborations on this generic model, its structure, and its behavioral patterns allow the author to gain insight into the processes of organizational change. Drivers of and resistance to change, external and internal factors, perception and actions of the management team are represented, analyzed, and consolidated towards the direction of a system of hypotheses—a theory—of organizational change. The analysis reveals that both developments in the environment and decisions by the managerial team determine the evolution of organizations. Additionally interests of stakeholders and the cognitive flexibility of the management team play a decisive role. In particular, the behavior of stakeholders can be used as a valuable source of information about the organizational environment.

Professor em. Dr. Dr. h. c. Peter Milling

VII

Acknowledgements Organizational phenomena have been a major topic of interest for me for a long time. In particular, the dynamic interplay of pressure for change and inertia has caught my attention. I have experienced its practical relevance and gained more and more interest in the theoretical side of this issue. Organizational change is situated at the interface between the system ‘organization’ and human beings. This dissertation concentrates on this interconnectedness of systemic drivers of and barriers to organizational change and on human interaction with this system. The dissertation grew out of my work during my time as an external PhD student and later as a research and teaching assistant at the Chair of Industrial Management (Industrieseminar) at the University of Mannheim. Additionally I profited from a research year at the University at Albany, State University of New York. The period as a doctoral student and assistant was interesting, challenging, and informative, which made it very valuable to me. Many people supported me during this phase and I would like to express my gratitude to them. I am greatly indebted to my advisor, Prof. Dr. Dr. h. c. Peter Milling, for giving me the possibility to work and develop under his guidance and for his continuing support and encouragement. I am also much obliged to researchers at the University at Albany. Here, I would like to express my gratitude to Professor George Richardson for many fruitful conversations and his support, as well as for co-refereeing this dissertation. Professor David McCaffrey aroused my enthusiasm for the case of the New York Stock Exchange, and Professor David Andersen helped to see the forest for the trees. Navid Ghaffarzadegan, Hyunjung Kim and Mohammad Mojtahedzadeh also made helpful suggestions. Additionally, I would like to thank Professor David Lane of the London School of Economics. Several years ago by his lecture he inspired me and aroused my enduring interest for system dynamics modeling and for systemic questions. My colleagues, Dr. Philipp Konecny, Dr. Christian Lehr, Switbert Miczka, Dr. Lena Oswald, Oliver Schmitzer, Prof. Dr. Jörn-Henrik Thun and Christian Weitert as well as our secretary Iris Scheuermann, have accompanied me during my years as a research and teaching assistant. They made it a pleasant time. In my personal environment, I have been able to rely on my close friends’ and family’s support, for which I am truly greateful. My sincere thanks go out to them all. I would also like to emphasize the assistance with final editing by Susanne Eschbach and, in particular, Dr. Lena Oswald. My parents have continuously encouraged me during my time as a doctoral student and on my entire path of life. I would like to thank them for their wonderful encouragement, for the motivation they gave me, and their loving support.

Nicole Zimmermann

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Contents Foreword.................................................................................................................... V Acknowledgements ................................................................................................ VII Contents ................................................................................................................... IX List of Figures ........................................................................................................ XIII List of Tables ........................................................................................................ XVII List of Abbreviations ............................................................................................ XIX A

The Challenge of Triggering Change in Organizations ................................... 1

B

Deterministic and Voluntaristic Theories of Organizational Change ............. 9 B.I

Incommensurable Drivers of Organizational Evolution ................................. 9 B.I.1

The Classical View of Adaptation to the Environment ..................... 11

B.I.1.a

Deterministic Adaptation and Impediments to the Adaptation Process ................................................................................... 11

B.I.1.b

Behavioral Elements of Adaptation ......................................... 15

B.I.2

Inertia and Routines as Determinants of Change ............................ 21

B.I.2.a

Inertia Leading to Environmental Selection ............................. 21

B.I.2.b

Routines Hindering and Driving Change ................................. 24

B.I.3

Transformation Triggered by Strategic Choice ................................ 28

B.II Reconciliation of Environmental Determinism and Managerial Choice ...... 33 B.II.1

Compatibility of Voluntaristic and Deterministic Views ..................... 33

B.II.2

Understanding Change by the Combination of Environmental and Managerial Forces ........................................................................... 37

B.II.3

A Reconciled Theory of Radical Change ......................................... 43

B.III Cognition and Attention as Drivers and Restraints of Alteration ................. 49 B.III.1 Perception of the Environment Through a Cognitive Managerial Lens ................................................................................................. 49 B.III.2 Selective Attention to Issues and Stakeholders ............................... 54 B.IV Need for a Dynamic Feedback View of Organizational Change ................. 61

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Contents

C

The Phenomenon of Inertia and Change Exemplified in a Case Study of the New York Stock Exchange ........................................................................ 65 C.I

A Method Mix for Studying Change in Organizations and Especially at the New York Stock Exchange ................................................................... 67 C.I.1

Contribution of Studying Change with System Dynamics and Case Study Methodologies .............................................................. 69

C.I.2

The Process of Model Conceptualization Supported by Case Study Research Methods................................................................. 72

C.I.3

Confidence Through Model Analysis and Testing............................ 79

C.II Reacting to Automation in the U.S. Securities Market ................................ 80 C.II.1

Automation of Order Clearing, Routing, and Information Systems .. 80

C.II.2

Moving Towards an Electronic Market ............................................. 87

C.II.3

The New York Stock Exchange’s Recast of Trading Systems ......... 93

C.III Structure and Behavior of Forces for Retention and Change ................... 100 C.III.1 A Perspective of Adaptation to the External Environment ............. 101 C.III.2 An Endogenous Struggle of Culture and Resistance ..................... 111 C.III.3 Managerial Impact on Change ....................................................... 120 C.III.4 Full Model Behavior ....................................................................... 139 C.IV Analyses of Model Structure and Behavior .............................................. 145 C.IV.1 Confidence in Model Structure and Parameterization .................... 146 C.IV.2 Validation of Model Behavior and Sensitivity ................................. 149 C.V Implications of the New York Stock Exchange’s Recast of Trading Systems ................................................................................................... 160 D

Generic Interpretation of Organizational-Environmental Forces, Feedback, and Change ................................................................................... 165 D.I

A Generic Model of Organizational Inertia and Change ........................... 165 D.I.1

Motivation for a Generic View ........................................................ 165

D.I.2

Generic Model Structure ................................................................ 168

D.II Structural-Behavioral Analysis and Causal Theory .................................. 182 D.II.1

Validation of the Generic Model ..................................................... 182

D.II.2

Effects of Reinforcing and Balancing Feedback on the Occurrence of Change ................................................................... 185

Contents

XI

D.III Possibilities of Managerial Intervention for Driving Change ..................... 192 D.III.1 Inertia and the Ambiguous Effects of the Responsiveness to Pressure ........................................................................................ 192 D.III.2 Effects of Increases in the Responsiveness of Attention ............... 198 D.IV Joint Management of Leverage Points ..................................................... 199 D.IV.1 Relationship Between the Responsiveness of Strategy to Pressure and Attention .................................................................. 200 D.IV.2 Policy Implications in Different Environments ................................ 205 D.V A Feedback Theory of Organizational Inertia, Change, and Attention...... 211 E

Realization of Change in Organizations ....................................................... 217

References ............................................................................................................. 227 Appendix................................................................................................................ 257

XIII

List of Figures Figure A-1: Innovation typology .................................................................................. 5 Figure B-1: Adaptive feedback .................................................................................. 12 Figure B-2: Impediments to adaptive feedback ......................................................... 15 Figure B-3: Limitations to adaptation......................................................................... 20 Figure B-4: A feedback view of strategic choice in a theory of organization ............. 31 Figure B-5: Sociological paradigms (Burrell and Morgan) ......................................... 34 Figure B-6: Convergence in the punctuated equilibrium model ................................. 44 Figure B-7: Determinants of stakeholder attention according to Mitchell, Agle, and Wood ............................................................................................... 59 Figure C-1: Trade participants and interactions in floor trade ................................... 66 Figure C-2: Percentage of leading stock exchanges enabling some or full e-trade (BOT) ..................................................................................................... 88 Figure C-3: NYSE market share in NYSE-listed securities (BOT) ............................. 90 Figure C-4: Adaptation process of stock exchanges ................................................. 93 Figure C-5: Spread.................................................................................................... 94 Figure C-6: Reference mode (BOT) .......................................................................... 98 Figure C-7: Diagramming conventions .................................................................... 100 Figure C-8: Sector diagram of the adaptation view ................................................. 101 Figure C-9: External influences in the remaining market (SFD) .............................. 103 Figure C-10: Diffusion of electronic trading in the securities market (BOT)............. 104 Figure C-11: External influences (CLD) .................................................................. 105 Figure C-12: Customer pressure for e-trade (SFD) ................................................. 106 Figure C-13: Limiting effect of e-trade on change ................................................... 106 Figure C-14: Customer pressure for e-trade (CLD) ................................................. 107 Figure C-15: Specialist participation from data (BOT) ............................................. 108 Figure C-16: Customer pressure for market quality (SFD) ...................................... 109 Figure C-17: Customer pressure for floor trade (CLD) ............................................ 110 Figure C-18: Adaptation view (BOT) ....................................................................... 111 Figure C-19: Sector diagram of the culture and resistance view ............................. 112 Figure C-20: Commissions and spread (SFD and BOT) ......................................... 113 Figure C-21: Resistance pressure from floor (SFD and effect) ............................... 114

XIV

List of Figures

Figure C-22: Resistance pressure from floor (SFD and CLD) ................................. 115 Figure C-23: Cultural pressure from floor (SFD and CLD) ...................................... 117 Figure C-24: Effect of institutional customers on power of floor firms ..................... 118 Figure C-25: Power of floor firms (SFD and CLD) ................................................... 119 Figure C-26: Culture and resistance (BOT) ............................................................. 120 Figure C-27: Sector diagram of the management view ........................................... 120 Figure C-28: Inertia and repetitive momentum (SFD) ............................................. 122 Figure C-29: Relationship between change and inertia........................................... 123 Figure C-30: Inertia and repetitive momentum (CLD) ............................................. 123 Figure C-31: Relationship between customer orientation and change (SFD) ......... 126 Figure C-32: Repetitive attention loop (SFD) .......................................................... 128 Figure C-33: Customer orientation (SFD) ............................................................... 129 Figure C-34: Perception bias from managerial attention (CLD) .............................. 129 Figure C-35: Spread and market share (SFD) ........................................................ 132 Figure C-36: Relationship between time at NBBO and market share ..................... 133 Figure C-37: Market share adjustment from speed and market quality (SFD) ........ 134 Figure C-38: Relationship between time to execution and market share ................ 134 Figure C-39: Relationship between market share and openness to change (SFD). 135 Figure C-40: Confidence effect of market share on openness to change ............... 136 Figure C-41: Market share (CLD) ............................................................................ 137 Figure C-42: Liquidity algorithms as a response to low market quality (SFD) ......... 138 Figure C-43: Adaptation, Culture and Resistance, and Mgmt (BOT) ...................... 139 Figure C-44: Underlying forces (BOT)..................................................................... 140 Figure C-45: Relationship between the time at the NBBO and market share ......... 141 Figure C-46: Comparison with reference mode (BOT) ............................................ 142 Figure C-47: Full NYSE model (CLD) ..................................................................... 143 Figure C-48: Importance of liquidity algorithms and market quality ......................... 144 Figure C-49: Sector diagram making explicit the model boundary .......................... 147 Figure C-50: Linear development of e-trade in market ............................................ 148 Figure C-51: Sensitivity for institutional customer pressure for e-trade ................... 151 Figure C-52: Sensitivity for resistance..................................................................... 152 Figure C-53: Sensitivity for resistance, power, and cohesiveness of floor firms ...... 154

List of Figures

XV

Figure C-54: Sensitivity for the fractional change in trading per pressure ............... 156 Figure C-55: Sensitivity for fractional change in customer orientation .................... 157 Figure C-56: Sensitivity for managerial parameters ................................................ 158 Figure C-57: Sensitivity for changes in stakeholder and management parameters 159 Figure D-1: Diffusion of B (SFD) ............................................................................. 169 Figure D-2: Adaptation pressure for strategy B (SFD) ............................................ 170 Figure D-3: Effect the relative quality B on the perceived inadequacy of strategy .. 171 Figure D-4: Adaptation pressure for strategy B (CLD) ............................................ 171 Figure D-5: Resistance pressure for strategy A (SFD) ............................................ 172 Figure D-6: Effect of adequacy of quality A on resistance pressure ........................ 173 Figure D-7: Resistance pressure for strategy A (CLD) ............................................ 173 Figure D-8: Inertia and repetitive momentum (SFD) ............................................... 174 Figure D-9: Effect of change on the decrease of inertia .......................................... 175 Figure D-10: Limitations to changes of strategy (SFD) ........................................... 176 Figure D-11: Attention to stakeholders (SFD) ......................................................... 177 Figure D-12: Repetitive momentum in the generic model (CLD) ............................. 178 Figure D-13: Limitations to changes in attention (SFD)........................................... 178 Figure D-14: Adaptation of Attention (CLD) ............................................................ 179 Figure D-15: Performance (SFD) ............................................................................ 180 Figure D-16: Relationship between performance and change (SFD) ...................... 181 Figure D-17: Performance (CLD) ............................................................................ 182 Figure D-18: Sensitivity for changes in stakeholder and management parameters 184 Figure D-19: Generic base run (BOT) ..................................................................... 186 Figure D-20: Phases of loop dominance (BOT and CLD) ....................................... 187 Figure D-21: Comparison of early and late radical adaptation (BOT) ..................... 189 Figure D-22: Relationship between difference in quality B and strategy inadequacy......................................................................................... 190 Figure D-23: Two environmental changes (BOT) .................................................... 191 Figure D-24: Effects of high inertia (BOT) ............................................................... 194 Figure D-25: Sensitivity to variations of inertia ........................................................ 195 Figure D-26: Sensitivity for change per perceived pressure.................................... 197 Figure D-27: Sensitivity for changes in adaptability of attention .............................. 199

XVI

List of Figures

Figure D-28: Delayed adaptation and early radical adaptation (BOT) ..................... 201 Figure D-29: Nonlinear relationship between fractional change and adaptation effectiveness ...................................................................................... 202 Figure D-30: Effect of attention to stakeholders on model behavior (BOT) ............. 204 Figure D-31: Adaptation of attention to stakeholder favoring B (CLD) .................... 205 Figure D-32: Reaction to quicker environmental change (BOT) .............................. 206 Figure D-33: Nonlinear relationship between the fractional change and adaptation effectiveness in the case of quick environmental change . 207 Figure D-34: Sensitivity to the variation in the permanent pressure for strategy A .. 208 Figure D-35: Inconsistent managerial setup (BOT and CLD) .................................. 210 Figure E-1: Full generic causal loop diagram (CLD) ............................................... 223

XVII

List of Tables Table B-1: Theoretical focus of the dissertation ........................................................ 10 Table B-2: Selected examples of multi-paradigm research in organizational change theory.......................................................................................... 40 Table C-1: Research design ..................................................................................... 69 Table C-2: Data sources for variable derivation ........................................................ 75 Table C-3: Exemplary derivation of variables, causal relationships, and behavior.... 78 Table C-4: Quantitative content analysis of the Exchanges Weblog ....................... 125 Table D-1: Strategies and their qualities ................................................................. 168

XIX

List of Abbreviations Amex

American Stock Exchange

BOT

behavior over time

CEO

chief executive officer

CLD

causal loop diagram

COO

chief operating officer

DEC

Digital Equipment Corporation

DMM

designated market maker

DOT

Designated Order Turnaround

i.e. (id est)

that is

NASDAQ

National Association of Securities Dealers Automated Quotations

NBBO

national best bid and offer

NGO

non-governmental organization

NYSE

New York Stock Exchange

PC

personal computer

SEC

U.S. Securities and Exchange Commission

SFD

stock and flow diagram

SIAC

Securities Industry Automation Cooperation

SOFFEX

Swiss Options and Financial Futures Exchange

vs.

versus

WHO

World Health Organization

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A The Challenge of Triggering Change in Organizations Organizational environments are dynamic, and the speed of environmental change as well as its direction is often difficult to anticipate. In order to survive in the long term, organizations need to be able to successfully cope with a changing environment. They have to respond to emerging technologies or strategies even before they are fully established in the market.1 For this purpose, they need to be crafted in a way that makes them adaptable systems. Quick and appropriate organizational reactions are desired, and the ability to trigger organizational adaptations and transformations is important, but this poses great challenges to organizations. There is wide agreement that—in particular for established organizations—change is a difficult task. These establishments are often incapable of responding effectively to shifts in their external environment.2 “[… E]xisting organizations, especially the largest and most powerful, rarely change strategies and structures quickly enough to keep up with the demands of uncertain, changing environments.”3 This failure to understand the need for change or to initiate it threatens the performance and even the survival of organizations.4 Three examples will elaborate more clearly the difficulties organizations encounter when the demands from their environments change. The world's largest food corporation Nestlé, which originated from a breast-milk substitute producer, took more than a decade to adapt to its customers’ demands for ethical conduct. In the early 1970s, critics addressed Nestlé for its aggressive marketing practices for infant formula in the developing world, such as the failure to label products appropriately as well as the use of personnel dressed like nurses who advised mothers to use milk substitutes. Critics argued that, in combination with the prevailing conditions of water contamination, illiteracy, and poverty, infants often received a diluted and contaminated meal. They attributed the resulting malnutrition, diarrhea, and higher mortality rates to the use of breast-milk substitutes.5 Nestlé reacted by only minor modifications to its marketing practices.6 It even sued some of its 1

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See Benner, Mary J.: Securities Analysts and Incumbent Response to Radical Technological Change: Evidence from Digital Photography and Internet Telephony, in: Organization Science, Vol. 21 (2010), No. 1, pp. 42 and 59. See ibid., p. 42; Tripsas, Mary and Giovanni Gavetti: Capabilities, Cognition, and Inertia: Evidence from Digital Imaging, in: Strategic Management Journal, Vol. 21 (2000), No. 10/11, p. 1147. See also Hannan, Michael T. and John Freeman: Structural Inertia and Organizational Change, in: American Sociological Review, Vol. 49 (1984), No. 2, p. 151; and Schaefer, Scott: Influence Costs, Structural Inertia, and Organizational Change, in: Journal of Economics and Management Strategy, Vol. 7 (1998), No. 2, pp. 237–238. Hannan, Michael T. and John Freeman: Organizational Ecology, Cambridge, MA [et al.] 1989, p. 12. See Hill, Charles W. L. and Frank T. Rothaermel: The Performance of Incumbent Firms in the Face of Radical Technological Innovation, in: The Academy of Management Review, Vol. 28 (2003), No. 2, p. 257. See Newton, Lisa H.: Truth is the Daughter of Time: The Real Story of the Nestle Case, in: Business and Society Review, Vol. 104 (1999), No. 4, p. 369. See Sethi, S. Prakash: Multinational Corporations and the Impact of Public Advocacy on Corporate Strategy: Nestlé and the Infant Formula Controversy, Boston 1994, p. 53.

N. Zimmermann, Dynamics of Drivers of Organizational Change, DOI 10.1007/978-3-8349-6811-1_1, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

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A The Challenge of Triggering Change in Organizations

critics for libel. The company continued to affirm that its marketing practices were in accordance with industry standards and law. Due to Nestlé’s reluctance to react to its critics, activists organized a boycott in 1977 that won support from the World Health Organization (WHO) and UNICEF. In 1980 the WHO drafted an industry-wide code of conduct. Discussions between the two parties began, and Nestlé slowly started to accept several of the boycotters’ demands.7 It had noticed that it needed to adapt its culture and management to changes in its socio-political environment.8 The boycott was called off when Nestlé accepted the code in 1984, seven years after the boycott started and more than a decade after its critics pointed to problems caused by its marketing practices. Although the Nestlé Company still has many critics, it has started to change the way it deals with its stakeholders. The next time when some European customers and Greenpeace aired their displeasure with a genetically modified candy bar produced by the organization, it reacted quickly by removing the bar from the European market. It also refrained from introducing further genetically engineered products. Although the organization was very reluctant to change its strategy in the infant formula case, with respect to candy bars it quickly aligned its strategic orientation with the public’s expectations. While Nestlé finally adapted to stakeholder demands, other organizations failed to undergo necessary change in their strategic orientation. Digital Equipment Corporation (DEC) used to be a leading computer manufacturer and a highly innovative company during the minicomputer generation in the 1970s and early 1980s. With the emergence of the much simpler personal computer (PC), DEC remained with its old strategy and continued to offer rather expensive and specialized all-in-one solutions including storage, processing, infrastructure, and applications. The emergence of the personal computer changed DEC’s market environment as well as the needs of a fast growing number of private customers. DEC’s management did not perceive this new group and thus failed to recognize the potential of the new product.9 The DEC culture kept the organizational focus on customers with a higher technological interest. Deeply embedded convictions about the prevalence of their technologically innovative products in the market led to a diminished perception of the significance of problems and of customer groups which developed in a radically altered environment.10 In the view of Kampas, the degree of environmental determinism, meaning the degree to which the market drives organizations, increased with the development of the PC as a dominant design in the computer market. DEC with its inwardly focused culture 7

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See Post, James E.: Assessing the Nestlé Boycott: Corporate Accountability and Human Rights, in: California Management Review, Vol. 27 (1985), No. 2, p. 123; and Sethi: Multinational Corporations and the Impact of Public Advocacy on Corporate Strategy, 1994, pp. 226–227 and 273–282. See Sethi: Multinational Corporations and the Impact of Public Advocacy on Corporate Strategy, 1994, pp. 121 and 226–227. See Schein, Edgar H. with Peter DeLisi, Paul J. Kampas, and Michael M. Sonduck: DEC is Dead, Long Live DEC: The Lasting Legacy of Digital Equipment Corporation, San Francisco, CA 2003, p. 291. See ibid., pp. 251–252.


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could not keep up with external developments.11 After many of its businesses had been sold already, the company was bought up by Compaq in 1998, which was later acquired by Hewlett-Packard. DEC thus represents an example of a company that did not align its strategy with changing environmental conditions. The failure to perceive and orient to a new customer group despite existing environmental pressure was significant for the absence of change. A relatively young company represents the third example of an organization having problems to reorient. The digital photography company Linco, founded in 1996, soon became the technological market leader in digital photo memory, and faced problems of inertia as early as 2001. This means it was not sufficiently open to demands from its environment. Linco originated from an exploratory research project within another company, became a formal business unit in 1993 and independent in 1996. Shortly after its founding, the CEO and management established the firm’s identity as a digital photography company.12 Here, identity is defined as what insiders and outsiders perceive to be the core of an organization. It is closely associated with norms and shared beliefs about legitimate behavior and manifests itself in capabilities, routines, and procedures.13 In order to increase internal identification with digital photography, management even handed out digital cameras to employees. In 2000, by the time the organization went public, Linco was also regarded from the outside as a photography company. Resource allocation, capital investment, and human resource policies were guided by identity. Because the technological and industry context were viewed through the lens of digital photography, Linco’s perception of other opportunities was limited. Even when USB flash drives—which overlapped the functionality of Linco’s digital film—were launched by its competitors, Linco was reluctant to adapt because flash memory did not fit its identity.14 Customers adopted the competitors’ product. From 2001 on, a new CEO tried to change the organization’s identity, but both internally and externally the company did not converge on a new identity for several years. Linco faced significant difficulty adopting identity-challenging technologies that violate self-reinforcing core beliefs.15 It is surprising how quickly identity manifested once the organization embarked on the digital photography strategy. This then created a reinforcing cycle between cognitive elements of the organization and its actions, preventing it from deviating from the direction taken. It becomes evident from the examples that many established organizations have problems adapting to a new strategy demanded or pursued by other market partici11

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See Kampas, Paul J.: The Impact of Changing Technology, in: Schein, Edgar H. (Ed.): DEC is Dead, Long Live DEC: The Lasting Legacy of Digital Equipment Corporation, San Francisco, CA 2003, pp. 138–139. Directly see Tripsas, Mary: Technology, Identity, and Inertia through the Lens of 'The Digital Photography Company', in: Organization Science, Vol. 20 (2009), No. 2, p. 444. See ibid., p. 450. See ibid., pp. 450–451. See ibid., p. 454.

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pants. Organization theorists argue that organizations are transformed all the time.16 For many of them, peripheral change or the fine-tuning of processes to further improve an old but also a relatively new strategy does not pose great challenges. DEC and Linco, for example, were very innovative in the minicomputer and digital photography business. However, they had great difficulties adopting a new strategy requiring a change in the organization’s core. In the view of Hannan and Freeman, the organizational core is comprised of stated goals, forms of authority, the core technology, as well as the marketing strategy, indicating which clients the organization orients to.17 Tushman and Romanelli hold a very similar notion of core strategy or strategic orientation. In contrast to a narrow understanding of strategic orientation as a product or business strategy, it not only tells what business the firm is in, but also how it competes in this area. Change of the strategic orientation can then be regarded as a shift in the business and product strategy as well as the structure, control systems, power relationships, or in the core values of an organization.18 The dissertation follows the broad definition of strategic orientation suggested by Hannan and Freeman as well as Tushman and Romanelli. This view of organizational change goes beyond the mere adoption of a technological innovation or disruptive technology with different technological features that customers value. Figure A-1 places the three examples discussed into a grid that measures the extent of technological innovation and the newness of the market served. The three market developments fall into very different categories, and it cannot be said that they all serve new markets or are radical technological innovations. Only digital photography can be subsumed under disruptive innovations.19 The concept described here additionally extends to non-technological changes in the market and includes all significant shifts in customer or stakeholder demands that impinge on an organization and require a change in the organization’s core. Many organizations seem to have difficulty with transformations that require such a form of rethinking. Organizations already fail to recognize the need and to make the decision to adopt a new strategy.20

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19

20

See March, James G.: Footnotes to Organizational Change, in: Administrative Science Quarterly, Vol. 26 (1981), No. 4, p. 563. See also Kimberly, John R. and Robert H. Miles: The Organizational Life Cycle: Issues in the Creation, Transformation, and Decline of Organizations, in: The Jossey-Bass Social and Behavioral Science Series, San Francisco, CA 1980, p. 2. See Hannan and Freeman: Structural Inertia and Organizational Change, 1984, p. 156. Directly see Tushman, Michael L. and Elaine Romanelli: Organizational Evolution: A Metamorphosis Model of Convergence and Reorientation, in: Cummings, Larry L. and Barry M. Staw (Ed.): Research in Organizational Behavior, Vol. 7, Greenwich, CT 1985, pp. 175–176. See Christensen, Clayton M: The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail, Boston, MA 1997, p. xxv. Christensen defines a disruptive technology as an inferior technology which has different features that customers value. See Christensen: The Innovator's Dilemma, 1997, p. xv. See Mellahi, Kamel: The Dynamics of Boards of Directors in Failing Organizations, in: Long Range Planning, Vol. 38 (2005), No. 3, Special Issue: Organizational Failure, p. 270; Shattock, Michael: The academic profession in Britain: A study in the failure to adapt to change, in: Higher Education, Vol. 41 (2001), No. 1/2, pp. 45–46; and Sheppard, Jerry Paul and Shamsud

5


new newness of market served

personal computers

ethical products

digital photography

existing incremental radical extent of technological innovation

Figure A-1: Innovation typology From the reluctance to change among organizations the problem arises of what enhances their adaptability. The recognition of drivers of change allows for a more appropriate strategic orientation of organizations that goes beyond the mere understanding of change obstacles. Theoretical positions concerting drivers of change are far from concordant with each other. Opinions on what triggers development in organizations are indeed diverse, as the following statement highlights. “Although development can be considered as the natural model of organizational behavior, there is no theoretical consensus as to which forces generate development or hold it back. There is a debate as to how far one should seek to interpret the development of organizations as the product of external forces rooted in the social and economic system, as opposed to interpreting it as the product of idiosyncratic purposive behavior on the part of those within organizations who decide on strategies. It is a debate over the significance of environmental forces as opposed to managerial action, over organizational dependence as opposed to autonomy. This has been a fundamental issue both in the economic theory of the firm and in organization theory […].”21 As the statement reveals, the question of what determines change is central in organization theory. Different theories propose contrasting drivers and also inhibitors of change. The disagreement among organization theories shows that there is need for a deeper exploration. This investigation in this dissertation will therefore be guided by the research question of what the drivers of change are. The query will be discussed also in relation to possible inhibitors of change that proved to be important in the short examples presented. Reasons for the difficulty to make the decision to change will be analyzed. As the DEC case showed, managerial perception of old and emerging stakeholder groups turned out to be essential. Also the Linco study revealed the significance of a cognitive lens in relation to self-reinforcing beliefs and routines. The

21

D. Chowdhury: Riding the Wrong Wave: Organizational Failure as a Failed Turnaround, in: Long Range Planning, Vol. 38 (2005), No. 3, Special Issue: Organizational Failure, p. 250. Child, John and Alfred Kieser: Development of organizations over time, in: Starbuck, William H. and Paul C. Nystorm (Ed.): Handbook of organizational design, Vol. 1. Adapting organizations to their environments, Oxford 1981, p. 28.

6


Nestlé example demonstrated differences in the organization’s reaction to a first and second customer demand of a similar kind. These aspects deserve consideration in their relation to drivers of change. Overall, this dissertation addresses the questions of, first, what drives organizational change, and second, whether prior change serves as a driver for future transformations. In this way, it addresses the problem of why organizations have difficulties making the decision to alter their core strategy. A better understanding of what prevents and drives change in organizations is expected to lead to better organizational strategies and enhance organizations’ adaptability. Previous research on organizational change has often investigated drivers of change from a static perspective. Many approaches illuminate single aspects and disregard interdependencies and their evolution over time. This investigation employs a long-term focus of behavior and structure of organizations. Change is analyzed by system dynamics modeling and simulation as this approach accounts for the system’s complexity as well as the interaction of hindering and driving forces for transformation. It is also able to incorporate cognitive aspects and show how the system’s history is captured in accumulations, which may create path-dependent behavior.22 The relation of structure to behavior helps understand organizations’ evolution and elicit triggers of change. For the reason of educing determinants of organizational evolution, in chapter B, organizational theories are discussed in their relation to drivers of change. First, single-driver theories are addressed. Second, there may be multiple drivers and inhibitors. Therefore, it is clarified whether it is legitimate from a philosophical point of view to combine drivers and theories of change. After this discussion on legitimacy, several examples of combined or multiple-paradigm theories follow that include several of the elements of the single-paradigm theories. As there still remains a need for a more dynamic consideration of drivers of change, a case study helps elicit causal relationships of change and its absence. Chapter C deals with a case study of the New York Stock Exchange’s move to electronic trading and its methodical analysis. In the beginning, the system dynamics method is described together with its potential to reveal complex causal relationships, link structure and behavior, and provide understanding for what determines change. In particular the use of system dynamics for case study research in combination with qualitative methods is elaborated. Subsequently, the reader is introduced to important developments in the securities market which led to the emergence of electronic trading. The NYSE’s initial lack of response and later radical adaptation to electronic trading are analyzed in more detail by system dynamics methods. This part of the inquiry particularly focuses on different drivers of change. As the NYSE-specific analysis provides many examples of more generic relevance, a further, generic model is developed in chapter D. It represents a more ge22

For an overview of the principles system dynamics modeling see Forrester, Jay W.: Industrial Dynamics, Cambridge, MA 1961, pp. 67–72.


7

neric system dynamics theory and investigates the interconnectedness of drivers of change and possibilities for managerial intervention for creating different patterns of change. Chapter E summarizes the findings and consolidates the implications.

9

B Deterministic and Voluntaristic Theories of Organizational Change There are many schools of thought concerned with organizations and their evolution. Innovation research, for example, focuses on process and product innovations in business.23 Institutional theories and industrial organization deal with market exchanges.24 Strategic management is concerned with the competitiveness of companies.25 Many theories pay attention to single aspects in organizations such as power, culture, or sensemaking.26 Additionally, there are practical approaches such as change management and organization development, aiming at successful change implementation. This dissertation, however, concentrates on organization theory and its sub-category of organizational change. The reason is their focus on behavioral elements of decision-making. Structure and process are both important in organization theory, and some theories even take into account cognitive elements of decisionmakers.27 The organization theory approach also incorporates many of the aspects of strategic management, the dynamic capabilities view, and innovation research. This, together with decision-behavioral elements, makes it particularly suited for the analysis of change in organizations.

B.I

Incommensurable Drivers of Organizational Evolution

The organization theory field combines many smaller organizational theories. These theories have various and often incommensurable opinions regarding the origin of change. Despite these differences, they hold similar views of the essence of organizations and consider them as socially constructed systems of human activity. Organi-

23

24

25

26

27

See Milling, Peter M.: Modeling innovation processes for decision support and management simulation, in: System Dynamics Review, Vol. 12 (1996), No. 3, pp. 215–218; Milling, Peter M.: Understanding and managing innovation processes, in: System Dynamics Review, Vol. 18 (2002), No. 1, pp. 75–80; and Rogers, Everett M.: Diffusion of Innovations, 5. Ed., New York, NY [et al.] 2003, pp. 136–157. See Nelson, Richard R. and Sidney G. Winter: An evolutionary theory of economic change, Cambridge, MA [et al.] 1982, pp. 3–4; and North, Douglass C.: Institutions, institutional change and economic performance, Cambridge, MA [et al.] 1990, pp. 93–96 and 108–109. See Chandler, Alfred D. Jr.: Strategy and Structure: Chapters in the History of the Industrial Enterprise, Cambridge, MA 1962, p. 11; and Porter, Michael E.: On Competition, updated and expanded Ed., Boston, MA 2008, pp. 3–4. See Pfeffer, Jeffrey: Managing With Power: politics and influence in organizations, Boston, Mass. 1992, pp. 8–13; Schein, Edgar H.: Organizational Culture and Leadership, 3. Ed., San Francisco, CA 2004, pp. 1–8; and Weick, Karl E.: Sensemaking in Organizations:, Thousand Oaks [et al.] 1995, pp. 4–16. See Scott, W. Richard: Reflections of a Half-Century of Organizational Sociology, in: Annual Review of Sociology, Vol. 30 (2004), No. 1, pp. 3–4 and 7–8.


10

B Deterministic and Voluntaristic Theories of Organizational Change

zations are goal-directed and maintain boundaries that reflect their goals.28 Hence they are social systems that have persisting elements and a purpose. Following the organizational literature in general, the view of organizations employed in this piece of work mainly focuses on business organizations, but also extends to non-profit organizations, as they obey similar processes and share properties important in the organization theory field. Phenomena discussed in organization theory—such as decision-making in organizations, organizational behavior, and particularly organizational change—also apply to administrative, charitable, or health care organizations. The characteristics of organizational theories employed in this dissertation are summarized in Table B-1. Characteristics of organization theory employed Similar definitions of organizations, but different views on drivers of change

Focus on behavior and decision-making in combination with organizational elements

Focus on organizational change

Primary focus on organizational level of investigation

Table B-1: Theoretical focus of the dissertation Research has approached organizations and their evolution at different levels of investigation: the individual, group, organization, and even the industry. This dissertation concentrates on decisions and behavior at the group and organizational level. Accordingly, it relies on an aggregate view of individuals although they may be influenced by the psychological micro-perspective. Since the focus is on the organization, reasons for changes in the market will also be outside the boundary of the subject matter. They may be described, but will be included only in so far as they impinge on decision-makers in the organization as part of their decision environment. Much attention has been paid to the relationship between the environment or market and the organization. Environmental drivers of change have been found in changes in demand, technological innovations, and institutional conditions.29 The perspective according to which organizations adapt to the environment is one of the most prominent views of organizational theory and change. 28

29

See Aldrich, Howard E.: Organizations and Environments, Stanford, CA 2008, pp. 4–6; and Aldrich, Howard E. and Martin Ruef: Organizations Evolving, 2. Ed., London [et al.] 2006, p. 4. See also Barnard, Chester I.: The Functions of the Executive, Cambridge, MA 1938, p. 65. See Romanelli, Elaine and Michael L. Tushman: Organizational Transformation as Punctuated Equilibrium: An Empirical Test, in: Academy of Management Journal, Vol. 37 (1994), No. 5, p. 1145. See also Abernathy, William J. and James M. Utterback: Patterns of Industrial Innovation, in: Technology Review, Vol. 80 (1978), No. 7, pp. 41–46; Haveman, Heather A., Michael V. Russo and Alan D. Meyer: Organizational Environments in Flux: The Impact of Regulatory Punctuations on Organizational Domains, CEO Succession, and Performance, in: Organization Science, Vol. 12 (2001), No. 3, p. 253–254 and 269; and Meyer, Alan D., Geoffrey R. Brooks and James B. Goes: Environmental Jolts and Industry Revolutions: Organizational Responses to Discontinuous Change, in: Strategic Management Journal, Vol. 11 (1990), No. -, Special Issue: Corporate Entrepreneurship, pp. 94–97.

B.I Incommensurable Drivers of Organizational Evolution

B.I.1 B.I.1.a

11

The Classical View of Adaptation to the Environment Deterministic Adaptation and Impediments to the Adaptation Process

Well into the second half of the 20th century, organization theory was dominated by views of rational adaptation. Many theories fall into the rational adaptation category, ranging from scientific management to industrial organization economics and resource dependence theories.30 In their core these ‘modern’ theories are based on the assumption of human rationality and utility maximization, and they rely on humans as decision-makers according to the concept of the homo economicus.31 Based on this cognitive and behavioral assumption of humans, entire organizations are assumed to rationally adapt to the environment. Hannan and Freeman describe rational adaptation as “designed changes in strategy and structure of individual organizations in response to environmental changes, threats, and opportunities.”32 Since the environment is assumed to set the point of time and the direction of adaptation, many authors call these theories deterministic.33 Management choice plays a minor role in these theories that were originally rather focused on content; a management team is only assumed to direct the organization to match the environmental demands and the organization to be able to change in this adaptive direction that management sets. In this way, classical organization theory is thus also based on the premise of the malleability of the entire organization.

30

31

32 33

A more detailed list of theories includes scientific management, Fordism, Weber’s bureaucracy, industrial organization economics, contingency theories, and resource dependence theories. See Whittington, Richard: Environmental Structure and Theories of Strategic Choice, in: Journal of Management Studies, Vol. 25 (1988), No. 6, pp. 524–526. A concept of man is an assumption or abstraction about the nature of human beings. The deterministic concept of the homo economicus can be traced back to classical 19th century economists, particularly to Wilfried Pareto and John Stuart Mill. See Persky, Joseph: Retrospectives: The Ethology of Homo Economicus, in: The Journal of Economic Perspectives, Vol. 9 (1995), No. 2, p. 222. See Hannan and Freeman: Structural Inertia and Organizational Change, 1984, p. 150. See Child, John: Organizational structure, environment and performance: The role of strategic choice, in: Sociology, Vol. 6 (1972), pp. 8 and 10; Mellahi, Kamel and Adrian Wilkinson: Organizational failure: a critique of recent research and a proposed integrative framework, in: International Journal of Management Reviews, Vol. 5/6 (2004), No. 1, pp. 23 and 27; Child, John: Strategic Choice in the Analysis of Action, Structure, Organizations and Environment: Retrospect and Prospect, in: Organization Studies, Vol. 18 (1997), No. 1, p. 45; and de Rond, Mark and Raymond-Alain Thietart: Choice, chance, and inevitability in strategy, in: Strategic Management Journal, Vol. 28 (2007), No. 5, pp. 539 and 546.

12

B Deterministic and Voluntaristic Theories of Organizational Change

The adaptation process to environmental drivers of change can be described by a feedback loop. As Figure B-1 reveals, a gap between the target organization’s and the environment’s strategic orientation leads to adaptation pressure, consequently aligning the target’s strategic orientation with environmental demands and reducing the gap. This constitutes a balancing, also called negative, goal-seeking or adaptive feedback relationship—here the balancing loop Deterministic Adaptation. ENVIRONMENT'S STRATEGIC ORIENTATION

Strategic Orientation + - + (B) Deterministic gap Adaptation pressure to adapt + strategic orientation

Note on the Nomenclature of Feedback Figures Loop Polarity: The letters (B) or (R) in the center of a feedback loop indicate the polarity of the entire feedback cycle. A balancing loop (B) is a self-correcting loop that seeks to achieve an equilibrium stage and to remain at this stage. The antipode of a balancing feedback loop is a reinforcing feedback cycle (R). It enhances what happens in the system. Arrow Polarity: The signs next to the arrows specify the polarity of the respective causal relationship. If x changes, a plus indicates a change of y in the same direction, a minus indicates a change of y in the opposite direction. The mathematical representation is shown in the following: +

x՜y ֜ -

x ՜ y ֜

y x y x

t

>0ǡ and for accumulations Y= ‫׬‬t=0ሺx+…ሻds+Yt0 ; t

+ total pressure for more floor trade from customers + pressure for more floor trade per customer + + dissatisfaction effect of REF. PRESSURE market quality on PER NON.INST. pressure CUSTOMER +

REF. MARKET QUALITY

Figure C-16: Customer pressure for market quality (SFD) 472

See Storkenmaier and Riordan: The Effect of Automated Trading on Market Quality, 2009, p. 22. 473 See Blume, Siegel and Rottenberg: Revolution on Wall Street, 1993, p. 108; and Ryan and Schneider: Institutional Investor Power and Heterogeneity, 2003, pp. 399 and 416–417.

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C The Phenomenon of Inertia and Change Exemplified in a Case Study of the NYSE

As is shown in an aggregate CLD in Figure C-17, the mechanisms explained above create a balancing feedback loop called Customer Pressure for Market Quality. A perceived inadequacy of market quality from a falling fraction of floor trade creates customer pressure for more floor trade. The implementation of electronic trading thus triggers pressure for the retention of the specialist system. ACCESS TO INFORMATION TECHNOLOGY

+ fraction of e-trade in remaining market +

+ rel. time to execution -

Fraction of Institutional + Customers FRACTION OF EQUITIES HELD BY INSTITUTIONS

+ (B) Customer Pressure for Speed

dissatisfaction with time per inst. customer + pressure for more e-trade

NYSE Fraction + of E-Trade specialist participation + market quality from specialist

pressure for more floor (B) Customer Pressure for Market + Quality pressure for floor - from customers

Figure C-17: Customer pressure for floor trade (CLD) Competition for order flow takes place on price and speed.474 Since the two types of customer pressure represent environmental drivers of organizational change, the NYSE can be regarded as adapting to the external forces of the market. Figure C-18 reveals the output produced when the model presented so far is simulated. The behavior over time (BOT) graph shows a quick adaptation of the NYSE (line 2) to the trend in the market (line 1). Since the customers and the NYSE react to what they perceive in the remaining market, there is an implementation delay of about two years. Its length depends on how strongly the NYSE reacts to pressure, i.e. on the fractional change per perceived pressure p.a.475 When change starts to become implemented, non-institutional customers perceive the disadvantages in market quality and start pressuring for the retention of the old system (line 4), creating a deceleration of the implementation of e-trade at the NYSE. The non-institutional customers’ pressure vanishes after some years since they become used to the new situation and since their number decreases.

474 475

See Boehmer, Jennings and Wei: Public Disclosure and Private Decisions, 2007, p. 318. Here, the fractional change per perceived pressure per year was set to 0.1 which can be interpreted as a fractional change of 0.5 per year (i.e. an implementation delay of two years) and a fractional change per perceived pressure of 0.2. The more willing the organization is to react to pressure (meaning the higher the reference fractional change in trading per pressure per year), the quicker the implementation of e-trade.

111

C.III Structure and Behavior of Forces for Retention and Change

NYSE Fraction of E-Trade 1 Dmnl 6 pressure unit

1

0.5 Dmnl 3 pressure unit

1 2

34

12

12 2

12

3

4

3 3

0 Dmnl 12 3 4 0 pressure unit 123 4123 4 1970 1980 1990

4 2000 Date

2010

1 "fraction of e-trade in remaining market" : adaptation "NYSE Fraction of E-Trade" : adaptation 2 2 "total pressure for more e-trade from customers" : adaptation total pressure for more floor trade from customers : adaptation

1

4 2020

3 4 2030

1

2

Dmnl Dmnl 2 3 pressure unit 4 pressure unit

Figure C-18: Adaptation view (BOT) This adaptive behavior does not yet give an adequate picture of reality. Even a further run with an assumed increase in the implementation time does not produce realistic behavior; it only results in a time-delayed, but smooth adaptation. Change happened later and more radically. Remembering the difference between the simulation results and the observed radical implementation of e-trade mainly between 2006 and 2007, the environment may be an important driver for change, but impediments to change deserve closer investigation. For this reason, the pressure for the floor system will now be elaborated.

C.III.2 An Endogenous Struggle of Culture and Resistance The pressure for the NYSE trading floor that prevented the adaptation to environmental drivers of change also originated from the floor firms (i.e. from specialists and floor brokers and their respective firms). They, first, met automation attempts with resistance, second, dominated the stock exchange’s culture, and third, they were powerful. These influences by stakeholders are added in bold face in the sector diagram of Figure C-19.

112


PRESSURE FOR E-TRADE (-) Pressure by institutional customers

MGMT DECISION ON EXTENT OF E-TRADE

MARKET CHARACTERISTICS

(-) Pressure by non-inst. cust. (-) Resistance by floor (+) Cultural pressure by floor (+) Power of floor

PRESSURE FOR FLOOR TRADE

Figure C-19: Sector diagram of the culture and resistance view First, the floor’s opposition to electronic trading is able to create a balancing mechanism Floor Resistance that diminishes the implementation of e-trade once it is launched. It has been said that the electronic market hypothesis or disintermediation effect will be limited by the extent at which there is resistance and by the extent at which intermediaries form cooperative groups.476 In 1971, an attempt to launch an Automated Trading System at the NYSE that would execute orders of 100 or less shares without human intervention was literally torn into pieces. On the weekend before its commissioning someone destroyed its cover with a saw. The system was perceived as a threat to the floor, and nobody really wanted it to work.477 In the years following, the Securities Industry Automation Cooperation (SIAC) which provides automation and data processing services to the NYSE made several attempts to automate the floor. These attempts usually were not very successful.478 A floor broker who was interviewed reported a rather hostile reaction of the floor to electronic trading. Formally, the development of resistance is expressed by the linkage of employability and income. Resistance arises when floor brokers and specialists have fewer possibilities to participate in trading and at the same time feel restrictions in their income. Floor firms earn money from the commissions they receive for trading, specialists also from the spread between their bid and offer (ask) price, and from trading for their own account.479 They could maintain their cartel for fixed commissions until the first 476

See Clemons and Weber: Information Technology and Screen-Based Securities Trading, 1997, p. 1706; and Picot, Bortenlänger and Röhrl: Organization of Electronic Markets, 1997, p. 115. Gasparino also expects floor firms to react with resistance. See Gasparino: King of the Club, 2007, p. 318. 477 See Blume, Siegel and Rottenberg: Revolution on Wall Street, 1993, pp. 194–198. 478 See Keith, Christopher and Allan Grody: Electronic Automation at the New York Stock Exchange, in: Guile, Bruce R. and James Brian Quinn (Ed.): Managing Innovation: Cases from the Services Industries, Washington, DC 1988, p. 92. 479 Blume, Siegel and Rottenberg: Revolution on Wall Street, 1993, p. 40; and Conroy and Winkler: Market Structure, 1986, p. 22.

113


half of the 1970s, but afterwards institutional investors gained in influence and commissions declined.480 As Figure C-20 a) illustrates, due to the power and number of institutional investors, the commission per share diminished. This is also illustrated in Figure C-20 b), comparing model output with historical data. While brokers still receive commissions, designated market makers are not any more allowed to charge them. Shrinking commissions negatively affect the floor’s earnings per share. a)

-

REF. COMMISSION PER SHARE + + commission per share

effect of inst. customers on commission

effect of inst. - customers on spread

+ NYSE spread + REF. SPREAD

+ +

floor earnings per share handled HALF SPREADS

b)

NYSE Commissions 1

$/share

0.75

12

data

12

0.5

12 12

0.25 0 1970

1980

model output

12

1990

1

2

12 2000 Time (Year)

commission per share from data : adaptation 2 commission per share : adaptation 2

1

2 2 2010 1

2

1 2

2

2 2020

2

1

1 2

2 2030 1 2

Figure C-20: Commissions and spread (SFD and BOT)481 Additionally, the effect of institutional investors on spread diminishes earnings. In reality this often took place piecemeal in events, such as the transitions from quoting

480 481

See Abolafia: Making Markets, 1996, p. 109; and Gasparino: King of the Club, 2007, p. 48. Data for commission per share was derived by dividing the floor firms’ entire income from commissions by the NYSE share volume. The numbers match specifications about the size of commissions by Tinic and West as well as Tully. See NYSE Euronext Inc.: Annual reported volume, turnover rate, reported trades (mils. of shares), no date-a, electronic source; NYSE Euronext Inc.: Income statement for NYSE member firms ($ in mils.), no date-g, electronic source; Tinic, Seha M. and Richard R. West: The Securities Industry under Negotiated Brokerage Commissions: Changes in the Structure and Performance of New York Stock Exchange Member Firms, in: The Bell Journal of Economics, Vol. 11 (1980), No. 1, p. 36; and Tully: Bringing Down the Temple, 2003, p. 126.

114


in eights of a dollar to sixteenth to pennies, but in the model this is a continuous effect. The average NYSE spread declined from about USD 0.20 to USD 0.03.482 a) (B) Floor Resistance from Profitability

<specialist participation> + proportional floor earnings per share traded + +

+ pcvd adequacy of profitability (R)

-

effect of profitability on resistance

total pressure for more floor trade from floor + +

resistance pressure for floor system per floor firm + +

desired earnings per share

TIME TO ADJUST DESIRED EARNINGS

<effect of employability on resistance>

REF. RESISTANCE PRESSURE PER FLOOR FIRM

b) Effect of Profitability on Resistance 1

effect

0.75 0.5 0.25 0 0

0.10

0.20

0.30 0.40 0.50 0.60 0.70 pcvd adequacy of profitability

0.80

0.90

1

Figure C-21: Resistance pressure from floor (SFD and effect) A decline in spread and commissions reduces the floor earnings per share. The spread captures the liquidity providers’ revenues.483 It is divided by two, i.e. by the number of half spreads since a floor participant does not necessary deal with both the buyer and seller. As can be seen in Figure C-21 a), when multiplied by the percentage at which the specialist participates in trades, the proportional specialist earnings result, which provide information on revenues per share traded. A perceived inadequacy of profitability leads to resistance pressure for the floor system. Resistance pressure for the floor system rises when adequacy falls below its normal level of 1, as Figure C-21 b) exhibits in more detail. Since there may always be some variation, it increases somewhat slower in the beginning. Since floor firms need a certain extent

482

See NYSE Euronext Inc.: Average NYSE Spreads, (in dollars, rounded to penny) (1994–2003), no date-b, electronic source. See also Huang and Stoll: Dealer versus auction markets, 1995, p. 323; and Bennett and Wei: Market structure, fragmentation, and market quality, 2006, p. 62. 483 See Storkenmaier and Riordan: The Effect of Automated Trading on Market Quality, 2009, p. 23.

115


of participation in order to remain in the business, resistance is already fully present when employability and thus its perceived adequacy fall to 50 percent. Yet, by the above formulation floor firms may also show opposition when their income decreases for other reasons than automation. They would thus only resist electronic trading if they could attribute the loss in their ability to earn money to lower possibilities of employment. Specialists and floor brokers are “less and less required to arrange trades” in an automated environment.484 It is not surprising that they showed resistance against the implementation of e-trade which would reduce the specialist participation and ability of specialists and floor brokers to carry on their profession. Figure C-22 a) illustrates the structural relationships in an SFD. Structurally this floor resistance from employability is highly similar to resistance from profitability. The floor’s perceived adequacy of employability adapts to a floating goal of desired specialist participation. In the same manner as it works with profitability (see Figure C-21 b), resistance pressure for the floor system may rise when adequacy falls below its normal level of 1. a) <specialist participation>

total pressure for more floor trade from floor +

(B) Floor Resistance from Employability

+ (R)

+

desired specialist participation

- pcvd adequacy of employability

resistance pressure for floor system per floor firm + +

effect of + employability on resistance

TIME TO ADJUST DESIRED PARTICIPATION

REF. RESISTANCE PRESSURE PER FLOOR FIRM

b) ACCESS TO INFORMATION TECHNOLOGY






NYSE Fraction + of E-Trade specialist participation

pressure for more floor +

(B) Resistance -

+

profitability of floor

-

resistance pressure for floor

Figure C-22: Resistance pressure from floor (SFD and CLD) The effects of employability and profitability are multiplied so that pressure for more floor trade only develops if low profitability results from the floor’s low participa484

ibid., p. 28.

116


tion in trading. Both effects together create the balancing feedback loop Floor Resistance which is shown in Figure C-22 b). As soon as e-trade is implemented, resistance builds up from the floor to reduce the amount of automated trading and to increase the floor’s involvement again. Second, a strong floor culture developed during the 20th century at the NYSE and helped exert pressure for the retention of the floor. The stock and flow structure in Figure C-23 a) shows how floor firms value the floor culture depending on their ability to make profits and on their merits in providing market quality. Concerning market quality, people on the floor strongly believed in the superiority of their manual system. “We’re a national asset.”485 They were convinced that only the auction process which involves the floor brokers and specialists is able to generate high levels of price improvement. Additionally, due to their high position and commissions, the specialist (and broker) profession used to be a license to make money.486 This further contributed to the floor’s valuation of its trading system. This culture has also emerged, has been deeply embedded for many decades, but may also decline. To the extent either the perceived adequacy of market quality or of profitability is below the value one, the floor reduces its valuation of the floor culture. The valuation is modeled as an accumulation because the strength of the floor culture only faded slowly, although its profitability steadily declined with the rising power of institutional investors, decimalization, and others. The valuation of the floor culture divided by its reference value serves as an indicator for the importance of cultural aspects, i.e. the relative valuation of the floor culture. Since individual valuation does not produce concerted action, the relative value needs to be adjusted by the cohesiveness of floor firms to determine the cultural multiplier of pressure from the floor. The latter variable moderates the strength of the resistance pressure discussed on the previous pages. The cohesiveness of floor firms is important and floor firms had established long-term relationships based on trust, but concerted action was not as high as, for example, among members of an orchestra who depend on each other for a good individual outcome. Despite individual valuation of the floor, high valuation does therefore not fully translate into concerted resistance, and resistance is reduced to about 70 percent of its potential value. As is depicted in Figure C-23 b), this closes a reinforcing loop from which many decades ago high pressure for the floor developed which then diminished only slowly due to the imprinting of culture.

485

Cited in Abolafia: Making Markets, 1996, p. 104, see also p. 133; and Tabb: The NYSE Floor, 2005, p. 54. 486 See Abolafia: Making Markets, 1996, p. 131; and Blume, Siegel and Rottenberg: Revolution on Wall Street, 1993, p. 25.

117


a) + effect of market quality on culture + + effect of profitability on culture

(R) Pressure from Floor Culture

+

fractional change in valuation of floor culture +

+

total pressure for more floor trade from floor +

cultural multiplier of pressure from floor + + Valuation of rel. valuation of Floor Culture + floor culture change in + by Floor + valuation - (R)

REF. FRACTIONAL CHANGE OF VALUATION PER YEAR

REF. VALUATION OF FLOOR CULTURE

DEGREE OF COHESIVENESS OF FLOOR FIRMS







NYSE Fraction + of E-Trade specialist participation + market quality from specialist + - profitability of floor

pressure for more floor +


+ +

Valuation of Floor Culture by Floor

Figure C-23: Cultural pressure from floor (SFD and CLD) Third, floor firms’ power constitutes a remarkable factor that determines the floor’s influence as well as the exerted pressure for the floor. Specialists in particular used to be the force at the exchange. In the beginning of the 1990s, they NYSE was still described as “wedded to the concept of the specialist market”487, but the floor’s power had begun to decline.488 According to an interviewee, during the months following the implementation of the Hybrid Market, one of the specialists attached around 20 stickers to his suit showing his opposition to electronic trading and to the way it was implemented. Although he used to be a member of the board of executives, his opposition was powerless at that point of time because the floor firms’ influence had already deteriorated. Picot et al. state that these firms’ power can prevent electronic trading. They define the floor’s power particularly by the volume the firms attract to the exchange.489 The floor’s ability to attract volume can be operationalized by the market quality floor brokers and in particular specialists provide, i.e. the market quality from 487

Clemons and Weber: London's big bang, 1990, p. 52. See Abolafia: Making Markets, 1996, p. 129. 489 See Picot, Bortenlänger and Röhrl: The Automation of Capital Markets, 1995, electronic source, no page. 488

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specialist participation in Figure C-25 a). Due to the absence of more detailed information, it is assumed that market quality raises the indicated power of floor firms in a proportional way. As a matter of completeness, the indicated power is also shaped by the effect of institutional customers on power. Over the last decades of the 20th century, the power of floor firms diminished in the manner institutional investors’ importance grew—particularly because they represent a large amount of order flow. Some floor firms were even taken over by institutional customers. The power of floor firms relative to that of institutions thus declines. This put specialists under pressure, and their role in providing liquidity was crowded out by computer-based trading.490 Figure C-24 shows that the existence of some institutional trading has no significant effect on the indicated power—as could be observed by about the end of the 1970s. The effect becomes more severe as the fraction of institutions rises. The increasingly negative slope of the relationship reflects what could be observed in reality. A possible effect that changing securities market regulations had on the power of the floor is outside the boundary of this investigation.491 Effect of Institutional Customers on Power of Floor Firms 1

effect

0.75 0.5 0.25 0 0

0.10

0.20

0.30 0.40 0.50 0.60 0.70 Fraction of Institutional Customers

0.80

0.90

1

Figure C-24: Effect of institutional customers on power of floor firms Floor firms have the same potential power (reference power of floor firms) as customers. As described in the beginning of chapter 0, the customers’ power is expressed by their group size or share of trading which is normalized to 100. Therefore, the reference power of floor firms also takes the value of 100. Although both groups have the same potential power, the real power distribution between the two groups is determined by the specific circumstances represented e.g. by the effects which shape the indicated power in the model (Figure C-25 a on page 119). The power of floor firms adapts with a time delay of two years to this indicated power of floor firms. Since the power represents the firms’ usefulness and legitimacy for the trading business, it shapes the total pressure for more floor trade from the floor. 490

See Abolafia: Making Markets, 1996, p. 131; and Lucas Jr., Oh and Weber: The defensive use of IT in a newly vulnerable market, 2009, p. 5. 491 For an analysis of the effects of regulation on the power of specialists see Abolafia: Making Markets, 1996, pp. 111–128.

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total pressure for more floor trade from floor [pressure unit] = resistance pressure for floor system per floor firm [pressure unit / entity] • cultural multiplier of pressure from floor [dmnl] • Power of Floor Firms [entity]

C-5

A decrease in market quality resulting from the introduction of electronic trading thus diminishes power and then the pressure for the floor. This creates the reinforcing mechanism Power shown in Figure C-25 b). It reveals that once electronic trading is initiated, the decreasing power of floor firms makes it easier to implement more. a) <market quality from sp. participation>

(R) Floor Power + effect of market quality on power

<cultural multiplier of pressure from floor> Power of

+

total pressure + for more floor trade from floor + +

effect of institutional customers on power -

indicated power of floor firms + + REF. POWER OF FLOOR FIRMS

Floor Firms + change in power of floor firms (B)

TIME TO CHANGE POWER OF FLOOR FIRMS





dissatisfaction with time per inst. customer +


pressure for more e-trade

NYSE Fraction + of E-Trade specialist participation + market quality from specialist

pressure for more floor

(R) Floor Power

+

Power of Floor Firms

+ -

Figure C-25: Power of floor firms (SFD and CLD) The model now incorporates drivers of change from the environment as well as impediments to these drivers from exchange-related stakeholders. These impediments are the product of the floor firms’ resistance, their culture, and power. When the model is simulated, the implementation of electronic trading is shaped by the pressure for and against electronic trading from the floor and from customers. Line 3 of Figure C-26 reveals that the pressure coming from the valuation of the floor culture and from resistance is influential and that it can defer and diminish the implementa-

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tion of electronic trading. Nevertheless, the system dynamics model is still not able to reproduce a behavior similar to the real course of events.

NYSE Fraction of E-Trade 1

1

Dmnl

0.75

3

12 1231231 3

3

12

0.5

12 2

3 12

0.25 0 123123123 1970 1980

3

1 23 1990

2000 Time (Year)

"fraction of e-trade in remaining market" : adaptation "NYSE Fraction of E-Trade" : adaptation 2 2 3 3 "NYSE Fraction of E-Trade" : pressures

2010

2020 1

2

1 2

3

2030 1

2 3

2 3

3

Figure C-26: Culture and resistance (BOT) So far it has been assumed that management implemented whatever the joint pressure of customers and stakeholders called for. Since it could be ruled out that the observed radical shift is based on sudden changes in the forces for floor and e-trade only, the role of management as a driver of electronic trading is worth consideration.

C.III.3 Managerial Impact on Change How the management team reacts to pressure from its environment is also shaped by the recognition and interpretation of this pressure which—as Figure C-27 displays—depends on the organization’s market share and inertia.

MARKET SHARE

(+) Market share from market quality

(-) Market share from speed

(-) Pressure by institutional customers


(+) Repetitive momentum (+) Repetitive attention (customer orientation)

INERTIA

PRESSURE FOR E-TRADE


(-) Pressure by non-inst. cust. (-) Resistance by floor (+) Cultural pressure by floor (+) Power of floor


Figure C-27: Sector diagram of the management view


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“A few years ago, the NYSE, owned by NYSE Euronext, was considered a dinosaur in the increasingly electronic exchange universe, hobbled by its slowmoving, human-run system and outdated infrastructure.”492 "Part of being very successful for a very long time and having a large market share [is that] the New York Stock Exchange did become complacent. We have to be receptive to change. We have to give our customers what they're looking for."493 The statement by the NYSE CEO reveals that since about the year 2005 the New York Stock Exchange management regards its past success and the resulting complacency as reasons for the missing reaction to changing demands in its environment. The past success and high levels of inertia made the NYSE inattentive so that the organization focused on what it had always done. The NYSE was not known to be an innovative organization.494 Inertia could grow over a long period of several decades because the NYSE had not changed the basic principles of its way of trading for about 100 years. During this convergence period, there was much opportunity for the institutionalization of routines and the growth of consistency. This idea is represented by the institutionalization rate in the SFD of Figure C-28 a). Inertia increases in a reinforcing way by a reference fraction of about 0.3 per year, but a further process limits inertia to a maximum value of 100 percent or 1. Additionally, fluctuation and an ongoing process of rethinking make an organization lose its inertness. An NYSE interviewee reported that new employees in middle and high positions were “grown from within the organization” and seldom came from outside bringing fresh ideas. While fluctuation in general reduces inertia, inner-organizational replacements hardly do. The NYSE’s fluctuation and inertia decrease rate is therefore assumed to be only 15 percent per year, which equals half the recent fluctuation rate in the financial sector of about 30 percent.495 The resulting inertia led to the observed complacency.

492

Pellecchia, Ray: Faster, Faster: High Tech Hits the NYSE Floor (29 Mar. 2010), in: NYSE Euronext Inc.: Exchanges: Blogging About NYSE Euronext Markets [Weblog], 2010, electronic source. 493 Statement by John Thain, CEO, quoted in Ewing: When CEOs Talk to Each Other, 2005, electronic source. 494 See Thain, John: Statement on A CEO's Views on IT and Innovation (28 Jun. 2007), in: McGee, Ken: Gartner Fellows Interviews, 2007, electronic source. 495 Data is based on the rate of total US hires in the finance and insurance industry for the three years before the CEO of the NYSE changed. See Bureau of Labor Statistics, U.S. Department of Labor: Job Openings and Labor Turnover Survey (JOLTS), 2009, electronic source.

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a)

b) REF. OPENNESS PER INERTIA +

REF. FRACT. institutionalization INSTITUTIONALIZATION + + + (R) (B) limiting effect on institutionalization

Inertia (B)

REF. FRACT. INERTIA DECREASE

+

+ inertia decrease

institutionalization + (R) Inertia (B)

+ inertia decrease + effect of change on inertia NYSE Fraction of Floor Trade

openness to change

+

effect of openness on change

(R) Repetitive Momentum REF. FRACT. CHANGE IN TRADING PER PRESSURE P.A. + + fract. change per pcvd pressure p.a.

change in fraction + of e-trade

NYSE Fraction of E-Trade

pcvd pressures

Figure C-28: Inertia and repetitive momentum (SFD) An extension of the inertia mechanism is shown in Figure C-28 b) which focuses on the stock and flow structure of the Repetitive Momentum Loop. The organization’s level of inertia is inversely related to its openness to change. This openness has an influence on how willing the organization is to react to perceived pressure from its environment, expressed by the fractional change per perceived pressure. The effect of openness on change is an s-shaped curve indicating that the organization quickly reacts to perceived pressure if it has a rather high openness. It becomes less responsive as openness decreases until its reactivity reaches a lower bound. Since the fractional change determines the change in the fraction of e-trade, and since change reduces inertia, the reinforcing feedback loop Repetitive Momentum is closed. The effect of change on inertia amplifies the reference fractional inertia decrease that was explained in relation to Figure C-28 a) on page 122. The effect was modeled in such a way that incremental—meaning slow—changes reduce inertia only slightly. Net inertia still develops during times of incremental change.496 As soon as changes become more profound, they quickly reduce inertia, but with an upper limit of 6.5 times the reference fractional decrease of 15 percent so that a very rapid and extensive transformation has the potential to fully destroy inertia. These assumptions lead to an s-shaped effect of change on inertia, shown by Figure C-29. This is consistent with statements by Nadler and Tushman as well as Wollin according to which discontinuous change is disruptive and involves the unlearning of routines, ways of thinking, and assumptions.497 The structure implies that inertia grows when no or very little changes take place, leading to a low openness to change and making changes unlikely. But once the pressure on the NYSE is sufficiently strong to force the imple496

See Kaplan, Sarah and Mary Tripsas: Thinking about technology: Applying a cognitive lens to technical change, in: Research Policy, Vol. 37 (2008), No. 5, pp. 795 and 800. 497 See Nadler and Tushman: Types of Organizational Change, 1995, p. 23; and Wollin: Punctuated equilibrium, 1999, p. 362.

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mentation of some e-trade, inertia diminishes and a reinforcing change mechanism is initiated. Effect of Change on Inertia Decrease 8

effect

6 4 2 0 0

0.100

0.200 0.300 "change in fraction of e-trade"

0.400

0.500

Figure C-29: Relationship between change and inertia How this mechanism fits in with other parts of the model can be seen in the CLD of Figure C-30. Due to its reinforcing character, the Repetitive Momentum Loop creates path-dependent behavior. Repetitive behavior may occur in both directions of stability and change, but does not explain how the NYSE switched from one to the other, and it does thus not explain the sudden NYSE transformation. The path dependent behavior is challenged, first, by customer pressure and its influence on customer orientation, and second, by inadequate performance. These two concepts will be explained on the following pages. ACCESS TO INFORMATION TECHNOLOGY Fraction of Institutional Customers + FRACTION OF EQUITIES HELD BY INSTITUTIONS

+

+ fraction of e-trade in + remaining market

rel. time to execution -

+


pressure for (B) Customer Pressure for Speed more e-trade openness to change Inertia

(R) Repetitive Momentum

NYSE Fraction of E-Trade specialist participation

+ (B) Customer Pressure for Market Quality

+ market quality from specialist

(R) Floor Power


(B) Resistance -

+ profitability of floor


Figure C-30: Inertia and repetitive momentum (CLD) The orientation of the NYSE’s market model on floor firms and its culture strongly contributed to the observed inertia. The NYSE had always been dedicated to providing the best market for individual investors.498 Its focus on the floor originated from 498

See Blume, Siegel and Rottenberg: Revolution on Wall Street, 1993, p. 108.

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the belief that the floor provided the best trading mechanism. As a result, the organization centered around and benefited floor brokers and specialists, disregarding the sell side’s, i.e. the institutional investment banks’ demands. The NYSE COO who used to work for several large financial institutions expressed this fact. "In my 20 years on the sellside, I never felt like a client of the New York Stock Exchange, … That's despite the fact that at Morgan Stanley, Credit Suisse, and UBS, I represented a pretty good chunk of their order flow.”499 He went on to say that the exchange was run for its owners—often floor broker and specialist firms—but not for investment houses. The NYSE admitted that it had not listened to large customers. It had restricted quick and anonymous trading with intent.500 But this view changed radically, and the focus shifted from floor firms to customers that use the NYSE for trading. Now, NYSE executives “… are trying to transform what was traditionally an inward-looking, exchange floor-dominated culture into an outward-looking, customer-focused culture. Instead of running the organization for the benefit of specialists, for example, Niederauer and Leibowitz say they are trying to satisfy the people that deliver the order flow—[i.e.] the broker-dealers.” “With its market share slipping away, the New York [Stock Exchange] can no longer afford such arrogance. It has had to reconnect with the sources of its order flow.” 501 Yet, an interviewee also reported that the rising customer focus was rather the result of the reinvention of the company than of falling market share. “…because what we were doing was, we were responding to a set of customer needs. There's a set of customers who said, "We want to trade instantaneously, electronically and anonymously. We do not want our orders being routed to the floor." So, we said okay — great. We have to respond to that group of customers, because if we don't they're going to go trade somewhere else.”502 “That push from customers was really how this all started. Making ourselves fit into Regulation NMS is also very important to us. But we moved in this direction because of the reaction from our customer base, which we hadn’t previously been listening to well enough.”503 “If you have a group of big and important customers who want to trade in a certain way and you don’t give them the capability and somebody else does, that’s where they’re going to go. So the first objective of the Hybrid Market is to allow those institutions that want to trade electronically, instantaneously and anonymously to do so.” 504 499

Statement by the NYSE COO and Executive Vice President Lawrence Leibowitz. See Chapman, Mehta and Scotti: Men At Work, 2007, p. 48. 500 See Pellecchia: On crisis and opportunity, 2006, electronic source. 501 Chapman, Mehta and Scotti: Men At Work, 2007, p. 48. 502 Thain: Statement on A CEO's Views on IT and Innovation, 2007, electronic source. 503 Pellecchia: I'd like an Auction Limit order with soy milk, please, 2006, electronic source. 504 ibid., electronic source.

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As a consequence, the NYSE shifted to “serving the needs of its customers,”505 and customer orientation has become central at the New York Stock Exchange. This also manifests itself in a large number of entries in its weblog and in utterances by its management. The importance of customer orientation for the change becomes obvious through a quantitative content analysis of the Exchanges Weblog of which Table C-4 lists the results. It was used and able to provide a quantitative confirmation of qualitative findings. According to Duriau, Reger, and Pfarrer content analysis has received increasing attention particularly in the investigation of constructs related to strategic management and managerial cognition as well as in longitudinal research.506 In the analysis made of the NYSE weblog, searches in March 2008 and December 2009 for the word customer(s) retrieve more entries than searches for floor, specialist(s), speed, fast (market), electronic, automated/automation, value, price improvement, and volatility. The rising awareness of complacency and particularly its impact on the incoming order flow served as a wake-up call. The organization finally noticed that its environment had changed.507 The management team became aware that its strategic orientation was in conflict with what its environment demanded, and it readjusted its attention towards those groups which were most dissatisfied: its large institutional customers who wanted to trade electronically. As a consequence of the growing awareness that the NYSE was not responding to their stakeholders’ demands, it made a strong move away from its inward floor orientation towards an adaptation to its customers. Key Words Entered on 11 Dec. 2009

Number of Hits

Customer(s) / client(s)

447

Floor / manual

411

Specialist(s) / DMM(s) / designated market maker(s)

379

Electronic / automated / automation

354

Speed / fast (market)

243

Value

222

Price improvement

151

Volatility

140

Market share / performance

125

Table C-4: Quantitative content analysis of the Exchanges Weblog 505

Statement by Louis G. Pastina, Executive Vice President, NYSE Operations, quoted in: NYSE Euronext Inc.: NYSE Euronext Appoints Todd B. Abrahall and Michael J. Rutigliano as Liaisons to NYSE Specialists and Brokers, 2008, electronic source. 506 See Duriau, Vincent J., Rhonda K. Reger and Michael D. Pfarrer: A Content Analysis of the Content Analysis Literature in Organization Studies: Research Themes, Data Sources, and Methodological Refinements, in: Organizational Research Methods, Vol. 10 (2007), No. 1, pp. 14–16 and 23. 507 See Pellecchia: Shaping the blog to a new mark, 2007, electronic source.

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The NYSE management team faces pressure from institutional as well as noninstitutional customers and floor firms. The majority of institutional customers expresses a desire or exerts total pressure for more e-trade. Many non-institutional ones apply total pressure for more floor trade from customers. Additionally, the floor firms impose total pressure for more floor trade from the floor which is a product of resistance, culture, and power of floor firms. These three types of pressure are shown in the bottom right corner of Figure C-31, and they were already discussed in the previous chapters. Customer orientation comes in when it concerns the perception of pressure. The management team’s perception of pressure is a result of customer and floor pressure and particularly its orientation to its stakeholders, i.e. its customer orientation as opposed to the orientation to the floor. As the figure and the equations C-6 and C-7 elucidate, customer orientation serves as a weighting factor for the perception of pressure that stakeholders exert on the NYSE. It tells how much weight the NYSE management attributes to the respective stakeholder pressure. In this way, a shift in customer orientation is able to explain the increased perceived pressure to implement e-trade and the resulting shift of the organization. REF. OPENNESS PER INERTIA

institutionalization + (R) Inertia (B) + inertia decrease + effect of change on inertia NYSE Fraction of Floor Trade effect of floor trade on change

+ openness to change

+


(R) Repetitive Momentum REF. FRACT. CHANGE IN TRADING PER PRESSURE P.A. + +

Customer Orientation

fractional change per pcvd pressure p.a.

change in fraction + of e-trade + + -

NYSE Fraction of E-Trade effect of e-trade on change

+ pcvd pressure for + more e-trade pcvd pressure for + more floor trade +

Figure C-31: Relationship between customer orientation and change (SFD)

pcvd pressure for more eTrade [pressure unit] = total pressure for more eTrade from customers [pressure unit] • Customer Orientation [dmnl]

C-6


pcvd pressure for more floor trade [pressure unit] = total pressure for more floor trade from customers [pressure unit] • Customer Orientation [dmnl] + total pressure for more floor trade from floor [pressure unit] • (1 – Customer Orientation [dmnl])

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C-7

An imbalance in the perceived pressure triggers change. When the perceived pressure for e-trade is higher than the perceived pressure for floor trade, the NYSE moves from floor to e-trade and vice versa. How the NYSE translates perceived pressure into change action confirms with earlier representations. The limiting effects of e-trade and floor trade on change still represent the idea that in the theoretical case when the customers still exert a large amount of pressure for e-trade (floor trade) although the fraction of e-trade (floor trade) is already high, the management team is less willing to fully react to the pressure. Depending on the fractional change per perceived pressure—i.e. the NYSE’s general openness to change—the organization reacts more or less quickly to the pressure it perceives in its environment. Overall this means, managerial attention provides the lens through which the NYSE interprets the pressure which affects the organization. A high or low value of customer orientation always expresses a bias towards one of the stakeholder groups. Figure C-32 indicates how the stock customer orientation itself is influenced and adapts. The management team can distribute its attention between floor firms and customers. First, the rate of change in the customer orientation shifts attention the quicker the larger the yearly fractional change in customer orientation becomes. Since the latter directly depends on the effect of openness on change that also affects changes in e-trade, the feedback loop Repetitive Attention is closed. This reinforcing feedback loop allows the organization to adapt to a level of customer importance at a specific point in time and to freeze here as inertia grows, biasing the perception of outside pressure. This relationship captures how a reinforcing character of Repetitive Attention perpetuates and how customer orientation changes when the organization becomes more open.

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REF. OPENNESS PER INERTIA

institutionalization + (R)

+ openness to change

+


-

REF. FRACT. CHANGE IN CUST. ORIENT. P.A. + fract. change in + cust. orient. per pressure p.a.

Inertia (B) + inertia decrease + effect of change on inertia NYSE Fraction of Floor Trade - (R) effect of floor trade on change

Floor Orientation

change in cust. orient.


change in fraction + of e-trade + + -

NYSE Fraction of E-Trade (B) effect of e-trade on change

(R) Repetitive Attention + pcvd pressure for + more e-trade pcvd pressure for + more floor trade +

Figure C-32: Repetitive attention loop (SFD) Second, as illustrated in Figure C-33, customer orientation adapts to the pressure which comes from stakeholder groups in a comparable way as the fraction of e-trade adjusts to pressure. This means customer orientation rises when the perceived pressure from customers is higher than the perceived pressure from the floor. Similar to the perception of pressure for change, the perception of pressure from stakeholders is biased by customer (or floor) orientation itself, leading to the following computation of change in the customer orientation: change in customer orientation [dmnl / year] = (pcvd pressure for more cust. orient. [pressure unit] • effect of cust. orient. on change [dmnl] – pcvd pressure for more floor orient. [pressure unit] • effect of floor orient. on change [dmnl]) • fract. change in cust. orient. per pressure p.a. [dmnl / pressure unit / year]

C-8

Importantly, the perceived pressure from customers and from the floor is different from that for e-trade and floor trade since one set of variables aggregates stakeholders and the second one aggregates their desires. Additionally, the balancing effect of customer (floor) orientation on change limits a further orientation to customers (the floor) when customer (floor) orientation is already high, but customers (the floor) continue to pressure for their aims. The management team would no longer be willing to fully react to this pressure.

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Floor Orientation

Customer Orientation change in cust. orient. + + (B) (B) - + effect of floor effect of cust. orient. on change orient. on change +

(R)

(R)

pcvd pressure from the floor

+

pcvd pressure from customers

+

+

Figure C-33: Customer orientation (SFD) As the relationship between pressure and customer orientation in Figure C-33 and Figure C-34 reveals, customer orientation adapts if there is a pressure imbalance. It does so even faster if the organization is open to change. The reinforcing feedback loop that shapes the Repetitive Attention is shown in a simplified causal form in Figure C-34. It shapes and biases the pressure for e-trade and for the floor in a similar way the Repetitive Momentum Loop does. ACCESS TO INFORMATION TECHNOLOGY

+

+ fraction of e-trade in + remaining market +

Fraction of Institutional Customers + FRACTION OF EQUITIES HELD BY INSTITUTIONS

Customer Orientation openness to change Inertia

(B) rel. time to execution -

+


pressure for (B) Customer Pressure for Speed more e-trade

(R) Repetitive Attention


NYSE Fraction of E-Trade specialist participation

+ (B) Customer Pressure for Market Quality

+ market quality from specialist

(R) Floor Power


(B) Resistance -



Figure C-34: Perception bias from managerial attention (CLD) In the case of the New York Stock Exchange, not only the acknowledgement of customer demands, but also performance represents a driver of change.508 Performance of the NYSE can be operationalized by several concepts—market share, market quality, liquidity, and speed being the most important ones. Two of these are of particular importance: Much of the discussion centers around the market quality that the 508

See Lucchetti: NYSE Plans to Revise Specialist-Trader Rules, p. C.4.

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participation of specialists provides. Additionally, market share is central because all concepts listed above have an influence on the NYSE’s market share of volume in NYSE-listed securities. Therefore, the determinants of market share and market quality as well as how these performance measures translate into managerial decisionmaking will be described in more detail. Two reinforcing liquidity mechanisms of the NYSE and of the remaining U.S. market are the main determinants of market share. Figure C-35 visualizes these loops. The mechanism is commonly referred to by the words “liquidity begets liquidity”.509 The same reinforcing mechanism is also reported for the London Stock Exchange.510 The feedback loop NYSE Spread from Liquidity on the right side sketches the mechanism for the NYSE, and the loop Spread in the Remaining Market mirrors it for the remaining trading venues. The further division of market share among other exchanges is not of interest so that the remaining market can be represented in an aggregate manner. The description will focus on the NYSE Spread from Liquidity loop. The NYSE market share is the central stock at the top of Figure C-35. Multiplied by the total U.S. share volume in NYSE-listed issues, it results in the NYSE trading volume expressed in shares per year. The relative trading volume in comparison to the total shares traded (which is another expression for market share) has an effect on the NYSE spread, i.e. the quoted price. Liquidity is often regarded as the most important factor characterizing the attractiveness of an exchange, which—since difficult to measure—is often described with relation to daily turnover and the size of the bid ask spread.511 This means, liquidity from market share will attract volume to the exchange—here orders with a price limit—and will increase market share again. The more limit orders there are and the more quoted depth (i.e. orders) there is at each price point, the smaller the spread. An analysis of market quality at the NYSE and NASDAQ also illustrates that a high consolidation of order flow to one exchange reduces an exchange’s spread.512 The effect of the relative trading volume on the NYSE spread is drawn as a linear function because, first, there is no further information on the shape of this relationship, and second, the idea that liquidity begets liquidity develops by the reinforcing character of the entire loop, not by this relationship between two variables. When market share (or trading volume) increase by one percent, the spread falls by 0.2 percent. The NYSE spread is additionally influenced by the reference spread and the effect of institutional customers on the spread. The latter decreased the spread and is supposed to symbolize events such as the transi509

Duncan Niederauer, Deputy CEO, NYSE Euronext, in: Niederauer, Duncan: Statement 17 in Questions and Answers, in: Thomson Financial: NYX - Q2 2007 NYSE Euronext Earnings Conference Call (2 Aug. 2007) [Conference Call Transcript], 2007a, electronic source; Tabb, Larry: Liquidity Begets Liquidity, in: Advanced Trading (2008a), issued Feb. 2008, p. 47; and Weber, Bruce W.: Adoption of electronic trading at the International Securities Exchange, in: Decision Support Systems, Vol. 41 (2006), No. 4, p. 741. Additionally, in an interview a specialist referred to the mechanism as “volume begets volume”. 510 See Clemons and Weber: London's big bang, 1990, p. 51. 511 See ibid., pp. 54–55. 512 See Bennett and Wei: Market structure, fragmentation, and market quality, 2006, p. 69.


131

tion to quoting in sixteenth and to pennies (commonly referred to as decimalization) that is supposed to have favored institutional customers. Data shows that the quoted spread used to average at USD 0.25, declined to USD 0.13 in 1991, and fell further to USD 0.06 for a sample of NYSE stocks from January 2002 to March 2003. During the latter period a NASDAQ sample averaged at USD 0.09.513 The system dynamics model explicitly refers to these sources of the quoted average spread, not to data on the median spread or on a spread measure that is not further defined.514 The model exhibits a spread of USD 0.23 in the beginning which falls to USD 0.13 in 1991 and to USD 0.07 in early 2003. It is thus able to closely replicate behavioral patterns observed in the real world. The simulated spread for the remaining market diminishes from USD 0.25 to USD 0.08 as compared to USD 0.09 at NASDAQ. Hendershott and Moulton observe a 10 percent increase in the NYSE spread directly after the introduction of the Hybrid Market.515 Others report a falling quoted spread or ambiguous results.516 In the simulation runs, the spread continuously decreases, too, but the relative spread of the NYSE increases by about 10 percent after the introduction of the Hybrid Market. Further influences on the spread such as inventory and informational issues are discussed particularly in the market microstructure literature.517 They are left out since they often concern short-term or external effects.

513

For data during the 1960s see Demsetz, Harold: The Cost of Transacting, in: The Quarterly Journal of Economics, Vol. 82 (1968), No. 1, p. 40; for data on the year 1991 see Huang and Stoll: Dealer versus auction markets, 1995, p. 323; and for data on the period from 2002 to 2003 see Bennett and Wei: Market structure, fragmentation, and market quality, 2006, p. 62. 514 Data on some measure of spread and on the median spread can be found in NYSE Euronext Inc.: Average NYSE Spreads, no date, electronic source; and United States Securities and Exchange Commission (SEC), Market 2000, 1994, exhibit 30. 515 See Hendershott and Moulton: Speed and Stock Market Quality, 2009, p. 26. 516 See Storkenmaier and Riordan: The Effect of Automated Trading on Market Quality, 2009, pp. 21 and 28. Gutierrez and Tse find a falling quoted spread for several order types and sizes, but they observe contrary evidence for large market orders and insignificant results for other order types and sizes. See Gutierrez, Jose A. and Yiuman Tse: NYSE execution quality subsequent to migration to hybrid, in: Review of Quantitative Finance and Accounting, Vol. 33 (2009), No. 1, p. 75. 517 Market microstructure, trading costs, and the spread are analyzed in Biais, Bruno, Larry Glosten and Chester Spatt: Market microstructure: A survey of microfoundations, empirical results, and policy implications, in: Journal of Financial Markets, Vol. 8 (2005), No. 2, pp. 221–235; Conroy and Winkler: Market Structure, 1986, pp. 22–30; and Madhavan: Market microstructure: A survey, 2000, pp. 208–211. For an analysis of transaction costs see also Demsetz: The Cost of Transacting, 1968, pp. 46–47.

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C The Phenomenon of Inertia and Change Exemplified in a Case Study of the NYSE NYSE Market Share

TOTAL U.S. SHARE VOLUME IN NYSELISTED ISSUES

<Time>

+

TIME FOR CHANGING MARKET SHARE

+ trading volume of the remaining market

+ + NYSE trading volume

indicated NYSE market share +

effect of relative trading volume on market's spread

effect of relative trading volume on NYSE spread

NYSE market share from NBBO (cons.) + (R) Market Liquidity and Spread

fraction of time at NBBO -

(R) NYSE Liquidity and Spread

+

+

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+

<effect of inst. customers on spread>

relative spread + of NYSE

NYSE spread - +

REF. SPREAD

Figure C-35: Spread and market share (SFD) The relative price expressed by the relative spread of the NYSE in comparison with the market is important. The better the relative spread, the higher the fraction of time at the NBBO, i.e. the fraction of time the NYSE displays the national best bid and offer. The specialist quote or a public limit order may set the best quote at the NYSE.518 “[The] NBBO is a key driver of market share.”519 How the time at the NBBO translates into market share is depicted in Figure C-36. In the consolidated market, before the implementation of Regulation NMS, the SEC’s trade-through rule required an order to be sent to the exchange displaying the best price.520 The line is situated slightly above the 45°line in order to capture that the NYSE is one market where liquidity consolidates whereas the remaining market consists of several trading venues where liquidity is scattered. The line rises below proportionally in the very beginning so as to account for the fact that an exchange that displays bad prices most of the time has difficulties to attract a sufficient depth of liquidity at the best price. A critical mass of liquidity is necessary for an exchange to attract volume.521 The market share from the NBBO of a single exchange reaches a maximum of 80 percent as not all trades are executed at exchanges and only reported later. Some are executed internally at large institutions and off the exchanges after the regular trading hours.522 Just from quoting at the NBBO a single exchange can thus not reach a 100 percent mar518

See Conroy and Winkler: Market Structure, 1986, pp. 22 and 34. A limit order is an order with a maximum (or minimum) price. 519 Statement by Duncan Niederauer, as Deputy CEO of the NYSE, quoted in: Chapman, Mehta and Scotti: Men At Work, 2007, p. 54. 520 A detailed description of the mechanism can be found in Hasbrouck, Joel, George Sofianos and Deborah Sosebee: New York Stock Exchange Systems and Trading Procedures, NYSE Working Paper, 1993, pp. 25–29. See also Securities and Exchange Commission (SEC): Adoption of Amendments to the Intermarket Trading System Plan, 1999, p. 70298. 521 See Picot, Bortenlänger and Röhrl: Organization of Electronic Markets, 1997, p. 115. 522 See United States Securities and Exchange Commission (SEC), Market 2000, 1994, p. II-7.

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ket share. Since the focus is on the consolidated market before the implementation of Regulation NMS and since further influences are left out at the moment, the NYSE market share from the NBBO directly translates into an indicated NYSE market share. The actual NYSE market share is a stock that adapts to the indicated variable with a time delay of one year. The feedback loop NYSE liquidity and spread is now closed. While benefiting exchanges with high market share, the reinforcing character of this feedback mechanism also aggravates situations of low liquidity.

market share from NBBO

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critical mass problem

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Figure C-36: Relationship between time at NBBO and market share While the time at the NBBO is a key driver of market share, the “other half is based on the specialist’s trading performance.”523 This may be an exaggeration, but the extra market quality the specialists and floor brokers provide as well as the trading speed are responsible for a market share adjustment. Execution quality has an influence on order routing decisions. Markets with low execution costs and fast executions receive more orders.524 The NYSE assumes that improving the quality of quotes will attract more orders, i.e. market quality from the specialist will improve market share.525 “My general view is if I get market quality right, market share will follow.”526 Therefore, as depicted in the bottom left of Figure C-37, market quality from specialist participation has an influence on the indicated NYSE market share via the market share adjustment. Since market quality ranges between the values 1 when there is no specialist participation and 1.1 when the specialist participates in 10 percent of the trades, it serves as a multiplier for the NYSE market share from the NBBO. High market quality from high floor involvement may thus adjust market share upwards by 10 percent. 523

Statement by Duncan Niederauer, Deputy CEO of the NYSE, quoted in: Chapman, Mehta and Scotti: Men At Work, 2007, p. 54. 524 See Boehmer, Jennings and Wei: Public Disclosure and Private Decisions, 2007, pp. 318 and 353. 525 See Chapman, Mehta and Scotti: Men At Work, 2007, p. 52. 526 Statement by Duncan Niederauer, President and Co-COO and Head of U.S. Cash Markets. Niederauer: Statement in speech on U.S. Cash Equities, 2007, electronic source. See also McAndrews and Stefanadis: The Emergence of Electronic Communication Networks in the U.S. Equity Markets, 2000, p. 3.

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C The Phenomenon of Inertia and Change Exemplified in a Case Study of the NYSE <wt. on time vs. spread among all customers> <effect of time to execution on market share>

<market quality from sp. participation>

indicated NYSE market share + + - market share - adjustment +

NYSE market share from NBBO (cons.) + fraction of time at NBBO

Figure C-37: Market share adjustment from speed and market quality (SFD) Additionally, customers do not only value market quality, but speed has gained significant importance. Customers decide to trade at electronic exchanges in order to have their orders executed more quickly.527 A slow time to execution has a negative effect on market share.528 Figure C-37 expresses this by the effect of the time to execution on market share. The nonlinear relationship is also drawn in Figure C-38. In the area around the point (1,1) where the speed at the NYSE and in the market are similar, customers are rather indifferent and market share is determined by the frequency an exchange sets the NBBO. Yet, the further the times to execution diverge, the more important speed becomes, and it turns into an important order winning criterion.529 Effect of Time to Execution on Market Share 1.1

effect

0.9

0.7

0.5 0

1

2

3 4 5 6 relative time to execution

7

8

9

Figure C-38: Relationship between time to execution and market share While competition originally focused on price, with the growth of institutional customers and technology, it increasingly shifted towards speed. Based on an analysis of customer order choices, Ellul et al. find that orders which are sent to an automated 527

See Ortega: NYSE's Thain Fails to Stem Losses in Market Share (Update 2), 2006, electronic source. 528 See Hendershott and Moulton: Speed and Stock Market Quality, 2009, p. 1. They also refer to several further authors, such as Boehmer, Jennings and Wei: Public Disclosure and Private Decisions, 2007, pp. 252–253. 529 For information on the shape of the relationship between a performance criterion and the customer reaction see Salge: Struktur und Dynamik ganzheitlicher Verbesserungsprogramme in der industriellen Fertigung, 2009, pp. 55–56; and Slack, Nigel, Stuart Chambers and Robert Johnston: Operations Management, 5. Ed., Harlow [et. al] 2007, pp. 69–70.

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system are less price sensitive than those sent to the floor.530 This suggests that customers attach a certain weighting to the time to execution as well as to the spread or price. As equation C-9 indicates, the weight on time vs. spread among all customers thus weights among the importance of the effect of time to execution on market share and the impact of the market quality from specialist participation. The resulting market share adjustment works as a multiplier of the NYSE market share from the NBBO to derive the indicated NYSE market share. market share adjustment [dmnl] = wt. on time vs. spread among all customers [dmnl] • effect of time to execution on market share [dmnl] + (1 – wt. on time vs. spread among all customers [dmnl]) • market quality from sp. participation [dmnl]

+

pcvd adequacy of market share -

+

confidence effect of market share

REF. OPENNESS PER INERTIA + openness to change -

+ desired market share TIME TO ADJUST DESD MARKET SHARE

C-9

Figure C-39: Relationship between market share and openness to change (SFD) The CEO explained that the NYSE had lost market share due to a general increase of electronic trading with which the NYSE could not keep up.531 JP Morgan Securities estimates that with every lost percentage point of market share the NYSE looses about USD 2.3 million in net income.532 Respectively, the organization’s projected earnings per share are expected to diminish by 2 cents per lost percentage point of market share.533 In the view of Storkenmaier and Riordan, the NYSE’s reluctance to change led to decreases in its market share, and the NYSE management reacted by the introduction of the Hybrid Market. Further authors support this view.534 “The decline [in the NYSE's share of trading NYSE-listed issues] underscores the urgency of adopting the Hybrid Market, a project that will cut the time to complete a

530

See Ellul, et al.: Order dynamics, 2007, p. 659. See CEO, NYSE Euronext in: Thain, John: Statement in an Interview with CSBN (6 Oct. 2006), in: NYSE Euronext Inc.: Exchanges: Blogging About NYSE Euronext Markets [Weblog], 2006, electronic source. 532 Cited in: Ortega: NYSE's Thain Fails to Stem Losses in Market Share (Update 2), 2006, electronic source. 533 See Lucchetti, Aaron and Alistair MacDonald: Stock-Exchanges Grudge Match: Old Rivals NYSE and Nasdaq Use Bids for European Markets As Latest Way to Pin Their Foe, in: Wall Street Journal - Eastern Edition (2006), issued 8 Sep. 2006, p. C.1. 534 See Storkenmaier and Riordan: The Effect of Automated Trading on Market Quality, 2009, p. 11. See also Ortega: NYSE's Thain Fails to Stem Losses in Market Share (Update 2), 2006, electronic source. 531

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trade to less than a second, just like all-electronic rivals.”535 They thus see a causal relationship between an inadequate market share and subsequent change, which is understandable with respect to the negative consequences of market share. The causal link from market share to the NYSE decision making can be seen in Figure C-39. When the management team perceives the NYSE market share to be inadequate as compared to the floating goal of desired market share, it loses its confidence that it is pursuing the right strategy. As Storkenmaier and Riordan reported, this confidence effect of market share increases the organization’s openness to change.536 Figure C-40 indicates how this happens. As soon as the adequacy level falls below the value of one, confidence decreases. This happens slowly for minor inadequacies close to the value one since there always may be some variation in market share, but the effect quickly aggravates. Since the product of inertia and the confidence effect are subtracted from the value one to derive the openness to change (equation C-10), a low confidence effect from low market share increases the openness to change. Confidence effect of market share on opennes to change 1

effect

0.75 0.5 0.25 0 0

0.20

0.40 0.60 0.80 pcvd adequacy of market share

1

1.20

Figure C-40: Confidence effect of market share on openness to change openness to change [dmnl] = 1 [dmnl] – Inertia [consistency unit] • confidence effect of market share [dmnl] • REF. OPENNESS PER INERTIA [dmnl / consistency unit]

C-10

Figure C-41 illustrates that the relationships market share Æ NYSE decision on e-trade Æ market share create two endogenous feedback mechanisms. Low market share creates more openness for electronic trading, and e-trade improves the relative time to execution and raises market share again. This closes the balancing feedback loop Market Share from Speed. A higher fraction of electronic trading also lowers market quality and thus market share, creating the reinforcing mechanism Market 535

CEO John Thain in: Ortega: NYSE's Thain Fails to Stem Losses in Market Share (Update 2), 2006, electronic source. 536 See Storkenmaier and Riordan: The Effect of Automated Trading on Market Quality, 2009, p. 11.

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Share from Market Quality. This indicates that a move towards electronic trading calls for even more electronic trading. Therefore, the implementation of e-trade has ambiguous effects on market share, but due to the rising importance of institutional customers and their weighting of speed, the effect of speed finally prevails. ACCESS TO INFORMATION TECHNOLOGY Fraction of Institutional Customers + FRACTION OF EQUITIES HELD BY INSTITUTIONS


-

spread


(R) Liquidity Market openness to Share + change +

Inertia

+ (B) Market Share from Speed

rel. time to execution

+

-




NYSE Fraction + of E-Trade (B) Customer specialist Pressure for participation Market Quality + (R) Floor market quality (R) Market Share Power from Market Quality from specialist (B) Resistance (R) Repetitive Momentum

-




Figure C-41: Market share (CLD) In order to reduce the reinforcing effects created by the Market Share from Market Quality Loop, the NYSE introduced liquidity algorithms which provide specialists and floor brokers with the ability to participate in electronic trading and improve market quality at the same time. When specialist participation and market quality dropped to a level much below that of the pre-Hybrid stage, the NYSE management made a significant revision of its new market model. It slightly amended the role of the specialists and changed their name to designated market makers (DMMs). While the management team did not depart from electronic trading, it implemented liquidity algorithms in order to make sure that floor brokers and DMMs participate in the electronic environment. It also introduced Special Liquidity Providers: six firms which complement and compete with DMMs and provide bids and offers for assigned securities.537 With the help of liquidity algorithms, DMMs and Special Liquidity Providers are able to constantly provide automated bids and offers also in electronic trading. The NYSE management made these changes because the electronic environment did not provide sufficient incentives for specialists to participate in trading. The transformation of specialists to DMMs and the introduction of liquidity algorithms are based on the belief that the DMMs’ provision of liquidity and quality improves the NYSE’s market share.538 537 538

See NYSE Euronext Inc.: Significant Progress in New NYSE Market Model, 2009, p. 2. See Niederauer: Statement in speech on U.S. Cash Equities, 2007, electronic source; Pellecchia: Comment on: Ten Things I Like About the Coming Changes at NYSE, 2008, electronic source.; Pellecchia: Comment on: We, Robots, 2008, electronic source; and Ross, Jim (Vice President of NYSE MatchPoint and ATS (alternative trading system) Strategy): Together, MatchPoint and Algorithms Take Performance to a New Level (29 Apr. 2009), in: NYSE Eu-

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C The Phenomenon of Inertia and Change Exemplified in a Case Study of the NYSE TIME TO DEVELOP ALGORITHMS

REF. LIQUIDITY ALGORITHMS effect of liquidity algorithms on + participation

<effect of floor trade on sp. participation>

Liquidity Algorithms

+

+ specialist participation + REF. SP PARTICIPATION

+

development of + liquidity algorithms +

market quality adequacy gap + -

(B) Liquidity Algorithms

effect of sp. participation on market quality + +

market quality from sp. participation

ALGORITHMS PER GAP PER YEAR

+

pcvd adequacy of market quality by customer

DESD ADEQUACY OF MARKET QUALITY

REF. MARKET QUALITY

Figure C-42: Liquidity algorithms as a response to low market quality (SFD) The causal mechanism between floor trade, specialist participation, and the perceived adequacy of market quality has been described and is depicted again from the top left to the bottom right of Figure C-42. Algorithms are introduced to increase participation and market quality when there is a gap between the perceived adequacy of market quality and its desired value one. Algorithms take about one and a half years to be initiated since the gap needs to be perceived as being problematic; this is why there is a third order smooth in the development decision instead of a development delay. Then, algorithms accumulate and positively affect specialist participation as expressed by equations C-11 and C-12. Together, this creates the balancing loop Liquidity Algorithms. effect of liquidity algorithms on participation [dmnl] = Liquidity Algorithms [algorithms] + REF. LIQUIDITY ALGORITHMS [algorithms] / REF. LIQUIDITY ALGORITHMS [algorithms]

C-11

specialist participation [dmnl] = effect of floor trade on sp. participation [dmnl] • effect of liquidity algorithms on participation [dmnl] • REF. SP PARTICIPATION [dmnl]

C-12

Floor algorithms serve the goal of offering greater choices to customers and serving their needs. In the view of the managing director of the company providing the algorithms, markets characterized by fragmentation and millisecond timescales demand algorithms which additionally bring human judgment.539 Even with the revision ronext Inc.: Exchanges: Blogging About NYSE Euronext Markets [Weblog], 2009, electronic source. See also Chapman, Mehta and Scotti: Men At Work, 2007, p. 56; and Lucchetti: NYSE Plans to Revise Specialist-Trader Rules, p. C.4. 539 See NYSE Euronext Inc.: NYSE Euronext Appoints Todd B. Abrahall and Michael J. Rutigliano as Liaisons to NYSE Specialists and Brokers, 2008, electronic source.

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of the Hybrid Market model, in the pursuit of customer orientation the NYSE remained committed to the advantages of the floor and combined this with high-speed automated trading.

C.III.4 Full Model Behavior The system dynamics model now includes exogenous pressure, stakeholder reactions, and the NYSE management’s response to these kinds of pressure, as it has been shaped by the past. Endogenous processes of the management’s decisionmaking create the rather radical shift from manual to electronic trading, as can be seen in line number 3 in the BOT graph of Figure C-43. The consideration of how inertia and market share impact the openness to change as well as the consideration of attention to customers explain the long period during which electronic trading is not implemented. They also account for the observed radical shift.


1 1

Dmnl

0.75

123 1231231

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2 3

3 2000 Date

3 2010

2020

2030

"NYSE Fraction of E-Trade" : adaptation 1 1 1 1 1 "NYSE Fraction of E-Trade" : pressures 2 2 2 2 2 2 3 3 3 3 3 "NYSE Fraction of E-Trade" : management 3

Figure C-43: Adaptation, Culture and Resistance, and Mgmt (BOT) Figure C-44 a) reveals the different forces leading to this shift. When the pressure for more e-trade (line 1 a) rises, a small portion of e-trade is implemented. At the same time when e-trade is implemented, the floor exerts pressure against it, trying to avert the change (line 2 a). Its pressure is less strong than that by institutional customers because the floor has lost much of its power and cannot truly slow down change. As Figure C-44 b) shows, a decline in market share (line 2 b) from the rising dissatisfaction with the trading mechanism also helps the implementation of electronic trading by increasing the openness to change. But the radical behavior comes about due to the decline of inertia from change (line 1 b). This drop is accommodated by a rise in the openness to change, allowing for more e-trade. The base run of the model now to a great extent matches the reference mode. It fits the idea that after the

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transformation the NYSE is much more aware of competition it faces from ECNs, alternative trading systems, and stock exchanges.540 a)

Pressure and E-Trade 1

50 pressure unit 1 Dmnl

3

3

3

1

25 pressure unit 0.5 Dmnl

2 0 pressure unit 1 3 0 Dmnl 12 12 12 312 31 231 2 3 23 2 1970 1980 1990 2000 2010 2020 Date

12 2030

"total pressure for more e-trade from customers" : management 1 pressure unit total pressure for more floor trade : management pressure unit 2 2 3 3 3 3 "NYSE Fraction of E-Trade" : management 3 Dmnl

b)

Market Share and Inertia 1 consistency unit 1 2 1 Dmnl

12

12

1

2

1

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2010

2020

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Inertia : management consistency unit 1 1 1 1 1 1 2 2 2 2 2 NYSE Market Share : management Dmnl 3 3 3 3 "NYSE Fraction of E-Trade" : management 3 Dmnl

Figure C-44: Underlying forces (BOT) As the reader may have noticed, the reviewed forces are able to explain the general behavior pattern of the radical shift from floor to e-trade. Environmental as well as endogenous drivers of change collectively create the behavioral pattern that can be observed in the real world. The inclusion of two external effects will additionally help to be more exact concerning the timing of the radical transformation. These external effects are a scandal involving the former CEO Richard Grasso and the implementation of Regulation NMS with the resulting fragmentation of the formerly consolidated market. The former NYSE CEO tried to cash in his USD 140 million retirement 540

See NYSE Euronext Inc., Annual Report pursuant to Section 13 or 15(d) of the Securities Exchange Act of 1934 for the fiscal year ended December 31, 2008, No. 1-33392, New York 2009a, p. 15.

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package. Due to the high amount, this was considered inappropriate and outrageous for a CEO of a non-profit company. As a result of the scandal, the CEO Grasso was asked to resign.541 His resignation and replacement diminished organizational inertia suddenly and exogenously, which is added to the model as a diminishing effect on organizational inertia in the year 2004. About two years after the scandal, the Securities and Exchange Commission implemented Regulation NMS affecting the reinforcing “liquidity begets liquidity” loop of market share. Regulation NMS reduces market share for two reasons. First, it requires markets to trade fast if they want to participate in the national market system in which orders are directly sent to the trading venue with the best price. Second, even if an exchange quotes the best price, after the regulatory change it may often only execute the first portion of the order if it does not have sufficient liquidity at the best price. As a consequence the remainder gets fragmented, which is represented by the comparatively lower thick line in Figure C-45. Technically, starting in mid-2005, a different function is used for translating the fraction of time at the NBBO into the NYSE market share from the NBBO. Studies provide evidence that market fragmentation results in reduced price efficiency, liquidity, and higher volatility.542 This weakens the reinforcing liquidity loop and diminishes the NYSE’s market share. The scandal and the regulatory change are thus responsible for the radical shift towards electronic trading to happen about two years earlier.

market share from NBBO

NYSE Market Share from NBBO 0.8

consolidated market

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fragmented market

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0.10

0.20

0.30

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0.90

1

Figure C-45: Relationship between the time at the NBBO and market share As can be seen in Figure C-46, the base run of the model now to a great extent matches the reference mode, and the radical automation of NYSE trading starts around late 2006. It also becomes obvious that, due to the regulatory change, the market share will not recover to the former high standard any more. This base run

541 542

See Gasparino: King of the Club, 2007, pp. 275í281. See Bennett and Wei: Market structure, fragmentation, and market quality, 2006, pp. 69 and 71; and Madhavan, Ananth: Consolidation, Fragmentation, and the Disclosure of Trading Information, in: The Review of Financial Studies, Vol. 8 (1995), No. 3, p. 594.

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reproduces the real events and behavior as closely as possible and marks the reference point for further model analysis.

Market Share and E-Trade 3

1

2

4

4

2

4

2 4

2

Dmnl

0.75

3

3

3 4

0.5 2 4

0.25 0 1 3 1970

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3 1 1980

3 1 1990

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3 1 2000 Date

4

4

1 2010

2020

2030

"fraction of e-trade from data (volume)" : base run 1 1 1 market share from data : base run 2 2 2 2 2 "NYSE Fraction of E-Trade" : base run 3 3 3 3 3 4 4 4 4 4 NYSE Market Share : base run

Figure C-46: Comparison with reference mode (BOT) In the beginning, while there is no pressure from customers, high institutionalization and inertia dominate the system dynamics model’s behavior. A significant level of market share as well as the reinforcing Liquidity Loop keep the management team inert and support the concentration on floor firms (see Figure C-47). Initially, the balancing Customer Pressure for Speed Loop is not able to affect greater changes in the way of trading. But once the organization makes small changes to its trading mechanism, the Repetitive Momentum Loop and the Repetitive Attention Loop, which explain path-dependent behavior, become less rigid. The Liquidity Loop around market share also looses much of its strength due to Regulation NMS and allows market share to decline. When the feedback from the relative time to execution to market share additionally gets stronger, i.e. the Market Share from Speed Loop, these mechanisms trigger a rethinking in the organization’s strategic orientation. Once some change is initiated, decreasing inertia also triggers more and more change so that the pendulum of the reinforcing Repetitive Momentum Loop swings to the opposite direction towards more electronic trading. Now the Repetitive Attention Loop also helps bias the perception of resistance from the floor. The adaptation process then slows down the more the adaptive Customer Pressure for Speed Loop approaches its goal.

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ACCESS TO INFORMATION TECHNOLOGY Fraction of Institutional Customers + FRACTION OF EQUITIES HELD BY INSTITUTIONS


-

spread


(R) Liquidity openness to Market change Share + + -

Inertia

+ (B) Market Share from Speed

rel. time to execution

+

-




NYSE Fraction + of E-Trade (B) Customer specialist Pressure for participation Market Quality + (R) Floor market quality (R) Market Share Power from Market Quality from specialist (B) Resistance (R) Repetitive Momentum

+ -

profitability of floor



Figure C-47: Full NYSE model (CLD) While responding to pressure from stakeholders, the NYSE management team also shaped the situation. This is the case in particular concerning floor firms and their participation by algorithms. The importance of the implementation of liquidity algorithms and the modification of specialists to designated market makers becomes obvious when comparing simulation runs of Figure C-48. When no algorithms are developed, market quality declines (line 2) due to the implementation of electronic trading, and floor firms as well as customers who prefer floor trading have a much greater potential to hold up electronic trading (line 4). A strong Liquidity Algorithms Loop weakens the balancing loops Customer Pressure for Market Quality and Floor Resistance. As a consequence, more electronic trading can be implemented as is shown in the base run behavior (line 3). This happens without significant shortcomings in market quality (line 1) so that several stakeholder groups can be satisfied at the same time. Market quality only reveals a short-term drop before sufficient algorithms are in place and then rises again. This is also what could be observed in reality. A recent study in the area of finance has investigated the impact of the NYSE’s migration to the Hybrid Market on market quality and speed. The latter has improved significantly as compared to pre-Hybrid. But the move to electronic trading has also reduced the proportion of shares which are price-improved by specialists and floor brokers.543 But since late 2008 participation has increased and so has market quality and the time the floor quotes at the NBBO. Less volume is routed away to other stock exchanges.544

543

See Gutierrez and Tse: NYSE execution quality subsequent to migration to hybrid, 2009, pp. 72 and 79. See also Chapman, Mehta and Scotti: Men At Work, 2007, p. 54. 544 See Clark: Growth in the NYSE's Liquidity Provider Programs, 2009, electronic source; NYSE Euronext Inc.: Cash Equities: Designated Market Makers, 2009b, electronic source, p. 1; and NYSE Euronext Inc.: NYSE Equities: NYSE Market Model Update, 2009c, electronic source, pp. 1–2.

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Liquidity Algorithms 3

1.2 Dmnl 1 Dmnl 1.1 Dmnl 12 0.5 Dmnl

12

1 Dmnl 0 Dmnl 3 4 34 1970 1980

1 2

12

34 1990

1 2

34 2000 Date

1

1 3

4

3 4

4

2 2010

34

1 2

1 2

2020

2030

"market quality from sp. participation" : base run 1 1 1 Dmnl "market quality from sp. participation" : no algorithms Dmnl 2 2 3 3 3 3 "NYSE Fraction of E-Trade" : base run Dmnl "NYSE Fraction of E-Trade" : no algorithms Dmnl 4 4 4

Figure C-48: Importance of liquidity algorithms and market quality By keeping the floor, the organization was able to reduce some of the negative consequences the shift has for some of its customers. The different behavior that liquidity algorithms cause explains the importance the floor still has for the NYSE. The NYSE’s hybrid strategy also serves those customers well who prefer market quality—something which could not have been achieved if the organization had turned into a purely electronic exchange. While institutional customers gained a lot of importance for the New York Stock Exchange, the organization did not entirely reorient towards electronic trading. It still kept many of its old values, particularly concerning market quality and specialist participation. Apart from allowing for e-trade, it kept the floor at a reduced level to add value by the provision of liquidity, price improvement, and reduction of volatility. After the number of specialist or DMM firms has declined for decades, in February 2010 a pivot firm of electronic trading joined the floor to become a DMM. Traders interpreted this as a sign of the continuing importance of a physical trading floor despite the dominance of electronic trading.545 Nevertheless, for a long time specialists and floor brokers dominated the exchange, and the management’s decision-making was highly biased to their favor. This has fundamentally changed, and they have lost the privileges they formerly had. The valuation of the floor is now assessed by the value it contributes to a fair and orderly as well as high quality market. In 2010, stock exchanges were competing on the basis of speed. One may imagine that once all stock exchanges have implemented electronic platforms and technology reaches physical boundaries, the importance of speed will deteriorate since it

545

See Peterson, Kristina and Jacob Bunge: Getco Becomes NYSE Market Maker, in: Wall Street Journal (2010), issued 12 Feb. 2010, p. C.5.

C.IV Analyses of Model Structure and Behavior

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then becomes not an order winner, but an order qualifier.546 Then, the NYSE’s involvement of specialists/DMMs may gain in importance again, giving the stock exchange an advantage over its competitors. The human element is thus likely to prove useful also in a highly electronic environment.

C.IV Analyses of Model Structure and Behavior Now, after the structure of the model as well as its behavior have been revealed, it will be important to gain more confidence in the system dynamics model. This is done with the help of several tests which are applied to its structure and behavior. The model needs to be able to describe the aspects that explain the respective problem, here the radical adaptation of the New York Stock Exchange to electronic trading. Cyert and March suggest the comparison of model output with actual data as an indicator of fit.547 This test alone is not yet sufficient because the comparison cannot validate the appropriateness of the model, but only serves as one indicator of fit. One of the benefits of the system dynamics method becomes apparent here: it is not only possible to test model behavior, but model structure as well. Model structure, parameterization, resulting behavior, as well as policies need to match the problem case of the NYSE.548 The model thus needs to be checked in all of these areas. It is important that in a homomorphous way the model elucidates the essential structural elements of the real system that generate important behavioral patterns. Parameter values also have to be adjusted to the case. Particularly if data is qualitative and ordinal measures are used, the influence of inaccuracy and estimation errors on the behavior must be checked in order to judge the quality of the model.549 Additionally, a thorough examination of behavior follows, although a fit of model behavior to behavior observed in the real world does not yet establish validity. Therefore, first, plausibility of the model itself is tested as well as, second, the consistency of model with real world behavior. The behavioral consistency analysis helps to make sure that the behavior of the model matches schematic patterns of behavior observed in the real world.550 Third, scenario tests are included in order to check the plausibility of behavior in possible different worlds. The model then constitutes a testing laboratory which can be used to check different policies. It can be employed to analyze the New York 546

The shift from speed being an order winner to an order qualifier will also change the shape of the function that transforms the relative speed into an adjustment of market share (effect of time to execution on market share, see Figure C-38 on page 134). 547 See Cyert and March: A Behavioral Theory of the Firm, 1963, pp. 93–96. Behavioral tests of models that involve transient behavior such as s-shaped growth or cycles are difficult to do with standard statistical measures. In the view of Barlas it is best to compare output visually to behavior patterns observed in reality. See Barlas: Formal aspects of model validity and validation in system dynamics, 1996, p. 194. 548 See Forrester and Senge: Tests for Building Confidence in System Dynamics Models, 1980, p. 213; and Milling: Der technische Fortschritt beim Produktionsprozeß, 1974, p. 209. 549 See Milling: Der technische Fortschritt beim Produktionsprozeß, 1974, pp. 212–213. 550 See ibid., pp. 214–216; and Sterman: Business Dynamics, 2000, p. 860.

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Stock Exchange’s possible decision-making rules and reactions to changes in their environment. The simulator may also add and subtract model structure in order to test the sensitivity of behavior policies to changes in the model boundary.551 Nevertheless, it is difficult to separate and label model tests since one test often provides information on several aspects.

C.IV.1 Confidence in Model Structure and Parameterization Concerning the structural elements, first, the structure of the model itself, second, its boundary, and third, parameters are important. To begin with, structural elements and relationships need to be validated. It is even more important to show this for soft variables, such as customer orientation, power of floor firms, and market quality. The derivation of model structure from aspects of data strengthens the confidence that all elements of model structure conceptually correspond to the real world.552 This structural analysis has been done in parallel with the model building process by the close linkage of variables and causal relationships to data from the case study. Model analysis is an iterative process in which the boundaries between construction and testing become blurred so that some tests could already be done and were described in the chapter on model construction. Second, the piecemeal building process of the model was important for the validation of structure since it led to the adequate model boundary. An adequate boundary aggregates the model in such a way that important feedback loops are still included.553 This was, for example, done in the representation of market share which is highly simplified in relation to reality, but still includes the important Liquidity and Spread Loops that bundle liquidity. A too narrow system boundary, i.e. when only environmental developments are taken into consideration, leads to inadequate model behavior.554 It also became obvious that the reactions of important stakeholders to the NYSE decisions and strategy need to be included in an endogenous manner. As Figure C-49 shows, these stakeholder responses are the customers’ development of 551

See Forrester and Senge: Tests for Building Confidence in System Dynamics Models, 1980, p. 225. 552 See ibid., p. 213. 553 See Größler, Andreas: Modelltests, in: Strohhecker, Jürgen (Ed.): System Dynamics für die Finanzindustrie, Frankfurt a. M. 2008a, pp. 261–262. The way commissions and prices were included into the model represent an example of aggregation. The impact of commissions on market share has not explicitly been modeled although there is evidence that execution costs also have an impact on order flow and market share. They were incorporated indirectly by including the price improvement of the specialist. This can also be regarded as a part of lowering execution costs. For evidence of the impact of execution costs see Boehmer, Jennings and Wei: Public Disclosure and Private Decisions, 2007, p. 317. 554 The work by Kim exemplifies the importance of setting the right system boundary for analyzing a problem. See Kim, Hyunjung: Broadening Boundary Perception in a Multi-organizational Context: Study of a Community Mental Health Program in New York State, in: Dangerfield, Brian C. (Ed.): Proceedings of the 26th International Conference of the System Dynamics Society, Athens 2008, pp. 9 and 14–15.

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dissatisfaction, reactions by floor firms, and their importance for market share. Additionally, the management decision process and its reaction to stakeholders are central as well. Since they interact with each other, including them in an endogenous manner proved necessary. While developments in the securities market were important for management and stakeholder dynamics, the market itself is not the focus of this dissertation and is thus not included in an endogenous manner. The causal mechanisms which led to these developments in the market were outside the model boundary. Several important market characteristics, such as the number of securities held by institutional investors as well as technological advancements were included exogenously. Some of them, e.g. the reasons for the growth of institutional investors or for the inauguration of Regulation NMS, were described verbally in order to gain a thorough understanding of the situation. MARKET SHARE - Spread (Liquidity) - Speed

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Figure C-49: Sector diagram making explicit the model boundary Several further aspects were outside the boundary. The system dynamics model makes simplifications concerning the representation of market share. To a large extent market share is determined by a simplified Liquidity Loop, but influences from e.g. order types and trade anonymity are left out. The latter has been integrated in an indirect manner as part of electronic trading. Additionally, already in the 1970s to 1990s, automation was put into practice in order to accelerate the manual trading process and to support the floor. The NYSE automated information systems, order routing, broker and specialist support, and else. These developments were excluded because, first, they do not allow for the leap from a trade that takes multiple seconds to sub-second speed, and second, they represent a continuation of the old market model, not a deviation from it. The same is true for enhancements in trading speed

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that occurred after the implementation of the Hybrid Market. While the Hybrid initially allowed for order execution within 300 milliseconds, it took only about 5 milliseconds to execute a trade in the year 2009. These factors are included indirectly by a higher fraction of electronic trading that is possible in and after 2009, but they are what can be called incremental modifications of the current strategic orientation as opposed to more radical transformations or discontinuities.555 The case study of the NYSE reveals how managerial and inertial forces as well as endogenous pressure from culture and resistance impinge on environmental drivers of change. Figure C-50 supports the importance of endogenous forces. Concerning the trend of the remaining market, two different assumptions are modeled: a linear growth of electronic trading as well as the more realistic limited exponential growth. These two different market behaviors help analyze how closely the adoption pattern of e-trade in the remaining marked shaped electronic trading within the NYSE. The resulting behavior shows no difference. Further runs revealed that the timing of e-trade in the market may slightly change the point of time of the implementation of e-trade at the NYSE, but the pattern of radical change is always prevalent. Thus the development of electronic trading in the market may be necessary for the NYSE to automate, but how the automation is executed and how the implementation process unfolds within the NYSE is subject to endogenous management and stakeholder dynamics. In the following sub-chapters, these endogenous aspects will be analyzed more closely, particularly the effects of each of these determinants on the implementation of e-trade, pressure for and against e-trade, and market share.

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For a grundlegend typology of incremental and discontinuous change see Nadler and Tushman: Types of Organizational Change, 1995, p. 22. Tushman and O’Reilly III emphasize the necessity of organizations to be ambidextrous and capable of mastering evolutionary and revolutionary change. See Tushman and O'Reilly III: Ambidextrous organizations, 1996, p. 24.


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Third, in addition to structural elements, parameters have been adapted to mirror the conditions of the case study for external validity. For instance, cohesiveness of floor firms was found to range on a fair average level. Additionally, the reference fractional inertia decrease at the NYSE was adapted to real-world data and the respective circumstances of the exchange. Many other parameters, such as the reference fractional change in trading and in customer orientation have been adjusted as well. Areas in which the model reacts sensitively to changes in parameters will be pointed out. When the initial market share parameter is changed, it is not only possible to simulate trajectories of the NYSE, but also that of its smaller competitors in the securities market. For a stock exchange that starts out with a market share of only a few percent and that otherwise has the same parameterization as the NYSE, the resulting behavior almost overlaps with that of the NYSE base run.556 The smaller market participant might need a different organizational setup in the area of inertia, for example. However, its simulation revealed a broader applicability of the system dynamics model. A validation test for extreme parameter assumptions serves as an internal parameter test and helps gain confidence in model structure because it illustrates the model’s reasonable behavior under extreme conditions. For example, the model shows sensible results when it is assumed that no technology develops or no institutional customers hold securities. In both cases e-trade is not implemented. Furthermore, tests in which e.g. the NYSE fraction of e-trade, customer orientation or the power of floor firms had extreme initial values produced sensible results. Tests with simultaneous changes in multiple parameters will be subject matter of the following chapter. Despite its ability to explain the interaction of drivers of change, the system dynamics model makes several simplifying assumptions. Floor brokerage and specialist firms are increasingly owned by large financial institutions. This has implicitly been incorporated by the power of floor firms which decreases with the rise of institutional customers. The effect of speed becoming an order qualifier instead on an order winner could more closely be investigated, but was not considered important for the phenomenon of inertia and change that rather concerned the NYSE’s and the securities market’s past.

C.IV.2 Validation of Model Behavior and Sensitivity Sensitivity analyses are conducted to analyze model robustness and the relationship between structure and behavior.557 They investigate the strength of the model’s reac556 557

Appendix B compares the simulation runs. See Sterman: Business Dynamics, 2000, p. 830. In the sensitivity analyses, the model is run 500 to 10000 times while a certain parameter or a group of parameters is changed. In all sensitivity analyses it is assumed that Regulation NMS

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tion to parameter changes, i.e. to changes concerning the model’s assumptions. Minor reactions to changing assumptions point to the robustness of the policies and results. If the model reacts sensitively, this can also point to important feedback mechanisms, to levers of human intervention and the likelihood of different scenarios. The sensitivity of the model behavior to changes in the pressure of institutional customers, for the pressure of floor firms, and for management attitudes will be presented. A sensitivity analysis for the pressure that develops out of the dissatisfaction of institutional customers was run, and its boundaries compare to the extreme conditions test in which no e-trade develops in the market. The reference pressure per dissatisfaction unit was varied from 0.01 to 5 with a normal value of 1. Figure C-51 indicates that the customer reaction is an important driver of change, meaning that extremely strong institutional customers can force an earlier and more gradual implementation of electronic trading. If institutional customers do not develop strong pressure, then in rare cases e-trade is not implemented to the full extent. If an organization thus operates in a niche market and enjoys stable relationships to customers that do not go along with the new trend, it may be relatively unaffected by a major shift of the main market. While the variation in the reference pressure from institutional customers has effects on model behavior, variation in the reference pressure per non-institutional customer neither has an effect on the timing nor on the extent of the implementation of e-trade.558

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comes into effect, but the effect of the Grasso scandal is left out since Regulation NMS is assumed not be influenced by the NYSE’s behavior, but the scandal is. The output graphs of the sensitivity test are shown in APPENDIX B. Here the reference pressure per non-institutional customer was changed in the range of 0.01 to 5 with a base-run value of 0.5.

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Figure C-51: Sensitivity for institutional customer pressure for e-trade The investigation of pressure from floor firms for the retention of the floor sheds light into the influence floor firms have on the implementation of electronic trading. A first step analyzes the effect of their resistance on the NYSE decision-making. The upper graph in Figure C-52 shows that the extent to which e-trade is implemented is not sensitive to variations in resistance from inadequacies in employability and profitability. Here, the reference resistance pressure per floor firm, the time to adjust desired participation and the time to adjust desired earnings were changed simultaneously to express differences in the strength of resistance and in their durability. Different parameter values create pressure to a greater or lesser extent for the specialist system (bottom graph). But they do not affect the decision for e-trade since resistance occurs only after the New York Stock Exchange started implementing at least some e-trade and had already shifted its attention towards institutional customers. In this case the management team’s reaction is more important than the re-

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Figure C-52: Sensitivity for resistance The power of floor firms may be a more sensitive determinant of the extent of e-trade. Floor firms gain power from the participation of the specialist in trading and the positive effect of participation on market quality. They lose power due to the rising number of institutional traders who like to bypass floor intermediaries. When the sensitivity of the model is checked for changes of the reference power of floor firms, the fraction of e-trade shows a very low sensitivity, too. In general, sensitivity analyses for resistance, the floor firms’ power, and even for their cohesiveness show that variances in each pressure for itself do not have much effect on the fraction of e-trade at

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the NYSE.559 It seems that a group of forces determines the implementation of e-trade, as simultaneous changes of the cultural variables have complementary effects. Therefore, the sensitivity of the model is checked for several floor constituencies at the same time—for the resistance variables as above, for the reference power of floor firms, and the degree of cohesiveness of floor firms. Here, here the output graphs show more variation. Management was parameterized as in the base scenario. Figure C-53 a) suggests that when all forces work together the effects on the implementation of e-trade are much greater. The effect of feedback becomes obvious in the figure on this and the following page. Different extents of the institutional pressure for e-trade (b) affect the fraction of e-trade (a) which then leads to more or less pressure for the floor (c) and differences in market share (d).560 Concerning the NYSE this reveals that even with a different value of stakeholder pressure change could not have been implemented much earlier. If culture, resistance and related aspects had been stronger and institutional pressure weaker, e-trade could have been held up and been implemented to a lesser extent. This shows that affected stakeholders have some influence on the extent to which change is implemented if they are sufficiently strong and cohesive at the same time. There are several feedback loops which include the floor’s reaction to the management team’s decisions. When the strength of only one of these loops is changed, the system exhibits ‘policy’ resistance and the behavior remains very similar to the base run. The management loops’ dominance is responsible for the immutable behavior. The simultaneous alteration of several floor parameters is much more effective because the influences are multiplicative and this strength of pressure is needed to affect the persistent management behavior. a)

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Figure C-53: Sensitivity for resistance, power, and cohesiveness of floor firms The management team’s importance has already become obvious in the descriptions above. The influence of the reinforcing loops which represent managerial decisions will be analyzed now. It will be tested how sensitively the system reacts when stakeholder parameters are kept at their base run values but management attributes are changed. These changes comprise the adaptability of the trading mechanism, i.e. the organization’s general responsiveness to pressure, as well as the management’s adaptability of customer orientation.

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Figure C-54 shows a sensitivity analysis of the organization’s general openness to change in which the management’s reference fractional change in trading per pressure p.a. was altered. Graph a) of Figure C-54 reveals that the management team’s general openness to pressure decides between an earlier and smoother adaptation and the observed radical change. The NYSE adapts earlier for high values of the reference fractional change. Radical change develops when the reference fractional change is low. Yet, the earlier adaptation is also delayed by about ten years in comparison to the market since the organization still starts with a high level of inertia. Missing adaptation leads to strong institutional pressure for electronic trading, as depicted in graph c) on the following page. Together with falling market share this pressure triggers some change which quickly amplifies by the reinforcing loops Repetitive Momentum and Repetitive Attention. Graphs a) and b) illustrate this phenomenon: Low initial change is followed by a radical shift and a resulting sudden depletion of inertia. Only when the NYSE management is not at all reactive to pressure, is e-trade not implemented to the full extent. The floor also reacts with pressure when automation threatens its survival (graph d), but this reaction only lasts several years. First of all, the floor firms become accustomed to the situation, and second, liquidity algorithms improve their situation by improving participation and market quality, depicted in graph e). a)

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Figure C-54: Sensitivity for the fractional change in trading per pressure A similar reaction to variations in the reference fractional change in customer orientation p.a and in the initial customer orientation can be observed. Customer orientation responds sensitively, shown in the lower part of Figure C-55. The little dip around the year 2008 in some of the simulation runs indicates pressure from the floor reducing customer orientation. The fraction of e-trade is somewhat affected from the changes in attention. A high initial as well as a responsive orientation to stakeholders is able to expedite the shift to e-trade, low adaptability of customer orientation slows down the process a little bit, but the radical behavioral pattern generally remains. A sensitivity analysis for different initial specifications of inertia has similar results and shifts the timing of the radical change (see Appendix B).

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Figure C-55: Sensitivity for fractional change in customer orientation The analyses of the management team’s general openness to change and of its concentration on institutional customers support the impact of the management and the organization on the implementation of electronic trading. Yet, so far only the reactivity to pressure has been analyzed. Figure C-56 below shows the model output for a sensitivity analysis in which additionally the levels of inertia and of customer orientation were altered. As before, it is assumed that the organization is at least somewhat responsive to performance and stakeholder pressure. The graph reveals that the shift towards e-trade could have happened somewhat faster had the organization been more reactive, and the implementation could also have gone slower if the organization had been more rigid in its reaction to outside pressure. Thus, management turns out to be an important factor concerning the implementation of electronic trading. By

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different levels of inertia, openness to change and customer orientation it particularly sets the point of time when change is initiated. Strong stakeholder pressure turned out to have an effect rather on the extent to which electronic trading is implemented. Overall, the sensitive behavior towards variation in managerial disposition reveals the scope of managerial choice. Given the historic environmental conditions, the management team is an important driver of change. It can trigger adaptive behavior to external forces quite early or remain inert for a long time and then rather change radically to the strong pressure that has built up. sens management 50% 75% 95% "NYSE Fraction of E-Trade" 1

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Figure C-56: Sensitivity for managerial parameters So far examples concerned the effect of changes in a single or a few variables on the implementation of electronic trading. Figure C-57 shows a sensitivity analysis for a simultaneous random change of environmental, stakeholder, and managerial conditions. Subjected to a careful sensitivity analysis, the results are robust to a wide range of parameters, and they are important in two ways. They portray the robustness of the model and additionally reveal the full range of theoretical possibilities of how an NYSE-like stock exchange may react to different environments and interact with pressure from multiple sides.

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The sensitive graphs of Figure C-57 make it obvious that the history and the future of the New York Stock Exchange could well have been different. Only in rare cases the shift to electronic trading would not have happened, but the extent to which etrade was implemented could have varied. The differences arise from variations in the pressure for and against automation, how vigorously the stakeholder groups pressure for their aims and how powerful their pressure is. Differing setups in the management can also lead to different trajectories. By their pressure and resistance the surrounding stakeholders exert influence on the extent to which the decision is carried out. In their combination the different stakeholder and management dispositions are able to generate different modes of behavior, ranging from smooth adaptation to radical change and even the decline of the organization. As the market share graph of Figure C-57 indicates, these theoretical possibilities lead to different positions of a stock exchange in the market so that multiple futures would theoretically have been possible.

C.V Implications of the New York Stock Exchange’s Recast of Trading Systems The present study provides an example of a radical organizational transformation. It presents an investigation into structure and behavior of organizations—here of the New York Stock Exchange—with particular focus on feedback relationship and the emergence of behavior. The different scenarios simulated by a system dynamics model have shown that, on the one hand, the change towards electronic trading is an adaptation process of the NYSE to its competitive environment. It proved those right who predicted stock exchanges to adopt electronic trading, and proved those wrong who expected the NYSE to be too inert to change and fail.561 Since technology provided for the possibility of e-trade and since there was a growth in institutional customers, electronic trading finally was implemented. Without these drivers for change from the environment, no change in the NYSE’s way of trading would have emerged. On the other hand, the mere adaptation point of view does not provide the full picture and cannot explain how and why the shift to electronic trading happened at the NYSE —i.e. the timing, pacing, and the significance of decision-making. The modeling process is indeed able to illustrate why the changes appeared to be this radical. It gave a structural account for path-dependent processes that have also been discussed in some of the literature on the punctuated equilibrium approach and 561

For an adaptive point of view for stock exchanges see Clemons and Weber: London's big bang, 1990, p. 56; Clemons and Weber: Information Technology and Screen-Based Securities Trading, 1997, p. 1695; Feldman: Electronic Marketplaces, 2000, p. 95; and Picot, Bortenlänger and Röhrl: The Automation of Capital Markets, 1995, electronic source; Picot, Bortenlänger and Röhrl: Organization of Electronic Markets, 1997, pp. 113–115; and Stoll: Electronic Trading in Stock Markets, 2006, pp. 154 and 173. For the NYSE’s expected decline see Naidu and Rozeff: Volume, volatility, liquidity and efficiency of the Singapore Stock Exchange before and after automation, 1994, p. 24; and Welles: Is it time to make the Big Board a black box, 1990, p. 74.

C.V Implications of the New York Stock Exchange’s Recast of Trading Systems

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on cognitive determinants of change.562 The management team is highly important for the way the organization reacts to its environment. A reinforcing process of repetitive momentum made it develop path-dependent behavior and concentrate on what it had always done. The strength of this reinforcing process prevented a smoother adaptation and was responsible for the observed radical behavior. Together with the management team’s generally weak conversion of pressure to action, it caused the missing adaptation. Missed adjustment then resulted in even higher pressure and a market share decrease that eventually overpowered the reluctance to change. For a substantial period of time there had been dissatisfaction pressure that was initially held up by management, but managerial inertia started to be overwhelmed by the pressure from institutional stakeholders. The slowly rising awareness of pressure together with the shift in management and with the effects of the falling market share functioned like a valve, so that the pressure was released. Since inertia is a part of reinforcing feedback loops, a small step towards change decreased inertia, meaning it created further replacement and rethinking among the management team. The New York Stock Exchange seized the opportunity for electronic trading quite late, but it did make the decision to change. It also decided against full automation and kept parts of its old structure in a customized way in alignment with the new trading system. This was a decision of the NYSE management that revealed high elements of choice. After the shift in its strategic orientation the NYSE expressed about itself: “We adapt and evolve.”563 This statement summarizes the environment and the management as dual drivers of change since the NYSE adapts to e-trade but finds its own way of evolution in combination with a trading floor and liquidity algorithms. Liquidity algorithms as well as the conversion of specialists into DMMs take a special position since they happened later than the initial move to e-trade. They provide empirical evidence of a post-revolutionary adjustment some time after the original transformation.564 The later modification led to more full alignment with the NYSE’s environment. This later adjustment can be regarded as part of the full transformation process towards e-trade because it fine-tunes the new strategy and implements learnings from the change process. By this post-revolutionary adjustment the NYSE also provided an example of how an organization is able to simultaneously address different stakeholder demands and to cope with conflicting demands of its environment.565

562

See Jansen: From Persistence to Pursuit, 2004, p. 277; Tripsas: Technology, Identity, and Inertia through the Lens of 'The Digital Photography Company', 2009, p. 447; Tushman and Romanelli: Organizational Evolution, 1985, p. 192; and Wollin: Punctuated equilibrium, 1999, p. 363. 563 Pellecchia, Ray: It's go time for go fast (6 Oct. 2006), in: NYSE Euronext Inc.: Exchanges: Blogging About NYSE Euronext Markets [Weblog], 2006b, electronic source. 564 See Sabherwal, Hirschheim and Goles: The Dynamics of Alignment, 2001, pp. 193–195. 565 For the need to cope with conflicting demands see Pache, Anne-Claire and Filipe Santos: When Worlds Collide: The Internal Dynamics of Organizational Responses to Conflicting Institutional Demands, in: Academy of Management Review, Vol. 35 (2010), No. 3, p. 471.

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In relation to managerial decision-making, the degree of attention to customers proved to be of specific importance. The existence of strong and then decreasing inertia is not able to fully explain why the shift towards electronic trading took place so late and was radical. An attentional bias towards the floor and a rather sudden acknowledgement of the customers’ importance supported the radical nature of the shift. Attention is part of a reinforcing and path-dependent process. The distribution of attention often biases the disposition and orientation of the management team towards a specific stakeholder group and its demands. If an organization accumulates inertia, it may reinforce its current attention to powerful groups such as floor firms and thus perpetuate tradition. While attention also follows the pressure that comes from the two stakeholder groups, as long as the management team is inert and committed to its current strategic orientation, adaptation takes place only slowly. Some existing literature on decision-making, change and adaptation take up the topic of attention to issues.566 The present case study, however, provides an example of organizational attention to legitimate stakeholders. It shows how the combination of a stakeholder group’s urgency from dissatisfaction and power determines the pressure for their respective aim. It takes into account how attention to customers developed and how it affected decisions of the management team. The case study of the NYSE gives first insights into the research question of what the drivers of change are.567 It shows that and clarifies how the evolution of the NYSE has been affected by multiple drivers. While including decision-making by the management team into the analysis proved to be necessary, narrowing the focus only to the management would not provide the desired results. Hereby, the analysis also reveals that one view alone also does not explain the NYSE’s trajectory. Both the environmental developments as well as the management team’s decisions create endogenous forces among stakeholder groups such as customer and floor firm pressure that returns to the management’s subsequent decision-making by means of feedback. The modular build-up of the system dynamics model reveals that while the environment represents an important driver of change, the endogenous dynamics of pressure by stakeholders and the management team also drive organizational evolution. Different determinants of change have to be seen in their interaction in order to make sense of the radical shift to electronic trading by the New York Stock Exchange. Jointly these drivers explain why “this old stone is shaking off the moss and rolling again.”568 As Morgan points out, each new perspective adds a little different 566

See Cho and Hambrick: Attention as the Mediator Between Top Management Team Characteristics and Strategic Change, 2006, p. 454, referring to Ocasio: Towards an Attention-based View of the Firm, 1997, p. 189; Ocasio and Joseph: An Attention-Based Theory of Strategy Formulation, 2005, pp. 56–57; and Simon: Administrative Behavior, 1976, p. 90. 567 For an answer to this question see also Milling, Peter M. and Nicole S. Zimmermann: Modeling Drivers of Organizational Change, in: Kybernetes, Vol. 39 (2010), No. 9/10, pp. 1452–1490. 568 Pellecchia, Ray: The right time (6 Oct. 2006), in: NYSE Euronext Inc.: Exchanges: Blogging About NYSE Euronext Markets [Weblog], 2006d, electronic source.

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understanding, thus leading to a more complete picture.569 Only from a multi-paradigm view is it possible to capture how elements of deliberate choice and deterministic adaptation drive the NYSE’s evolution. The combination of perspectives shed light into the dynamics of how changes unfold. The investigation of the case study of the New York Stock Exchange and the three-step development of the system dynamics model point out that parameters have an influence on loop dominance and can put loops as well as entire model sectors out of action. This suggests that different boundaries: the environment, stakeholders, and the management—i.e. the assumption of what needs to be included in the analysis—create different assumptions about the prevalence of drivers of organizational change. Due to the focus on feedback relationship and the emergence of behavior, this investigation helps open the black box of the process of organizational change. The analysis reveals that the implementation of electronic trading at the New York Stock Exchange could have followed a different trajectory. Organizational decline due to the high inertia would have well been another possible path that also many researchers and industry experts assumed the NYSE would take. As shown by the last sensitivity graph, an early adaptation to electronic trading was also within the realm of possibilities. The importance of the pressure for the floor also became obvious in the runs in which no liquidity algorithms were included and the pressure against e-trade was higher. The failure to introduce algorithms in combination with an even stronger floor could have somewhat impeded the full implementation of electronic trading. The system dynamics analysis also demonstrates that the management team’s initial orientation to specialists, floor brokers and non-institutional customers was successful at delaying the adaptation process by masking the pressure for the automated system. The historical importance of the floor and the low initial customer orientation averted the shift towards electronic trading in the 1990s. A management team which does not direct its attention to customers even in times of performance crisis could have caused a further delay in e-trade. Since it is clear now why the NYSE followed its specific trajectory, it will be interesting to further investigate how other modes of behavior can be achieved by a management team when it faces rapid environmental change.

569

See Morgan: Images of Organization, 2006, pp. 337–341.

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D Generic Interpretation of Organizational-Environmental Forces, Feedback, and Change D.I D.I.1

A Generic Model of Organizational Inertia and Change Motivation for a Generic View

Although the New York Stock Exchange takes a special position among U.S. and even world-wide stock exchanges, its behavior is typical for a class of organizations. Organizational ‘dinosaurs’ which frequently are long-established corporations but also younger organizations often have difficulties adapting to a changing environment. They exhibit inertia and often adapt late or not at all. The short recall of several known examples will demonstrate this similar pattern of behavior. Just like the NYSE did for a long period of time, the computer manufacturer DEC also failed to undergo necessary change in its strategy and culture. The innovation of the personal computer altered the organization’s market environment. A new group of private customers emerged who had different preferences and were served by new competitors. Since DEC’s management did not perceive the needs of this customer group, it missed major market opportunities.570 The organizational culture remained focused on clients with a strong technological interest. The grown belief that technologically sophisticated computing products will prevail in the market was deeply embedded in DEC’s culture and resulted in a reduced perception of the radically altered environment with its new solutions and stakeholder groups.571 The DEC example shows that apparently similar reasons led to the failure to change at DEC and at the NYSE. In both cases a missing orientation to an important stakeholder group has had significant impact on the biased perception of pressure to change. Attention to customers was also a main factor leading to the demise of the camera and film manufacturer Polaroid. Although the company did invest in a new technology, it misjudged the rising importance of a new group of customers and their preference for digital photography. The failure to depart from its established business model was grounded in the management team’s cognitive representations. Adequate adaptation to the new environment would have required change in strategic beliefs.572 The case of Nestlé was somewhat different from DEC and Polaroid. Customers accused Nestlé of and boycotted it for its unethical marketing practices in the developing world. The question did thus not center on the adoption of a new technology, but rather on a new strategy of ethical conduct and respective marketing. At the same time it is also an example of a long-term neglect of stakeholder preferences 570

See Schein: DEC is Dead, Long Live DEC, 2003, p. 291. See ibid., pp. 74. 572 See Tripsas and Gavetti: Capabilities, Cognition, and Inertia, 2000, p. 1158. 571


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D Generic Interpretation of Organizational-Environmental Forces, Feedback, and Change

and of a strong orientation towards its taken for granted strategy. The company regarded the boycotters’ complaints as an ephemeral phenomenon and paid little attention to them. Even more than a decade after Nestlé’s marketing practices had been denunciated, the organization was still regarded as not having undergone a true ideological change.573 Finally Nestlé learned, and also started to promote its ethical behavior on its company website. In the three cases described above, organizational inertia played a critical role. In relation to managerial cognition and bounded rationality, it often resulted in the failure to attend to an important group of stakeholders. In this respect, the reaction of selected organizations to the personal computer, digital photography, and to a rising demand of consumers for corporate ethical conduct bears resemblance to the NYSE’s reaction to the emergence of e-trade and the growing emphasis on speed vs. price among institutional customers. The similarities between the different examples give reason into a further investigation of the causalities that generate behaviors at the NYSE and that were also apparent at Nestlé, DEC and Polaroid. These causal relationships and their informational value for the research questions guiding this investigation will be analyzed in the following sub-chapters. They will concentrate on causal relationships and dynamics among drivers of change and the influence of previous changes on further transformations. Causal relations and resulting behavior will be examined with a generic system dynamics model. It will portray the characteristics of a ‘canonical situation model’. According to Lane, such a model is general and applies to a specific domain (or class) of systems and is often derived from a more specific application case. Depending on the parameter and policy choices employed, it is able to generate significantly different modes of behavior. These types of generic models serve “as general theories of structure and behaviours of a domain.”574 The system dynamics model is supposed to explain behavior observed in the three cased described. Although the emphasis in system dynamics modeling is on modeling a problem instead of a system, many models which are developed for specific purposes are able to make generic contributions, e.g. by revealing causal relationships that generate problems also in related cases. Forrester points out that in the best of cases a generalized model is created. It is a theory for a particular class of systems that can be adapted to specific circumstances by parameterization. The generalized structure explains phenomena and modes of behavior encountered in similar situations.575 System dynamics models are structural theories of social and in 573

See Post: Assessing the Nestlé Boycott, 1985, p. 123; Richter, Judith: Holding Corporations Accountable: Corporate Conduct, International Codes, and Citizen Action, New York 2001, pp. 77–78; and Sethi: Multinational Corporations and the Impact of Public Advocacy on Corporate Strategy, 1994, p. 70. 574 Lane, David C. and Chris Smart: Reinterpreting 'generic structure': Evolution, application and limitations of a concept, in: System Dynamics Review, Vol. 12 (1996), No. 2, p. 102, see also p. 91. 575 See Forrester: Industrial Dynamics—A Response to Ansoff and Slevin, 1968, p. 607.

D.I A Generic Model of Organizational Inertia and Change

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particular of socio-economic systems.576 Hence, a system dynamics model as the one described above reaches beyond the explanatory value of the single case of the New York Stock Exchange. It does not only shed light onto behavior and structural causalities of a specific example, but is able to explain generic characteristics of specific social phenomena. Since the generic system dynamics model accounts for a specific class of organizations—i.e. established organizations that face environmental change—on the continuum between small and middle range theories it is situated somewhere towards the latter. It represents a dynamic theory that is able to explain adaptive, inertial, and radical patterns of behavior observed in the real world. The system dynamics approach can not only be used for supporting concrete decisions in organizations. It can also be used to understand the structure of a decision context and behavioral patterns in general. There are many examples when system dynamics models were employed to test, enlarge, or develop theories. E.g. Größler developed a concept model that links key strategic competitive factors in production: i.e. time, cost, quality, and flexibility. Rudolph and Repenning showed how interruptions in the organizational routines can lead to organizational collapse. Sastry explicitly tested and enlarged Tushman and Romanelli’s punctuated equilibrium theory.577 Based on a concrete example, Saysel and Barlas developed a generalized simplification process that has been applied, for example, by Kopinsky et al. In the view of Saysel and Barlas, generic structures, simplified from one or several case-specific models, are able to transfer insights within or across application domains.578 Schwaninger and Grösser also demonstrated the use of a case study as a means and locus of theory building in the area of product launch strategies. Based on SD modeling and a case study, they exemplified how to develop theory that is applicable to an entire class of systems and is thus closer to a middle range than a small range theory.579 This places the generic system dynamics model or theory which is developed here in the tradition of previous system dynamics research. 576

See Barlas: Formal aspects of model validity and validation in system dynamics, 1996, p. 187; and Größler, Andreas: Struktur und Dynamik strategischer Fähigkeiten in der Produktion, Mannheim 2007, p. 150. For information on system dynamics models or simulation models as a theory see Cohen and Cyert: Computer Models in Dynamic Economics, 1961, p. 113; Forrester: System dynamics, systems thinking, and soft OR, 1994, p. 253; and Kopainsky and Luna-Reyes: Closing the Loop, 2008, pp. 474 and 483. 577 See Größler: Struktur und Dynamik strategischer Fähigkeiten in der Produktion, 2007, in particular p. 158; Rudolph and Repenning: Disaster Dynamics, 2002, p. 24; Sastry: Problems and Paradoxes in a Model of Punctuated Organizational Change, 1997, p. 266; and Sastry, M. Anjali: Understanding Dynamic Complexity in Organizational Evolution: A System Dynamics Approach, in: Lomi, Alessandro and Erik R. Larsen (Ed.): Dynamics of Organizations: Computational Modeling and Organization Theories, Menlo Park, CA [et al.] 2001, p. 400. 578 See Saysel, Ali Kerem and Yaman Barlas: Model simplification and validation with indirect structure validity tests, in: System Dynamics Review, Vol. 22 (2006), No. 3, p. 260. For a further application see Kopainsky, Birgit, et al.: A Blend of Planning and Learning: Simplifying a Simulation Model of National Development, in: Simulation & Gaming (2010), pp. 6–7. 579 See Schwaninger and Grösser: System Dynamics as Model-Based Theory Building, 2008, pp. 448, 457 and 461.

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D.I.2


Generic Model Structure

Causally, the generic model of the organization-environment relationship is highly similar to the NYSE-specific model. The managerial decision-making structure was kept in order to adequately capture the reaction of organizational decision-makers to their environment. This part has been condensed already. Pressure from stakeholders for the new strategy—called strategy B—is represented by one feedback loop as it was also in the NYSE case. The pressure from the old stakeholders for the retention of the old system—here for strategy A—was simplified since in the case of the NYSE the floor firms displayed idiosyncratic features of their culture and power in relation to the NYSE management that cannot be expected to hold in the majority of cases. The causal loops and the respective stock and flow structure will be described next. The order of description will be the same as for the NYSE model: starting with the new developments in the market and stakeholder pressure for a new strategy— here for strategy B—resistance from stakeholders favoring the old strategy A will be added. Finally, the managerial mechanisms will be presented. In an analogous manner to floor and e-trade, the two strategies A and B will be discussed. Table D-1 gives an overview. Strategies may stand for a focus on mini vs. personal computers, for analog vs. digital photography, or for a pure cost strategy vs. a strategy that incorporates customer demands for ethical conduct. While the focal organization and the remaining market pursue the traditional strategy A, a new strategy B is developed, e.g. by a competitor in the market. This new strategy offers a different set of qualities that are preferred by a group of stakeholders. Strategy A (old strategy)

Strategy B (new strategy)

floor trade, analog photography, minicomputer, …

e-trade, digital photography, PC, …

price quality in trading, Quality A resolution quality in photography, computing power

high

often low

speed in trading, Quality B digital editing ability in photography, applications of home computing

low

high

Table D-1: Strategies and their qualities In the base scenario, the development is assumed to be no single invention, but to be a process such as in the case of electronic trading that takes about 15 years to develop and on average 5 more years to be implemented in the market. The long development seems reasonable: The interest in ethical conduct started to increase in the 1980s and is still current in the year 2010. Digital photography started to develop in the 1980s, and became the dominant design around the year 2000. Yet, the struc-

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ture also allows the testing of alternative developments. The development of strategy B, pictured on the left of Figure D-1, diffuses in the remaining market with the respective time delay of 5 years, as in the NYSE case.580 It nd increases the fraction of stakeholders favoring B. The latter fraction, when multiplied by the total number of stakeholders, results in the number of stakeholders favoring B, shown in the upper part of the figure. Compared to the NYSE case, these external influences on the model have been simplified.581 TOTAL NO. OF STAKEHOLDERS number of stakeholders favoring B + +

DEVELOPMENT OF STRATEGY B

+

diffusion of B in remaining market -

fraction of stakeholders favoring B +

+ TIME TO DIFFUSE B IN REMAINING MARKET

REF.QUALITY B OF STRATEGY B

desired quality B +

REF. QUALITY B OF STRATEGY A

Figure D-1: Diffusion of B (SFD) Many stakeholders prefer B because it succeeds at discovering the potential that is unused by the old strategy A. Strategy B offers a special quality B such as speed in the case of stock trading or usage in households in the case of personal computers. On the bottom right of Figure D-1, the desired quality B forms from the diffusion of B in the remaining market which works as a weight for the reference quality B of strategy A and B respectively. The quality B of strategy B is set to 1 whereas that of strategy A is a rather small number (here 0.1). The computation of the desired quality B is broken down in the following equation D-1: desired quality B [quality unit] = diffusion of B in remaining market [dmnl] • REF. QUALITY B OF STRATEGY B [quality unit] + (1 – diffusion of B in remaining market [dmnl]) • REF. QUALITY B OF STRATEGY A [quality unit]

D-1

The concept of the diffusion of a new strategy that entails an idiosyncratic quality does not only comply with the emergence of e-trade and the growing emphasis on speed vs. price among institutional customers. It also extends to the increasing preference for digital storage in photography, and the applications of home computing as well as the availability of mobile telephony also serve as examples of the rising quality B of a new product or strategy. Apart from the invention of new technological appli580 581

Appendix E lists the model equations of the generic model. For simplification of parameters and their causes see Saysel and Barlas: Model simplification and validation with indirect structure validity tests, 2006, pp. 256–257.

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cations, the mechanism also extends to developments that require a new strategy that involves a rethinking in the management or a change of the corporate culture. In the case of ethical conduct, the development of B may be an increasing interest in ethics, resulting in a group of stakeholders demanding ethical behavior from organizations.582 Quality B could in this case be interpreted as a product’s ethical quality. These market changes are similar to what Christensen called disruptive innovation. The latter is a technologically inferior product with features different from the old product but which customers value. Christensen subsumes digital photography and the emergence of ECNs under disruptive innovations.583 The concept described in the generic model of this dissertation extends to non-technological changes in the market to include significant shifts in customer or stakeholder demands that impinge on an organization. While disruptive technologies rather describe changes in the market, the focus is on transformations that require radical change and re-thinking in organizations. + <desired quality B>

-

pcvd inadequacy of strategy per stakeholder B

rel. quality B +

REF.QUALITY B OF STRATEGY B

(B) Adaptation Pressure for B

+

+ total stakeholder pressure for more B

quality B + + -

REF. QUALITY B OF STRATEGY A

+ + stakeholder pressure for more B

REF. PRESSURE PER STAKEHOLDER FAVORING B

+

Orientation to Strategy A

change in strategy +

Orientation to Strategy B

+ pcvd pressure from stakeholders favoring B

Figure D-2: Adaptation pressure for strategy B (SFD) Those stakeholders favoring strategy B compare the developments of this new strategy and its quality dimension to the focal organization’s orientation, as described in the upper left of Figure D-2. In this respect, the focal organization’s quality B, that derives from its orientation to strategy B rather than to the old strategy A, gets compared with the desired quality B in the market. The resulting relative quality B, shown in Figure D-3, is a measure for the adequacy of strategy that those stakeholders favoring B perceive. This perceived inadequacy of strategy per stakeholder B is the 582

For more information on the rising demand of ethical conduct see Miczka, Switbert, et al.: Walk the Talk: Implementing Ethical Conduct in Industrial Production Systems, in: Strohhecker, Jürgen and Andreas Größler (Ed.): Strategisches und operatives Produktionsmanagement: Empirie und Simulation, Wiesbaden 2009, pp. 91–92. For an opposite view see Carrigan, Marylyn and Ahmad Attalla: The myth of the ethical consumer - do ethics matter in purchase behaviour?, in: The Journal of Consumer Marketing, Vol. 18 (2001), No. 7, pp. 569–573. 583 See Christensen: The Innovator's Dilemma, 1997, p. xxv. For a definition of disruptive technology see Christensen: The Innovator's Dilemma, 1997, p. xv.

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inverse of the relative quality B, at the time when the focal organization lags behind the market. Inadequacy remains at zero for when the focal organization outperforms it. Perceived Inadequacy of Strategy per Stakeholder Favoring B perceived inadequacy

1 0.75 0.5 0.25 0 -1

-0.80 -0.60 -0.40 -0.20 0 0.20 "rel. quality B"

0.40

0.60

0.80

1

Figure D-3: Effect the relative quality B on the perceived inadequacy of strategy Just as in the NYSE-specific case, each stakeholder perceiving an inadequacy exerts stakeholder pressure for more B that adds up to total stakeholder pressure for more B. Compared to the NYSE case in which the individual reference pressure per stakeholder favoring B was set to one, it is here set to 0.6 in order to refrain from the special power that institutional clients had at the NYSE. In more general terms the pressure may also be interpreted as a stakeholder desire that the focal organization perceives or does not notice. The weighting of this total pressure with the organization’s attention to stakeholders favoring B results in the perceived pressure for more B which then leads to the focal organization’s change in strategy towards more orientation to strategy B. In the area of stakeholder theory, it is established knowledge that stakeholder pressure motivates organizations to implement new practices.584 This closes the balancing feedback loop Adaptation Pressure for B that is also shown in a simplified CLD in Figure D-4. DEVELOPMENT OF STRATEGY B

+ desired quality B

rel. quality B + (B) Adaptation Pressure for B Orientation to Strategy B

pressure for more B

+

Figure D-4: Adaptation pressure for strategy B (CLD)

584

See Eesley and Lenox: Firm responses to secondary stakeholder action, 2006, pp. 775–777; and Sarkis, Joseph, Pilar Gonzalez-Torre and Belarmino Adenso-Diaz: Stakeholder pressure and the adoption of environmental practices: The mediating effect of training, in: Journal of Operations Management, Vol. 28 (2010), No. 2, p. 164.

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It can be expected that resistance from the stakeholder favoring the old strategy follows the implementation of the new strategy B.585 The resulting resistance pressure is composed similar to the non-institutional customers’ resistance pressure rather than the more complicated structure described for the NYSE floor firms. The mediation of resistance by cultural aspects is left out since it also represents a phenomenon that was special at the NYSE. The power of the stakeholders favoring A is expressed by their group size, e.g. by the number of customers desiring the old product. Additionally permanently powerful stakeholders may exist whose power is independent of their group size and thus constant over time. They may be floor firms, employees, and else. In the base scenario their number is kept at zero, but it can be varied. The respective stock and flow structure is depicted in Figure D-5.

<no of stakeholders favoring A>

(B) Resistance Pressure for A

(R)

desired quality A by stakeholders favoring A

TIME TO ADJUST DESIRED QUALTIY

+

stakeholder resistance pressure for more A + +

quality A +

REF. QUALITY A OF STRATEGY A

total stakeholder pressure for more A +

+ +

+

+ -

pcvd adequacy of quality A

PERMANENTLY POWERFUL STAKEHOLDERS FAVORING A

+ effect of quality A on resistance REF. RESISTANCE PRESSURE PER STAKEHOLDER FAVORING A

Figure D-5: Resistance pressure for strategy A (SFD) The orientation to strategy A involves a specific quality, such as market quality of stock exchanges, resolution quality in analog photography, or the computation capacity of mini-computers. This results in an absolute quality A that the focal organization offers. Quality A is presented as an absolute quality since the difference to the past quality, not to that of competitors is essential for the rise of resistance pressure. The perceived adequacy of quality A results from this comparison of the actual quality value with the floating goal of desired quality A by stakeholders favoring A. Stakeholder resistance pressure for more A rises when adequacy falls below its normal level of one, as Figure D-6 exhibits in more detail. The effect of quality on resistance is slightly inversely s-shaped to account for a convergence on a maximum value of one and a lower rise of resistance when adequacy is close to one.

585

See Oreg: Personality, context, and resistance to organizational change, 2006, p. 79; and Beer: Organization Change and Development, 1980, p. 103.

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Effect of Quality on Resistance 1

effect

0.75 0.5 0.25 0 0

0.10

0.20

0.30 0.40 0.50 0.60 0.70 pcvd adequacy of quality A

0.80

0.90

1

Figure D-6: Effect of adequacy of quality A on resistance pressure Individual stakeholder resistance pressure multiplied with the sum of the number of stakeholders favoring A and the permanently powerful stakeholders favoring A results in the total stakeholder pressure for more A. The total stakeholder pressure for more A is thus computed as follows: total stakeholder pressure for more A [pressure unit] = stakeholder resistance pressure for more A [pressure unit / entity] D-2 • (no of stakeholders favoring A [entity] + PERMANENTLY POWERFUL STAKEHOLDERS FAVORING A [entity]) Since the total stakeholder pressure for more A feeds back to the management’s decision-making, the balancing feedback mechanism Resistance Pressure for A is closed. How this loop fits in with the structure described before is shown in Figure D-7. It represents the direct antipode to the adaptation loop, but only gets triggered once adaptation has started. DEVELOPMENT OF STRATEGY B

+ desired quality B

rel. quality B + (B) Adaptation Pressure for B

pressure for more B


pressure for more A -

Orientation to + Strategy B

quality A

Figure D-7: Resistance pressure for strategy A (CLD)

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Concerning managerial decision-making, management is represented in an analogous way to the NYSE case with a repetitive momentum and a repetitive attention mechanism. The Repetitive Momentum Loop, shown on the right part of Figure D-8, influences the change rate by which the focal organization shifts its orientation between the accustomed strategy A and the new strategy B. The rate of change in strategy reduces inertia which—together with the confidence effect of performance— determines the organization’s actual openness to change. This openness then affects the yearly fractional change per perceived pressure and feeds back to the rate of change in strategy.586 The reference fractional change in strategy determines the general rigidity of this feedback loop and organization’s normal responsiveness to pressure that is independent of the current situation. REF. OPENNESS PER INERTIA

REF. FRACT. institutionalization INSTITUTIONALIZATION + + + (R) (B) limiting effect on institutionalization

Inertia (B)

REF. FRACT. INERTIA DECREASE

+

+ inertia decrease

institutionalization + (R) Inertia

+ openness to change (R) Repetitive Momentum

(B)

REF. FRACT. CHANGE IN STRATEGY PER PRESSURE P.A. + +

+ inertia decrease +

fract. change per pcvd pressure p.a.

effect of change on inertia Orientation to Strategy A

+ effect of openness on change

-

change in + strategy


pcvd pressures

Figure D-8: Inertia and repetitive momentum (SFD) As depicted in the left part of Figure D-8, inertia itself grows by institutionalization, in congruence with writings of the organizational evolution and organization ecology literature. Researchers stated and also found evidence that, as time passes, institutionalization processes contribute to inertia. During times in which changes are not required, inertia and persistence consolidate.587 Mollona pointed out the distinction of resource-like inertia and cognitive inertia by two different accumulations.588 In the 586

A technically somewhat different, but conceptually similar mechanism can be found in Larsen and Lomi: Resetting the clock, 1999, pp. 412 and 419–420; and Sastry: Problems and Paradoxes in a Model of Punctuated Organizational Change, 1997, pp. 244 and 270–272. Sterman and Wittenberg also show that reinforcing feedback loops produce path-dependent behavior and lock-in. See Sterman, John D. and Jason Wittenberg: Path Dependence, Competition, and Succession in the Dynamics of Scientific Revolution, in: Organization Science, Vol. 10 (1999), No. 3, pp. 332–333. 587 See Hannan and Freeman: Structural Inertia and Organizational Change, 1984, pp. 152–155; Péli: Fit by Founding, Fit by Adaptation, 2009, pp. 344–345. For empirical evidence see Audia, Locke and Smith: The Paradox of Success, 2000, p. 849; and Starbuck and Milliken: Challenger, 1988, pp. 323–324, 329 and 331. 588 See Mollona, Edoardo: A Competence View of Firms as Resource Accumulation Systems: A Synthesis of Resource-Based and Evolutionary Models of Strategy-Making, in: Morecroft,

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present stock and flow diagram, the accumulation of the orientation to strategy A or B shows similarity to the concept of resource-like inertia since the stocks are inert and inflexible in the way that they comprise the accumulation of their history. The variable inertia itself, which particularly symbolizes the institutionalized routines and inflexibility in the thinking of the focal organization’s management, has similarity to the concept of cognitive inertia. Inertia decreases by the replacement of old with new employees who bring new ideas to the organization as well as by the learning of new and unlearning of old patterns of thinking and behavior. This unlearning increases when change takes place.589 The s-shaped relationship between a change in strategy and its effect on the decrease of inertia is shown in Figure D-9. Its shape symbolizes a less than proportional disruption of routines and thinking when changes are incremental. Effect of Change on Inertia 8

effect

6 4 2 0 0

0.100

0.200 0.300 change in strategy

0.400

0.500

Figure D-9: Effect of change on the decrease of inertia The rate of change in strategy not only affects inertia, but is itself affected mainly by perceived pressure from stakeholders for more A (or B), illustrated in Figure D-10. Depending on which pressure is greater, the organization shifts to strategy A (or B). Additionally, and only in the case that the orientation to A or to B is already very high, a limiting effect influences the rate of change so that it looks as follows: change in strategy [dmnl / year] = (pcvd pressure from stakeholders favoring B [pressure unit] • effect of B on change [dmnl] - pcvd pressure from stakeholders favoring A B [pressure unit • effect of A on change [dmnl]) • fract. change per pcvd pressure p.a. [dmnl / pressure unit / year]

D-3

John, Ron Sanchez and Aimé Heene (Ed.): Systems Perspectives on Resources, Capabilities, and Management Processes, Amsterdam [et al.] 2002, p. 111. For a distinction of resource and routine rigidity see Gilbert, Clark G.: Unbundling the Structure of Inertia: Resource Versus Routine Rigidity, in: Academy of Management Journal, Vol. 48 (2005), No. 5, p. 742. 589 See Nadler and Tushman: Types of Organizational Change, 1995, p. 23; and Wollin: Punctuated equilibrium, 1999, p. 362.

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The limiting effects work when stakeholders still exert pressure for a strategy that is almost fully implemented. The management team becomes hesitant in reacting to the full pressure. Orientation to Strategy A effect of A on change

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Figure D-10: Limitations to changes of strategy (SFD) Phenomena such as the concentration on floor firms do not represent an unparalleled example. The Polaroid management misjudged the rising importance of a new group of customers and their preference for digital photography. DEC, for instance, was a highly client-oriented organization. The company even maintained the Digital Equipment Corporation Users Society in order to provide for the possibility of mutual exchange, feedback, and learning. At the same time, exactly this strong relationship to loyal customers and its culture made the DEC management inattentive to the growth of a new group of customers that favored the PC. DEC appeared to be customer oriented, but concentrated only on one customer group.590 The organization’s cultural inertia was responsible for the lacking attention to an altered environment with different stakeholders. Magness supports in an empirical analysis that “stakeholder status is impermanent, and determined through the eyes of the decisionmaker.“591 Therefore, it is also necessary to include the concept of attention to stakeholders into the generic model of organizational inertia and change.

590 591

See Schein: DEC is Dead, Long Live DEC, 2003, pp. 74 and 252. Magness: Who are the Stakeholders Now, 2008, p. 177, abstract.

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REF. OPENNESS PER INERTIA + institutionalization + (R)

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Figure D-11: Attention to stakeholders (SFD) Via the openness to change, inertia also affects the attention to stakeholders. Figure D-11 demonstrates that openness increases the annual fractional change in attention per pressure and thus allows for a faster reaction of attention to pressure from stakeholders. The organization also has a general responsiveness of attention, the yearly reference fractional change in attention. It represents its normal openness to new trends and stakeholder groups, independent of the situation. How the adaptation to pressure takes place is further detailed in Figure D-13 and will be described in the next paragraph. Back to Figure D-11, a modified accumulated attention to stakeholders also weights the types of pressure and results in an altered perceived pressure from stakeholder favoring A (or B) for more A (or B). Since the perceived pressure returns to the change in strategy and to inertia by means of feedback, the reinforcing Repetitive Attention Loop is closed. The weighting relationship of attention has generic value because it not only could be found at the NYSE, but González-Benito and González-Benito found a similar relationship not for stakeholder attention, but managerial environmental awareness. The latter increases the perceived pressure for environmental issues and the implementation of environmental practices.592 The full Repetitive Attention Mechanism as well as the Repetitive Momentum Loop shown in the CLD of Figure D-12 exhibit reinforcing behavior.

592

See González-Benito, Javier and Óscar González-Benito: The role of stakeholder pressure and managerial values in the implementation of environmental logistics practices, in: International Journal of Production Research, Vol. 44 (2006), No. 7, p. 1368.

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D Generic Interpretation of Organizational-Environmental Forces, Feedback, and Change + desired quality B


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Figure D-12: Repetitive momentum in the generic model (CLD) While in particular the freeze of attention is a result of high inertia, attention also shows adaptive behavior. The adaptive mechanism is displayed in Figure D-13. The change in attention is positive and attention to stakeholders favoring B rises when the perceived pressure from stakeholders favoring B is higher than that from stakeholders favoring A. The balancing effect of attention to B (A) limits a further orientation to stakeholders favoring B (A) when attention to these stakeholders is already very high. While certain stakeholders may continue to exert pressure, the management team would not be willing any more to fully react to these forces and to further change its attention. The rate of change in attention is thus computed as follows: change in attention [dmnl / year] = (pcvd pressure from stakeholders favoring A [pressure unit] • effect of attention to A on change [dmnl] D-4 – pcvd pressure from stakeholders favoring B [pressure unit] • effect of attention to B on change [dmnl]) • fract. change in attention per pressure p.a. [dmnl / pressure unit / year]

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As Figure D-13 explains structurally, the perception of pressure from stakeholders itself is biased by the current distribution of attention, leading to the following computation of the perceived pressure, here exemplified for the perceived pressure from stakeholders favoring B: pcvd pressure from stakeholders favoring B [pressure unit] = total stakeholder pressure for more B [pressure unit] • Attention to Stakeholders Favoring B [dmnl]

D-5

Attention serves as a weighting factor for the incoming forces from stakeholder pressure. This structure creates two reinforcing mechanisms which bias the perception of forces towards those stakeholders the management team listens to. Nevertheless, while attention may be biased, it also adapts to existing total stakeholder pressure for more A or B. Once those who demand change receive more attention, this triggers change. Then the interests of the stakeholders demanding change are better met so that they do not need to exert as much pressure any more. These feedback mechanisms of the adaptation of attention are detailed in the causal loops of Figure D-14. pressure for more B

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Figure D-14: Adaptation of Attention (CLD) A further important feedback relationship can be found between managerial decision-making and performance. In different organizations, performance may represent varying concepts such as market share, the sales level, or the size of the customer base. Therefore, its representation is kept general and plain to match all of these interpretations. It is assumed that—unlike in the NYSE case—past performance does not reinforce the current value.593 The idea that different effects adjust performance upwards and downwards are also known from works by Salge and Sterman, for example, in which cases market share is affected by effects of quality and attractive-

593

For information on the simplification of decision rules and the aggregation of stock and flow structure see Saysel and Barlas: Model simplification and validation with indirect structure validity tests, 2006, p. 257.

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ness.594 A similar computation of performance, by which a normal or reference performance is adjusted by a multiplier effect and real performance then adapts to this indicated value, has also been proposed by Milling.595 Figure D-15 indicates that quality A and the relative quality B that come with the pursuit of strategy A or B both adjust performance. Which of these effects prevails depends on the weight that customers attribute to the qualities, e.g. the weight on speed vs. price or on digital editing vs. resolution quality in photography. The weight on quality B vs. quality A directly emanates from the distribution of stakeholder preferences (i.e. the fraction of stakeholders favoring B). The performance adjustment amends the reference performance, set here to 0.5 performance units. Since information about an organization’s offerings needs to diffuse in the market and customers may show some loyalty, performance adapts to its indicated value with a time delay of one year.

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Figure D-15: Performance (SFD) In the NYSE case, the descent of market share helped the introduction of a new strategy, the Hybrid Market.596 In a more general sense, this idea conforms to the concept of aspiration levels and failure-induced change of the behavioral theory of organizations. If performance falls below the aspiration level, the organization is more likely to search for a solution and undergo change.597 Figure D-16 clarifies this process by a detailed SFD. If performance is below the aspiration level of desired performance, it is perceived as inadequate and decision-makers lose confidence in the current strategy. This confidence effect of performance is weak for minor inadequacies as they may reflect normal variations of performance not related to the organization’s strategy. For greater perceived shortcomings the effect quickly aggravates and increases the organization’s openness to change. The relationships and their 594

See Salge: Struktur und Dynamik ganzheitlicher Verbesserungsprogramme in der industriellen Fertigung, 2009, p. 51; and Sterman: Business Dynamics, 2000, p. 393. 595 See Milling: Der technische Fortschritt beim Produktionsprozeß, 1974, pp. 195–197. 596 See Storkenmaier and Riordan: The Effect of Automated Trading on Market Quality, 2009, p. 11. 597 See Cyert and March: A Behavioral Theory of the Firm, 1963, p. 121; and March and Simon: Organizations, 1958, pp. 173–174 and 184. For empirical support see e.g. Greve, Henrich R.: Performance, Aspirations, and Risky Organizational Change, in: Administrative Science Quarterly, Vol. 43 (1998), No. 1, pp. 74–75.

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strength were already specified for the NYSE and described on page 136. These causal relationships also comply with the view of Lant and Mezias who maintain that the impetus for change and for the adaptation to the environment is triggered by a gap between current and desired performance.598 They also found empirical evidence that historical performance provides the most robust description of aspiration levels.599 +

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Figure D-16: Relationship between performance and change (SFD) In the view of Forrester and Senge, a model of the loss and gain of market share should include the effect of different companies’ contrasting policies on market share.600 This has been achieved by the linking of qualities A and B to performance and further linking the latter to the openness to change. The full resulting feedback cycles involving performance and the orientation to a strategy are shown by bold arrows in Figure D-17. Low performance increases the openness to change, and—in the case of a pressure imbalance in favor of strategy B—the organization orients towards strategy B, increases its relative quality B, and performance increases in an adaptive manner. This balancing mechanism is called Performance Adaptation. It has a reinforcing side effect since the reorientation further reduces quality A and diminishes performance. As in the NYSE case, this Performance Decline Loop makes the organization reorient to the alternative direction even more quickly.

598

See Lant, Theresa K. and Stephen J. Mezias: An Organizational Learning Model of Convergence and Reorientation, in: Organization Science, Vol. 3 (1992), No. 1, p. 48. 599 See Lant: Aspiration Level Adaptation, 1992, pp. 641–642. 600 See Forrester and Senge: Tests for Building Confidence in System Dynamics Models, 1980, p. 221.

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Figure D-17: Performance (CLD) The structure of the generic system dynamics model has now fully been specified. It includes the environment as an external driver of change. Endogenously it incorporates stakeholders in the close environment of the focal organization and managerial decision-making as it relates to cognitive elements. The model’s explanatory power will be analyzed in the next chapters.

D.II Structural-Behavioral Analysis and Causal Theory D.II.1

Validation of the Generic Model

Validation of a generic structure is more difficult than gaining confidence in a model that maps a specific example. Nevertheless it is possible.601 In particular as regards to generic system dynamics models it is useful to distinguish two different kinds of validation: internal and external, both of which will be addressed. Internal validity exists if the model is consistent and sound.602 It can be tested just as in a case-specific model. This has initially been done by tests of model structure. Very fundamentally, the generic model is dimensionally consistent.603 Additionally, it is possible to have confidence in the structure because the behavior it produces is insensitive to changes in the choice of integration frequency (time step) and method.604 Many aspects of structural validity were already described together with the model structure. It has 601

See Größler: Struktur und Dynamik strategischer Fähigkeiten in der Produktion, 2007, p. 152; Lane, David C.: Can We Have Confidence in Generic Structures?, in: The Journal of the Operational Research Society, Vol. 49 (1998), No. 9, p. 942; and Lane and Smart: Reinterpreting 'generic structure', 1996, p. 113. 602 See Größler: Struktur und Dynamik strategischer Fähigkeiten in der Produktion, 2007, p. 146. 603 See Forrester and Senge: Tests for Building Confidence in System Dynamics Models, 1980, pp. 215–216; and Größler: Modelltests, 2008, p. 262. 604 See Sterman: Business Dynamics, 2000, p. 872.

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been laid out how model variables and in particular parameters correspond to reality. For instance, the importance of attention has been established in relation to several examples, and there is theoretical and empirical evidence for an effect of performance on decisions of the management team. Parameters shaping inertia were compared to institutionalization and employee turnover, and the reference change in attention might represent the intensiveness of the search for new trends and stakeholder groups, e.g. by means of consultation of market search institutions. Parameters in this case cannot be compared to exact data. Since the model includes many soft variables and parameters, an exact quantification is not possible and it is not necessary. In the view of Richardson, the degree of accuracy is always judged against model purpose.605 Forrester mentions that for many purposes it is sufficient to estimate parameters within the plausible range because it will not affect results significantly.606 For this reason, different numerical parameters values are rather understood as qualitative values such as low, rather low, medium, or high levels of e.g. reference fractional changes in inertia or attention. Extreme conditions tests were useful to validate the causal structure because they uncover whether the model produces results that are inconsistent with e.g. physical laws. In this way, they are a means to uncover inconsistencies in assumptions made. A sensitivity analysis including a broad number of extreme and simultaneous parameter changes also produces sensible outcomes, as shown in Figure D-18. It assumes the environment to proceed as before and investigates how organizations with different managerial setups that may also face varied degrees of resistance pressure may react to this environmental development. It results in orientations to strategy B ranging between 20 and 100 percent, rather high sensitivity in performance, as well as high variation in inertia and attention—all of them within reasonable bounds. The analysis serves as an extreme test of the system dynamics model, but it also demonstrates that many different behaviors ranging from the failure to change to smooth adaptation are within the realms of possibility. According to Lane as well as Milling, the aim is to have a homomorphous mapping of encountered phenomena.607 The quality of this mapping can and will be analyzed in particular with the family member test suggested by Forrester and Senge. This test is part of the behavioral validation of a generic model. It checks the applicability of the generic model to a class of phenomena, and it tests whether a modification of parameter values is able to generate behavior appropriate for different organizations within a class.608 The validation procedure helps to clarify whether different modes of 605

See Richardson and Pugh III: Introduction to System Dynamics Modeling with DYNAMO, 1981, p. 230. 606 See Forrester: Industrial Dynamics, 1961, p. 171. 607 See Lane: Can We Have Confidence in Generic Structures, 1998, p. 942; and Milling: Der technische Fortschritt beim Produktionsprozeß, 1974, p. 212. 608 See Forrester and Senge: Tests for Building Confidence in System Dynamics Models, 1980, p. 220; Lane: Can We Have Confidence in Generic Structures, 1998, p. 942; and Lane and Smart: Reinterpreting 'generic structure', 1996, p. 110.

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behavior known to occur in the class of systems that the generic model stands for can be reproduced. In this way, it also establishes external validity of the model. generic sens all 50% 75%

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Figure D-18: Sensitivity for changes in stakeholder and management parameters


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In close relation to the family member test, behavioral correspondence can be investigated by the behavioral anomalies and the surprise behavior test.609 Behavioral anomalies and surprise behavior can be used on a continuous basis in the modeling process to detect flaws in the model’s assumptions.610 In the present study, tracing back the reasons for the behavior has helped to decide whether the anomalies required a modification of assumptions or conveyed surprise behavior that advances the understanding of the system.611 System understanding is also enhanced by the testing of different policies and the sensitivity of recommendations to parameter changes. This way it becomes evident which policies lead to a system improvement. Additionally, the further analysis of policy sensitivity provides information on the robustness of strategy and policy recommendations.612 The generic model structure includes elements of the environment, inertia, cognition as well as stakeholder reactions. With the structure laid out, the next step will be the investigation into the generic model’s behavior. The tests mentioned above that have not yet been presented will be elaborated. For this behavioral analysis, a simulation period of 50 years was chosen. While this time horizon may seem long at first glance, it roughly equals two to three times the implementation time of important innovations and of customer demand changes. The rising demand for corporate ethical conduct and for digital cameras constitutes an example.

D.II.2

Effects of Reinforcing and Balancing Feedback on the Occurrence of Change

In the base run scenario, the generic model exhibits a radically changing behavior similar to the NYSE case. While a smooth s-shaped adaptation takes place in the remaining market, expressed by the grey line 1 in Figure D-19, the target organization (line 2) initially does not react, but then shows a much steeper s-shaped growth than the remaining market.

609

See Lane: Can We Have Confidence in Generic Structures, 1998, p. 942. See Forrester and Senge: Tests for Building Confidence in System Dynamics Models, 1980, p. 220. 611 See ibid., pp. 220–221. 612 See Richardson and Pugh III: Introduction to System Dynamics Modeling with DYNAMO, 1981, pp. 349–352. 610

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cline mechanisms are of minor importance. When the organization finally reacts, stakeholder preferences have almost completely shifted to strategy B so that there is only minor resistance from the few favoring A. The structural-behavioral relationships discussed above have a direct connection to the research question of whether prior change increases or reduces the occurrence of subsequent change. This question has mainly been analyzed by statistical means, whereas this dissertation will give a causal explanation. While a significant number of studies have found that prior change increases the occurrence of subsequent transformations, the results were not unambiguous and a proportion of studies found contrary or inconclusive evidence.613 Relating the two different behavioral patterns exhibited in Figure D-21 below to the causal structure of Figure D-20 above, will help explain in what cases change increases the occurrence of subsequent transformations. Line 2 in the upper part of Figure D-21 reveals the behavior of an organization which—in comparison with the base run organization—is more aware of the existence and desires of a new customer group, less inert and more reactive to perceived pressure. After only a short period during which the Repetitive Momentum and Attention Loops reinforce the old strategy, around period 20 the management team radically changes its strategy and adapts to the situation in the market. This ‘earlier radical adopter’ might represent companies such as Canon or Nikon that introduced digital cameras already around the year 1990, or it might be one of the stock exchanges that implemented a significant portion of electronic trading in the late 1980s. For example, the London Stock Exchange was inert during the 1970s, but radically adapted in 1986 despite the opposition of the disadvantaged.614 As in the base run, the radical adaptation reduces inertia (line 2 in the lower part of Figure D-21) and the Repetitive Momentum Loop switches to the direction of further change. The repetitive loops allow the organization to become more malleable; its openness to change increases. Lower inertia entails a greater willingness to react to pressure and to adapt its attention towards those who demand a transformation. This means the organization’s ability to react to pressure and thus its ability to change increases. Change propagates due to the reduction of inertia. This is the same for both organizations, the inert one and the earlier radical adopter.

613

Among others, the following studies support the hypothesis that prior change increases the occurrence of subsequent changes: Amburgey, Kelly and Barnett: Resetting the Clock, 1993, for change of the same type, pp. 66 and 69; Collins, et al.: Learning by doing, 2009, p. 1333; Dobrev, Kim and Carroll: Shifting Gears, Shifting Niches, 2003, p. 274; Kelly and Amburgey: Organizational Inertia and Momentum, 1991, for change of the same type, negative relationship for change of different type, p. 606. Inverse correlations were found in Beck, Brüderl and Woywode: Momentum or Deceleration, 2008, p. 426; Beck and Kieser: The Complexity of Rule Systems, Experience and Organizational Learning, 2003, p. 807; and Wischnevsky and Damanpour: Radical strategic and structural change, 2008, p. 65. 614 See Michie: The London Stock Exchange, 1999, pp. 593–595.

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When the earlier adopter (line 2 of the upper part of Figure D-21) closes the gap to its environment around period 22, the environment still changes (line 1) and requires further adaptation from the focal organization. The repetitive mechanisms are flexible, and the adaptation mechanisms pull the organization towards a greater implementation of strategy B. In this scenario, the initial change leads to subsequent changes.

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However, the situation is different in the base run scenario (line 3). Here, the adaptation takes place later, and once the focal organization reaches its environment, the latter does not change any further. There may be other unrelated changes in the focal organization’s environment, but the shift from strategy A to strategy B has fully taken place. In this case the initial change does not lead to any subsequent transformations. The reason is that the balancing Adaptation Pressure for B Loop by which stakeholders exert pressure for more B is inactive. Thus, in the base run case, the transformation process comprises one adaptive shift that decreases the occurrence of further changes. While both transformation reduce inertia and increase the malleability of the focal organization, the occurrence of subsequent alterations also depends on the further development of the organizational environment. The importance of the relation between the focal organization’s ability to change and the pressure to adapt also becomes obvious when two environmental changes are simulated. Figure D-23 displays a modified version of the base run scenario. It overlaps the base run until period 30, but then a second shift takes place, triggered by stakeholders favoring strategy B, to which the remaining market adapts with an average time delay of five years (line 1). For reasons of feasibility, this has been modeled as a move back to strategy A instead of a shift towards strategy C. The stakeholders originally favoring B now pressure for the diminution of strategy B (meaning for less B). Technically, this is achieved by making the perceived inadequacy of the strategy the inverse of the relative quality B. Figure D-22 displays the relationship which is established hereby, by which not only an underachievement in the relative quality B (below 0) leads to a perceived inadequacy, but also an overachievement gets sanctioned. Stakeholders favoring B now not only exert pressure for more B, but also for less B when the focal organization’ relative quality B reaches values above market average. Since the market transformation takes both directions, it needs to be assumed that both over- and underachievement in quality B leads to some kind of pressure for change. Perceived Inadequacy of Strategy per Stakeholder Favoring B perceived inadequacy

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Line 2 of Figure D-23 exhibits the known delayed but radical adaptation process of the focal organization that is upheld by the working of the reinforcing character of the Repetitive Momentum and Attention Loops which then also enforce how radical the behavior is. The reaction to the second market move is very different, as the organization now quite quickly adapts to the new direction of the market and implements what is implemented in the market with a time delay of only little more than one year. The behavior in the scenario including two environmental changes is different from the base run in which the organization remained with strategy B. The reason is that, while the repetitive loops are flexible, the Adaptation Pressure for B loop, by which the organization adapts to the market, gains in importance again.

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Figure D-23: Two environmental changes (BOT)615 A second reason why the adaptation to the second market shift takes place more quickly is that the management team has already focused its attention on those stakeholders who matter and who demand the second change. The stakeholders originally favoring B now pressure for the diminution of strategy B. As a consequence, attention remains with stakeholders who originally favored B. The quick adaptation is thus possible because, first, inertia is lower than in the outset and the Repetitive Momentum Loop more flexible, and second, because the management team already focuses its attention on those stakeholders who demand the subsequent change. The hypothetical twofold change scenario is a clear example for the effects of previous change on the occurrence of further alterations. Since initial change enhances the organization’s ability to change, the existence of a continuously strong adaptive mechanism increases the occurrence of subsequent change. But the base

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run scenario reveals in comparison that without any further activity of adaptive mechanisms, future change fails to appear. Hence, there is no single answer to the question whether prior change increases or reduces the occurrence of subsequent change. While it is clear that it increases the malleability of the organization and its ability to change, the strength of the adaptive mechanism, meaning the standing of the organization in relation to its environment, is also important. A one-time environmental shift leads to a new equilibrium, but a dynamic and competitive environment that has not yet finished changing or that frequently transforms may trigger subsequent change in the focal organization. Behavior is shaped by the interrelationship of the management setup, endogenous forces, and environmental requirements. The consideration of how balancing and reinforcing feedback mechanisms are intertwined is necessary and important.

D.III Possibilities of Managerial Intervention for Driving Change In the latter scenario, reduced inertia and the focus of attention proved to be important for the quicker adaptation to the second environmental change. Attention and inertia will therefore be analyzed as possible leverage points for intervention. It will be investigated how the management team’s influence on inertia and attention can act as a driver of change and shape the evolution of an organization and its alignment with the environment. This view concurs with Bowen’s position indicating that while affected by the pressure that arises from the system’s structure, decision-makers still have the ability to either follow these types of pressure or make an autonomous decision.616

D.III.1 Inertia and the Ambiguous Effects of the Responsiveness to Pressure The effect that a diminished level of inertia has on the behavior of the organization will be considered. At the NYSE, one interviewee reported the reason for high inertia within the organization to be rooted in the inward-orientation of recruiting. People were grown from within, and there was very little turnover with people from outside the NYSE. This was different with Polaroid; 90 percent of the employees initially involved in the development of digital photography were new to the company. They developed a sound product, but its commercialization was thwarted by the management team’s grown convictions and beliefs in an old business model that did not fit digital photography. In the management area, no turnover had taken place. Once the management started to change, new people from outside were less entangled and 616

See Bowen: System dynamics, determinism, and choice, 1994, pp. 87–88 and 90. See also Lane: Should System Dynamics be Described as a 'Hard' or 'Deterministic' Systems Approach, 2000, p. 10.

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locked in the old business model and embraced market developments with greater openness.617 The replacement of culturally and ideologically aligned employees and managers with more open ones could have been a viable solution for the NYSE and Polaroid. Therefore the effect of different degrees of the decrease in inertia on the model behavior will be simulated to test flexible turnover and unlearning. For example, a consequence of the London Stock Exchange’ move to e-trade was a permanent transformation of its membership. The stock exchange re-emerged as a more dynamic institution.618 Additionally, the strength of institutionalization processes in organizations deserves attention. Even at the relatively young digital photography company Linco, the opportunity for entering the USB flash drives business bypassed unnoticed. The company’s quickly arising insistence on its identity as a photo memory or digital film producer refrained it from exploiting all options that opened up, e.g. in the area of MP3 players or flash memory.619 Similar effects of a quick socialization of new employees have been reported for the Intel Corporation.620 Quick institutionalization can thus also cause high inertia and lock-in. It may result in a missing adaptation to the market and to the opportunities it offers. In a similar vein, DEC’s inertia did not result from missing employee turnover. While the rate at which employees left the company may have been small, the organization grew rapidly and had a strong inflow of new employees from outside. But the culture of technological overconfidence and missing market orientation was enforced by the leadership style and development programs which decreased the organization’s ability to react to changed environmental circumstances.621 At DEC the institutionalization process worked particularly well. The effects of an especially high institutionalization as well as of a low turnover rate on an organization’s strategy are shown in Figure D-24. They lead to a very slow adaptation to the market and have long-term side effects on performance that are detrimental to any company having fixed costs.

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See Tripsas and Gavetti: Capabilities, Cognition, and Inertia, 2000, pp. 1152–1157. See Michie: The London Stock Exchange, 1999, pp. 633 and 441. 619 See Tripsas: Technology, Identity, and Inertia through the Lens of 'The Digital Photography Company', 2009, pp. 450–451. 620 Directly see Burgelman: Strategy as Vector and the Inertia of Coevolutionary Lock-in, 2002, p. 354. 621 See Gibbons, Tracy C.: DEC's "Other" Legacy: The Development of Leaders, in: Schein, Edgar H. (Ed.): DEC is Dead, Long Live DEC: The Lasting Legacy of Digital Equipment Corporation, San Francisco, CA 2003, pp. 97–102; and Schein: DEC is Dead, Long Live DEC, 2003, pp. 80–89. 618

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Figure D-24: Effects of high inertia (BOT) Since the processes described above resulted in high inertia and missed opportunities for change, an analysis of the effects of low inertia is promising for testing the management team’s different possibilities of intervention. For this reason, the sensitivity for parameter changes of the initial level of inertia (ini inertia), the reference fractional institutionalization and the reference fractional inertia decrease is tested. The scenarios show the management team’s ability to intervene and to create an organization that is more flexible and adaptive to change. The upper part of Figure D-25 illustrates the area of parameter changes by a grey box and the variable which it may affect by a black box. The lower part of the figure displays the results of the sensitivity runs in comparison with the diffusion of B in the remaining market. The simulation runs reveal that reduced institutionalization as well as a higher rate of inertia decrease are able to trigger an earlier and often also radical orientation to strategy B. It proves beneficial to bring in new people with fresh ideas and a greater openness. The reduction of strong institutionalization processes, i.e. in the form of special trainings or the creation of an open-minded culture different from, for example, the engineering culture present at DEC also turns out to be valuable. They represent ways in which the management team can make the organization more malleable and drive change. Sensing opportunities and threats in shifting markets is a necessary requirement for an organization’s ability to adapt to a changed market environment.622 An open-minded management that exhibits low inertia is a necessary requirement for 622

See O'Reilly III, Charles A. and Michael L. Tushman: Ambidexterity as a dynamic capability: Resolving the innovator's dilemma, in: Research in Organizational Behavior, Vol. 28 (2008), p. 191.

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Figure D-25: Sensitivity to variations of inertia It turns out that the lower inertia the better for the adaptive ability of the organization. Nevertheless, the comparison of the focal organization’s to the market’s orientation to strategy B in Figure D-25 indicates that even very low levels of inertia are not able to trigger an immediate adaptation. Even in the extreme situation in which no additional amount inertia develops (i.e. when fractional institutionalization and inertia decrease are equal at 0.2), several years pass during which the focal organization does not react. The level of inertia has an influence on how quickly the organization adapts attention and reacts to perceived pressure. The reference reaction time is still

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rather long—or expressed differently, the reference fractional change in strategy per pressure is low. The latter is a measure for the speed and the intensiveness of the reaction to perceived pressure. It decides when the system achieves a critical load that it reacts to this pressure by the amendment of its strategy.623 Therefore, the potential of a more intense reaction to perceived pressure will be tested. It distinguishes the rather change aversive base run organization from, e.g., a more decentralized organization. In the latter one, employees are free to initiate their own changes if they perceive pressure to do this or see room for improvement in their respective area. Größler, Grübner, and Milling also illustrate that it might represent an organization that has previously reacted to a complex environment by a more complex internal structure and built-in flexibility that is autonomous of the management function.624 In this way, changes may take place more easily despite initial managerial inertia. At the same time, the repetitive momentum mechanism is not put out of action and alterations still transform the organization and reduce inertia. The upper part of Figure D-26 demonstrates that ceteris paribus an increase of the reference change in strategy per pressure does not have a great impact on the system’s behavior. The increase in the change per pressure somewhat reduces the strength of the Repetitive Momentum Loop which keeps the organization locked at its initial strategy. The radical shift is triggered by the concurrence of the strong Adaptation Pressure for B Loop and the Performance Adaptation Loop with the sudden decrease of the formerly dominant Repetitive Momentum Loop.625 As the bottom graph of Figure D-26 shows, even extreme values of the reference change in strategy per pressure do not lead to the desired result of an early and smooth adaptation. The transformation of perceived pressure into change action is still hampered by inertia and a biased perception of pressure, and adaptation takes place somewhat quicker in the beginning, but not in line with the rest of the industry. A result which may not be intuitive is that a somewhat quicker adaptation in the beginning even results in a much slower implementation of strategy B in the end. The reason for the different behavior is rooted in the difference of the dominance of the Adaptation Pressure for B Loop, not in the nonlinearity of the effect of change on inertia. In their overall behavior simulation runs with a linear effect of change on inertia are hardly different from those shown. While the restrictive character of the Repetitive Momentum Loop is reduced, adaptation initially is quicker and pressure by stakeholders favoring B 623

See Merten, Peter P.: Loop-based Strategic Decision Support Systems, in: Strategic Management Journal, Vol. 12 (1991), No. 5, p. 375. However, the view employed here to some extent deviates from that of Merten since it does not consider binary but continuous effects. 624 See Größler, Andreas, André Grübner and Peter M. Milling: Organisational adaptation processes to external complexity, in: International Journal of Operations & Production Management, Vol. 26 (2006), No. 3/4, pp. 256 and 272–273. 625 It was also tested whether the limiting effects that come into play when the orientation to a strategy is already very high affect the behavior. These limiting effects were explained and displayed in Figure D-10 on page 178, and they limit an overreaction to pressure. The simulation tests did not find any differences in the pattern of behavior when it was assumed that the management fully implements all perceived pressure.

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never builds up as strongly as in the base run scenario. This means the Adaptation Pressure for B Loop never becomes as dominant. Minor inadequacies that arise when the organization is, in general, reactive only create reactions with less than proportional strength. generic sens change 50% 75% 95% Orientation to Strategy B 1

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can be combined with the previously discussed measures to decrease the general level of inertia.626 The combination of a higher reference change and lower inertia has some effect, but it is minor. Even in the best of cases, this only leads to an adaptation delayed by four to five years. Additionally, the effect of attention on the dominance of the loops discussed is worth consideration. Therefore it will now be analyzed how the management team’s influence on attention can leverage the evolution of an organization.

D.III.2 Effects of Increases in the Responsiveness of Attention Attention as a possible lever for management intervention can work in two different ways. An organization can either try to equally distribute its attention to stakeholder groups, or it can change its attention more flexibly than in the base case when stakeholders start to exert pressure for the implementation of another strategy. An equal distribution of attention may be desirable, but it would be unrealistic to assume that a management team is completely unaffected by past developments and by the intensity with which stakeholder groups exert pressure on the organization. The change in importance of new stakeholder groups and the process of allocating attention to them would remain unclear. Hence, there may be a small variability in the initial level of attention, but the main focus will be on the examination of attention allocation over time and on the effects of an enhancement of the responsiveness of attention. An organization that changes attention more easily is simulated by increasing the yearly reference fractional change in attention. This represents a management team that trains employees to sense rising stakeholder groups more quickly, commissions market surveys, or buys information from market research institutes. In an empirical study of small and medium-sized firms, Durand identified differences in the effectiveness of these measures. He found that investment in market information is a factor reducing forecast errors, but that investment in employee capability increases them because it does not sufficiently shift attention outside the organization.627 While there is need for further research into the effects of different measures, it is assumed that an effective measure is chosen by the management team. These measures all aim at being informed about customers’ and other stakeholders’ desires as well as about the organizational strategy’s reputation among these groups in order to then direct adequate attention to them. The simulation reveals a sensitive reaction to an enhancement of the flexibility of attention. The sensitivity to these measures can be seen in Figure D-27. Compared to the base run (black line) it brings forward the change by two to three years. The 626

A sensitivity analysis was run in which the reference fractional change in strategy was changed as described in the upper part of Figure D-26 and inertia was varied as explained in Figure D-25. 627 See Durand, Rodolphe: Predicting a Firm's Forecasting Ability: The Roles of Organizational Illusion of Control and Organizational Attention, in: Strategic Management Journal, Vol. 24 (2003), No. 9, pp. 829 and 833.

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D.IV Joint Management of Leverage Points

significance of attention also becomes obvious from the sensitivity runs portraying parameter choices for the responsiveness of attention lower than in the base run. Even a minor reduction of the reference change in attention compared to the base run significantly reduces the adaptability of the organization. generic sens attention 50% 75% 95% Orientation to Strategy B 1

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Figure D-27: Sensitivity for changes in adaptability of attention The model reacts sensitively to changes in the responsiveness of attention, but even a high responsiveness does not trigger a quick adaptation to changes in the organizational environment. Merely reducing the restrictive character of the Repetitive Attention Loop, e.g. by the active fostering of search activities for new stakeholders and their desires is thus no solution. Nevertheless, it can be said that ceteris paribus higher attention to the new stakeholders favoring B is favorable for a quicker adaptation process, and a greater flexibility in adaptation is desirable. It increases the speed of adaptation by a couple of months or years.

D.IV Joint Management of Leverage Points So far the sensitivity to managerial intervention has been analyzed while one lever was changed and the other parameters were kept at the base run values. Now, an investigation into the joint influence on points of leverage will give an idea of the freedom of action of the management team. Changing one lever only revealed that the decrease of inertia and enhancement in the attentiveness has positive outcomes, whereas an increase in the reaction to pressure has mixed effects on organizational adaptation. The simultaneous amendment of several leavers can show whether these findings are stable in different situations as well.

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D.IV.1 Relationship Between the Responsiveness of Strategy to Pressure and Attention The behavior of an early radical adopter was described on page 189, and it is again depicted below by line 2 of Figure D-28. The early radical adopter may represent the London Stock Exchange or one of the early adopters of digital photography. In comparison to this early radical adopter, a second organization is described that seems highly flexible at first glance and should adapt to environmental changes in an instantaneous manner. The organization starts out with a low level of inertia and rather high turnover, high flexibility in attention, a somewhat higher level of attention at the outset, and a strong reaction to perceived pressure. While an equal distribution of attention would be difficult to explain, a somewhat higher level of attention than in the base run can still be legitimized. An organization may try to focus on all stakeholders to be aware if dissatisfaction arises, but it is likely to put more emphasis on those groups who proved important in the past. Contrary to expectations, while the focal organization adapts more quickly in the beginning, it never manages to catch up with its competitors (line 1), so that its behavior resembles a delayed adaptation with a time lag of about three years. It is also remarkable that in the end it even lags behind the early radical adopter. The behavior of this ‘delayed adopter’ is shown by line 3 of Figure D-28. In those runs in which only one lever was improved and the other parameters were kept at base run values, the increase of the reference change in strategy per pressure had ambiguous effects. Since the change in strategy takes a special position among the possible managerial levers for change, it will be further investigated. It will be interesting to see the effects of the variation of the responsiveness to perceived pressure when the management team simultaneously influences inertia and attention to make the organization more adaptive. Therefore, in the following simulation runs, inertia is at a desirable low level, attention changes quickly, and the model’s sensitivity for changes in the reference fractional change in strategy is tested.

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Figure D-28: Delayed adaptation and early radical adaptation (BOT) Since the market is assumed to implement customer requirements, quick adaptation is desired, and it results in the highest performance. In order to measure the adaptation and implementation effectiveness, a new and cumulative measure needs to be introduced. Performance has been modeled in a rather aggregate way. Additionally, it is formulated in such a way that minor inadequacies in its determining factors have a less than proportional influence on it. Therefore, the cumulative strategic orientation gives a finer grained picture of the implementation effectiveness than cumulative performance. Technically, the computation of the adaptation effectiveness was calculated by accumulating the orientation to strategy B over the modeling period. The higher cumulative orientation, the earlier or more fully the organization adapted, and the better. The level of the cumulative orientation to Strategy B is derived as follows: Cumulative Orientation to Strategy B = 50

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tional change in strategy does not necessarily lead to better results. There is a maximum point somewhere in the middle for the reference change in strategy. Initially, increasing the organization’s responsiveness to pressure turns out to be useful, but usefulness peaks at a value for the reference fractional change of about 0.06. It plateaus and then decreases for values above 0.1 representing an organization that very quickly responds to perceived pressure. While an organization should not neglect pressure for change it senses in its environment, the results indicate that it should not focus its entire energy on reacting to all different kinds of pressure it perceives. If an organization wants to follow the market as closely as possible, it can be beneficial to wait and see whether an innovation or customer demand gets substantial and receives some establishment in the market. In order to explain the reason for the split results, three strategies that represent a low (1), a desirable (2), and a high (3) fractional change per pressure will be analyzed and compared.

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Figure D-29: Nonlinear relationship between fractional change and adaptation effectiveness The effects on performance of the three different strategies differ only slightly because, first, in all three cases the organization follows the market fairly quickly, and second, performance is fairly aggregated. In the transition phase, the delayed adopter rather satisfies those favoring strategy A, whereas the smooth adopter pleases stakeholders favoring B. Nevertheless, the analysis of the different patterns of behavior is important because smooth or delayed adaptation might have positive


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and negative side effects on reputation and customer loyalty.628 Knowing about them gives decision-makers higher planning reliability. The upper part of Figure D-30 presents the different behavioral patterns of the three strategies during the period 10 to 30. The strategy leading to the greatest implementation effectiveness is shown by line 2. The organization adapts after an initial period during which the organization is somewhat restrictive in implementing change. In comparison, an organization with a low fractional change (line 1) lags behind due to its lacking transformation of pressure into change. The organization with a high fractional change, i. e the delayed adopter (line 3), takes a special position because it initially adapts even more quickly than the smooth adopter depicted by line 2. But after the initial period, it yet stays behind the organization exhibiting a low fractional change, before in the end it catches up again.629 The reason for the different patterns of behavior becomes apparent from differences in the degree to which the organizations focus their attention on stakeholders favoring B (lower part of Figure D-30). In an organization that is less reactive to pressure, attention shifts rather quickly to those stakeholders favoring B while it only changes slowly in an organization that reacts very flexibly.

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It is often argued that being a first mover as well as adapting early is the most effective and desirable strategy in terms of customer loyalty and reputation. See Lieberman, Marvin B. and David B. Montgomery: First-Mover (Dis)Advantages: Retrospective and Link with the Resource-Based View, in: Strategic Management Journal, Vol. 19 (1998), No. 12, pp. 1113–1114 and 1122. 629 It has also been tested whether the limiting effect that comes into play if the orientation to a strategy is already very high affects the behavior. Here as well, the behavioral pattern was not affected.

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the focal organization remains locked to strategy A. This initial lack of response triggers a strong growth of stakeholder pressure for more B. While the Repetitive Momentum Loop is still strong and does not allow for much change, the growing stakeholder pressure affects attention. A real world example constitutes Nestlé’s customers who expressed their wish for ethical marketing practices, but initially did not see their demands fulfilled at all by Nestlé’s marketing strategy. This dissatisfaction pressure triggers the shift towards higher attention to this stakeholder group. The causal structure of this balancing Adaptation of Attention Mechanism has already been described in Figure D-14 on page 179, but is again illustrated in relation to the full causal structure in Figure D-31. Once the organization becomes more aware of the new stakeholder group, it starts to react to pressure for more B, and this change reduces the rigidity of the Repetitive Momentum and Attention Loop, leading to further change until the organization has adapted. (B) Adaptation of Attention to B

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Figure D-31: Adaptation of attention to stakeholder favoring B (CLD) The explanations above clarified the importance of attention for the behavior of the organization. While attention by itself does not determine the fate of an organization, it has an effect on how radical as well as on how fully the organization adapts. A strategic reorientation is supported by a shift of attention. The quick shift of attention is further enhanced by the fact that attention itself also biases the perception of pressure. As a consequence, perceived pressure for more B further diverges from perceived pressure for more A. The behavior is thus influenced by the high interaction between variables and by the mutual interference of balancing and reinforcing loops that include elements of the management, stakeholders, and the environment.

D.IV.2 Policy Implications in Different Environments The investigation into the joint influence on points of leverage has given an idea of the freedom of action of management. In order to test policy sensitivity and the ro-

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bustness of the policy implications of levers of change, further analyses were conducted. They also provide information on the interaction of managerial decisionmaking and environment as drivers of change. So far, low inertia, high flexibility in the distribution of attention, and a medium responsiveness to pressure proved to be useful. However, it remains to see whether this is also the case in different environments. Therefore, reactions to a quicker environmental change will be simulated as well as the impact of strong resisting groups. First, different organizational dispositions were combined with a different development in the environment. Figure D-32 depicts a quicker environmental transformation (line 1) in comparison with the known environmental change (line 2) and the respective response to these two different developments by the smooth adopter. The quick transformation may represent two situations: firstly, a faster development of a new strategy in the market in general. Secondly, the focal organization may have a different reference group in the market and may orient towards those who are first at implementing a new strategy. Multiple simulations were conducted with both environmental developments, further detailed in Appendix C.630 They evidenced the advantage of low inertia and responsive attention over the broad range of parameters tested and are thus an indicator for policy robustness.


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change proves to be best. If the environmental transformation takes place more quickly, the peak of the adaptation effectiveness moves von 0.06 to 0.08 for the reference change, but the general relationship and conclusion remains the same, as can be derived from Figure D-33.631 A medium responsiveness of the reference fractional change in strategy per pressure gives the best results. The counterintuitive effect of the responsiveness to pressure thus still holds if the organization is embedded in a different environment. The simultaneous amendment of several leavers is able to show that the recommendations to keep inertia low, attention flexible, and the responsiveness to pressure at a medium level are stable also in different situations.

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Figure D-33: Nonlinear relationship between the fractional change and adaptation effectiveness in the case of quick environmental change Second, concerning different environmental developments, not only the speed of innovation in the market can be considered. Differences may also arise in the close stakeholder environment. In the previous simulation runs it was assumed that all stakeholders finally shift from favoring the old to favoring the new strategy. The case of the New York Stock Exchange exemplified by floor firms that there may exist a stakeholder group that continues to be powerful and to exert pressure for the retention of the old system. This might also represent a situation in which an advocacy group exerts pressure or a permanently loyal customer group exists that favors the old strategy. Figure D-34 reveals how such a group may affect the base run organization. The permanently powerful stakeholders favoring A were varied between the base run value 0 and the value 100. In the latter case the simulation represents circumstances in which a customer group orients to strategy B, but an equally powerful group continues to exert pressure for strategy A. Within certain bounds the decisionmakers react rather sensitively. Initially, they implement strategy B, but the resulting resistance causes a resurgence of the importance of the stakeholders favoring A. If 631

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resistance is strong, the management reorients towards those who exert strong pressure. This is exemplified by the dashed line in Figure D-34. It displays the attention to stakeholders favoring B in a situation when the permanently powerful stakeholders favoring A are set to 100. Attention initially shifts to stakeholders favoring B, but when resistance develops, it partially shifts back to those favoring A. With fading resistance attention slowly moves towards stakeholders favoring B again. In summary, the resulting variance in the level of orientation to strategy B shows that powerful stakeholders who develop resistance against the management’s decisions and actions can be powerful at shaping the extent to which a new strategy is implemented. generic sens permanent A 50% 75% 95%

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Figure D-34: Sensitivity to the variation in the permanent pressure for strategy A At first glance, it does not seem bad if the organization partially shifts back to its old strategy. Indeed, if it serves loyal customers who favor strategy A, this does not pose a problem. However, the permanently powerful stakeholders might represent a group that can significantly impede an organization’s operations such as changeaverse employees. Then they are able to deliver low quality work or damage an organization’s reputation. When at the same time customers already desire strategy B, this poses a problem for performance. In this case the organization needs to take appropriate action to address both stakeholder groups. This is exactly why the NYSE implemented liquidity algorithms. They allowed for an almost complete shift to electronic trading (strategy B), without giving up market quality (quality A). The high specialist involvement and resulting market quality pleases those favoring the floor while customers that want to trade electronically do not have to accept restrictions to fast trading. Concerning the other examples that


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have been discussed this may mean that a photography company shifts towards the production of digital cameras but keeps offering a limited range of traditional models. Since this may tie up too many resources, a strategy that moves towards resolution quality within digital photography could be an alternative solution. It may mean an industry-wide implementation of a minimum ethical code of conduct while further pursuing a profit maximizing strategy. The simulation results and the examples described above reveal that the forces developing from the decisions of the focal organization also provide it with feedback and may in some cases suggest a further revision of the strategy. In the latter example which involved permanent stakeholders, the balancing Resistance Pressure for A Loop became more active. Figure D-35632 portrays a further example in which there is high resistance from permanent stakeholders. In this organizational-environmental setup, permanent stakeholders encounter a very flexible organization with a low level of inertia and a high reference change in attention. Yet, despite its general flexibility, the organization is at the same time reluctant to react to perceived pressure. The simulation run reveals that once the environment (line 1) starts to change, the organization quickly adapts its attention (line 2) to those who become dissatisfied and starts to change its strategy (line 3). The strategic change triggers resistance so that in period 18 the organization quickly shifts attention back to those resisting. Attention then slightly oscillates. Extreme combinations of strong stakeholders and a high responsiveness of attention have the tendency to create oscillatory behavior for which the balancing feedback loops Adaptation of Attention to A (and B) are responsible, shown in the lower part of Figure D-35. The two accumulation delays—attention and strategy—cause oscillations if both Adaptation of Attention Loops are rather active. It hardly happens when management parameters are within reasonable ranges, but extreme pressure by stakeholders in combination with an extremely flexible attention may create oscillatory behavior. This also makes sense in reality. It is intuitive that if an organization changes its attention quickly and if stakeholders react to changes in strategy by varying their pressure, then organizational attention shifts quickly between the stakeholder groups. Hence, having a highly flexible attention (i.e. a very large reference fractional change in attention) is not recommendable in all situations.

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Figure D-35 portrays a CLD of the full generic model.

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D.V A Feedback Theory of Organizational Inertia, Change, and Attention

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the managerial policy recommendations for intervening into the system the following can be said:

Independent of the flexibility of attention and responsiveness to pressure, it turns out that the lower inertia the better.

Flexibility in attention is beneficial, but when resistance pressure is strong, the usefulness of flexibility in attention has limits.

Irrespective of inertia and attention, a medium responsiveness of the strategy to pressure for change proves to be best.

Overall, for the organization to be highly adaptive, a low level of inertia needs to be accompanied by a relatively reactive attention and by a medium level of responsiveness to pressure.

D.V A Feedback Theory of Organizational Inertia, Change, and Attention The generic system dynamics model, which builds on the NYSE-specific one, represents a causal theory of organization-environment relations. It is able to give more general answers to the research questions of, first, what the drivers of change in organizations are, and second, whether previous change enhances the occurrence of subsequent transformations. It also explains why and how organizations may have difficulty to adequately adapt to their environment. Concerning the validity of the model and its general applicability for answering the research questions, it can be pointed out that a single causal structure was able to generate different modes of organizational behavior. Simulation runs demonstrated the possibility of failure to change due to inertia, of radical and of smooth adaptation. This bears comparison with the family member test that analyzes whether one system dynamics structure is suitable not only for a single case but for a class of phenomena and situations and whether it is able to generate behavior appropriate for the class.633 In the concrete case here, it explained how different managerial policies create idiosyncratic behavioral patterns and how these policies interact with environmental settings. Concerning the question of drivers of change, the analysis revealed that both the environmental developments and managerial choice are determinants of an organization’s evolution. They are further affected by stakeholders as well as aspects of the management team’s cognitive inertia and attention. The close investigation of the generic base run related the model’s behavior to the underlying causal structure. It shows that organizational change is a multi-faceted process and that the evolution of the focal organization is driven by the complex interaction of reinforcing and balancing feedback relationships. Mechanisms of environmental adaptation that represent 633

See Forrester and Senge: Tests for Building Confidence in System Dynamics Models, 1980, p. 220; and Lane and Smart: Reinterpreting 'generic structure', 1996, p. 110.

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the environment as a driver of change interfere with managerial choice and bounded rational decision-making that thwarts and supports adaptation mainly by reinforcing mechanisms. An adaptive mechanism of stakeholder dissatisfaction aligns the organization with its environment. The modeling process made clear that environmental developments are important to generate change. Differences in the speed of change in the market or different reference points create variations in the behavior of the focal organization. An organization needs to adapt to the demands, be it preferences of a customer group or claims of other stakeholder groups that can affect the organization. Otherwise, the misalignment causes stakeholders to be dissatisfied and organizational performance to deteriorate. Thus, a balancing adaptation mechanism serves as one driver of change. The strength and dominance of this mechanism, however, can differ to a great extent, as the simulation runs have shown. Actions do not follow from environmental conditions per se. The managerial scope of decision proved substantial and makes the management team and its deliberate choices a further driver of change. The generic base run showed that the reinforcing mechanisms of the management team have a great effect on how the organization reacts to alterations in the environment. These mechanisms enforce an organization’s lock-in on its accustomed strategy A. But due to their reinforcing character, these feedback loops also amplify changes once it is initiated due to the reduction of inertia and due to a reorientation of the management team’s attention. The influence of bounded rationality and cognitive elements as part of the choices of decision-makers became obvious in the examples discussed. The case of the NYSE and further instances of Polaroid and DEC revealed that behavior is influenced by bounded rationality as well as routine behavior, expressed by reinforcing structures in both models. E.g., the grown belief in the traditional business model at the NYSE and Polaroid as well as DEC’s cultural blindness prevented a reorientation to new stakeholders. Inertia and the attention to stakeholders shaped the decisionmakers’ exercise of choice. These are central elements in the system dynamics model. The simulation of different organizational and environmental setups supported the importance of these elements for the evolution of organizations. This endorsed prior statements that the exercise of choice requires a prior perception and interpretation of the environment.634 Attention to stakeholders is an important aspect of managerial cognition and serves as a filter for information from the environment. The causal structure of the system dynamics model together with the simulation runs demonstrate that the direct influence of a performance inadequacy on change does not necessarily trigger transformations. Rather attention to stakeholders reacts more flexibly, so that a performance decline together with strong pressure from stakeholders loosens the reinforc634

See Child: Organizational structure, environment and performance, 1972, pp. 4–5; and Child: Strategic Choice in the Analysis of Action, Structure, Organizations and Environment, 1997, p. 48.


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ing Repetitive Attention Loop. The resulting shift in attention is more important for triggering change than performance itself. The shift in attention makes the management team aware of what it has previously neglected and gives the transformation a direction and momentum. Overall, there is strong evidence for the coexistence of the environment and the management as drivers of change. An organization’s evolution depends of the interconnection between these two mechanisms as well as influences from stakeholders, inertia, and attention. It is hence not possible to speak of ‘one’ driver of change. Behavioral outcomes are diverse. Organizational trajectories are driven by the combination of environmental and managerial, i.e. of deterministic and deliberate elements. Milling observes that behavior of firms is controlled by exogenous factors that exert high pressure. But it is not only determined by these forces. Organizational policies have considerable effects and often even cause negative organizational outcomes. There is feedback and mutual influence between the organization and the environment. Social economic systems are able to react to their environment, but also to anticipate its evolution and actively exert influence on it. ”Action and reaction, stimulus and response are tied together in a complex, causal relationship.”635 Several authors suggest a recursive view between structuring and structure.636 On the one hand, decisions of agents in social systems are shaped by the structure of the system. On the other hand, there is a feedback process between decisions and how they create the future decision environment. The second research question of whether prior change serves as a driver of subsequent transformations has also been answered in relation to the interconnectedness of change determinants. The focal organization’s reaction to a two-fold transformation in its environment provided useful insights into the organization’s adaptive ability. As a result of the mutual existence and even interdependence of drivers of change, it can neither be said that past change strictly increases nor decreases the occurrence of future transformations. Several authors found similar evidence. By testing the effect of performance on change, Greve could not fully explain change by per635

Milling, Peter: Business Systems as Control Systems, in: D'Amato, Vittorio and Carlo Maccheroni (Ed.): Dynamic Analysis of Complex Systems, Milano 1989, p. 44. See also Milling: Systemtheoretische Grundlagen zur Planung der Unternehmenspolitik, 1981, p. 106. 636 See Black, Laura J., Paul R. Carlile and Nelson P. Repenning: A Dynamic Theory of Expertise and Occupational Boundaries in New Technology Implementation: Building on Barley's Study of CT Scanning, in: Administrative Science Quarterly, Vol. 49 (2004), No. 4, p. 603; Bloomfield, B: Cosmology, Knowledge and Social Structure: The case of Forrester and system dynamics, in: Journal of Applied Systems Analysis, Vol. 9 (1982), pp. 3–15 (cited in Lane); and Lane: Should System Dynamics be Described as a 'Hard' or 'Deterministic' Systems Approach, 2000, p. 18. Giddens, Orlikowski and Barley hold similar views on structuration. See Barley, Stephen R.: Technology as an Occasion for Structuring: Evidence from Observations of CT Scanners and the Social Order of Radiology Departments, in: Administrative Science Quarterly, Vol. 31 (1986), No. 1; Giddens, Anthony: The Construction of Society: Outline of the Theory of Structuration, Berkeley, CA [et al.] 1984; and Orlikowski, Wanda J.: The Duality of Technology: Rethinking the Concept of Technology in Organizations, in: Organization Science, Vol. 3 (1992), No. 3.

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formance shortfall. Apart from performance effects, recent experience with change lowers inertia and enhances the organizational capability to change.637 Similar to Greve’s analysis, the simulation experiments evidenced that the adaptability of organizations increases by prior change; but this does not necessarily mean the organization will continue to change in the future. Depending on the strength of the adaptive mechanism at that point of time, the organization will find a new equilibrium or continue to evolve. Here again, the interrelationship of the managerial orientation, the organizational past, stakeholder and environmental forces contribute to organizational behavior. Managerial levers of change have received special attention because the setup of the organization and its management team decides about the adaptive or radical pattern of change. Inertia, the flexibility of attention, and the responsiveness of the strategy to pressure serve as policy levers. By their manipulation, the management team can determine the strength of repetitive feedback loops and manage the response to adaptive and resistant feedback pressure. Concerning inertia, an overall low level turned out to increase an organization’s adaptive ability. Therefore, given a certain type of environmental change, the decisions that guide the setup of the management team and its inertia are influential in deciding how the organization reacts to a given exogenous change. A restrictive organization with a homogeneous management team that drives all decisions is likely to overlook important developments in its environment and neglect the rise of new stakeholder groups or of shifting stakeholder preferences. A management team can enhance its organization’s adaptive ability by the active recruitment of employees from outside the organization who bring fresh ideas and are less inward oriented to the accustomed routines as people grown from within. Apart from the active management of the composition of decision-makers in an organization, their institutionalization plays a decisive role. If they are too quickly engrained into the organizational culture and routines, the adaptive ability of the organization is thwarted.638 The management team’s level of inertia is thus an important determinant of organizational outcomes and an important driver as well as inhibitor of change. A low level of inertia is best combined with a high, but not too high responsiveness of attention. This finding derives from a test for policy robustness that included different environments. The combination of a highly flexible reaction of attention with strong and permanent stakeholder pressure for the old strategy A is able to create oscillatory behavior. This is because attention is influenced by several feedback loops. They link the disposition of the management team and the respective strength 637

See Greve: Performance, Aspirations, and Risky Organizational Change, 1998, pp. 78–79 and 81. See also Amburgey, Kelly and Barnett: Resetting the Clock, 1993, pp. 66 and 69–70. 638 Quick institutionalization has also been exemplified for the Intel Corporation and for Linco. See Burgelman: Strategy as Vector and the Inertia of Coevolutionary Lock-in, 2002, p. 354; and Tripsas: Technology, Identity, and Inertia through the Lens of 'The Digital Photography Company', 2009, pp. 450–451.


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and urgency of stakeholder pressure and once again provide evidence for the interconnectedness of determinants of organizational evolution. Additionally, the split behavior resulting from modifications of the reference fractional change in strategy per pressure was insightful for demonstrating the interrelation of a managerial lever for change with environmental aspects. It pointed out that a quick transformation of pressure to strategy has counterintuitive effects on the further development of pressure so that the implementation is somewhat delayed in the end. In this way the interrelationship between the management and the environment as drivers becomes obvious again. While the environment requires the organization to change, first, managerial decisions have an influence on the organization’s reaction to external forces, and second, initial decisions of the management feed back to the environment. They affect the pressure from the environment that the decision-makers will encounter in the future. The generic system dynamics model thus relates behavior and structure, and it points out levers of managerial intervention. It helps explain when the environment and when the management team may be more influential in triggering or upholding change. As such, it unravels the interaction of the environment, the management, and of stakeholders in the closer environment of the organization, and it offers a structural theory of the interrelation of drivers of change in organizations.

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E Realization of Change in Organizations Many different streams of literature emphasize the importance of organizational change. It is considered necessary that organizations are flexible and adapt to a changing environment.639 However there are many examples of organizations that have failed to change adequately. This dissertation therefore investigated the underlying forces of organizational change and its absence. It concentrated on the reasons for change and its absence. In particular it pursued the question of what are the drivers of change and what forces may inhibit organizational alteration. Since it is often argued that there is repetitive momentum once a shift is initiated, the dissertation also analyzed whether previous change increases or reduces the occurrence of subsequent transformations. In order to answer these questions, the literatures focusing on different drivers of change have been outlined. Their assumptions about organizations and the resulting triggers of change differ. Organizational ecology does not allow organizations the capability of adapting to the environment. The traditional behavioral theory on the other hand assumes them to be malleable, its decision-makers bounded rational, but generally willing and able to adapt to the environment. It grants the environment the role of a trigger of change. The strategic choice approach questions the prominence of the environment. According to this theory, management and decision-makers in an organization have much greater freedom of decision and action than the rather deterministic theories assume. It regards management as the most important driver of change while not denying the influence of organizational and environmental constituencies. A possible combination of epistemological points of view and of drivers of change has been heavily discussed in the sociological and organizational literature. While based on different assumptions, recently there has been an increased use of the combination of theories and a more joint consideration of drivers of change. The analysis of the case study of the New York Stock Exchange’s move towards electronic trading enriches this discussion from a multi-paradigm view. The NYSE is a peculiar example since it adapted later than most of its competitors, but it also stands for a class of organizational dinosaurs. It serves as an example of inertia and change, and the causal and dynamic analysis proved helpful for clarifying the interaction of drivers of change. The system dynamics methodology was chosen because it is able to provide a long-term and process analysis of important drivers of change and their interdependence. The piecemeal development of the system dynamics model of the NYSE’s move to e-trade revealed the importance of a simultaneous consideration of multiple drivers of change. In the NYSE-specific case it proved necessary to consider the environment and management both as drivers of change and to additionally include stake639

See Benner: Securities Analysts and Incumbent Response to Radical Technological Change, 2010, pp. 42 and 59; and Hill and Rothaermel: The Performance of Incumbent Firms in the Face of Radical Technological Innovation, 2003, p. 257.


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E Realization of Change in Organizations

holder dynamics such as cultural and resistance pressure that develops as a response to managerial decisions. Sensitivity analyses with different parameter constellations opened the managerial decision space. At the same time they elucidated the impact by which stakeholders and the market environment constrain the organization. The NYSE provided an example of how an organization is able to simultaneously address different stakeholder demands and to cope with conflicting demands of its environment. The case study explained how and why the NYSE shifted towards electronic trading. It pointed to the importance of reinforcing feedback loops of repetitive momentum and attention that create path-dependent behavior and constrain the organization’s vision and its willingness to change. The strength of these reinforcing processes prevented a smoother adaptation and created the observed radical behavior. This in combination with the further feedback relationships of the model added a causal explanation to the process view of change in organizations. The structures which were important in the NYSE case turned out to have generic relevance since, first, sub-elements of the structure are discussed in separate literatures, and second, since there are similarities to other cases such as the delayed adoption of digital photography at Polaroid or the missing reorientation from mini to personal computers at DEC. The generic system dynamics model and simulation process contributed even more to the idea of the interconnectedness of drivers of change. It provided a structural theory of how an organizational management team, its cognitive side, stakeholders, and the environment are connected and how they are able to create different outcomes of smooth adaptation, radical transformation, or organizational trajectories guided by inertia as a response to an environmental shift. Concerning the research question of what triggers change, the structuralbehavioral analysis of the generic model made clear that there is no single or most important determinant of change. Behavior is driven by the combination of environmental and managerial, i.e. of deterministic and deliberate factors. In this way the present study enriched the stream of literature focusing on organizational choice and on the importance of managerial freedom of action.640 It shows that organizational decision makers do have an influence on how the organization is maintained.641 In particular if an organization wants to be adaptive, the management team has a role to play in asset selection and orchestration.642 In addition to the previous theoretical and empirical studies on managerial choice, the present analysis provides a structural-causal explanation of the relationship between the environment, management, and 640

E.g. Child: Organizational structure, environment and performance, 1972, p. 19; Mellahi and Wilkinson: Organizational failure, 2004, pp. 23 and 27; and de Rond and Thietart: Choice, chance, and inevitability in strategy, 2007, pp. 539 and 546. 641 For similar views also see for example Chandler: Strategy and Structure, 1962, pp. 8 and 383; Child: Organizational structure, environment and performance, 1972, pp. 13–14; and Cyert and March: A Behavioral Theory of the Firm, 1963, pp. 240–241. 642 See Augier and Teece: Dynamic Capabilities and the Role of Managers in Business Strategy and Economic Performance, 2009, p. 417.


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organizational outcomes. Decision-makers exercise choice, but their choices are also influenced by conditions provided by their respective environment. Much research has also focused on the environment as a driver of change. Changes in demand, technological innovations, and institutional change have been recognized in the literature as triggers of organizational transformations.643 The simulation analyses make clear that a shift in the environment is needed to trigger any major change. However, even in a generally adaptive organization, specifications in the intensity to which the organization implements perceived pressure for change can lead to different patterns of behavior. In particular, the hump-shaped relationship between the reference fractional change and implementation effectiveness has pointed to the nonlinear emergence of patterns. Behavioral outcomes depend on the continuous interaction and feedback between managerial decisions and stakeholder reactions in the closer organizational environment. Drivers of change are thus intertwined. Highly biased repetitive loops may restrict information intake and conversion to change so that the behavior exhibited may be very different than that of other organizations and well influenced by the managerial setup. The existence of strong reinforcing loops is able to postpone change, but once they lose their strength, they often trigger a radical transformation. A more flexible adaptation on the other hand may also be triggered by the flexible setup of management. Additionally, the relation of the focal organization to its environment has to be taken into account and the existence and strength of stakeholders pressuring for the adoption of a new strategy. Thus, the management, the environment and the setup of stakeholders in the organization’s close environment determine the evolution of organizations. This also means that it cannot be said that prior change increases the occurrence of change in the future. A universally valid answer to the second research question therefore does not exist. Prior change enhances the organization’s adaptability to the environment, but it then also depends on the evolution of this environment and on the compatibility of managerial attention to stakeholders with environmental demands whether one transformation leads to further change. Managerial attention to stakeholders was important in this process although it is not the only determinant of change. In most organizational theories it is outside the boundary of consideration. Previous work on attention exists in the area of the behavioral theory. Its concept of problemistic search represents a search attention to the environment.644 This rather represents attention to issues and solutions; stakeholder attention is rarely discussed. Seldom examples include the business ethics literature in which a normative concept of stakeholder salience has received recognition. But here as well, the explanatory and descriptive side remains a minor matter. 643

See Abernathy and Utterback: Patterns of Industrial Innovation, 1978, pp. 41–46; Haveman, Russo and Meyer: Organizational Environments in Flux, 2001, pp. 253–254 and 269; Meyer, Brooks and Goes: Environmental Jolts and Industry Revolutions, 1990, pp. 94–97; and Romanelli and Tushman: Organizational Transformation as Punctuated Equilibrium, 1994, p. 1145. 644 See Cyert and March: A Behavioral Theory of the Firm, 1992,. pp. 188–190.

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What has remained unsolved, however, is the development of managerial attention to stakeholders.645 This has been addressed in this dissertation. This work pointed to the importance of managerial or also an organization’s attention to stakeholders and integrated it with managerial cognition and inertia. The present system dynamics model adds to the existing literature by providing a structural account and theory of how managerial attention to stakeholders evolves. It includes the determinants of stakeholder attention as well as the behavioral effects of attention for the strategy of the entire organization. The present causal model has shown that many factors have an influence on the salience of a stakeholder claim within an organization. It is important to distinguish the real stakeholder pressure from the perceived one. An essential aspect of the postulated system dynamics model and theory is that attention to stakeholders shapes the extent to which different types of real pressure are perceived. Additionally, the degree to which perceived pressure then is implemented also depends on the organization’s inertia and its general disposition or willingness to react to pressure. Hence the relationship between stakeholder attributes and the resulting pressure on the one hand and the organizational outcome on the other hand is complex. Knowing about this complex relationship is important because in many cases an attentional shift precedes a larger strategic change. Thus adaptation, choice, and cognitive-attentional elements have an influence on the evolution of organizations. Theories focusing on one driver of change are informative, but only illuminate one part. The need for the combination of drivers of change also points to the requirement to combine elements of several organizational theories. It has been elaborated in chapter B.II.1 that difficulties with the assumptions and philosophical foundations of theories arise when radically different theories are combined. Nevertheless, the differences among the organization theories discussed here are not as great since they all reside within Burrell and Morgan’s functionalist paradigm.646 The analysis of the New York Stock Exchange as well as the more generic investigation provided support for the usefulness of the combination of organizational theories. It allows for the analysis of organizational phenomena not only with one, but with two or multiple lenses. As a consequence, the observer receives a richer and more multi-faceted picture of an organization. In the present example it contains elements of environmental determinism and managerial decision-making at the same time. Stakeholder influences and cognitive aspects also enrich the picture. As proponents of the multi-paradigm perspective affirm, it allows for a more comprehensive consideration of phenomena than a single paradigmatic perspective. Mutually exclusive views of the social world have incommensurable as well as continuous el-

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Mitchell, Agle, and Wood’s theory of stakeholder salience represents a first step into this direction. See Mitchell, Agle and Wood: Toward a Theory of Stakeholder Identification and Salience, 1997, pp. 868 and 879–880. 646 See Burrell and Morgan: Sociological Paradigms and Organisational Analysis, 1979, p. 25–30.


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ements, and this diversity is a possibility for learning.647 In the present study, the integration of different elements emerged naturally. Poole and Van de Ven argue that a multi-paradigm perspective is able to clarify the relations between different points of view.648 The elicitation of the causal structure in combination with the structuralbehavioral analysis in the NYSE and the generic case clearly support this statement. It explicitly shows how phenomena that are discussed in different perspectives are entangled. However, one has to be aware that the diverse picture is still a limited view which is more informative than the investigation of organizations by one lens only, but will never be exhaustive. The multi-paradigm analysis also allows for an investigation of different levels of abstraction. This dissertation combines a group and organizational level of abstraction with phenomena such as bounded rationality and cognition which are usually placed at the micro level. It succeeds in doing so by providing a causal explanation of how inertia and attention to stakeholders develop and lead to bounded rational decisions in organizations. The aggregated view of system dynamics makes it possible to integrate the bounded rational reasoning of the decision-making group with organizational outcomes.649 Latour emphasizes the usefulness of the combination of different levels of abstraction because the levels do not stand by themselves, but are linked.650 In the system dynamics model presented above, this linkage forms by shared team properties including shared mental models.651 These team properties of managerial attention and cognition derive from individual (bounded) rationality. Although individual understandings and representations differ, they are understood as what is shared among individuals in a group.652 The focus is on their behavior and change over time. The two team concepts of managerial attention and cognition bridge the gap between the underlying individual and micro-level to the organizational level. The present analysis is able to reveal how a shared cognition evolves over time.

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See Willmott: Breaking the Paradigm Mentality, 1993, pp. 701–703 and 708. See also Gioia and Pitre: Multiparadigm Perspectives on Theory Building, 1990, pp. 598–599; and Morgan: Images of Organization, 2006, pp. 8 and 337–339. 648 See Poole and Van de Ven: Using Paradox to Build Management and Organization Theories, 1989, p. 576. 649 On the linkage of bounded rationality and system dynamics see Größler, Andreas, Peter Milling and Graham Winch: Perspectives on rationality in system dynamics—a workshop report and open research questions, in: System Dynamics Review, Vol. 20 (2004), No. 1, pp. 77–78. 650 See Latour, Bruno: We Have Never Been Modern, Cambridge, MA 1993, p. 121. 651 Klein and Kozlowski subsume shared mental models, norms, and team cohesion under the concept of shared team properties. See Klein, Katherine J. and Steve W. J. Kozlowski: From Micro to Meso: Critical Steps in Conceptualizing and Conducting Multilevel Research, in: Organizational Research Methods, Vol. 3 (2000), No. 3, p. 215. 652 For a typology of group mental model-like concepts and their modes of analysis see Kim: In search of a mental model-like concept for group-level modeling, 2009, pp. 213 and 216–219.

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Cognitive change, as explained by the structural-behavioral investigation of the system dynamics model, can also be compared to a double-loop learning process.653 The Adaptation Pressure and Performance Adaptation Loops, depicted in Figure E-1, serve as a first-order learning structure by which the organization reduces the gap between its strategic orientation and that of the environment. Information feedback from the organizational environment does not only trigger new decisions. It also involves double-loop learning by altering the mental model decision-makers have of the real world. It thus exerts influence on managerial inertia, cognition and in particular on managers’ attention to stakeholders, as described by the Adaptation of Attention Loops. This influence not always leads to better mental models; more important, they may be biased in just a different way. As part of this process, the decision-makers’ mental models alter the information collected or perceived from the environment. In this way, attention to stakeholders serves as a weight on incoming pressure, and perception alters attention even further. This confirms prior findings that durable organizational change not only derives from adaptation processes, but require a further learning cycle by which cognition changes as well.654 The system dynamics model adds a causal structure that explains how cognitive changes develop and interact with adaptive processes. This view accounts for the claim that the mind and the environment need to come to terms which each other by mutual adaptation.655 In particular perceived pressure is shaped by both, the environment and managerial perception. In the case that management is biased, it may distort the perception of the need to change. In future research, the analysis of perception and attention in relation to stakeholders in the organization’s environment may be enlarged: it may incorporate managerial attention to more than two stakeholder groups. In the present generic model, the investigation has been limited to two possible organizational strategies and two stakeholder groups because this is the standard situation when an organization needs to decide between an old and a new strategy. Concerning stakeholder attention, the analysis of the New York Stock Exchange’s move to electronic trading included three stakeholder groups: institutional customers, non-institutional customers, and floor firms. It is thus also possible to integrate and analyze more than two stakeholder groups and the effect of their demands on the evolution of organizations if the situation analyzed is characterized by diversified stakeholders.

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For a causal account of double loop learning see Radzicki, Michael J.: Mr. Hamilton, Mr. Forrester, and a Foundation for Evolutionary Economics, in: Journal of Economic Issues, Vol. 37 (2003), No. 1, p. 154; and Sterman: Business Dynamics, 2000, pp. 18–19. 654 See Schimmel and Muntslag: Learning Barriers, 2009, p. 413. 655 See Gigerenzer, Todd and the ABC Research Group: Simple Heuristics That Make Us Smart, p. 22. They refer to a concept developed by Brunswik. See Brunswick, Egon: Scope and Aspects of the Cognitive Problem, in: University of Colorado at Boulder, Department of Psychology (Ed.): Contemporary Approaches to Cognition: A Symposium Held at the University of Colorado, Cambridge, MA 1957, p. 5.

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pressure for more B




quality A (B) Adaptation of Attention to A

Figure E-1: Full generic causal loop diagram (CLD) It would additionally be possible to tackle the levers of change in more detail. The process of inertia decrease could be fine-tuned and elaborated. In particular the effects of employee turnover could be compared to other measures such as job rotation systems, more diversified recruiting, unlearning and else. The institutionalization process is equally important. It would be interesting to investigate what measures have limiting effects on the growth of inertia without hampering the growth of organizational experience and competence. Experience has been associated with better performance because team members learn to work with each other. They learn about each other’s mental models.656 In this respect the inclusion of experience into the analysis would prove helpful. In further research it could for example be incorporated in the way Sastry included it in her system dynamics model of the punctuated equilibrium approach.657 The reason why experience was excluded from the present analysis is that it neither represents a central element in the NYSE’s transition from manual to electronic trading nor were the other examples related to missing experience. The omission of unethical marketing practices is not linked to competence, and Polaroid, for instance, had already built its experience in digital photography, but failed to change its business model. While floor trading requires a lot of human intervention and experience, this is different with e-trade. Participants are anonymous and a computer matches orders without any human intervention. Of course, traders also need to become accustomed to the elec656

657

See Huber, George P. and Kyle Lewis: Cross-understanding: Implications for Group Cognition and Performance, in: Academy of Management Review, Vol. 35 (2010), No. 1, p. 2010. For a formal representation of experience in the punctuated equilibrium approach see Sastry: Problems and Paradoxes in a Model of Punctuated Organizational Change, 1997, pp. 243–246 and 249.

224


tronic environment, but the growth of experience is much less important than in settings that involve human interaction. Therefore, the effects of experience with a certain strategy on performance were excluded in the present study. Since competence with a new strategy can be important in cases of organizational change and since it might conflict with low inertia, the present study could be enlarged in this direction. In a similar manner, as the introduction of new trading technology did not require the active support of those working with it, difficulties that can arise in the implementation phase of a new strategy or technology are outside the boundary of this dissertation. It was not essential in the examples analyzed, but inner-organizational difficulties such as employee resistance could be incorporated in the further investigation of cases in which implementation effectiveness depends on human support. Concerning the implementation of change initiatives, future research might include the effectiveness and direction of measures management takes.658 If the change concerns the transition from an old to a new technology the direction of change is straightforward. Nevertheless, it is within the realms of possibility that the management team launches change initiatives that are ineffective or have unintended consequences which go beyond resistance from those favoring the old strategy. The analysis of these effects, however, represents an implementation problem that may build up on the present study. The latter is rather concerned with the cognitive side and with the circumstances that determine the decision to initiate change. The present study contributed to organizational change theory, but the knowledge derived from the investigation of New York Stock Exchange and of the generic system dynamics model can be used in practice. Several concrete recommendations can be given. The analyses have shown the scope for managerial action is great, but in order to maintain adequate performance, it is also necessary that an organization is in accordance with the demands its environment poses. Outcomes can be ameliorated in different ways; by increasing adaptability or the overall benefits of adaptation. In order for this to happen, the management team can reduce managerial and organizational inertia by lowering institutionalization and by enhancing the loss of inertia. It may reduce institutionalization by training and by the active maintenance of diversity. The decrease of inertia can be enhanced by turnover, job rotation systems, more diversified recruiting, learning, and else. In addition to inertia, the organization’s general handling of perceived pressure turned out to be a lever for change. A medium responsiveness of its strategy to pressure proved best for an organization’s adaptability. While a low transformation of pressure into action will hamper and delay change, it is beneficial to wait and see whether a stakeholder claim gets substantial and then quickly react to it.

658

E.g. Baum and Singh: Dynamics of Organizational Responses to Competition, 1996, p. 1287 and Delacroix and Swaminathan: Cosmetic, Speculative, and Adaptive Organizational Change in the Wine Industry, 1991, p. 657.


225

Concerning managerial attention to stakeholders, rather high flexibility proved best. Decision-makers may commission market surveys or mandate market research institutes so as to early perceive emergent stakeholder groups and their claims. At the same time they need to make sure that their attention does not swing back as a result of a too high reaction to pressure in combination with strong stakeholder forces. Examples of the NYSE and DEC revealed that organizations often have strong relationships to existing stakeholders—here to floor firms and to business clients, respectively. But organizational managers often neglect emerging groups and their desires. The mere awareness of this problem and in particular knowledge of how it causally develops can help the organization. The inclusion of this knowledge into day-to-day business would increase decision-makers’ consciousness of the risk of growing inertia and failure to attend to important stakeholder groups. This can reduce their inward orientation and make the organization and its management more attentive to changes in its environment. The outcome of change initiatives can be increased by a reduction of negative consequences that come along with all organizational changes. As the case of the NYSE exemplified, it is not necessary to imitate the environment. For instance, by keeping the floor, the NYSE was able to reduce the negative consequences electronic trading had for some of its stakeholder groups. This shows that—while adopting a new strategy—it can be useful not to forget about the advantages of the old one. The NYSE did this by simultaneously striving for speed and market quality. Digital photography increasingly strives for resolution quality. PCs now also achieve high computation power. Customers who demand ethical behavior by the entire supply chain do not forget about price aspects either. It is important that organizations adapt and fulfill the demands of their environment, but they do not need to mechanically imitate other organizations. They are free to develop intelligent strategies to meet demands of several stakeholder groups at the same time. Managerial action thus bears great importance. Decision-makers shape the way an organization reacts to its environment. The overall evolution on an organization depends on the combination of the environment, its stakeholders, and organizational decision-makers. They constructively irritate and inspire each other. Nevertheless, the management team has sufficient freedom of action to shape its response to external factors and to enact its future decision environment.

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Yadav, Manjit S., Jaideep C. Prabhu and Rajesh K. Chandy: Managing the Future: CEO Attention and Innovation Outcomes, in: Journal of Marketing, Vol. 71 (2007), No. 4, pp. 84–101. Zander, Alvin: Resistance to Change—Its Analysis and Prevention, in: Advanced Management, Vol. 4 (1950), No. 5, pp. 9–11. Zimmermann, Nicole S. and Peter M. Milling: Data Generation from Weblogs in Qualitative Research and System Dynamics, in Proceedings of the 17th International Annual EurOMA Conference, 2010, pp. 1–10.

257

Appendix Appendix A: Financial Glossary

auction market

A market in which a market maker conducts an auction for specific securities and allows for negotiation over the price.

designated market maker A market professional who has the responsibility to provide a fair and orderly market for the securities he has been assigned. He combines a physical and an automated auction that includes algorithmic quotes. His responsibilities are to bring together demand and supply and to quote at the NBBO a specified percentage of time. designated order turnaround (DOT)

NYSE system which allows brokerage firms to electronically transmit orders directly to the specialist.

Direct+

A high-speed electronic system for immediate automatic execution of limit orders that was implemented at the NYSE in 2001.

DMM

See designated market maker.

DOT

Designated Order Turnaround. It is an electronic system that allows brokers to route orders directly to the specialist instead of a floor broker.

Financial Industry Regulatory Authority

A private regulatory body governing the business between brokers, dealers, and the investing public. It was formerly known as Securities Industry Regulatory Authority (SIRA).

floor broker

Broker physically located on the NYSE trading floor who competes with other brokers to receive the best price for his customer.

institutional investor

Financial institutions such as banks, insurance companies, investment funds, pension funds, proprietary trader organizations and else that frequently engage in securities trading.

Intermarket Trading System

System that gives market professionals the opportunity to send orders to other markets if these markets display a better price.

liquidity algorithm

With the help of liquidity algorithms, the NYSE’s DMMs and Special Liquidity Providers are able to constantly provide automated bids and offers also in electronic trading.

N. Zimmermann, Dynamics of Drivers of Organizational Change, DOI 10.1007/978-3-8349-6811-1, © Gabler Verlag | Springer Fachmedien Wiesbaden GmbH 2011

258

Appendix

market maker

By providing a bid and an offer price, a market maker makes sure that a specific group of securities can always be traded.

NBBO

National best bid and offer, the highest bid and lowest offer in the U.S. securities market.

order flow

Incoming orders.

proprietary trader

A firm trading with its own instead of the customers’ money.

SEC

The Securities and Exchange Commission is the regulating body of the U.S. securities industry.

specialist

A market professional who manages the auction market trading in the specific securities he (or she) has been assigned. He has the responsibility to provide a fair and orderly market, brings together demand and supply, and steps in with his own money in order to match imbalances in the market.

spread

Difference between the asking price at which shares of a certain security are offered and the bid price which someone is willing to pay for shares of this security.

Supplementary Liquidity Providers

Electronic, high-volume members who are incented to add liquidity on the NYSE.

Total Consolidated Tape The Total Consolidated Tape aggregates shares matched at U.S. exchanges and volume of transactions effected otherwise than on an exchange which are reported to the Financial Industry Regulatory Authority. trade-through rule

The trade-through rule helps price protection by demanding that an order is not traded through inferior markets, but should instead be directed to the market which offers the best price.

259

Appendix

Appendix B: Simulation Runs and Sensitivity Analyses of the NYSE Model

Insensitive reaction in response to changes in stakeholder power. sens power 50% 75% 95% "NYSE Fraction of E-Trade" 1

100%

0.75

0.5

0.25

0 1970

1980

1990

2000 Date

2010

2020

2030

2000 Date

2010

2020

2030

sens power 50% 75% 95% 100% total pressure for more floor trade from floor 60

45

30

15

0 1970

1980

changed parameter ref. power of floor firms

1990

base run value 100

parameter range 10 – 500

260

Appendix

Insensitive reaction to changes in the cohesiveness of floor firms sens cohesiveness 50% 75% 95% "NYSE Fraction of E-Trade" 1

100%

0.75

0.5

0.25

0 1970

1980

sens cohesiveness 50% 75% 95%

1990

2000 Date

2010

2020

2030

2000 Date

2010

2020

2030

100%

total pressure for more floor trade from floor 80

60

40

20

0 1970

1980

1990

changed parameter degree of cohesiveness of floor firms

base run value 0.7


261

Appendix

Simulation run representing an exchange with a small market share

Fraction of E-Trade 12

12

12

1

1

2

Dmnl

0.75 1

0.5 0.25 0 12 1970

12

12 1980

1 2 12 1990

12 1 2000 Date

2

"NYSE Fraction of E-Trade" : small exchange 1 "NYSE Fraction of E-Trade" : base run 2 2

changed parameters ini market share grasso effect strength

.

2010 1

2020 1

2

1 2

base run value 0.8747 1

2030 1

2

2

parameter value 0.03 0

262

Appendix

Pressure by non-institutional customers sens non-inst pressure 50% 75% 95% "NYSE Fraction of E-Trade" 1

100%

0.75 0.5 0.25 0 1970

1980

1990

2000 Date

2010

2020

2030

2010

2020

2030

sens non-inst pressure 50% 75% 95% 100% total pressure for more floor trade from customers 20 15 10 5 0 1970

1980

1990

changed parameter ref. pressure per non-inst customer

2000 Date

base run value 1


263

Appendix

Sensitivity for differences in inertia sens inertia 50% 75% 95% "NYSE Fraction of E-Trade" 1

100%

0.75 0.5 0.25 0 1970

sens inertia 50% 75%

1980

95%

1990

2000 Date

2010

2020

2030

2000 Date

2010

2020

2030

100%

Inertia 1 0.75 0.5 0.25 0 1970

1980

changed parameters ref. fract. institutionalization ini inertia

1990

.


parameter range 0.2 – 0.35 0.05 – 0.95

264

Appendix

Appendix C: Simulation Runs and Sensitivity Analyses of the Generic Model

Comparative sensitivity analyses with quick environmental change: Inertia generic sens inertia flex 50% 75% 95%

100%


0.75

0.5

0.25

0

0

5

10

15

20

25 30 Time (Year)

35

40

45

50

20

25 30 Time (Year)

35

40

45

50

generic sens inertia flex quick 50% 75% 95% 100% Orientation to Strategy B 1

0.75

0.5

0.25

0

0

5

10

15

changed parameters base run value ref. fract. change in strategy per pressure p.a. 0.02 ref. fract. change in attention p.a. 0.05 ref. fract. inertia decrease 0.15 ref. fract. institutionalization 0.3 ini inertia 0.9 ini attention to stakeholders favoring B 0.1

parameter range 0.06 0.3 0.15 – 0.2 0.2 – 0.3 0.5 0.3

265

Appendix

Comparative sensitivity analyses with quick environmental change: Attention generic sens attention flex 50% 75% 95% Orientation to Strategy B 1

100%

0.75

0.5

0.25

0

0

5

10

15

20

25 30 Time (Year)

35

40

45

50

20

25 30 Time (Year)

35

40

45

50

generic sens attention flex quick 50% 75% 95% 100% Orientation to Strategy B 1

0.75

0.5

0.25

0

0

5

10

15

changed parameters base run value ref. fract. change in strategy per pressure p.a. 0.02 ref. fract. change in attention p.a. 0.05 ref. fract. inertia decrease 0.15 ini inertia 0.9 ini attention to stakeholders favoring B 0.1

parameter range 0.06 0.05 – 0.5 0.28 0.2 0.3

266

Appendix

Appendix D: NYSE Model Equations

access to information technology

ACCESS TO INFORMATION TECHNOLOGY = WITH LOOKUP( Time , ([(1970,0)-(2030,1)],(1970,0),(1975,0), (1980,0.09),(1985,0.18),(1990,0.57),(1995,0.9),(1997,0.97),(1999,1), (2030,1) ) ) Units: Dmnl State of technology that is necessary for electronic trading. Access to Information Technology 1

0.5

0 1970

1980

1990

2000 Time (Year)

2010

2020

2030

ALGORITHMS PER GAP PER YEAR = 50 Units: algorithms/(Year*Dmnl) "change in cust. orient." = ( pcvd pressure from customers * "effect of cust. orient. on change" - pcvd pressure from the floor * "effect of floor orient. on change" ) * "fract. change in cust. orient. per pressure p.a." Units: Dmnl/Year "change in fraction of e-trade" = ( "pcvd pressure for more e-trade" * "effect of e-trade on change" - pcvd pressure for more floor trade * effect of floor trade on change ) * "fract. change per pcvd pressure p.a." Units: Dmnl/Year change in fraction of institutional customers = ( indicated fraction of institutional customers - Fraction of Institutional Customers ) / TIME TO BECOME CUSTOMER Units: Dmnl/Year

267

Appendix

change in power of floor firms = ( indicated power of floor firms - Power of Floor Firms ) / TIME TO CHANGE POWER OF FLOOR FIRMS Units: entity/Year change in valuation = Valuation of Floor Culture by Floor * fractional change in valuation of floor culture Units: valuation unit/Year commission per share = "REF. COMMISSION PER SHARE" * "effect of inst. customers on commission" Units: $/share Part of the floor’s earnings. confidence effect of market share = WITH LOOKUP( pcvd adequacy of market share , ([(0,0)-(1.2,1)],(0,0),(0.2,0.04),(0.4,0.14),(0.5,0.22),(0.6,0.33), (0.7,0.5), (0.8,0.75),(0.9,0.95),(0.95,0.985),(1,1),(1.2,1) ) ) Units: Dmnl Effect by which performance inadequacies increase the management team's openness to change. Minor inadequacies have less than proportional effect, but the effect on openness quickly rises before it slowly approaches the limit of a fully open organization in the case of organizational collapse. Confidence Effect of Market Share confidence effect

1 0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 pcvd adequacy of market share

1

cultural multiplier of pressure from floor = "rel. valuation of floor culture" * DEGREE OF COHESIVENESS OF FLOOR FIRMS Units: Dmnl Effect of culture and cohesiveness.

268

Appendix

Customer Orientation = INTEG( "change in cust. orient." , INI CUSTOMER ORIENTATION ) Units: Dmnl Attention to and orientation towards institutional and non-institutional customers. DEGREE OF COHESIVENESS OF FLOOR FIRMS = 0.7 Units: Dmnl Degree to which floor firms need to rely on each other. Cooperative groups may react with resistance. degree of trading professionalization = WITH LOOKUP( Fraction of Institutional Customers , ([(0,0)-(1,1)],(0,0.1),(0.1,0.27),(0.2,0.45),(0.3,0.63),(0.4,0.8), (0.5,0.92),(0.6,0.98), (0.7,0.995),(0.8,1),(0.9,1),(1,1) ) ) Units: Dmnl Portfolio management, information patterns-based trading, hedging, etc. Degree of Trading Professionalization professionalization

1 0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Fraction of Institutional Customers

1

DESD ADEQUACY OF MARKET QUALITY = 1 Units: Dmnl desired earnings per share = SMOOTH ( proportional floor earnings per share traded , TIME TO ADJUST DESIRED EARNINGS ) Units: $/share The floor's floating goal of earnings. "desired market quality from sp. part. by customers" = SMOOTH3 ( "market quality from sp. participation" , TIME TO CHANGE DESD MARKET QUALITY ) Units: Dmnl Floating goal of desired market quality.

269

Appendix

desired market share = SMOOTH ( NYSE Market Share , TIME TO ADJUST DESD MARKET SHARE ) Units: Dmnl Floating goal of desired market share. desired specialist participation = SMOOTH ( specialist participation , TIME TO ADJUST DESIRED PARTICIPATION ) Units: Dmnl The floor's floating goal of desired specialist participation. development of liquidity algorithms = SMOOTH3 ( market quality adequacy gap * ALGORITHMS PER GAP PER YEAR , TIME TO DEVELOP ALGORITHMS ) Units: algorithms/Year Algorithms take about one and a half years to be initiated since a quality gap needs to be perceived as being problematic; this is why there is a third order smooth in the development decision instead of a development delay. "dissatisf. with time per inst. customer" = WITH LOOKUP( relative time to execution , ([(0,0)-(10,1)],(0.9,0),(1,0), (9,1) ) ) Units: dissatisfaction unit/entity Institutional customers' extent of dissatisfaction with or dislike of the NYSE's relative speed of execution. Dissatisfaction with Time per Institutional Customer

dissatisfaction

1 0.75 0.5 0.25 0 1

2

3

4 5 6 relative time to execution

7

8

9

270

Appendix

dissatisfaction effect of market quality on pressure = WITH LOOKUP( pcvd adequacy of market quality by customer , ([(0.9,0)-(1.05,1)],(0.9,1),(0.91,0.98),(0.92,0.95),(0.93,0.9),(0.94,0.75), (0.95,0.5),(0.96,0.25),(0.97,0.1),(0.98,0.05),(0.99,0.02),(1,0),(1.05,0) ) ) Units: Dmnl Inversely s-shaped. Slowly approaches maximum, and slowly starts in the beginning because minor inadequacies cause less than proportional reactions. Dissatisfaction Effect of Market Quality on Pressure dissatisfaction effect

1 0.75 0.5 0.25 0 0.900

0.920 0.940 0.960 0.980 pcvd adequacy of market quality by customer

1

effect of captial distribution on customers = WITH LOOKUP( FRACTION OF EQUITIES HELD BY INSTITUTIONS , ([(0,0)-(1,1)],(0,0),(0.1,0.19),(0.2,0.36),(0.3,0.52),(0.4,0.62),(0.5,0.71), (0.6,0.79),(0.7,0.86),(0.8,0.91),(0.9,0.96),(1,1) ) ) Units: Dmnl Institutions participate in trading in a more than proportional way Æ concave function. Effect of Capital Distribution on Customers 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 FRACTION OF EQUITIES HELD BY INSTITUTIONS

1

271

Appendix

effect of change on inertia = WITH LOOKUP( ABS ( "change in fraction of e-trade" ) , ([(0,0)-(0.5,7)],(0,1),(0.05,1.4),(0.1,2.4),(0.15,4.2),(0.2,5.4),(0.3,6.2), (0.5,6.5) ) ) Units: Dmnl Small changes have an underproportional effect on consistency loss. This allows an organization to change very slowly without disruption in its internal consistency. The consistency decrease from change represents turnover rates which became higher, but it also captures changes in the people's thinking even if they remain in the organization Effect of Change on Inertia 8

effect

6 4 2 0 0

0.100

0.200 0.300 "change in fraction of e-trade"

0.400

0.500

"effect of cust. orient. on change" = WITH LOOKUP( Customer Orientation , ([(0,0)-(1,1)],(0,1),(0.25,1),(0.5,0.95),(0.8,0.75),(0.9,0.5),(0.96,0.04), (0.98,0.005),(1,0) ) ) Units: Dmnl Limit to the willingness to further react to pressure. Effect of Customer Orientation on Change 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Customer Orientation

1

272

Appendix

"effect of e-trade on change" = WITH LOOKUP( "NYSE Fraction of E-Trade" , ([(0,0)-(1,1)],(0,1),(0.5,1),(0.75,0.95),(0.9,0.75),(0.95,0.5),(0.99,0.01), (1,0) ) ) Units: Dmnl Limit to the willingness to further react to pressure. Effect of E-Trade on Change 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 "NYSE Fraction of E-Trade"

1

effect of employability on resistance = WITH LOOKUP( pcvd adequacy of employability , ([(0,0)-(1.2,1)],(0,1),(0.5,1),(0.55,0.97),(0.6,0.88),(0.75,0.5),(0.9,0.13), (0.95,0.05),(1,0),(1.2,0) ) ) Units: Dmnl Inversely s-shaped. Slowly approaches maximum, when adequacy is only 0.5 and slowly starts in the beginning because minor inadequacies cause less than proportional reactions. Effect of Employability on Resistance 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 pcvd adequacy of employability

1

"effect of floor orient. on change" = WITH LOOKUP( Floor Orientation , ([(0,0)-(1,1)],(0,1),(0.25,1),(0.5,0.95),(0.8,0.75),(0.9,0.5),(0.96,0.04), (0.98,0.005),(1,0) ) ) Units: Dmnl Limit to the willingness to further react to pressure. The graph has the same shape as the one indicating the effect of customer orientation on change.

273

Appendix

effect of floor trade on change = WITH LOOKUP( NYSE Fraction of Floor Trade , ([(0,0)-(1,1)],(0,1),(0.5,1),(0.75,0.95),(0.9,0.75),(0.95,0.5),(0.99,0.01), (1,0) ) ) Units: Dmnl Limit to the willingness to further react to pressure. The graph has the same shape as the one indicating the effect of e-trade on change. "effect of floor trade on sp. participation" = WITH LOOKUP( NYSE Fraction of Floor Trade , ([(0,0)-(1,1)],(0,0.1),(1,1) ) ) Units: Dmnl It is the special feature of the Hybrid Market that even when all trades are electronic, there is some specialist participation. In general, the effect of floor trade on specialist participation is assumed to grow linearly. Effect of Floor Trade on Specialist Participation 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 NYSE Fraction of Floor Trade

1

EFFECT OF GRASSO SCANDAL = 1 + PULSE ( 2004, GRASSO SCANDAL DURATION ) * GRASSO EFFECT STRENGTH Units: Dmnl In January 2004 a scandal around the former CEO Richard Grasso triggered a change of the CEO that decreased inertia.

274

Appendix

"effect of inst. customers on commission" = WITH LOOKUP( number of institutional customers , ([(10,0)-(100,1)],(10,1),(20,0.95),(65,0.06),(75,0.02),(100,0) ) ) Units: Dmnl Institutional customers became so powerful that they were able to strongly reduce the amount of money they need to pay for the floor's services. Effect of Institutional Customers on Commission 1

effect

0.75 0.5 0.25 0 0

10

20

30 40 50 60 70 80 number of institutional customers

90

100

"effect of inst. customers on spread" = WITH LOOKUP( number of institutional customers , ([(10,0)-(100,1)],(10,1),(25,0.95),(44,0.82),(55,0.6),(65,0.33),(80,0.15), (100,0.05) ) ) Units: Dmnl Regulatory effects that came with the rise of institutional customers such as the transition of quoting in eights of a dollar to sixteenth to pennies. Effect of Institutional Customers on Spread 1

effect

0.75 0.5 0.25 0 0

10

20

30 40 50 60 70 80 number of institutional customers

90

100

275

Appendix

effect of institutional customers on power = WITH LOOKUP( Fraction of Institutional Customers , ([(0,0)-(1,1)],(0,1),(0.2,0.98),(0.4,0.93),(0.6,0.75),(0.8,0.5),(1,0.1) ) ) Units: Dmnl Institutional customers diminish the floor's power since they are powerful as well. Some fraction of institutions has a low effect, but the strength of the effect rises Effect of Institutional Customers on Power 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Fraction of Institutional Customers

1

effect of liquidity algorithms on participation = ( Liquidity Algorithms + "REF. LIQUIDITY ALGORITHMS" ) / "REF. LIQUIDITY ALGORITHMS" Units: Dmnl Liquidity Algorithms allow the floor to participate also in electronic trades and thus increase specialist participation. Due to the balancing nature of the Liquidity Algorithms Loop, specialist participation remains in reasonable bounds, although it may oscillate slightly around the goal value. effect of market quality on culture = WITH LOOKUP( pcvd adequacy of market quality by customer , ([(0.9,-1)-(1.1,1)],(0.93,-0.7),(1,0),(1.07,0.7) ) ) Units: Dmnl Floor participants reduce their valuation of their own culture and way of doing things if they do not provide adequate market quality. Effect of Market Quality on Culture 0.8

effect

0.4 0 -0.4 -0.8 0.930

0.950 0.970 0.990 1.010 1.030 1.050 pcvd adequacy of market quality by customer

1.070

276

Appendix

effect of market quality on power = WITH LOOKUP( "market quality from sp. participation" , ([(1,0)-(1.1,1)],(1,0),(1.1,1) ) ) Units: Dmnl The higher market quality is, i.e. the higher the floor's contribution is, the more powerful is the floor. Effect of market Quality on Power 1

effect

0.75 0.5 0.25 0 1

1.020 1.040 1.060 1.080 "market quality from sp. participation"

1.100

effect of openness on change = WITH LOOKUP( openness to change , ([(0,0)-(1,1)],(0,0.1),(0.1,0.11),(0.2,0.14),(0.3,0.21),(0.4,0.3),(0.5,0.435), (0.6,0.63),(0.7,0.81),(0.8,0.92),(0.9,0.97),(1,1) ) ) Units: Dmnl Low openness to change may reduce fractional change to 10 percent of its reference value. The effect of openness on change is an s-shaped curve indicating that the NYSE quickly reacts to perceived pressure if it has a rather high openness. It becomes less responsive as openness decreases until its reactivity reaches a lower bound. Effect of Openness on Change 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 openness to change

1

277

Appendix

effect of profitability on culture = WITH LOOKUP( pcvd adequacy of profitability , ([(0,-0.5)-(2,0.5)],(0,-0.5),(1,0),(2,0.5) ) ) Units: Dmnl Floor participants reduce their valuation of their own culture and way of doing things if they are not profitable. Effect of Profitabiltiy on Culture 0

effect

-0.125 -0.25 -0.375 -0.5 0

0.20

0.40 0.60 pcvd adequacy of profitability

0.80

1

effect of profitability on resistance = WITH LOOKUP( pcvd adequacy of profitability , ([(0,0)-(1.1,1)],(0,1),(0.5,1),(0.55,0.97),(0.6,0.88),(0.75,0.5),(0.9,0.13), (0.95,0.05),(1,0),(1.1,0) ) ) Units: Dmnl Inversely s-shaped. Slowly approaches maximum, when adequacy is only 0.5 and slowly starts in the beginning because minor inadequacies cause less than proportional reactions. Effect of Profitability on Resistance 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 pcvd adequacy of profitability

1

EFFECT OF REGULATION = STEP ( 1, 2005.5) Units: Dmnl Regulation NMS changed the situation in the market. It came into effect in the year 2005.

278

Appendix

effect of relative trading volume on market's spread = WITH LOOKUP( trading volume of the remaining market / TOTAL U.S. SHARE VOLUME IN NYSE- LISTED ISSUES , ([(0,0.8)-(1,1.2)],(0,1.1),(0.5,1),(1,0.9) ) ) Units: Dmnl The market with the higher trading volume usually has more quoted depth which reduces the spread. Effect of Relative Trading Volume on Market's Spread 1.1

effect

1.05 1 0.95 0.9 0 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1 "relative trading volume of the remaining market ( = trading volume / TOTAL )

effect of relative trading volume on NYSE spread = WITH LOOKUP( NYSE trading volume / TOTAL U.S. SHARE VOLUME IN NYSE- LISTED ISSUES, ([(0,0.8)-(1,1.2)],(0,1.1),(0.5,1),(1,0.9) ) ) Units: Dmnl The graph has the same shape as the one indicating the effect of relative trading volume on market’s spread.

279

Appendix

"effect of sp. participation on market quality" = WITH LOOKUP( specialist participation , ([(0,1)-(0.1,1.1)],(0,1),(0.01,1.015),(0.02,1.03),(0.04,1.055),(0.06,1.075), (0.08,1.09),(0.1,1.1) ) ) Units: Dmnl The curve is concave due to the diminishing marginal utility of specialist participation. This non-linear relationship bases on the fact that there is an absolute limit to the effect that specialist participation can have on market quality. This value always depends on the specific security, but on average it can be assumed that specialist involvement is needed in no more than 10 percent of trades. Then, specialists are able to increase market quality by 10 percent. Effect of Specialist Participation on Market Quality 1.1

effect

1.075 1.05 1.025 1 0

0.020

0.040 0.060 specialist participation

0.080

0.100

effect of time to execution on market share = WITH LOOKUP( relative time to execution , ([(0,0.5)-(9,1.136)],(0,1.1),(0.3,1.04),(0.5,1.02),(0.75,1.005),(1,1), (1.5,0.995), (2,0.99),(3,0.985),(4,0.965),(5,0.91),(6,0.82),(7,0.73), (9,0.5) ) ) Units: Dmnl Upward or downward adjustment of market share based on the NYSE's relative speed. Effect of Time to Execution on Market Share 1.2

effect

1 0.8 0.6 0.4 0

FINAL TIME = 2030 Units: Year

1

2

3 4 5 6 relative time to execution

7

8

9

280

Appendix

floor earnings per share handled = commission per share + NYSE spread / HALF SPREADS Units: $/share The floor makes money from commissions and the half-spread, i.e. the difference between the price and the mid-point between the bid and ask quote. Floor Orientation = INTEG( - "change in cust. orient." , 1- INI CUSTOMER ORIENTATION ) Units: Dmnl The NYSE’s attention to and orientation towards floor firms. "fract. change in cust. orient. per pressure p.a." = "REF. FRACT. CHANGE IN CUST. ORIENT. P.A." * effect of openness on change Units: Dmnl/(Year*pressure unit) Flexibility of attention. Mix of the NYSE management team's general flexibility of attention and situational factors. "fract. change per pcvd pressure p.a." = "REF. FRACT. CHANGE IN TRADING PER PRESSURE P.A." * effect of openness on change Units: Dmnl/(Year*pressure unit) Responsiveness of the NYSE’s strategy. Mix of the management team's general responsiveness to pressure and situational factors. "fract. of e-trade among foreign competitors" = SMOOTH3 ( "Fraction of largest foreign competitors allowing some e-trade" , "TIME TO FULLY IMPLEMENT E-TRADE" ) Units: Dmnl FRACTION OF CAPITAL HELD BY INSTITUTIONS FROM NYSE DATA = WITH LOOKUP (Time, ([(1950,0)-(2010,1)],(1950,0.072),(1970,0.282),(1990,0.414), (1992,0.417),(1993,0.411),(1995,0.437),(1997,0.477),(1999,0.432), (2000,0.47),(2001,0.483) ) ) Units: Dmnl Data has been taken from NYSE Facts and Figures. There is not data for 1960, so the table goes back to the year 1950. It serves as a comparison to data from ICI.org which is used as model input.

281

Appendix

"fraction of e-trade in remaining market" = SMOOTH3 ( degree of trading professionalization * ACCESS TO INFORMATION TECHNOLOGY , "TIME TO DEVELOP E-TRADE POSSIBILITIES" ) Units: Dmnl Adoption of e-trade in the market. FRACTION OF EQUITIES HELD BY INSTITUTIONS = WITH LOOKUP( Time , ([(1970,0)-(2030,1)], (1970,0.185154),(1971,0.20556),(1972,0.201695), (1973,0.224875),(1974,0.258656),(1975,0.263879),(1976,0.247148), (1977,0.267071),(1978,0.290532),(1979,0.282315),(1980,0.274132), (1981,0.2911),(1982,0.324992),(1983,0.354418),(1984,0.376369), (1985,0.398256),(1986,0.374692),(1987,0.390658),(1988,0.359247), (1989,0.364516),(1990,0.375896),(1991,0.370787),(1992,0.372372), (1993,0.400006),(1994,0.422731),(1995,0.420065),(1996,0.433062), (1997,0.424297),(1998,0.433194),(1999,0.419878),(2000,0.450718), (2001,0.480608),(2002,0.508861),(2003,0.523507),(2004,0.548993), (2005,0.572488),(2006,0.587098),(2007,0.615491),(2015,0.666667), (2030,0.714912) ) ) Units: Dmnl Fraction of equities held by institutions such as mutual funds, insurance companies, etc. (see ICI.org). Fraction of Equities Held by Institutions

Dmnl

0.8

0.4

0 1970

1980

1990

2000 Date

2010

2020

2030

Fraction of Institutional Customers = INTEG( change in fraction of institutional customers , 0.25) Units: Dmnl This is a number that relates to the percentage of shares traded by institutional customers at the NYSE. It ranged around 25 percent in 1970.

282

Appendix

"Fraction of largest foreign competitors allowing some e-trade" = WITH LOOKUP( Time , ([(1970,0)-(2030,1)],(1970,0),(1976,0),(1977,0.0454545), (1982,0.0909091),(1986,0.181818),(1987,0.227273),(1988,0.363636), (1989,0.5),(1991,0.681818),(1994,0.772727),(1996,0.909091), (1997,0.954545),(2000,1),(2030,1) ) ) Units: Dmnl See the graph on page 88. fraction of time at NBBO = WITH LOOKUP( relative spread of NYSE , ([(0.82,0)-(1.22,1)],(0.82,0.99),(1.22,0.01) ) ) Units: Dmnl This variable expresses the effect of the spread on the trade execution time. A relative spread of 1.02 equally distributes the shares at the NBBO between the NYSE and remaining market. This little shift of the graph to the right represents the fact that the NYSE is more consolidated since it is a single stock exchange whereas the remaining market consists of several exchanges. Fraction of Time at NBBO 1

fraction

0.75 0.5 0.25 0 0.750

0.850

0.950 1.050 relative spread of NYSE

1.150

1.250

fractional change in valuation of floor culture = ( effect of profitability on culture + effect of market quality on culture ) * "REF. FRACTIONAL CHANGE OF VALUATION PER YEAR" Units: Dmnl/Year GRASSO EFFECT STRENGTH = 1 Units: Dmnl Expresses by what factor the Grasso scandal increased the ref. fract. inertia decrease.

Appendix

283

GRASSO SCANDAL DURATION = 0.5 Units: Year Duration of uncertainty and turbulence from scandal. HALF SPREAD = 2 Units: Dmnl Number of half spreads contained in the spread. indicated fraction of institutional customers = effect of capital distribution on customers Units: Dmnl indicated NYSE market share = ( "NYSE market share from NBBO (cons.)" * ( 1- EFFECT OF REGULATION ) + "NYSE market share from NBBO (fragm.)" * EFFECT OF REGULATION ) * market share adjustment Units: Dmnl Market share from NBBO, moderated by adjustments from speed and market qualtiy. indicated power of floor firms = effect of market quality on power * effect of institutional customers on power * "REF. POWER OF FLOOR FIRMS" Units: entity Inertia = INTEG( institutionalization - inertia decrease , INI INERTIA ) Units: consistency unit Inward-orientation of thinking, cognitive inertia, … inertia decrease = Inertia * "REF. FRACT. INERTIA DECREASE" * EFFECT OF GRASSO SCANDAL * effect of change on inertia Units: consistency unit/Year Management team turnover, unlearning, …

284

Appendix

INI CUSTOMER ORIENTATION = 0.1 Units: Dmnl The initial customer orientation represents the minimum amount of attention that the NYSE management attributes to its customers. INI MARKET SHARE = 0.8747 Units: Dmnl INI INERTIA = 0.9 Units: consistency unit Initial value = effect of (ref. fract. consistency decrease / ref. fract. institutionalization) = 0.9 INITIAL TIME = 1970 Units: Year institutionalization = "REF. FRACT. INSTITUTIONALIZATION" * Inertia * limiting effect on institutionalization Units: consistency unit/Year Growth of inertia, e.g. by cultural institutionalization, learning, etc. limiting effect on institutionalization = WITH LOOKUP( Inertia , ([(0,0)-(1,1)],(0,1),(0.2,1),(0.4,0.99),(0.6,0.9),(0.75,0.75),(0.9,0.5), (0.97,0.25),(1,0) ) ) Units: Dmnl This effect counteracts the reinforcing institutionalization loop. The more the organization is consistent, the more it slows consistency growth down. Limiting Effect on Institutionalization

limiting effect

1 0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Inertia

1

Appendix

285

Liquidity Algorithms = INTEG( development of liquidity algorithms , 0) Units: algorithms Liquidity Algorithms allow the floor to participate also in electronic trades. This mathematical formulation chosen allows Liduidity Algorithms to rise and to decline. Even in extreme situations, their value stays in reasonable bounds. Therefore, a fuzzy min and max formulation is not chosen here. market quality adequacy gap = DESD ADEQUACY OF MARKET QUALITY - pcvd adequacy of market quality by customer Units: Dmnl Difference between desired and actual adequacy. "market quality from sp. participation" = "effect of sp. participation on market quality" * "REF. MARKET QUALITY" Units: Dmnl Quality attribute of NYSE floor trading. This is what stock exchanges used to compete on. In particular it includes price quality, but also volatility, quoted depth (volume) at each liquidity point, etc. Market quality may also adjust market share upwards. market share adjustment = "wt. on time vs. spread among all customers" * effect of time to execution on market share + ( 1- "wt. on time vs. spread among all customers" ) * "market quality from sp. participation" Units: Dmnl Upwards or downwards adjustment of market share, independent of the part of market share which is set by the time at the NBBO. "no of non-institutional customers" = "TOTAL NO. OF CUSTOMERS" * ( 1- Fraction of Institutional Customers ) Units: entity Normalized number of private or retail customers. number of institutional customers = Fraction of Institutional Customers * "TOTAL NO. OF CUSTOMERS" Units: entity Normalized number of institutional customers.

286

Appendix

"NYSE Fraction of E-Trade" = INTEG( "change in fraction of e-trade" , 0) Units: Dmnl Fraction of fully automated trading at the NYSE. NYSE Fraction of Floor Trade = INTEG( - "change in fraction of e-trade" , 1) Units: Dmnl Fraction to which trades are executed manually on the floor. NYSE Market Share = SMOOTH3I ( indicated NYSE market share , TIME FOR CHANGING MARKET SHARE , INI MARKET SHARE ) Units: Dmnl The NYSE’s fraction of total U.S. consolidated share volume in NYSE-listed issues. "NYSE market share from NBBO (cons.)" = WITH LOOKUP( fraction of time at NBBO , ([(0,0)-(1,0.8)],(0.01,0.01),(0.07,0.05),(0.25,0.27),(0.5,0.54),(0.75,0.77), (0.78,0.79),(0.99,0.8) ) ) Units: Dmnl The better the relative spread, the higher the fraction of time at the NBBO, i. e. the fraction of time the NYSE displays the national best bid and offer. The line rises below proportionally in the very beginning so as to account for the fact that an exchange that displays bad prices most of the time has difficulties to attract a sufficient depth of liquidity at the best price. A critical mass of liquidity is necessary for an exchange to attract volume. market share from NBBO

NYSE Market Share from NBBO (cons.) 1 0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 fraction of time at NBBO

1

287

Appendix

"NYSE market share from NBBO (fragm.)" = WITH LOOKUP( fraction of time at NBBO , ([(0,0)-(1.22,0.6)],(0.01,0.01),(0.07,0.05),(0.123,0.115),(0.2,0.19), (0.35,0.28), (0.65,0.43),(0.8,0.49),(0.99,0.52) ) ) Units: Dmnl Both NBBO calculations follow a highly similar graphical shape, except that for the fragmented market, it levels off on a lower level. Due to fragmentation of orders, one exchange is not able to dominate the market to the extent at which this was possible before. market share from NBBO

NYSE Market Share from NBBO (fragm.) 0.6 0.45 0.3 0.15 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 fraction of time at NBBO

1

NYSE spread = "REF. SPREAD" * effect of relative trading volume on NYSE spread * "effect of inst. customers on spread" Units: $/share The spread is the difference between the bid and the asking price. NYSE time to execution = "NYSE Fraction of E-Trade" * "TIME TO EXECUTION E-TRADE" + NYSE Fraction of Floor Trade * TIME TO EXECUTION FLOOR TRADE Units: second/trade The time it takes to execute a trade, i.e. the time between order submission and execution. NYSE trading volume = NYSE Market Share * TOTAL U.S. SHARE VOLUME IN NYSE- LISTED ISSUES Units: share/Year The trading volume can be used to measure the model’s fit to data.

288

Appendix

openness to change = 1 - Inertia * confidence effect of market share * "REF. OPENNESS PER INERTIA" Units: Dmnl Readiness to change that is limited by inertia, but may be inhanced in the case of a performance threat. pcvd adequacy of employability = specialist participation / desired specialist participation Units: Dmnl pcvd adequacy of market quality by customer = "market quality from sp. participation" / "Desired Market Quality From Sp. Part. by Customers" Units: Dmnl pcvd adequacy of market share = NYSE Market Share / desired market share Units: Dmnl pcvd adequacy of profitability = proportional floor earnings per share traded / desired earnings per share Units: Dmnl "pcvd pressure for more e-trade" = "total pressure for more e-trade from customers" * Customer Orientation Units: pressure unit The management team's biased perception of institutional pressure for more e-trade. pcvd pressure for more floor trade = total pressure for more floor trade from customers * Customer Orientation + total pressure for more floor trade from floor * ( 1 - Customer Orientation ) Units: pressure unit The management team's biased perception of pressure for more floor trade.

Appendix

289

pcvd pressure from customers = ( "total pressure for more e-trade from customers" + total pressure for more floor trade from customers ) * Customer Orientation Units: pressure unit Biased perception of pressure from institutional and non-institutional customers. pcvd pressure from the floor = total pressure for more floor trade from floor * Floor Orientation Units: pressure unit Biased perception of pressure from floor. Power of Floor Firms = INTEG( change in power of floor firms , "REF. POWER OF FLOOR FIRMS" ) Units: entity The degree of influence of the floor. "pressure for more e-trade per inst. customer" = "dissatisf. with time per inst. customer" * "REF. PRESSURE PER DISSATISF. UNIT" Units: pressure unit/entity Pressure or desire for quicker and more electronic trading per institutional customer. pressure for more floor trade per customer = dissatisfaction effect of market quality on pressure * "REF. PRESSURE PER NON.INST. CUSTOMER" Units: pressure unit/entity Resistance and dissatisfaction pressure per stakeholder due to dissatisfaction with the extent of market quality offered by the NYSE. proportional floor earnings per share traded = floor earnings per share handled * specialist participation Units: $/share Floor earnings per share, adjusted by the extent of specialist participation in trading. It gives a better measure of total earnings. "REF. COMMISSION PER SHARE" = 1 Units: $/share Traditional, fix commission per share.

290

Appendix

"REF. FRACT. CHANGE IN CUST. ORIENT. P.A." = 0.03 Units: Dmnl/(Year*pressure unit) The management team's general flexibility of attention. It may represent the degree to which the organization 'looks outside' and seeks information on important stakeholders. "REF. FRACT. CHANGE IN TRADING PER PRESSURE P.A." = 0.02 Units: Dmnl/(Year*pressure unit) The NYSE's general propensity to react to perceived pressure. It may also represent the degree of decentralization or employee empowerment. "REF. FRACT. INERTIA DECREASE" = 0.15 Units: Dmnl/Year Due to the statement of a NYSE employee that people were grown from within, I assume that only half of them came from ousite the organization. Thus the assumed external turnover rate is half of that of the finance and insurance industry (30.0 % / 2 = 15.0 %). "REF. FRACT. INSTITUTIONALIZATION" = 0.3 Units: Dmnl/Year Institutionalization grows by a fraction of 0.3 of current inertia per year. Since ref. institutionalization is higher than ref. consistency decrease, the organization becomes inert over the years. "REF. FRACTIONAL CHANGE OF VALUATION PER YEAR" = 0.12 Units: Dmnl/Year This variable allows floor culture to diminish only slowly with an average delay of about 8 years. "REF. LIQUIDITY ALGORITHMS" = 1 Units: algorithms "REF. MARKET QUALITY" = 1 Units: Dmnl

Appendix

291

"REF. OPENNESS PER INERTIA" = 1 Units: Dmnl/consistency unit Openness per difference to maximum possible inertia of 1. "REF. POWER OF FLOOR FIRMS" = 100 Units: entity The reference power of floor firms equals the total power of customers—allowing for two theoretically equally powerful groups. Their power balance shifts endogenously over time. "REF. PRESSURE PER DISSATISF. UNIT" = 1 Units: pressure unit/dissatisfaction unit Pressure or customer desire for quicker trading and more electronic trading per institutional customer. "REF. PRESSURE PER NON.INST. CUSTOMER" = 0.5 Units: pressure unit/entity Pressure from dissatisfaction per non-institutional customer is half the pressure of institutional customers since non-institutional customers are less powerful. "REF. RESISTANCE PRESSURE PER FLOOR FIRM" = 1 Units: pressure unit/entity "REF. SP PARTICIPATION" = 0.1 Units: Dmnl Maximum of 10 percent. "REF. SPREAD" = 0.22 Units: $/share It relates to the traditional value of the year 1970. "REF. VALUATION OF FLOOR CULTURE" = 1 Units: valuation unit Maximum.

292

Appendix

"rel. valuation of floor culture" = Valuation of Floor Culture by Floor / "REF. VALUATION OF FLOOR CULTURE" Units: Dmnl relative spread of NYSE = NYSE spread / spread in market Units: Dmnl Determines the average time the NYSE quotes at the NBBO. relative time to execution = NYSE time to execution / time to execution in market Units: Dmnl Attribute that has become highly important for the order routing decision. It expresses the relation between the NYSE's and the market`s speed. resistance pressure for floor system per floor firm = effect of employability on resistance * effect of profitability on resistance * "REF. RESISTANCE PRESSURE PER FLOOR FIRM" Units: pressure unit/entity Resistance per floor firm due to dissatisfaction with the extent to which it is able to participate in trading. SAVEPER = 0.25 Units: Year [0,?] The frequency with which output is stored. specialist participation = "effect of floor trade on sp. participation" * effect of liquidity algorithms on participation * "REF. SP PARTICIPATION" Units: Dmnl Fraction of trades executed against money or shares of a specialist. spread in market = "REF. SPREAD" * effect of relative trading volume on market's spread * "effect of inst. customers on spread" Units: $/share Difference between the bid and the asking price at exchanges other than the NYSE.

Appendix

293

TIME FOR CHANGING MARKET SHARE = 1 Units: Year Short reaction time of one year since market share represents liquidity and routing decision and thus adjusts quickly. TIME STEP = 0.0078125 Units: Year [0,?] The time step between iterations of calculations. TIME TO ADJUST DESD MARKET SHARE = 3 Units: Year Medium adjustment time of 3 years for performance measures. TIME TO ADJUST DESIRED EARNINGS = 5 Units: Year Since earnings deteriorate, the floor gets used to a worse situation rather slowly which explains the relatively long adjustment time of 5 years. TIME TO ADJUST DESIRED PARTICIPATION = 5 Units: Year Since participation deteriorates, the floor gets used to a worse situation rather slowly which explains the relatively long adjustment time of 5 years. TIME TO BECOME CUSTOMER = 5 Units: Year There is a time delay to the indicated fraction so as to neglect shorttime changes and to account for the delay between the creation and the amendment a portfolio of securities. TIME TO CHANGE DESD MARKET QUALITY = 10 Units: Year The adjustment time is long (10 years) because market quality mainly falls and people only slowly get used to a worse situation.

294

Appendix

TIME TO CHANGE POWER OF FLOOR FIRMS = 2 Units: Year Adjustment time of 2 years means a rather quick, but not instantaneous adaptation. TIME TO DEVELOP ALGORITHMS = 1.5 Units: Year Algorithms take about one and a half years to be initiated since the gap needs to be perceived as being problematic. "TIME TO DEVELOP E-TRADE POSSIBILITIES" = 5 Units: Year Delay time between the technical development and implementation of e-trade. The delay of 5 years can be divided into the market's reaction and implementation time. "TIME TO EXECUTION E-TRADE" = 1 Units: second/trade Electronic trading is considered fast trading, and a trade is considered fast if it has sub-second speed. Therefore the time it takes to execute a trade in an electronic environment is set to 1 second. The concept of the time to execution is similar to the notion of latency. Advantages of technology are not be taken into consideration here and a constant time to executon of 1 second in electronic trading is assumed. TIME TO EXECUTION FLOOR TRADE = 9 Units: second/trade The least amount of time it takes to execute a trade manually. According to the NYSE, this is 9 seconds. time to execution in market = "fraction of e-trade in remaining market" * "TIME TO EXECUTION E-TRADE" + ( 1- "fraction of e-trade in remaining market" ) * TIME TO EXECUTION FLOOR TRADE Units: second/trade Speed of trading in the market.

Appendix

295

"TIME TO FULLY IMPLEMENT E-TRADE" = 5 Units: Year Equivalent to the time to develop e-trade possibilities for real data. "TOTAL NO. OF CUSTOMERS" = 100 Units: entity Normalized number of customers. "total pressure for more e-trade from customers" = "pressure for more e-trade per inst. customer" * number of institutional customers Units: pressure unit Pressure by the entire group of institutional customers. total pressure for more floor trade = total pressure for more floor trade from customers + total pressure for more floor trade from floor Units: pressure unit Pressure for more floor trade from non-institutional customers and the floor. total pressure for more floor trade from customers = pressure for more floor trade per customer * "no of non-institutional customers" Units: pressure unit Total pressure by the entire group of non-institutional customers for more floor trade. total pressure for more floor trade from floor = cultural multiplier of pressure from floor * resistance pressure for floor system per floor firm * Power of Floor Firms Units: pressure unit Total pressure by the entire floor for more floor trade.

296

Appendix

TOTAL U.S. SHARE VOLUME IN NYSE- LISTED ISSUES = WITH LOOKUP( Time , ([(1970,0)-(2030,4e+012)], (1970,3.5e+009),(1980,1.4e+010), (1990,4.8e+010),(1995,1.1e+011),(2000,3.2e+011),(2005,5.2e+011), (2007,8.5e+011),(2008,1.2e+012),(2009,1.5e+012),(2011,2e+012), (2015,2.5614e+012),(2020,2.98246e+012),(2030,3.57895e+012) ) ) Units: share/Year Data is taken from NYSE Facts and Figures: Historical > Annual reported volume, turnover rate, reported trades (mils. of shares), and Market Activity > Consolidated Volume in NYSE Listed Issues. Data after 2009 is assumed. Total U.S. Share Volume in NYSE-listed Issues

share/Year

4e+012 3e+012 2e+012 1e+012 0 1970

1980

1990

2000 Date

2010

2020

2030

trading volume of the remaining market = ( 1 - NYSE Market Share ) * TOTAL U.S. SHARE VOLUME IN NYSE- LISTED ISSUES Units: share/Year The trading volume can be used to measure the model’s fit to data. Valuation of Floor Culture by Floor = INTEG( change in valuation , 1) Units: valuation unit The higher the market quality the floor is able to provide, the higher it values its own contribution and culture. The more profitable the floor is, the more it values its own way of doing things. In 1970, it is still at almost 100 percent. "wt. on time vs. spread among all customers" = ACCESS TO INFORMATION TECHNOLOGY * Fraction of Institutional Customers Units: Dmnl Importance of time among customers.

Appendix

297

Appendix E: Generic Model Equations

Attention to Stakeholders Favoring A = INTEG ( - change in attention, 1 - INI ATTENTION TO STAKEHOLDER FAVORING B) Units: Dmnl Orientation towards the stakeholders favoring the ‘old’ strategy A. Attention to Stakeholders Favoring B = INTEG ( change in attention, INI ATTENTION TO STAKEHOLDER FAVORING B) Units: Dmnl Orientation towards the stakeholders favoring the ‘new’ strategy B. change in attention = ( ABS ( pcvd pressure from stakeholders favoring B * effect of attention to B on change ) - pcvd pressure from stakeholders favoring A * effect of attention to A on change ) * "fract. change in attention per pressure p.a." Units: Dmnl/Year change in performance = ( indicated performance – Performance ) / TIME FOR CHANGING PERFORMANCE Units: performance unit/Year change in strategy = ( pcvd pressure from stakeholders favoring B * effect of B on change - pcvd pressure from stakeholders favoring A * effect of A on change ) * "fract. change per pcvd pressure p.a." Units: Dmnl/Year

298

Appendix

confidence effect of performance = WITH LOOKUP ( pcvd adequacy of performance, ([(0,0)-(1.2,1)],(0,0),(0.2,0.04),(0.4,0.14),(0.5,0.22),(0.6,0.33),(0.7,0.5), (0.8,0.75),(0.9,0.95),(0.95,0.985),(1,1),(1.2,1) ) ) Units: Dmnl Effect by which performance inadequacies increase the management team's openness to change. Minor inadequacies have less than proportional effect, but the effect on openness quickly rises before it slowly approaches the limit of a fully open organization in the case of organizational collapse. Confidence Effect of Performance confidence effect

1 0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 pcvd adequacy of performance

1

desired performance = SMOOTH ( Performance, TIME TO ADJUST DESD PERFORMANCE ) Units: performance unit Floating performance goal. desired quality A by stakeholders favoring A = SMOOTH ( quality A, TIME TO ADJUST DESIRED QUALTIY) Units: quality unit Floating goal of desired quality A. desired quality B = diffusion of B in remaining market * "REF.QUALITY B OF STRATEGY B" + ( 1 - diffusion of B in remaining market ) * "REF. QUALITY B OF STRATEGY A" Units: quality unit Expectation by customers/stakeholders.

299

Appendix

DEVELOPMENT OF STRATEGY B = WITH LOOKUP ( Time, ([(0,0)-(50,1)],(0,0),(5,0),(10,0.18),(15,0.57),(20,0.9),(22,0.97),(24,1), (50,1) ) ) Units: Dmnl Invention of strategy B. Development of Strategy B 1

Dmnl

0.75 0.5 0.25 0 0

5

10

15

20 25 30 Time (Year)

35

40

45

50

DEVELOPMENT OF STRATEGY B QUICK = WITH LOOKUP ( Time, ([(0,0)-(50,1)],(0,0),(7,0),(12,1),(50,1) ) ) Units: Dmnl Quicker invention of strategy B, or different reference group. Development of Strategy B Quick 1

Dmnl

0.75 0.5 0.25 0 0

5

10

15

20 25 30 Time (Year)

35

40

45

diffusion of B in remaining market = SMOOTH3 (DEVELOPMENT OF STRATEGY B * ( 1 - SWITCH QUICK DEVELOPMENT ) + SWITCH QUICK DEVELOPMENT * DEVELOPMENT OF STRATEGY B QUICK, TIME TO DIFFUSE B IN REMAINING MARKET ) Units: Dmnl Adoption of strategy B in market.

50

300

Appendix

effect of A on change = WITH LOOKUP ( Orientation to Strategy A, ([(0,0)-(1,1)],(0,1),(0.5,1),(0.75,0.95),(0.9,0.75),(0.95,0.5),(0.99,0.01), (1,0) ) ) Units: Dmnl Limit to the willingness to further react to pressure. Effect of A on Change 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Orientation to Strategy A

1

effect of attention to A on change = WITH LOOKUP ( Attention to Stakeholders Favoring A, ([(0,0)-(1,1)],(0,1),(0.25,1),(0.5,0.95),(0.8,0.75),(0.9,0.5),(0.96,0.04), (0.98,0.005), (1,0) ) ) Units: Dmnl Limit to the willingness to further react to pressure. Effect of Attention to A on change 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Attention to Stakeholders Favoring A

1

effect of attention to B on change = WITH LOOKUP ( Attention to Stakeholders Favoring B, ([(0,0)-(1,1)],(0,1),(0.25,1),(0.5,0.95),(0.8,0.75),(0.9,0.5),(0.96,0.04), (0.98,0.005),(1,0) ) ) Units: Dmnl Limit to the willingness to further react to pressure. The graph has the same shape as the one indicating the effect of attention to A on change.

301

Appendix

effect of B on change = WITH LOOKUP ( Orientation to Strategy B, ([(0,0)-(1,1)],(0,1),(0.5,1),(0.75,0.95),(0.9,0.75),(0.95,0.5),(0.99,0.01), (1,0) ) ) Units: Dmnl Limit to the willingness to further react to pressure. The graph has the same shape as the one indicating the effect of A on change. effect of change on inertia = WITH LOOKUP ( ABS ( change in strategy ), ([(0,0)-(0.5,7)],(0,1),(0.05,1.4),(0.1,2.4),(0.15,4.2),(0.2,5.4),(0.3,6.2), (0.5,6.5) ) ) Units: Dmnl Small changes have a less than proportional effect on consistency loss. This allows an organization to change incrementally without disruption in its internal consistency. The consistency decrease from change represents turnover rates, but it also captures changes in the people's thinking even if they remain in the organization. Effect of Change on Inertia 8

effect

6 4 2 0 0

0.100

0.200 0.300 change in strategy

0.400

0.500

302

Appendix

effect of openness on change = WITH LOOKUP ( openness to change, ([(0,0)-(1,1)],(0,0.05),(0.1,0.06),(0.2,0.1),(0.3,0.18),(0.4,0.3),(0.5,0.435), (0.6,0.63), (0.7,0.81),(0.8,0.92),(0.9,0.97),(1,1) ) ) Units: Dmnl Low openness to change may reduce fractional change to 10 percent of its reference value. The effect of openness on change is an s-shaped curve indicating that the organization quickly reacts to perceived pressure if it has a rather high openness. It becomes less responsive as openness decreases until its reactivity reaches a lower bound. Effect of Openness on Change 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 openness to change

1

effect of quality A on performance = WITH LOOKUP ( quality A, ([(0,1)-(1,1.1)],(0,1),(1,1.1) ) ) Units: Dmnl Effect that pushes performance upward proportionally to the extent to which the organization outperforms in quality A. Effect of Quality A on Performance 1.1

effect

1.075 1.05 1.025 1 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 quality A

1

303

Appendix

effect of quality A on resistance = WITH LOOKUP ( pcvd adequacy of quality A, ([(0,0)-(1.1,1)],(0,1),(0.1,0.99),(0.15,0.97),(0.2,0.93),(0.5,0.5),(0.8,0.07), (0.85,0.03),(0.9,0.01),(1,0),(1.1,0) ) ) Units: Dmnl Inversely s-shaped. Slowly approaches maximum, and slowly starts in the beginning because minor inadequacies cause less than proportional reactions. Effect of Quality A on Resistance 1

effect

0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 pcvd adequacy of quality A

1

"effect of rel. quality B on performance" = WITH LOOKUP ( "rel. quality B", ([(-0.9,0)-(1,2)],(-0.9,0),(-0.7,0.53),(-0.6,0.7),(-0.5,0.85),(-0.4,0.93), (-0.3,0.97),(-0.2,0.99),(0,1),(0.25,1.005),(0.5,1.07),(0.7,1.2),(1,1.4) ) ) Units: Dmnl Effect that may push performance upward or downward depending on the organization's achievement regarding quality B relative to the market. It is formulated as an order winning criterion. Effect of Relative Quality B on Performance 2

effect

1.5 1 0.5 0 -1

-0.60

-0.20 0.20 "rel. quality B"

FINAL TIME = 50 Units: Year Time bounds of the simulation.

0.60

1

304

Appendix

"fract. change in attention per pressure p.a." = "REF. FRACT. CHANGE IN ATTENTION P.A." * effect of openness on change Units: Dmnl/(pressure unit*Year) Flexibility of attention. Mix of the management team's general flexibility of attention and situational factors. "fract. change per pcvd pressure p.a." = "REF. FRACT. CHANGE IN STRATEGY PER PRESSURE P.A." * effect of openness on change Units: Dmnl/pressure unit/Year Responsiveness of the strategy to pressure. Mix of the management team's general responsiveness to pressure and situational factors. fraction of stakeholders favoring B = diffusion of B in remaining market Units: Dmnl It is assumed that the market is adapted to the demands of market participants. indicated performance = "REF. PERFORMANCE" * performance adjustment Units: performance unit Inertia = INTEG ( institutionalization-inertia decrease, INI INERTIA ) Units: consistency unit Inward-orientation of thinking, cognitive inertia, … inertia decrease = Inertia * "REF. FRACT. INERTIA DECREASE" * effect of change on inertia Units: consistency unit/Year Management team turnover, unlearning, … INI ATTENTION TO STAKEHOLDER FAVORING B = 0.1 Units: Dmnl The initial attention represents the minimum amount of attention that the management team attributes to its stakeholders.

305

Appendix

INI INERTIA = 0.9 Units: consistency unit Initial value = effect of (ref. fract. consistency decrease / ref. fract. institutionalization) = 0.9 INITIAL TIME = 0 Units: Year Initial time bounds of the simulation. institutionalization = "REF. FRACT. INSTITUTIONALIZATION" * Inertia * limiting effect on institutionalization Units: consistency unit/Year Growth of inertia, e.g. by cultural institutionalization, learning, etc. limiting effect on institutionalization = WITH LOOKUP ( Inertia, ([(0,0)-(1,1)],(0,1),(0.2,1),(0.4,0.99),(0.6,0.9),(0.75,0.75),(0.9,0.5), (0.97,0.25),(1,0) ) ) Units: Dmnl This effect counteracts the reinforcing institutionalization loop. The more the organization is consistent, the more the effect slows consistency growth down. Limiting Effect on Institutionalization

limiting effect

1 0.75 0.5 0.25 0 0

0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 Inertia

no of stakeholders favoring A = "TOTAL NO. OF STAKEHOLDERS" - number of stakeholders favoring B Units: entity Normalized number of stakeholders.

1

306

Appendix

number of stakeholders favoring B = fraction of stakeholders favoring B * "TOTAL NO. OF STAKEHOLDERS" Units: entity Normalized number of stakeholders. openness to change = 1 - Inertia * confidence effect of performance * "REF. OPENNESS PER INERTIA" Units: Dmnl Readiness to change that is limited by inertia, but may be inhanced in the case of a performance threat. Orientation to Strategy A = INTEG ( - change in strategy, 1 ) Units: Dmnl Fraction to which the focal organization's strategy is oriented to the ‘old’ strategy A. Orientation to Strategy B = INTEG ( change in strategy, 0 ) Units: Dmnl Fraction to which the focal organization's strategy is oriented to the ‘new’ strategy B. pcvd adequacy of performance = Performance / desired performance Units: Dmnl pcvd adequacy of quality A = quality A / desired quality A by stakeholders favoring A Units: Dmnl

307

Appendix

pcvd inadequacy of strategy per stakeholder B = WITH LOOKUP ( "rel. quality B", ([(-1,0)-(1,1)],(-1,1),(0,0),(1,0) ) ) Units: Dmnl Stakeholders' extent of dissatisfaction with or dislike of the focal organization's strategy/offerings. Perceived Inadequacy of Strategy per Stakeholder B pcvd inadequacy

1 0.75 0.5 0.25 0 -1

-0.60

-0.20 0.20 "rel. quality B"

0.60

1

pcvd pressure from stakeholders favoring A = total stakeholder pressure for more A * Attention to Stakeholders Favoring A Units: pressure unit The management team's biased perception of stakeholder pressure for A. pcvd pressure from stakeholders favoring B = total stakeholder pressure for more B * Attention to Stakeholders Favoring B Units: pressure unit The management team's biased perception of stakeholder pressure for B. Performance = INTEG ( change in performance, "REF. PERFORMANCE" * effect of quality A on performance) Units: performance unit May represent market share, sales volume, size of customer base, etc. performance adjustment = "wt. on quality B vs. quality A" * "effect of rel. quality B on performance" + ( 1 - "wt. on quality B vs. quality A" ) * effect of quality A on performance Units: Dmnl Upward or downward adjustment of performance by quality A and B.

308

Appendix

PERMANENTLY POWERFUL STAKEHOLDERS FAVORING A = 0 Units: entity A group that can significantly impede an organization’s operations such as change-averse employees. quality A = Orientation to Strategy A * "REF. QUALITY A OF STRATEGY A" Units: quality unit Quality of the 'old' strategy, such as price quality in trading, resolution quality in photography. This is what organizations in the respective area used to compete on. quality B = Orientation to Strategy B * "REF.QUALITY B OF STRATEGY B" + Orientation to Strategy A * "REF. QUALITY B OF STRATEGY A" Units: quality unit Achievement in the ‘new’ quality by focal organization. "REF. FRACT. CHANGE IN ATTENTION P.A." = 0.05 Units: Dmnl/pressure unit/Year The management team's general flexibility of attention. It represents the degree to which the organization 'looks outside' and seeks information on important stakeholders. "REF. FRACT. CHANGE IN STRATEGY PER PRESSURE P.A." = 0.02 Units: Dmnl/pressure unit/Year An organization's general propensity to react to perceived pressure. It may represent the degree of decentralization or employee empowerment. "REF. FRACT. INERTIA DECREASE" = 0.15 Units: Dmnl/Year Reference inertia decrease has been adapted to a rather low rate of annual turnover in order to represent an organization that accumulates inertia rather quickly.

Appendix

309

"REF. FRACT. INSTITUTIONALIZATION" = 0.3 Units: Dmnl/Year Institutionalization grows by a fraction of 0.3 of current inertia per year. Since ref. institutionalization is higher than ref. consistency decrease, the organization becomes inert over the years. "REF. OPENNESS PER INERTIA" = 1 Units: Dmnl/consistency unit Openness per difference to maximum possible inertia of 1. "REF. PERFORMANCE" = 0.5 Units: performance unit "REF. PRESSURE PER STAKEHOLDER FAVORING B" = 0.6 Units: pressure unit/entity Medium extent of pressure or desire of new stakeholder for strategy B. "REF. QUALITY A OF STRATEGY A" = 1 Units: quality unit Attribute of the new strategy. High value of quality A. "REF. QUALITY B OF STRATEGY A" = 0.1 Units: quality unit Degree to which strategy A can fulfill quality B. Strategy A has a low value of quality B. "REF. RESISTANCE PRESSURE PER STAKEHOLDER FAVORING A" = 1 Units: pressure unit/entity High extent of resistance pressure of old stakeholder for old strategy. "REF.QUALITY B OF STRATEGY B" = 1 Units: quality unit An attribute of the new strategy B. E.g. speed as the attribute of electronic trading, ability to store photos electronically, high ethical compliance, etc.

310

Appendix

"rel. quality B" = quality B - desired quality B Units: quality unit The relative quality B espresses the difference between the focal organization's quality B and what is desired by customers/stakeholders. SAVEPER = 0.25 Units: Year [0,?] The frequency with which simulation output is stored. stakeholder pressure for more B = pcvd inadequacy of strategy per stakeholder B * "REF. PRESSURE PER STAKEHOLDER FAVORING B" Units: pressure unit/entity Pressure or customer desire for more strategy B per stakeholder favoring B. stakeholder resistance pressure for more A = "REF. RESISTANCE PRESSURE PER STAKEHOLDER FAVORING A" * effect of quality A on resistance Units: pressure unit/entity Resistance per stakeholder due to dissatisfaction with the extent of quality A offered. SWITCH QUICK DEVELOPMENT = 0 Units: Dmnl Can switch on and off a different environment. TIME FOR CHANGING PERFORMANCE = 1 Units: Year Reaction time of stakeholders. TIME STEP = 0.0078125 Units: Year [0,?] The time step between iterations of calculations.

Appendix

311

TIME TO ADJUST DESD PERFORMANCE = 3 Units: Year Medium delay time. TIME TO ADJUST DESIRED QUALTIY = 5 Units: Year Long delay/adjustment time to changes in desired attributes. TIME TO DIFFUSE B IN REMAINING MARKET = 5 Units: Year Time delay between the invention and implementation of the new strategy in the market. "TOTAL NO. OF STAKEHOLDERS" = 100 Units: entity Normalized number of stakeholders. total stakeholder pressure for more A = stakeholder resistance pressure for more A * ( no of stakeholders favoring A + PERMANENTLY POWERFUL STAKEHOLDERS FAVORING A ) Units: pressure unit Total pressure by the entire group of stakeholders favoring A for more A. total stakeholder pressure for more B = stakeholder pressure for more B * number of stakeholders favoring B Units: pressure unit Total pressure by the entire group of stakeholders favoring B for more B. "wt. on quality B vs. quality A" = fraction of stakeholders favoring B Units: Dmnl Importance of strategy B and quality B among stakeholders.

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